### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

1 

## Residual Risk Assessment for the Off

- Site Waste and Recovery Operations Source Category in Support of the February 2015

Risk and Technology Review Final Rule

by EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation February 2015

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

2 

## Table of Contents

1 Introduction

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

................................ ................................ ................................ .............................. 4 2.1 Emissions and so urce data

................................ ................................ ........................... 4 2.2 Dispersion modeling for inhalation exposure assessment

................................ ........... 5 2.3 Estimating chronic human inhalation exposure

................................ .......................... 7 2.4 Acute Risk Screening and Refined Assessments

................................ ........................ 8 2.5 Multipathway and environmental risk screening

................................ ...................... 10 2.6 Dose

- Response Assessment

................................ ................................ ...................... 13 2.6.1 Sources of chronic dose

- response information

................................ .................. 13 2.6.2 Sources of acute dose

- response information

................................ ...................... 21 2.7 Risk Characterization

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

................................ ................................ ................................ ............... 26 2.7.2 Mixtures

................................ ................................ ................................ ............. 28 2.7.3 Facility

- wide Risks

................................ ................................ ............................ 28 2. 7.4 MACT

- Allowable Risk

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

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

................................ ................................ . 29 3.2 Risk Characterization

................................ ................................ ................................ 35 4 General Discussion of Uncertainties and How They Have Been Addressed

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

................................ ................................ ............. 38 4.2 Uncertainties in the Dose

- Response Relationships

................................ ................... 39 5 References

................................ ................................ ................................ ........................ 49

Index of Tables

Table 2.2

- 1 AERMOD version 12345 model options for RTR modeling

................................ 6 Table 2.6

- 1 Dose

- Response Values for Chronic Inhalation Exposure to Carcinogens

.......... 16 Table 2.6

- 2 Dose

- Response Values for Chronic Inhalation Exposure to Noncarcinogens

.... 18 Table 2.6

- 3 Dose

- Response Values for Acute Exposure

................................ ........................ 24 Table 3.1

- 1 Summary of Emissions from the OSWRO Source Category Used in the Residual Risk Assessment and Availability of Dose

- Response Values

................................ ................. 30 Table 3.2

- 1 Summary of Source Category Level In halation Risks for OSWRO

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

- 2 Summary of Environmental Risks Screen Results for OSWRO

.......................... 37 Table 3.2

- 3 Source Category Contribution to Facility

- Wide Cancer Risks

............................ 38
Appendices
Appendix 1 Technical Support Document for HEM3 Modeling Appendix 2 Meteorological Data for HEM3 Modeling Appendix 3 Analysis of data on short

- term emission rates relative to long

- term emission

rates Appendix 4 Technical Support Document for TRIM

- Based Multipathway Screening

Scenari o for RTR: Summary of Approach and Evaluation

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

3 Appendix 5 Detailed Risk Modeling Results Appendix 6 Acute Impacts Refined Analysis Appendix 7 Dispersion Model Receptor Revisions and Additions for OSWRO

Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model AEGL Acute exposure guideline l evel ASTDR US Agency for Toxic Substances and Disease Registry CalEPA California Environmental Agency CTE Central Tendency Estimate ERPG Emergency Response Planning Guideline HAP Hazardous Air Pollutant HEM Human Exposure Model HI Hazard i ndex HQ Hazard q uotient IRIS Integrated Risk Information System MACT Maximum Achievable Control Technology MIR Maximum Individual Risk MOA Mode of a ction NAC National Advisory Committee NAAQS National Ambient Air Quality Standard NATA National Air Toxics Assessment NEI National Emissions Inventory NPRM Notice of Proposed Rulemaking PB

- HAP Persistent and Bioaccumulative

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

- organ

- specific hazard index URE Unit risk estimate

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

4 

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

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

- based standards for categories of sources (e.g., petroleum refineries, pulp and paper mills, etc.) [ 1 ]. EPA has largely completed the initial Maximum Achievable Control Technology ( MACT ) standards as required under this p rovision. 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 additional risk reductions are necessary to protect public health with an ample margin of safety or to prevent an adverse environmental effect, EPA must develop standards to address these remaining risks. This second stage of the regulatory process is known as the residual risk stage. For each source category for which EPA issued MACT standards, the resid ual 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 Pla n” and in June of 2007, we received a letter with the results of that consultation. Subsequ ent to the consultation, in Ju ly of 2009 a meeting was held with an SAB panel for a formal peer review of the “Risk and Technology Review (RTR) Assessment Methodolo gies” [ 2 ]. We received the final SAB report on this review in May of 2010 [ 3 ]. Where appropriate, we have responded to the key messages from this review in developing our current risk assessment s and we will be continuing our efforts to improve our asses sments by incorporating updates based on the SAB recommendations as they are developed and become available . Our responses to the key recommendations of the SAB are ou tlined in a memo entitled, “EPA’s Actions in Response to the Key Recommendations of the Science Advisory Board Review of R isk and Technology Review (RTR) Risk Assessment Methodologies” [ 4 ].
This document contains the methods and the results of baseline risk assessments (i.e., after the implementation of the MACT standard) performed for the off

- site waste and recovery operations (OSWRO) source category . The methods discussion includes descriptions of the methods used to develop refined estimates of chronic inhalation exposures and human health risks for cancer and noncancer endpoints, as we ll as descriptions of the methods used to screen for acute health risks, chronic non

- inhalation health risks, and adverse environmental effects.



## 2 Methods

2.1 Emissions and source data The starting point for this assessment involved generating a list of facilities potentially subject to the OSWRO MACT. The initial list, which included over 400 facilities across the United

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

5 States, was developed from databases maintained for the Resource C onservation and Recovery Act (RCRA) and the EPA’s Office of Enforcement and Compliance Assurance (OECA), as well as from databases used in development of the original OSWRO MACT standards . The facilities were checked against compliance databases , including the Air Facility System (AFS), and title V operating permit information to ensure that the facilities

were still operating, were subject to the OSWRO MACT and were major sources of HAP emissions. T he resulting facility list, which consisted of 53 f ac i liti es, was used to c r e a te a subset of the 2005 NEI that included facility, process and emissions data. Data from nine corporate entities w ere also gathered through a CAA section 114 questionnaire. Where data for a facility was available from the questionnaire , th e questionnaire data was used ; NEI data

w ere used for the remaining facilities , where OSWRO emissions could be identified

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

acility and industry representatives were contacted to clarify details and resolve issues with the data for several sources

. In the end, data for 38 facilities were included in the modeling dataset

. Details on the development of the emissions and source data for this source category are discussed in a memorandum entitled
, “ Development of the RTR Emissions Dataset for the Off

- Site Waste and Recovery Operations Source Category ” available in the docket for this rule making

. Section 3 below provides a summary of the emissions.



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

- and short

- term inhalation exposure concentrations and associated health risk from each facility in the source category of interest were estimated using the Human Exposure Model in combinati on with the American Meteorological Society/EPA Regulatory Model (AERMOD) disper sion modeling system ( HEM3 ). The approach used in applying this modeling system is outlined below, and further details are provided in Appendix 1 of this document, Technical S upport document for HEM3 Modeling

. The HEM3 performs three main operations: atmospheric dispersion modeling, estimation of individual human exposures and health risks, and estimation of population risks. This section focuses on the dispersion modeling co mponent. The exposure and risk characterization components are discussed in other subsections of Sections 2 and 3.

The di spersion model in the HEM3 system, AERMOD version 12345 , is a state

- of

- the

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

- by

- hour dispersion from the emission sources into the surrounding atmosphere. Hourly emission rates used for this simulation are generated by evenly dividing the total annual emission rate from the inventory into the 8,760 hours of the year.
The first step in t he application of the HEM3 modeling system is to predict ambient concentrations at locations of interest. The AERMOD model options employed are summarized in Table 2.2

- 1 and are discussed further below.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

6 Table 2.2

- 1 AERMOD version 1 2345 mo del options for RTR modeling

Modeling Option Selected Parameter for chronic exposure Type of calculations Hourly Ambient Concentration Source type Point and Area
Receptor orientation Polar (13 rings and 16 radials) Discrete (census block centroids) and user

- supplied receptors Terrain characterization Actual from USGS 1

- degree DEM data Building downwash Not Included Plume deposition/depletion Not Included Urban source option Included if source in urbanized area
Meteorology 1 year represent ative NWS from nearest site (over 8 00 stations)



### In HEM3 , meteorological data are ordina rily selected from a list of over 8 00 National Weather Service (NWS) surface observation stations acr oss the continental United States, Alaska, Hawaii, and Puerto Rico. In most cases the nearest station is selected as representative of the conditions at the subject facility. Ideally, when considering off

- site meteorological data most site

- specific dispe rsion 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 dat a, we modeled only a single year, typically 2011 . While the selection of a single year may result in under

- prediction of long

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

- prediction at others. For each facility identified by its characteristic latitude and longitude coordinates, the closest meteorological station was used in the dispersion modeling. The average distance between a modeled facility and the applica b le meteorological station was 2 3 miles ( 37 km). Appendix 2 of this document ( Meteorological Data Processing Using AERMET for HEM 3 ) provides a complete listing of stations and assumptions along with further details used in processing the data through AERMET (the AERMOD meteorological data pre

- processing program)

. The sensitivity of model results to the selection of the nearest weather station and the use of one year of meteorological data is discussed in “ Risk and Technology Review (RTR) Risk Assessment Methodologies ” [ 2 ].

The HEM3 system estimates ambient concen trations at the geographic centroids of census blocks (using the 20 1 0 Census), and at other receptor locations that can be specified by the

user. See Appendix 7 to this document ( Dispersion Model Receptor Revisions and Additions for the Off

- Site Waste and Recovery Operations Source Category ) for a discussion of user receptors and centroid location changes specific to this source category. The model accounts for the effects of multiple facilities when estimating concentration impacts at each block centroid . Typically we combined only the impacts of facilities within the same source category, and assessed chronic exposure and risk only for census blocks with at least one

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

7 resident (i.e., locations where people may reasonably be assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambient concentrations were calculated as the annual average of all estimated short

- term (one

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

.
I n contrast to the development of ambient concentrations for evaluating long

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

- case short

- term (one

- hour) concentrations were estimated both at the census block centroids and at points nearer the facility that represent locations where people may be present for sho rt 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 ac ute exposures, and since the emissions data contain only annual emission totals, we generally apply the assumption to all source categories that the maximum one

- hour emission rate from any source is ten times the average ann ual hourly emission rate for that source. More description of our short

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

– Acute Risk Screening and Refined Assessments

.
Census block elevations for HEM3 modeling were determined nationally fr om the US Geological Service 1 /3 Arc Second National Elevation Dataset , which have a spatial resolution of about 1 0 meters. Elevations of polar grid points used in estimating short

- and long

- term ambient concentrations were assumed to be equal to the high est elevation of any census block falling within the polar grid sector corresponding to the grid point. If a sector does not contain any blocks, the model defaults the elevation to that of the nearest block. If an elevation is not provided for the emissi on source, the model uses the average elevation of all sectors within the innermost model ring. AERMOD adjusts the plume’s flow if nearby elevated hills are expected to influence the wind patterns. For details on how hill heights were estimated and used in the AERMOD modeling, see Appendix 1 of this document, Technical Support Document for HEM3 Modeling

.



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

- term or long

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

We did not address short

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

- term activity would, on average, reduce risk estimate s about 25 percent for particulate HAP ; it will also reduce risk estimates for gaseous HAP , but typically by much less. Secon d, basing exposure estimates on average ambient concentrations at census block

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

8 centroids may underestimate or overestimate actual exposure concentrations at some residences. Further reducing exposure estimates for the most highly exposed residents by mode ling their short

- term behavior could add a systematic low bias to these results.
We did not address long

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

- term population mobility does not bias the estimate of the theoretical MIR nor does it affect the estimate of cancer incidence since the total population number remains the same. It does, however, affect the shape of the distribution of individual risks across the affected population, shifting it toward higher estimated individual risks at the upper end and reducing the number o f people estimated to be at lower risks, thereby increasing the estimated number of people at specific risk levels

.



### 2.4 Acute Risk Screening and Refined Assessments In establishing a scientifically defensible approach for the assessment of potential health risks due to acute exposures to HAP, we 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 assess ment was endorsed by the National Academy of Sciences in its 1993 publication “Science and Judgment in Risk Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to Congress” in 1999.
The assessment methodology is designed to elimi nate from further consideration those facilities for which we have confidence that no acute adverse health effects of concern will occur. To do so, we use what is called a tiered, iterative approach to the asse ssment. This means that we beg i n with a scre ening assessment, which relies on readily available data and uses conservative assumptions that in combination approximate a worst

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

- or site

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

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

- specific data can be used to determine that acute exposures are not a concern, and significant additional data collection is not necessary. Acute health risk screening was performed as the first step. When we identify acute impacts which exceed their relevant benchmarks, we pursue refining our acute screening estimates to the extent possible. In some cases, this may include the use of a facility

- specific emissions multiplier to estimate the peak hourly emission rates from the average rates (rather than the default factor of 10). In other cases, this may entail determining the actual physical layout

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

9 and boundaries of a facility to more accurately gauge where people might reasonably be exposed for an hour.
When screening for potentially significant acute exposures, we used an estimate of the highest hourly ambient concentration at any off

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

- term emissio n rates, we generally apply the assumption that the maximum one

- hour emission rate from any source is ten times the average annual hourly emission rate for that source. (The average hourly emission rate is defined as the total emissions for a year divided by the total number of operating hours per year.) This choice of a factor of ten for acute screening was originally based on engineering judgement.
To develop a more robust peak

- to

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

- term emissions dataset from a number of sources located in Texas (originally reported on by Allen et al. 2004) [ 7 ]. In that report, the Texas Environmental Research Con sortium Project compared hourly and annual emissions data for volatile organic compounds for all facilities in a heavily

- industrialized 4

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

- month time period in 2001. We obtaine d the dataset and performed our own analysis, focusing on sources which reported emitting high quantities of HAP over short periods of time (see Appendix 4 of this document, Analysis of data on short

- term emission rates relative to long

- term emission rates ). Most peak emission events were less than twice the annual average, and the highest was a factor of 74 times the annual average, and the 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 ana lysis of the data from the Texas Environmental Research Consortium suggests that this factor should cover more than 99 percent of the short

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

- term emissions of particulate HAP. In the absence of source category

- specific data, we use this same default approach for particulate emissions as well.

W hen we identify acute impacts which exceed their relevant benchmarks, we pursue refini ng our acute screening estimates to the extent possible. For the off

- site waste and recovery operations source category, we conducted a review of the layout of the polar receptors around the facilities to determine the maximum off

- site acute impact for th e facilities that did not screen out during the initial run. Appendix 5 to this document ( Draft Detailed Modeling Results ) contains the initial acute results for the source category, while Appendix 6 to this document (

Acute Impacts Refined Analysis ) conta ins the refined acute results.
In summary, w e used conservative assumptions for emission rates, meteorology, and exposure location , and refined data where available . We used the following worst

- case assumptions in our screening approach:

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

10  Peak 1

- hour emissions were assumed to equal 10 times the average 1

- hour emission rates.

 For facilities with multiple emission points, peak 1

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

 For facilities with multiple emission points, 1

- h our concentrations at each receptor were assumed to be the sum of the maximum concentrations due to each emission point, regardless of whether those maximum concentrations occurred during the same hour.

 Worst

- case meteorology (from one year of local meteo rology) was assumed to occur at the same time the peak emission rates occur. The recommended EPA local

- scale dispersion model, AERMOD, is used for simulating atmospheric dispersion.

 A person was assumed to be located downwind at the point of maximum impac t during this same worst

- case 1

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

 The maximum impact was compared to multiple short

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



### 2.5 Multipathway and environmental risk screening The potential for significant human health risks due to exposures via routes other than inhalation (i.e., multipathway exposures) was screened by first de termining whether any sources emitted any hazardous air pollutants known to be persistent and bioaccumulative in the environment (PB

- HAP) 1 

### . The PB

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

HAP are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury compounds, methoxychlor, polychlorinated biphenyls, polycyclic org anic matter (POM) , toxaphene, and trifluralin

. Currently, the multipathway screen can be used to quantitatively estimate exposures and risks for the following five PB

- HAP compounds: lead, cadmium compounds, chlorinated dibenzodioxins and furans, mercury compounds, and polycyclic organic matter (POM). These compounds are the focus of the screen because, based on current emissions and toxicity considerations, they are expected to pose the vast majority of the non

- inhalation risks to humans from air emissions at sources subject to residual risk provision s of the CAA. Potential impacts on human health from non

- inhalation exposures to cadmium compounds, chlorinated dibenzodioxins and furans, mercury compounds, and polycyclic organic matter are evaluated using our screening scenario developed for use with t he EPA’s peer

- reviewed Total Risk Integrated Methodology

– Fate, Transport, and Ecological Exposure

1 Although the two

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

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

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

11 ( TRIM.FaTE 2 

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

- specific emission rates of each of the four PB

- HAP listed above are large e nough to create the potential for significant non

- inhalation human health risks under reasonable worst

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

- HAP using a hypothetical upper

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

- HAP emissions (of the four listed PB

- HAP above) from each facility in the source category were compared to the emission rate threshold values for each of the four 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 der ived emission levels for each of the four PB

- HAP listed above at which the maximum human health risk would be 1

- in

- 1 million for lifetime cancer risk or a hazard quotient of 1.0 for noncancer impacts. We call this application of the TRIM.FaTE model the Ti er I screen. See Appendix 4 of this document ( Technical Support Document for TRIM

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

- level emission rates developed for screening potentially significant multi

- pathway impacts.
In evaluating the potential multi

- pathway risks from emissions of lead compounds, rather than developing a screening emission ra te for them , we compared maximum est imated chronic atmospheric concentrations with the current National Ambient Air Quality Standard (NAAQS) for lead. Values below the NAAQS were considered to have a low potential for multi

- p athway risks

.
Emissions of p olycyclic o rganic ma tter ( analyzed as benzo(a)pyrene ), polychlorinated biphenyls, lindane (gamma

- hch), chlordane, hexachlorobenzene, methoxychlor, toxaphene, heptachlor, and trifluralin were identified in the emissions inventories for the OSWRO source cate gory. The EPA has developed a screening approach to examine the potential for adverse environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental screen focuses on the following seven environmental HAP:

 Five persistent bioaccumulative HAP (PB

- HAP)

– cadmium, dioxins/furans, polycycl ic organic matter (POM), mercury (both inorganic mercury and methyl mercury) and lead.

 Two acid gases

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

2 EPA’s Total Risk Integrated Methodology (General Information) http://www 2.epa.gov/fera/total

- risk

integrated

- methodology

- trim

- general

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

12 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 faci lities in the source categor y emitted any of the seven environmental HAP. If one or more of the seven environmental HAP evaluated are emitted by at least one facility in the source category we proceed to the second step of the evaluation.
For cadmium, me rcury, POM and dioxins/furans, the environmental screening analysis consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst

- cast environmental conditionals to determine whether the facility

- specific off

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

- site emissions from a facility do not exceed the Tier I thresholds the facility “passes” the screen, and therefore, is not evaluated fu rther under the screening approach. If off

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

.
In Tier II of the environmental screening analysis, the screening emission thresholds are adjusted to acco unt for local meteorology and the actual location of lakes in the vicinity of facilities that did not pass the Tier I screen. If off

- site emission s from a facility do not exceed the Tier II thresholds, the facility passes the screen, and is typically not e valuated further. If off

- site emissions from a facility exceed the Tier II thresholds, the facility does not pass the screen and, therefore, may have the potential to cause adverse environmental effects. Such facilities are evaluated further to investigate factors such as the magnitude and characteristics of the area of exceedance.
For acid gases, t he environmental screening analysis evaluates the p otential phytotoxicity and reduced productivity of plants due to chronic exposure to acid gases. The environmental risk screening methodology for acid gases is a single

- tier screen that compares the average off

- site ambient air concentration over the model ing 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 the re is an adverse environmental effect.
For lead compounds, we currently do not have the ability to calculate media concentrations using the TRIM.FaTE model. However, air concentrations of lead are already calculated as part of the human health exposure and risk analysis using the HEM

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

13 effects which ca n include “effects on soils, water, crops, vegetation, man

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

- being.” 3



### 2.6 Dose

- Response Assessment

2.6.1 Sources of chronic dose

- response information Dose

- response assessment (carcinogenic and non

- carcinogenic) for chronic exposure (either by inhalation or ingestion) for the HAP reported in the emissions inven tory for the OSWRO

source category were based on the EPA Office of Air Quality Planning and Standards’ existing recommendations for HAP [ 9 ], also used for NATA [ 10 ]. This information has been obtained from various sources and prioritized according to (1) c onceptual consistency with EPA risk assessment guidelines and (2) level of peer review received. The prioritization process was aimed at incorporating into our assessments the best available science with respect to dose

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

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

cancer. The RfC is defined as an “ estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including

se nsitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. ” 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 appreciable risk of deleterious effects during a lifetime. ” The URE is defined as “ the upper

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

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

- day ...” EPA disseminates dose

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

3 A secondary standard, as defined in Section 109(b)(2), must “specify a level of air quality the attainment and maintenance of which, in the judgment of the Administrator, based on [air qualit y] 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 no t limited to, “effects on soils, water, crops, vegetation, man

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

- being.”

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

14 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 independen t 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 assess ments 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 opt ion for carcinogens for which the mode of action (MOA) has not been identified. We expect future EPA dose

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

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

. 2) US Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part of the US Department of Health and Human Services, develops and publishes Minimum Risk Levels (MRLs) [ 12 ] for inhalation and oral exposure to many toxic substances. As stated on the ATSDR web site: “ Following discussions with scienti sts within the Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a practice similar to that of the EPA's Reference Dose (RfD) and Reference Concentration (RfC) for deriving substance specific health guidance levels for non neo plastic endpoints .” The MRL is defined as “an estimate of daily human exposure to a substance that is likely to be without an appreciable risk of adverse effects (other than cancer) over a specified duration of exposure.” ATSDR describes MRLs as substanc e

- 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 effe cts 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 effe cts 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 4 

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

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

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

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

- Technical Support Document for the Determination of Non

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

15 In developing chronic risk estimates, we adjusted dose

- response values for some HAP based on professional judgment, as follows:
1) In the case of HAP categories such as glycol ethers and cyanide compounds, the most conservative dose

- response value of the chemical category i s used as a surrogate for other compounds in the group f or which dose

- response values are not available. This i s done in order to e xamine, under conservative assumptions, whether these HAP that lack dose

response values may pose an unacceptable risk and require further examination, or screen out from further assessment.
2) Where possible for emissions of unspecified mixtures of HAP cat egories such as met al compounds and POM, we apply category

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

- response value for the category.
3) In 2004, the EPA determined that the Chemica l Industry Insti tute of Toxicol ogy ( CIIT )

cancer dose

- response value for formaldehyde (5.5 x 10

- 9 

### per μg/m 3 

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

- response value (1.3 x 10

- 5 

### per μg/m 3 

### ), and we switched from using the IRIS value to the CIIT value in risk as sessments supporting regulatory actions. S ubsequent research published by EPA suggested that the CIIT model was not appropriate and in 2010 EPA returned to using 1991 IRIS value

. EPA has been working on revising the formaldehyde IRIS assessment and the N ational Academy of Sciences (NAS) completed its review of the EPA’s draft assessment in April of 2011

. 5 

### EPA will follow the NAS Report recommendations and will present results obtained by implementing the biologically

- based dose

- response (BBDR) model for formaldehyde. EPA will compare these estimates with those currently presented in the External Review draft of the assessment and will discuss their strengths and weaknesses. As recommended by the NAS committee, appropriated sensitivity and uncertainty an alyses will be an integral component of implementing the BBDR model

. The draft IRIS assessment will be revised in response to the NAS peer review and public comments and the final assessment will be posted on the IRIS database

. In the interim, we will p resent findings using the 1991 IRIS value as a primary estimate and EPA may also consider other information as the science evolves.
4) A substantial propo rtion of POM reported to EPA’s N ati onal E mission I nventory (NEI) is not speciated into individua l comp ounds. As a result, it i s necessary to apply the same simplifying assumptions to assessment s that are used in NATA [ 17 ]. The NATA approach partitions POM into eight different non

- overlapping “groups” that a re modeled as separate pollutants. Each POM grou p comprises POM species of similar carcinogenic potency, for which we can apply the same URE.

The emissions inventor y for the OSWRO source category include s emissions of HAP with available chronic quantitative inhalat ion dose

- response values. Of these
, 5 6 are classified as known, probable, or possible carcinoge ns, with quantitative cancer do se

- response values

5 http://www.nap.edu/catalog.php?record_id=13142

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

16 available. These 5 6 HAP, their quantitati ve inhalation chronic cancer do se

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

- 1. Eighty

- two HAP have quantitative inhalation chronic noncancer threshold values available

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

- 2

.

Table 2.6

- 1 Dose

- Response Values for Chronic Inhalation Exposure to Carcinogens
URE (unit risk estimate for cancer) 6 

### = cancer risk per μg/m 3 

### of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & De velopment, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard Assessment. HEAST = EPA Health Effects Assessment Tables , Conv. Oral = Oral unit risk converted to inhalation. Pollutant CAS Number 7 

### URE 1 1



### (1/μg/m3) Source

1,1,2,2

- Tetrachloroethane 79345 0.000058 IRIS 1,1,2

- Trichloroethane 79005 0.000016 IRIS 1,2

- Dibromo

- 3

- chloropropane 96128 0.002 CAL 1,2

- D iphenylhydrazine 122667 0.00022 IRIS 1,3

- Butadiene 106990 0.00003 IRIS 1,3

- Dichloropropene 542756 0.000004 IRIS 1,3

- Propane sultone 1120714 0.00069 CAL 1,4

- Dioxane 123911 0.00000 5 IRIS 8 2,4,6

- Trichlorophenol 88062 0.0000031 IRIS 2,4

- Dinitrotoluene 121142 0.000089 CAL 2,4

- Toluene diamine 95807 0.0011 CAL 2,4

- Toluene diisocyanate 584849 0.000011 CAL 2

- Nitropropane 79469 0.0000056 EPA OAQPS 3,3'

- Dichlorobenzidine 91941 0.00034 CAL 4,4'

- methylene Bis(2

- chloroaniline) 101144 0.00043 CAL 4,4'

- Methylenedianiline 101779 0.00046 CAL 4

- Dimethylaminoazobenzene (p

Dimethylaminoazobenzene 60117 0.0013 CAL Lindane (gamma

- HCH) 58899 0.00031 CAL Acetaldehyde 75070 0.0000022 IRIS Acetamide 60355 0.00002 CAL Acrylamide 79061 0.00016 IRIS Acrylonitrile 107131 0.000068 IRIS Allyl c hloride 107051 0.000006 

### CAL Aniline 62533 0.0000016 

### CAL

6 The URE is the upper

- bound excess cancer risk estimated to result from continuous lifetime exposure to an agent at a concentration of 1 μg/m 3 in air. URE’s are considered upper bound estimates meaning they represent a plausible upper limit to the true value.
7 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have used a surrogate 3

- digit ide ntifier corresponding to the group’s position on the CAA list of HAP.
8 IRIS external review draft value, September 2011.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

17 Table 2.6

- 1 Dose

- Response Values for Chronic Inhalation Exposure to Carcinogens
URE (unit risk estimate for cancer) 6 

### = cancer risk per μg/m 3 

### of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & De velopment, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard Assessment. HEAST = EPA Health Effects Assessment Tables , Conv. Oral = Oral unit risk converted to inhalation. Pollutant CAS Number 7 

### URE 1 1



### (1/μg/m3) Source

Benzene 9 71432 0.0000078 IRIS Benzidine 92875 0.1072 IRIS Benzyl c hloride 100447 0.000049 CAL Bis(2

- ethylhexyl)phthalate 117817 0.0000024 CAL Bromoform 75252 0.0000011 IRIS Carbon t etrachloride 56235 0.000006 IRIS Chlordane 57749 0.0001 IRIS Chlorobenzilate 510156 0.000078 HEAST Dichloroethyl e ther 111444 0.00033 IRIS Epichlorohydrin (1

- Chloro

- 2,3

- e poxypropane) 106898 0.0000012 IRIS Ethyl b enzene 100414 0.0000025 CAL Ethyl c arbamate 51796 0.000464 CAL Ethylene d ibromide 106934 0.0006 IRIS Ethylene d ichloride 107062 0.000026 IRIS Ethylene o xide 75218 0.000088 CAL Ethylene t hiourea 96457 0.000013 CAL Ethylidene d ichloride (1,1

- Dichloroethane) 75343 0.0000016 CAL Formaldehyde 10 50000 0.000013 IRIS Heptachlor 76448 0.0013 IRIS Hexachlorobenzene 118741 0.00046 IRIS Hexachlorobutadiene 87683 0.000022 IRIS Hydrazine 302012 0.0049 IRIS Methyl tert

- butyl e ther 1634044 0.00000026 CAL Methylene c hloride 75092 0.000000 13 IRIS Naphthalene 91203 0.000034 CAL Nitrobenzene 98953 0.00004 IRIS Nitrosodimethylamine 62759 0.0 22 IRIS N

- nitrosomorpholine 59892 0.0019 CAL

o

- Toluidine 95534 0.000051 CAL P

- dichlorobenzene (1,4

- dichlorobenzene ) 106467 0.000011 CAL Pentachlorophenol 87865 0.0000051 CAL Polychlorinated b iphenyls (PCB) 1336363 0.0001 

### IRIS

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

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

- 06 per ug/m 3

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

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

18 Table 2.6

- 1 Dose

- Response Values for Chronic Inhalation Exposure to Carcinogens
URE (unit risk estimate for cancer) 6 

### = cancer risk per μg/m 3 

### of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & De velopment, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard Assessment. HEAST = EPA Health Effects Assessment Tables , Conv. Oral = Oral unit risk converted to inhalation. Pollutant CAS Number 7 

### URE 1 1



### (1/μg/m3) Source

Polycyclic Organic Matter 11 (POM) 2

- Acetylaminofluorene 53963 0.00208 CAL Propylene oxide 75569 0.0000037 

### IRIS Tetrachloroethene 127184 0.00000 026 IRIS Toxaphene 8001352 0.00032 

### IRIS Trichloroethylene 79016 0.00000 48 IRIS Vinyl c hloride 75014 0.0000088 IRIS



### Table 2.6

- 2 Dose

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

Pollutant CAS Number 12 

### RfC (mg/m 3 

### ) Source 12

1,1,1

- Trichloroethane (methyl chloroform) 71556 5 IRIS

- M 1,1,2

- Trichloroethane 79005 0.4 CAL 1,2,4

- Trichlorobenzene 120821 0.2 HEAST 1,2

- Dibromo

- 3

- chloropropane 9612 8 0.0002 IRIS

- M 1,2

- Epoxybutane 10688 7 0.02 IRIS

- M 1,3

- Butadiene 106990 0.002 IRIS

- M 1,3

- Dichloropropene 54275 6 0.02 IRIS

- H

11 POM without a chemical

- specific URE are assigned a URE associated with a mixture of POM compounds having similar characteristics. Details of this method, also used in the 2002 National Air Toxics Assessment, are available at http://www.epa.gov/ttn/a tw/nata2002/02pdfs/pom_approach.pdf

.

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

19 Table 2.6

- 2 Dose

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

Pollutant CAS Number 12 

### RfC (mg/m 3 

### ) Source 12

1,4

- Dichlorobenzene (P

- dichlorobenzene) 106467 0.8 IRIS

- M 1,4

- Dioxane 12391 1 0.03 IRIS

- D 13 2,4

- Dinitrotoluene 12114 2 0.007 CAL 2,4

- Toluene diisocyanate 58484 9 0.00007 IRIS

- M 2

- Nitropropane 7946 9 0.02 IRIS

- L 4,4'

- Methylenedianiline 10177 9 0.02 CAL 4,4 '

- Methylene diphenyl diisocyanate 10168 8 0.0006 IRIS

- M Acetaldehyde 75070 0.009 IRIS

- L Acetonitrile 7505 8 0.06 IRIS

- M Acrolein 107028 0.00002 IRIS

- M Acrylamide 79061 0.006 IRIS

- M Acrylic a cid 79107 0.001 IRIS

- M Acrylonitrile 10713 1 0.002 IRIS

- M Allyl chloride 10705 1 0.001 IRIS

- L Aniline 6253 3 0.001 IRIS

- L Benzene 71432 0.03 IRIS

- M Benzidine 9287 5 0.01 CAL Bis(2

- ethylhexyl)phthalate 11781 7 0.01 CAL Carbon d isulfide 75150 0.7 IRIS

- M Carb on t etrachloride 56235 0.1 IRIS

- M Chlordane 5774 9 0.0007 IRIS

- L Chlorine 7782505 0.00015 ATSDR Chlorobenzene 108907 1 CAL Chloroform 67663 0.098 ATSDR Cresol s (mixed) 1319773 0.6 CAL

m

- Cresol 10839 4 0.6 CAL

o

- Cresol 95487 0.6 CAL p

- Cresol 10644 5 0.6 CAL Cumene 98828 0.4 IRIS

- M Dichlorvos 6273 7 0.0005 IRIS

- M Diethanolamine 111422 0.003 CAL Dimethyl formamide 6812 2 0.03 IRIS

- M

13 IRIS external review draft value, September 2011.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

20 Table 2.6

- 2 Dose

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

Pollutant CAS Number 12 

### RfC (mg/m 3 

### ) Source 12

Epichlorohydrin (1

- Chloro

- 2,3

e poxypropane ) 10689 8 0.001 IRIS

- M Ethyl b enzene 100414 1 IRIS

- L Ethyl chloride 7500 3 10 IRIS

- M Ethylene d ibromide 106934 0.009 IRIS

- M Ethylene d ichloride 107062 2.4 ATSDR Ethylene g lycol 107211 0.4 CAL Ethylene o xide 75218 0.03 CAL Ethylene thiourea 9645 7 0.003 CAL Ethylidene d ichloride (1,1

- Dichloroethane) 7 534 3 0.5 HEAST Formaldehyde 50000 0.0098 ATSDR Glycol Ethers 14 Ethylene glycol ethyl ether 11080 5 0.2 IRIS

- M Ethylene glycol methyl ether 10986 4 0.02 IRIS

- M Glycol ethers 18 1 0.02 IRIS

- M Hexachlorobenzene 11874 1 0.003 CAL Hexachlorobutadiene 8768 3 0.09 CAL Hexachlorocyclopentadiene 77474 0.0002 IRIS

– M/H Hexachloroethane 67721 0.0 3 IRIS

- L Hexane 11054 3 0.7 IRIS

- M Hydrazine 302012 0.0002 CAL Hydrochloric a cid (hydrogen chloride) 7647010 0.02 IRIS

- L Hydrof luori c acid (hydrogen fluoride) 766439 3 0.014 CAL Isophorone 7859 1 2 CAL Lindane (gamma

- HCH) 5889 9 0.0003 CAL Maleic anhydride 10831 6 0.0007 CAL Methanol 67561 4 CAL Methyl b romide 74839 0.005 IRIS

- H Methyl c hloride 74873 0.09 IRIS

- M Met hyl isobutyl k etone 108101 3 IRIS

- L/M Methyl m ethacrylate 80626 0.7 IRIS

- M/H Methyl tert

- butyl e ther 1634044 3 IRIS

- M Methylene c hloride 75092 0.6 IRIS

– M/H

14 The RfC value for ethylene glycol methyl ether was used as a surrogate for all glycol ethers without an RfC.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

21 Table 2.6

- 2 Dose

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

Pollutant CAS Number 12 

### RfC (mg/m 3 

### ) Source 12

Naphthalene 91203 0.003 IRIS

- M Nitrobenzene 9895 3 0.009 IRIS

- M Pentachlorophenol 87865 0.1 CAL Phenol 108952 0.2 CAL Phosphine 780351 2 0.0003 IRIS

- L Phthalic anhydride 8544 9 0.02 CAL Propionaldehyde 123386 0.008 IRIS

– L/M Propylene d ichloride 78875 0.004 IRIS

- M Propylene oxide 7556 9 0.03 IRIS

- M Styrene 100425 1 IRIS

- M Styrene oxide 9609 3 0.006 CAL Tetrachloroethene 12718 4 0. 04 IRIS

- M Titanium tetrachloride 755045 0 0.0001 

### ATSDR Toluene 108883 5 IRIS

- H Trichloroethylene 79016 0. 002 IRIS

- H Triethylamine 12144 8 0.007 IRIS

- L Vinyl a cetate 108054 0.2 IRIS

- H Vinyl c hloride 75014 0.1 IRIS

- M Vinylidene c hloride 75354 0.2 IRIS

– H/M Xylenes (mixed) 1330207 0.1 IRIS

- M

m

- Xylene 16 108383 0.1 IRIS

- M

o

- Xylene 15 95476 0.1 IRIS

- M p

- Xylene 16 106423 0.1 IRIS

- M



### 2.6.2 Sources of acute dose

- response information Hazard identification and dose

- response assessment information for preliminary acute inhalation exposure assessments are based on the existing recommendations of OAQPS for HAP [ 18 ]

. Depending on availability, the results from screening acute assessments ar e compared to both “no effects” reference levels for the general public, such as the California Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelin es

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

22 (ERPGs), with the recognition that the ultimate interpretation of any potential risks associated with an estimated exceedance of a particular reference level depends on the definition of that level and any limitations expressed therein. Comparisons amo ng different available inhalation health effect reference values (both acute and chronic) for selected HAP can be found in a n EPA document of graphical arrays [ 19 ].
California Acute Reference Exposure Levels (RELs ). The California Environmental Protecti on Agency (CalEPA) has developed acute dose

- response reference values for many substan ces, expressing the results as a cute inhalation Reference Exposure Levels (RELs).
The acute REL ( http://www.oehha.ca.gov/air/pdf/acuterel.pdf ) is defined by CalEPA as

“ t he concentration level at or below which no adverse health effects are anticipated for a specified exposure duration . [ 20 ]

. RELs are based on the most sensitive, relevant, adverse heal th effect reported in the medical and toxicological literature. RELs are designed to protect the most sensitive individuals in the population by the inclusion of margins of safety. Since margins of safety are incorporated to address data gaps and uncerta inties, exceeding the REL does not automatically indicate an adverse health impact. ” Acute RELs are developed for 1

- hour (and 8

- hour) exposures. The values incorporate uncertainty factors similar to those used in deriving EPA’s Inhalation Reference Concen trations (RfCs) for chronic exposures (and, in fact, California also has developed chronic RELs).
Acute Exposure Guideline Levels (AEGLs ). AEGLs are developed by the National Advisory Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) f or Haza rdous 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 that “the primary purpose of the AEG L program and the NAC/AEGL Committee is to develop guideline levels for once

- in

- a

- lifetime , short

- term exposures to airborne concentrations of acutely toxic, high

- priority chemicals.” In detailing the intended application of AEGL values, the document stat es that
, “ It is anticipated that the AEGL values will be used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, and possibly the international community in conjunction with chemical emergency response, planning, and prevention p rograms. 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, including susceptible i ndividuals, could

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

23 experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. However, the effects are not disabling and are transient and reversible upon cessation of exposure. ”

“ AEGL

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

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

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

“ Airborne concentrations below AEGL

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

corres ponding 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 guideline s represent concentrations for exposure of the general population (but not particularly sensitive persons) for up to 1 hour associated with effects expected to be mild or transient (ERPG

- 1), irreversible or serious (ERPG

- 2), and potentially life

- threatenin g (ERPG

- 3).

ERPG values ( http://www.aiha.org/get

involved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Pages/default.a spx ) are described in their supporting documentation as follows: “ERPGs are air concentration guidelines for single exposures to agen ts and are intended for use as tools to assess the adequacy of accident prevention and emergency response plans, including transportation emergency planning, community emergency response plans and incident prevention and mitigation.”

ERPG

- 1 and ERPG

- 2 va lues 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 pe rceiving 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 healt h effects or symptoms which could impair an individual's ability to take protective action. ”

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

24 The emissions inventory for the OSWRO source category include s emissions of 57 HAP with relevant and available quantitative acute dose

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

- 3

.

Table 2.6

- 3 Dose

- Response Values for Acute Exposure

Pollutant CAS Number AEGL

- 1

(1

- hr) (mg/m 3 

### ) AEGL

- 2

(1

- hr) (mg/m 3 

### ) ERPG

1 (mg/m 3 

### ) ERPG

- 2 (mg/m 3 

### ) REL 1,1

- Dimethylhydrazine 5714 7 7.4 1,2

- Epoxybutane 10688 7 210 410 1,3

- Butadiene 10699 0 1500 12000 22 440 1,4

- Dioxane 12391 1 61 1200 3 2,4

- Toluene diisocyanate 58484 9 0.14 0.59 0. 071 1.1 Acetaldehyde 75070 81 490 18 360 0.47 Acetonitrile 7505 8 22 540 Acrolein 107028 0.069 0.23 0. 11 0. 34 0.0025 Acrylic a cid 79107 4.4 140 3 15 0 6 Acrylonitrile 10713 1 10 130 22 77 Allyl chloride 10705 1 8.8 170 9.4 130 Aniline 6253 3 30 46 Benzene 71432 170 2600 16 0 48 0 1.3 Benzyl chloride 10044 7 5.2 52 0.24 Biphenyl 92524 61 Carbon d isulfide 75150 40 500 3.1 160 6.2 Carbon t etrachloride 56235 280 1200 130 63 0 1.9 Chlorine 7782505 1.5 5.8 2.9 8.7 0.21 Chlorobenzene 108907 46 690 Chloroform 67663 310 24 0 0.15 Chloromethyl methyl ether 10730 2 1.6 3.3 Cumene 98828 250 1500 Dichlorvos 6273 7 0.99 5.1 Dimethyl formamide 6812 2 270 6 30 0 Dimethyl sulfate 7778 1 0.12 0.62 Epichlorohydrin ( 1

- Chloro

- 2,3

- e poxypropane) 106898 22 91 19 76 1.3 Ethyl a crylate 140885 34 150 0.041 12 0 Ethyl b enzene 100414 140 4800 Ethylene d ibromide 106934 130 180 Ethylene d ichloride 107062 200 810 Ethylene o xide 75218 81 90 Formaldehyde 50000 1.1 17 1. 2 1 2 0.055

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

25

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 Glycol Ethers 16 Ethylene glycol ethyl ether 11080 5 0.37 Ethylene glycol methyl ether 10986 4 0.093 Glycol ethers 18 1 0.093 Hexachlorobutadiene 8768 3 11 32 Hexane 110543 12000 Hydrazine 302012 0.13 17 0. 65 6.5 Maleic anhydride 10831 6 0.8 8 Methanol 67561 690 2700 260 13 00 28 Methyl b romide 74839 820 19 0 3.9 Methyl c hloride 74873 1900 83 0 Methyl hydrazine 6034 4 3.2 Methyl iodide 7488 4 150 290 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 Phosphine 780351 2 2.8 0.7 Propionaldehyde 123386 110 620 Propylene oxide 7556 9 170 690 12 0 5 90 3.1 Styrene 100425 85 550 210 110 0 21 Tetrachloroethene 127184 240 1600 68 0 14 00 20 Titanium tetrachloride 755045 0 0.54 7.8 5 20 Toluene 108883 750 4500 190 110 0 37 Trichloroethylene 79016 700 2400 54 0 27 00 Triethylamine 12144 8 2.8 Vinyl a cetate 108054 24 630 18 260 Vinyl c hloride 75014 640 3100 130 0 130 00 180 Xylenes (mixed) 1330207 560 4000 22

m

- Xylene 17 108383 22

o

- Xylene 18 95476 22 p

- Xylene 18 106423 22

16 The acute REL for ethylene glycol methyl ether was used as a surrogate for glycol e ther compounds without an acute REL. 17 The REL for mixed xylenes was used as a surrogate.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

26 

### 2.7 Risk Characterization

2.7.1 General

The final product of the risk assessment is the risk characterization, in which the information from the previous steps is integrated and an overall conclusion about risk is synthesized that is complete, informative, and useful for decision makers. In gen eral, the nature of this risk characterization depends on the information available, the application of the risk information and the resources available. In all cases, major issues associated with determining the nature and extent of the risk are identifi ed 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/OST P September 2007 Memorandum on Updated Principles for Risk Analysis 18 

### , 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, itera tive analytical approach, we have attempted to reduce both uncertainty and bias to the greatest degree possible in these assessments, within the limitations of available time and resources. We provide summaries of risk metrics (including maximum individua l cancer risks and noncancer hazards, as well as cancer incidence estimates) along with a discussion of the major uncertainties associated with their derivation to provide decision makers with the fullest picture of the assessment and its limitations.

For each carcinogenic HAP included in the assessment that has a potency estimate available, individu al and population cancer risks we re calculated by multiplying the corresponding lifetime average exposure estimate by the appropriate URE. This calculated can cer 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 exposure. Because UREs for most HAP are upper

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

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

- of

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

- Life Exposure to Carcinogens [ 26 ]

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

- 1), 3 (for children aged 2

- 15), or 1.6 (for 70 years of exposure beginning at birth), as needed in risk assessments . In this case, this has the effect of increasing the estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a

18 Memorandum f

or t he Heads o f Executive Departments a nd Agencies

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

- 24.pdf )

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

27 small fraction of the total POM emissions may be reported as individual compounds, EPA expresses carcinogenic potency for compounds in this group in terms of benzo[a]pyrene equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism of action as does benzo[a]pyrene. For this reason
, EPA implem entation policy [ 27 ] recommends applyi ng the Supplemental Guidance to all carcinogenic PAH s for which risk estimates are based on relative potency. Accordingly, we applied the Supplemental Guidance to all unspeciated POM mixtures.
Increased cancer incidence for the entire receptor population within the area of analysis wa s estimated by multip lying 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 estimat e of the number of cancer cases per year.

In the case of benzene, the high end of the reported cancer URE range was used in our assessments to provide a conservative estimate of potential cancer risks. Use of the high end of the range provides risk estim ates that are approximately 3.5 times higher than use of the equally plausible low end value. If the estimated benzene

– associated risks exceed 1 in a million, we also evaluate d the impact of using the low end of the URE range on our risk results.

Unlike linear dose

- response assessments for cancer, noncancer health hazards generally are not expressed as a probability of an adverse occurrence. Instead, “risk” for noncancer effects is expressed by comparing an exposure to a reference level as a ratio. The “hazard quotient” (HQ) is the estimated exposure divided by a reference level (e.g., the RfC). For a given HAP, exposures at or below the reference level (HQ≤1) are not likely to cause adverse health effects. As exposures increase above the reference lev el (HQs increasingly greater than 1), the potential for adverse effects increases. For exposures predicted to be above the RfC, the risk characterization includes the degree of confidence ascribed to the RfC values for the compound(s) of concern (i.e., hi gh, medium, or low confidence) and discusses the impact of this on possible health interpretations.
The risk characterization for chronic effects other than cancer is expressed in terms of the HQ for inhalation, calculated for each HAP at each census blo ck centroid. As discussed above, RfCs incorporate generally conservative uncertainty factors in the face of uncertain extrapolations, such that an HQ greater than one does not necessarily suggest the onset of adverse effects. The HQ cannot be translated to a probability that adverse effects will occur, and is unlikely to be proportional to adverse health effect outcomes in a population

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

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

28 2.7.2 Mixtures Since most or all receptors in these assessments receive exposures to multiple pollutants

r ather than a single pollutant, we estimated the aggregate health risks associated with all the exposures from a particular source category combined.
To combine risks across multiple carcinogens, our assessments use the mixtures guidelines’ [ 28 , 29 ] default a ssumption of additivity of effects, and combine risks by summing them using the independence formula in the mixtures guidelines.
In assessing noncancer hazard from chronic exposures, in cases where different pollutants cause adverse health effects via com pletely different modes of action, it may be inappropriate to aggregate HQs. In consideration of these mode

- of

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

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

- organ

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

. Because impacts on organs or systems that occur abov e 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 inhalati on screening and the variable nature of emissions and potential exposures, acute impacts were screened on an individual pollutant basis, not using the TOSHI approach.

2.7.3 Facility

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

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

- wide assessment are summarized below in the r isk characterization section of this document. The complete results of the facility

- wide assessment are provided in Appendix 5 of this document, Detailed Risk Modeling Results

.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

29 2.7.4 MACT

- Allowable Risk The emissions data in the data set for the OSWRO source category are estimates of actual emissions on an annual basis. The risk results presented in the following sections are b ased on these actual emissions. For the OSWRO source category, estimates of MACT

- allowable emissions were also estimated, and the risk results based on tho se emissions are presented below as well. For more detail, please refer to the MACT

- Allowable memo in docket for this action ( MACT

- Allowable Emissions for the Off

- Site Waste and Recovery Operations Source Cat egory , located in Docket EPA

- HQ

- OAR

- 2012

- 0360

. )



## 3 Risk Results for the OSWRO Source Ca tegor y

3.1 Source Category Description and Results

The O SWRO source category includes facilities that store, treat, recycle, or dispose of certain wastes, used oil, or used so lvents from off

- site locations. Specifically, facilities in the OSWRO source category handle off

- site material, which is defined as any material generated from commercial, industrial, agricultural, and mining operations or community activities that is recycled, reused, reprocessed, disca rded, or is being accumulated, stored, or treated (physically, chemically, biologically, or thermally) before being disc arded, discharged, or recycled. The activities that take place at OSWRO sources vary widely depending on the specific treatment, storag e, or other handling processes that are being conducted (e.g., treatment of wastewater, physical separation of waste mixtures, storage of wastes, reprocessing of wastes for recovery or recycling purposes, and other processes). Potential HAP emission sourc es at OSWRO operations include tanks, surface impoundments, containers, oil

- water separators, process vents, equipment leaks, and individual drain systems and oth er material conveyance systems.

The data set for the OSWRO source category contains 38 faci lities identified with a n OSWRO MACT code 806 in the 2005 National

- scale Air Toxics Assessment (NATA) National Emissions Inventory (NEI) , as explained in Section 2.1 of this document

. The 2005 NATA NEI was updated for this category with data from industry and enhanced quality assurance of facility and emission locations

. In the NEI, t hirty seven (37) of these facilities are identified as major sources one facility is identified as an area source , however we believe that all 38 facilities are major sources

.
The emissions for the O SWRO source category data set (of 38 facilities) are summarized in Table 3.1

- 1 . The total HAP emissions for the source category are approximately 80 tons per year. Based on these data, the HAP emitted in the largest quantities ac ross these 38 facilities

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

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

- trichloroethane, ethyl benzene, acetaldehyde, glycol ethers, hexane, trichloroethylene, formaldehyde, styrene, propionaldehyde, benzene, 2,4

- toluene diamine, carbon tetrachloride, chloroform, ethylene dichloride, ethylene glycol, 1,2

- epoxybutane, naphthalene, carbon disulfide, 1,1,2

- tri chloroethane, vinyl acetate, phenol, hydrofluoric acid, chlorobenzene,

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

30 ethylene oxide, methylene diphenyl diisocyanate, cresols (mixed), nitrobenzene, acrylonitrile, vinylidene chloride, aniline, methyl methacrylate, propylene dichloride, phthalic anhydrid e, lindane (gamma

- hch), 2,4

- toluene diisocyanate, titanium tetrachloride, 2

- nitropropane, m

cresol (3

- methylphenol), p

- cresol (4

- methy p henol)
, dimethyl formamide, acetamide, and 2,4

- d, salts and esters . These 5 2 chemicals account for 99 percent of the tot al emissions by mass. Persistent and bioaccumulative HAP (PB HA P) 19 

### reported as emissions from these facilities include p olycyclic o rganic ma tter ( analyzed as benzo(a)pyrene ), polychlorinated biphenyls, lindane (gamma

- hch), chlordane, hexachlorobenzene, me thoxychlor, toxaphene, heptachlor, and trifluralin

. The following environmental HAP are emitted from the Off

- Site Waste and Recovery Operations source category and are included in the environmental risk screen: POM, hydrogen chloride and hydrogen fluorid e.

Table 3.1

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

Assessment and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 38 facilities in data set) Prioritized Inhalation Dose

- Response

Value Identified by OAQPS b
PB

HAP? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? Toluene 15 25 

 

 Methylene Chloride 11 21 

 

 

 Methanol 8 26 

 

 Tetrachloroethene 8 22 

 

 

 Xylenes (mixed) 7 24 

 

 Hydrochloric Acid 6 7 

 

 Methyl Tert

- butyl Ether 5 11 

 

 

 Acetonitrile 3 13 

 

 Methyl Isobutyl Ketone 3 24 

 Chlorine 2 3 

 

 1,1,1

- trichloroethane 2 9 

 

 Ethyl Benzene 1 21 

 

 

 Acetaldehyde 1 3 

 

 

 Glycol Ethers

  Glycol Ethers 1 8 

 

 Ethylene Glycol Ethyl Ether 0.002 2 

 

 Ethylene Glycol Methyl Ether 0.002 1 

 

 H exane 0.8 14 

 

 Trichloroethylene 0.6 16 

 

 

 Formaldehyde 0.4 12 

 

 

 Styrene 0.4 17 

 



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

- 453K

- 04

- 001A, as referenced in the NPRM and provided on the EPA’s Technology Transfer Network website for Fate, Exposure, and Risk Assessment at http://www 2.epa.gov/fera/risk

- assessment

- and

- modeling

air

- toxics

- risk

- assessment

- reference

- library

.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

31 Table 3.1

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

Assessment and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 38 facilities in data set) Prioritized Inhalation Dose

- Response

Value Identified by OAQPS b
PB

HAP? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? Propionaldehyde 0.3 2 

 

 Benzene 0.3 15 

 

 

 2,4

- toluene Diamine 0.3 2 

 Carbon Tetrachloride 0.3 8 

 

 

 Chloroform 0.3 11 

 

 Ethylene Dichloride 0.3 8 

 

 

 Ethylene Glycol 0.3 9 

 1,2

- epoxybutane 0.2 2 

 

 Naphthalene 0.2 16 

 

 Carbon Disulfide 0.2 6 

 

 1,1,2

- trichloroethane 0.2 6 

 

 Vinyl Acetate 0.2 7 

 

 Phenol 0.2 10 

 

 Hydrofluoric Acid 0.1 4 

 

 Chlorobenzene 0.1 7 

 

 Ethylene Oxide 0.1 1 

 

 

 Methylene Diphenyl Diisocyanate 0.1 1 

 

 Cresols (mixed) 0.1 6 

 Nitrobenzene 0.1 6 

 

 Acrylonitrile 0.1 6 

 

 

 Vinylidene Chloride 0.1 2 

 Aniline 0.07 5 

 

 

 Methyl Methacrylate 0.06 6 

 

 Propylene Dichloride 0.05 4 

 Phthalic Anhydride 0.05 4 

 Lindane (gamma

- hch) 0.05 3 

 

 

 2,4

- toluene Diisocyanate 0.05 3 

 

 

 Titanium Tetrachloride 0.04 4 

 

 2

- nitropropane 0.04 3 

 

 M

- cresol (3

- methylphenol) 0.04 2 

 P

- cresol (4

- methy Phenol) 0.04 2 

 Dimethyl Formamide 0.04 9 

 

 Acetamide 0.04 2 

 2,4

- d, Salts And Esters 0.04 3 P

- dichlorobenzene 0.03 4 

 

 1,3

- butadiene 0.03 2 

 

 

 O

- cresol 0.03 1 



### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

32 Table 3.1

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

Assessment and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 38 facilities in data set) Prioritized Inhalation Dose

- Response

Value Identified by OAQPS b
PB

HAP? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? Dichloroethyl Ether 0.03 2 

 Chlordane 0.03 5 

 

 

 Hexachlorobenzene 0.03 6 

 

 

 Hexachlorobutadiene 0.03 4 

 

 

 Hexachloroethane 0.02 5 

 1,4

- dioxane 0.02 3 

 

 

 Epichlorohydrin 0.02 4 

 

 

 Acetophenone 0.02 4 Methoxychlor 0.02 4 

 Maleic Anhydride 0.02 4 

 

 2,4

- dinitrotoluene 0.02 2 

 

 Dibutylphthalate 0.02 3 Ethyl Acrylate 0.02 6 

 Cumene 0.02 8 

 

 Isophorone 0.02 1 

 Polychlorinated b iphenyls 0.02 3 

 

 1,1,2,2

- tetrachloroethane 0.01 4 

 Vinyl Chloride 0.01 4 

 

 

 M

- xylene 0.01 4 

 

 Acrylamide 0.01 4 

 

 Toxaphene 0.01 3 

 

 Hexachlorocyclopentadiene 0.01 3 

 Bis(2

- ethylhexyl)phthalate 0.01 3 

 

 1,2,4

- trichlorobenzene 0.01 3 

 P

- xylene 0.01 2 

 

 Dibenzofuran 0.009 1 Carbaryl 0.008 3 Dimethyl Phthalate 0.008 3 Ethyl Carbamate 0.008 3 

 Acrylic Acid 0.008 4 

 

 Methyl Chloride 0.008 3 

 

 Triethylamine 0.008 4 

 

 Pentachlorophenol 0.007 2 

 

 Biphenyl 0.007 3 

 4

- nitrobiphenyl 0.007 1 N

- nitrosomorpholine 0.007 1 

 Nitrosodimethylamine 0.007 1 



### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

33 Table 3.1

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

Assessment and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 38 facilities in data set) Prioritized Inhalation Dose

- Response

Value Identified by OAQPS b
PB

HAP? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? Heptachlor 0.007 5 

 

 Methyl Bromide 0.006 2 

 

 Pentachloronitrobenzene 0.006 1 O

- toluidine 0.006 3 

 4

- nitrophenol 0.005 1 O

- xylene 0.005 2 

 

 3,3'

- dimethylbenzidine 0.005 1 4,4'

- methylene Bis(2

- chloroaniline) 0.005 1 

 Dimethylcarbamoyl Chloride 0.005 1 Benzidine 0.005 2 

 

 2,4

- dinitrophenol 0.005 1 Ethyl Chloride 0.005 1 

 Ethylene Dibromide 0.004 2 

 

 

 3,3'

- dimethoxybenzidine 0.004 1 2,4,6

- trichlorophenol 0.004 1 

 Hydrazine 0.004 3 

 

 

 1,3

- propane Sultone 0.004 1 

 Captan 0.004 3 2,4,5

- trichlorophenol 0.003 1 Chlorobenzilate 0.003 1 

 Diethanolamine 0.003 2 

 Dimethyl Sulfate 0.002 3 

 Propylene Oxide 0.002 2 

 

 

 4,4'

- methylenedianiline 0.002 1 

 

 Ethylene Thiourea 0.002 1 

 

 Quinone 0.002 1 Allyl Chloride 0.002 1 

 

 

 Methyl Iodide 0.002 1 

 Benzyl Chloride 0.002 3 

 

 1,3

- dichloropropene 0.002 1 

 

 P

- dimethylaminoazobenzene 0.002 1 

 Ethylidene Dichloride 0.002 2 

 

 N,n

- dimethylaniline 0.001 2 1,2

- dibromo

- 3

- chloropropane 0.001 1 

 

 1,1

- dimethylhydrazine 0.001 1 

 1,2

- diphenylhydrazine 0.001 1 

 Benzotrichloride 0.001 1

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

34 Table 3.1

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

Assessment and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of

Facilities Reporting HAP ( 38 facilities in data set) Prioritized Inhalation Dose

- Response

Value Identified by OAQPS b
PB

HAP? Unit Risk Estimate for Cancer? Reference

Concentration for Noncancer? Health

Benchmark Values for Acute Noncancer? Phosphorus, White 0.001 1 Hydroquinone 0.001 2 Polycyclic Organic Matter

  2

- acetylaminofluorene 0.001 1 

 

 Dichlorvos 0.0008 1 

 

 3,3'

- dichlorobenzidine 0.0007 1 

 Chloromethyl Methyl Ether 0.0006 1 

 Bromoform 0.0004 1 

 Styrene Oxide 0.0004 1 

 Acrolein 0.0002 2 

 

 Trifluralin 0.0001 2 

 Methyl Hydrazine 0.00003 1 

 Phosphine 0 1 

 



a Notes for how HAP were speciated for risk assessment:

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

b Specific dose

- response values for each chemical are identified on EPA’s Technology Transfer N etwork website 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 Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

35 

### 3.2 Risk Characterization

This section presents the results of the risk assessment for the O SWRO source category based on the model ing methods described in the previous sections

. All baseline risk results are developed using the best estimates of actual HAP emissions summarized in the previous section. The basic chronic inhalation risk estimates presented here are the maximum indivi dual 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 potenti al non

- inhalation risks from PB

- HAP. Also presented are the HAP “drivers,” which are the HAP that collectively contribute 90 percent of the maximum cancer risk or maximum hazard index at the highest exposure location , as well as a summary of the results of our facility

- wide assessments and our analysis of risks associated with the maximum allowed emissions under the current MACT standards

. A detailed summary of t he facility

- specific risk assessment results is available in Appendix 5 of this document, Draft Detailed Risk Modeling Results

.
Table 3 .2

- 1 summarize s the chronic and acute inhalation risk results for this source category. The results of the chronic inhalation cancer risk assessment for the OSWRO source category estimate that the maximum lifetime individual cancer risks posed by these facilities could be as high as 9

- in

- 1 million. The major contributors to this risk are benzidine, and 2,4

- toluene diamine . Approximately 210 ,000 people were estimated to have cancer risks at or abov e 1

- in

1 in a million as a result of the emissions from this source category . The maximum chronic

non

- cancer target organ specific hazard index (TOSHI) value for the source category could be up to 0.6 associated with emissions of chlorine, indicating no s ignificant potential for chronic

non

- cancer impacts.
Analysis of potential differences between chronic inhalation risks based on actual emission levels and the maximum emissions allowed under the MACT standards (i.e., MACT

allowable emissions) was condu cted. See the memo, MACT

- Allowable Emissions for the Off

Site Waste and Recovery Operations Source Category , for more details on the calculation of these allowable risks. The maximum individual lifetime cancer risk based on allowable emissions is estimat ed to be 20

- in

- 1 million, while the maximum chronic noncancer TOSHI value is estimated to be 1.

Worst

- case acute hazard quotients ( HQs ) were calculated for every emitted HAP that has an acute benchmark , based on actual emissions . For cases where the screening acute HQ was greater than 1, we further refined the estimates by determining the highest HQ value that might occur outside facility boundaries. Analysis of potential off

- site impacts for the OSWRO source category identifie d no facility with an acute value greater than 1

. As presented above, t he PB

- HAP emitted by facilities in this category includ e polycyclic organic matter (POM) ( 2

- acetylaminofluorene analyzed as benzo(a)pyrene TEQ) ( 1 facilit y), polychlorinated biphenyls (3 facilities), hexachlorobenzene (6 facilities), chlordane (5 facilities), lindane (gamma hch) (3 facilities), methoxyclor (4 facilities), toxaphene (3 facilities), heptachlor (5 facilities), and trifluralin (2 facilities). To identify potential multipathway health risks from PB

- HAP other than lead, we first performed a screening

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

36 analysis (Tier I ) that compared emissions of PB

- HAP emitted fro m the OSWRO source category (based on actual emissions) to screening emission rates ( see section 2.5) , where those screening emission rates are available. Of the PB

- HAP emitted by this category, the POM compound 2

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

- acetylaminofluorene were below the screening va lue for POM. The remaining PB

HAP do not currently have multipathway screening values and so are not evaluated for potential non

- inhalation risks. As shown in Table 3.1

- 1, these HAP are not emitted in appreciable quantities from off

- site waste and recove ry operations.
We conducted a screening

- level evaluation of the potential adverse environmental risks associated with emissions of the following environmental HAP from the Off

- Site Waste and Recovery Operations source category: POM, hydrogen chloride
, and hydrogen fluoride. The results of the environmental screening analysis are summarized in Table 3.2

- 2 . In our Tier I analysis, emissions of POM did not exceed the threshold emission rates for any of the ecological benchmarks (either the LOAEL or NOAEL ) for any facility in the source category

. Therefore, we did not conduct a Tier II environmental screening analysis. For hydrogen fluoride and hydrogen chloride, the average modeled concentration around each facility (i.e., the average concentration of a ll off

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

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

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

Table 3.2

- 1 Summary of Source Category Level Inhalation Risks for OSWRO

Result HAP “Drivers” Facilities in Source Category Number of Facilities Estimated to be in Source Category 20 38 n/a
Number of Facilities Identified in the NEI and Modeled in Risk Assessment 38 n/a
Cancer Risks Maximum Individual Lifetime Cancer Risk (in 1 million) 9 benzidine, 2,4

- toluene diamine Number of Facilities with Maximum Individual Lifetime Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a Greater than or equal to 10 in 1 million 0 n/a

Greater than or equal to 1 in 1 million 8 benzidine, 2,4

- toluene diamine
, n itrosodimethylamine , n

n itrosomorpholine , e thylene oxide
, l indane (gamma

- HCH) , h exachlorobenzene , h eptachlor
, f ormaldehyde , n itrobenzene , c arbon tetrachloride , b enzene , h ydrazine
, e thylene dichloride , e thyl carbamate
,

20 As explained in Section 2.1 of this document, the list of facilities began with 53 facilities and there are 38 facilities included in the modeling file used for this risk assessment.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

37 Table 3.2

- 1 Summary of Source Category Level Inhalation Risks for OSWRO

Result HAP “Drivers”

a cetamide , n aphthalene , a crylonitrile
, 2,4

- t oluene diisocyanate , c hlordane
, t oxaphene
, 

### e thyl benzene Chronic Noncancer Risks Maximum Respiratory Hazard Index 0.6 chlorine Num ber of Facilities with Maximum Respiratory Hazard Index: Greater than 1 0 n/a Acute Noncancer Results Maximum Acute Hazard Quotient 1 glycol ethers (REL) Number of Facilities With Potential for Acute Effects 0 n/a Population Exposure Number of People Living Within 50 Kilometers of Facilities Modeled 35,000,000 n/a Number of People Exposed to Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a Greater than or equal to 10 in 1 million 0 n/a Greater than or equal to 1 in 1 million 2 1 0 ,000 n/a Number of People Exposed to Noncancer Respiratory Hazard Index: Greater than 1 0 n/a Estimated Cancer Incidence (excess cancer cases per year) 0.02 n/a Contribution of HAP to Cancer Incidence: Benzidine 7 3% n/a 2,4

- Toluene diamine 19 % n/a



### Table 3.2

- 2 Summary of Environmental Risks Screen Results for OSWRO

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

### Tier I Screen Tier II Screen 1 

### NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL PB

HAP POM None None

- - 0% 0%
Acid Gases 3 

### HF NA N one

- - NA 0% HCL 4 

### N A N one

- - NA 0%

NA

– Not Applicable. 1

- Tier II screen is performed for PB

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

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

- HAP the percent area is based on the Tier II results, if a Tier II an alysis is performed. Otherwise, the percent area is based on the Tier I results.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

38 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 NO AEL and a LOAEL, we have listed the results under the LOAEL column. 4

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



### The facility

- wide MIR and TOSHI, available in Appendix 5 of this document ( Draft Detailed Risk Results ) , are based on emission s, as collected in the NEI, from all emissions sources at the identified facilities. The results of the facility

- wide assessme nt are summarized in Table 3.2

- 3 . The results indicate that 1 7 facilities with OSWRO processes have facility

- wide cancer MIR gre ater than or equal to 1

- in

- 1 million. The maximum facility

- wide MIR is 20 0 in a million due to emiss ions of beryllium compounds from cement manufacturing , with the OSWRO source category contributing less than 1 percent of the risk. The maximum facility

wide TOSHI is 4 , and occurs at 2 facilities. At one, the HI is driven mainly by emissions of chlorine from industrial inorganic chemical manufacturing processes and synthetic organic chemical manufacturing proc esses , while the other is driven mainly by emissions of beryllium compounds from cement manufacturing

. In each instance, t he source category contributes less than 1 percent to the facility

- wide TOSHI.

Table 3.2

- 3 Source Category Contribution to Facility

- Wide Cancer Risks

OSWRO Number of Facilities Binned by Facility

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

- Wide

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

> 90% 7* 4 0 0 1 1 

### 50

- 90% 4 3 0 0 7 

### 10

- 50% 1 0 0 0 1 

### < 10% 9 5 4 1 1 9 

### Total 2 1 * 1 2 4 1 38 *

* Includes one facility for which both source category and facility

- wide modeled risk is 0.



### 4 General Discussion of Uncertainties and How They Have Been Addressed

4.1 Exposure Modeling Uncertainties

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

specific parameters and reasonably r epresentative meteorology. If anything, the population

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

39 exposure estimates are biased high by not accounting for short

- or long

- term population mobility, and by neglecting processes like deposition, plume depletion, and atmospheric degradation. Additional ly, 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 closure occurring during the 70

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

- year chronic exposure period, leading to a potential downward bias in both the MIR and popula tion risk estimates.
A sensitivity analysis performed for the 1999 NATA found that the selection of the meteorology dataset location could result in a range of chronic ambient concentrations which varied from as much as 17% below the predicted value to as much as 84% higher than the predicted value. This variability translates directly to the predicted exposures and risks in our assessment, indicating that the actual risks could vary from 17% lower to 84% higher than the predicted values.
We have purpose ly biased the acute screening results high, considering that they depend upon the joint occurrence of independent factors, such as hourly emissions rates, meteorology and human activity patterns. Furthermore, in cases where multiple acute threshold values are considered scientifically acceptable we have chosen the most conservative of these threshold values , erring on the side of overestimating potential health risks from acute exposures. In the cases where these results indicated the potential for exceed ing short

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

.



### 4.2 Uncertainties in the Dose

- Response Relationships

In the sections that fol low, separate discussions are provided on uncertainty associated with cancer potency factors and for noncancer reference values. Cancer potency values are derived for chronic (lifetime) exposures. Noncancer reference values are generally derived for chro nic exposures (up to a lifetime), bu t may also be derived for acute (

- term (>24 hours up to 30 days), and subchronic (>30 days up to 10% of lifetime) exposure durations, all of which are derived based on an assumption of continuous exposu re 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 (cancer 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 HAP emitted by the source category included in an assessment , some HAP have no peer

- reviewed cancer potency values or reference values for chronic no n

- cancer or acute effects (inhalation or ingestion) . Since exposures to these pollutants cannot be included in a quantitative risk

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

40 estimate, an understatement of risk for these pollutants at environmental exposure levels is possible.
Additionally, chroni c 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 t hese reviews, a dose

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

Cancer assessment

The discussion of dose

- response uncertainties in the estimation of cancer risk below focuses on the uncertainties associated with the specific approach currently used by the EPA to develop cancer potency factors. In general, these same uncertainties atten d 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 Carcinog en Risk Assessment [ 30 ] (herein referred to as Cancer Guidelines ). “The primary goal of EPA actions is protection of human health; accordingly, as an Agency policy, risk assessment procedures, including default options that are used in the absence of scientific data to the contrary, should be health protective.” The approach adopted in this document is consistent with this approach as described in the Cancer Guidelines

. For cancer endpoints EPA usually derives an oral slope factor for ingestion and a unit risk value for inhalation exposures. These values allow estim ation 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 a pproach, 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 population s is based upon EPA’s assessment of the scientific database for a pollutant using EPA’s guidance documents and other peer

- reviewed methodologies. The EPA Cancer Guidelines describes the Agency’s recommendations for methodologies for cancer risk assessment . EPA believes that cancer risk estimates developed following the procedures described in the Cancer Guidelines and outlined below generally provide an upper bound estimate of risk. That is, EPA’s upper bound estimates represent a “plausible upper limit to the true value of a quantity” (although this is usually not a true statistical confidence limit). 21 

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

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

21 IRIS glossary at htt p://ofmpub.epa.gov/sor_internet/registry/termreg/searchandretrieve/glossariesandkeywordlists/search.do?detai ls=&glossaryName=IRIS%20Glossary 22 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of which i s considered to be equally plausible, and which is based on maximum likelihood estimates.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

41 underestimate risk, EPA generally relies on conservative default approaches. 23 

### EPA also uses the upper bound (rather than lower bound or central) estimates in its assessments, although it is no ted 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 expresse d qualitatively. Uncertainties may vary substantially among cancer risk assessments associated with exposures to different pollutants, since the assessments employ different databases with different strengths and limitations and the procedures employed ma y differ in how well they represent actual biological processes for the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk characterizations present estimates demonstrating the impact on the assessment of alternative choice s, data, models and assumptions [ 31 ]. Some of the major sources of uncertainty and variability in deriving cancer risk values are described more fully below.
(1) The qualitative similarities or differences between tumor responses observed in experimenta l animal bioassays and those which would occur in humans is a source of uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites observed in an experimental animal bioassay are necessarily predictive of the sites at which tu mors would occur in humans. 24 

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

- estimation or over

- estimation of human cancer risks.
(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead to differences in risk predictions. For example, the measure of dose is commonly expressed in units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may support development of a pharmacokinetic model for the absorption, distribution, metabolism and excretion of an agent, which may result in improved dose metrics (e.g., average blood concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative uncertainties result when the appropriate choice of a dose metric is uncertain or when dose metric estimates are them selves uncertain (e.g., as can occur when alternative

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

- assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from th em 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 underestimat ed (although defaults are not intended to overtly overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Principles and Practices, EPA/100/B

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

- final.pdf

. 24 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer studies indicate that the agent under study can have carcinogenic potential in humans.” and “Target organ concordance is not a prerequi site for evaluating the implications of animal study results for humans.”

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

42 pharmacokinetic models are available for a compound). Uncertainty in dose estimates may lead to either over or underestimation of risk.
(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to humans, EPA uses scaling methodologies (relating expected response to differences in physical size of the species), which introduce another source of uncertainty. These methodologies are based on both biological data on differ ences in rates of process according to species size and empirical comparisons of toxicity between experimental animals and humans. For a particular pollutant, the quantitative difference in cancer potency between experimental animals and humans may be eit her greater than or less than that estimated by baseline scientific scaling predictions due to uncertainties associated with limitations in the test data and the correctness of scaled estimates.
(4) EPA cancer risk estimates, whether based on epidemiolo gical or experimental animal data, are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which there is a specified excess risk of cancer, which is used as the point of departure (or POD) for the remainder of the calculation. Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed though use of the 95% lower confidence limit on the dose at which the specified excess risk occurs (the BMDL), decreasing the likelihood of understat ing risk. EPA has generally utilized the multistage model for estimation of the BMDL using cancer bioassay data (see further discussion below).
(5) Extrapolation from high to low doses is an important, and potentially large, source of uncertainty in canc er risk assessment. EPA uses different approaches to low dose risk assessment (i.e., developing estimates of risk for exposures to environmental doses of an agent from observations in experimental or epidemiological studies at higher dose) depending on th e available data and understanding of a pollutant’s mode of action (i.e., the manner in which a pollutant causes cancer). EPA’s Cancer Guidelines express a preference for the use of reliable, compound

- specific, biologically

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

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

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

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

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

- dose linear responses, although the slope of this relationship may not be the same as the slope estimated by the straight line approach.
EPA most frequently utilizes a linear low

- dose extrapolation approach as a baseline science

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

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

43 and variability. EPA believes that linear dose

- response models, when appropriately applied as part of EPA’s cancer risk assessment pro cess, 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 enc ouraged the exploration of sigmoidal type functions (e.g., log

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

- shaped dose

- response functions for a population. This report notes sources of variability in the human population: “One might expect these individual tolerances to vary exten sively in humans depending on genetics, coincident exposures, nutritional status, and various oth er susceptibility factors...” Thus, if a distribution of thresholds approach is considered for a carcinogen risk assessment, application would depend on abili ty of modeling to reflect the degree of variability in response in human populations (as opposed to responses in bioassays with genetically more uniform rodents). Note also that low dose linearity in risk can arise for reasons separate from population var iability: due to the nature of a mode of action and additivity of a chemical’s effect on top of background chemical exposures and biological processes. As noted above, EPA’s current approach to cancer risk assessment typically utilizes a straight line app roach 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 va lue to central estimate BMD values, although these differences are generally not a large contributor to uncertainty in risk assessment (Subramaniam et. al., 2006) [ 33 ]. It is important to note that earlier EPA assessments, including the majority of those f or 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 o n 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 with in the human population

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

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

44 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 Substan ces 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 25 

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

- specific data; where data are available, uncertainty factors

ma y also be developed using compound

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

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

For non

- cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) for exposures via ingestion, and a Reference Concentration (RfC) for inhalation exposures. These values provide an estimate (with uncertainty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a continuous inha lation exposure (RfC) to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. 26 

### To derive values that are intended to be “without appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach [ 34 ],[ 35 ] which includes consideration of both uncertainty and variability.
EPA begins by evaluating all of the avail able 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 mod eling of the available data, and then determines the appropriate point of departure (POD) for derivation of the reference value. A POD is determined by (in order of preference): (1) a statistical estimation using the benchmark dose (BMD) approach; (2) use of the dose or concentration at which the toxic response was not significantly elevated (no observed adverse effect level

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

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

- assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report , Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). The refore, 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 t he environment, default assumptions are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practices, EPA/100/B

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

- final.pdf

.

26 See IRIS glossary

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

45 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 assessmen t. 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 difference s); (3) uncertainty in extrapolating from data obtained in a study with less

- than

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

. When scientifically sound, peer

- reviewed assessment

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

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

- fold factor. If an extrapolation step or adjustment is not relevant to an assessment (e.g., if applying human toxicity data and an interspecies extrapolation is not required) the associated UF is not used. The major adjustment steps are described mor e fully below.
1) Heterogeneity among humans is a key source of variability as well as uncertainty. Uncertainty related to human variation is considered in extrapolating doses from a subset or smaller

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

- specific data on human variation, a 10

- fold UF is used to account for uncertainty associated with human variation. Human variation may be larger or smaller; however, data to examine the potential magnitude of human variability are often unavailable. In some situations, a smaller UF of 3 may be applied to reflect a known lack of significant variability among humans.
2) Ex trapolation from results of studies in experimental animals to humans is a necessary step for the majority of chemical risk assessments . When interpreting animal data, the concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. rodents) is

ex trapolated to estimate the human response. While there is long

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

- related differences (allometric relationships) indicate that typically humans are more sensitive than rodents when compared on a mg/kg/day basis. The default choice of 10 for the interspec ies 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 u ncertainties; however, such dosimetric adjustments are not always

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

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

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

- species extrapolation, a UF of 3 may be used for the remaining component.
3) In the case of reference values for chronic exposures where only data from shorter durations are available (e.g., 90

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

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

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

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

- related lead and the associated risk (73FR at 66979). EPA also explicitly considered the uncertainties associated with both the human health evidence and the exposure and risk analyses when developing the NAAQS for lead . For example, EPA considered uncertainties in the relationship between ambient air lead and blood lead levels (73FR at 66974), as well as uncertainties between blood lead levels and loss of IQ points in children (73FR at 66981). In considering the evide nce and risk analyses and their associated uncertainties, the EPA Administrator noted his view that there is no evidence

- or risk

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

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

47 0.15 μg/m 3 

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

- 66981, November 12, 2008).

We also note the uncertainties associated with the health

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

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

- making by the Office of Air Quality Planning and Standards (OAQPS) and by the Office of Drinking Water (ODW). By comparison to most other environmental toxicants, the degree of uncertainty abou t the health effects of lead is quite low. It appears that some of these effects, particularly changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral development, may occur at blood lead levels so low as to be essentia lly without a threshold. The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two meetings (07/08/1985 and 07/22/1985) and considered it inappropriate to develop an RfD for inorganic lead.” EPA’s IRIS assessment for Lead and compou nds (inorganic) (CASRN 7439

- 92

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

.
We note further that because of the multi

- pathway, multi

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

related multi

- pathway exposures from industrial sources like primary and secondary lead smelting operations. It also considered background lead exposures from other sources (like contaminated drinkin g water and exposure to lead

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

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

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

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

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

specific or health effect

- specific information (e.g., simple irritation effects do not vary appreciably between human individuals, hence a value of 3 is typically used), or based on the purpose for the reference value (see the following par agraph). The UFs applied in acute reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

48 extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. Additional adjustments are often applied to account for uncertainty in extrapolation from observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an acute reference value a t another exposure duration (e.g., 1 hour).
Not all acute reference values are developed for the same purpose and care must be taken when interpreting the results of an acute assessment of human health effects relative to the reference value or values b eing exceeded. W here relevant to the estimated exposures, the lack of threshold values at different levels of severity should be factored into the risk characterization as potential uncertainties.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

49 

## 5 References

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

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

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

– MACT I Petroleum Refining Sources and P ortland 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. Mem orandum from Dave Guinnup to Docket EPA

- HQ

- OAR

- 2010

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

5

. US EPA, 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 40 CFR Part 51. http://www.epa.gov/scram001/guidance/guide/appw_05.pdf
6 . US EP A, 2004. Users’ guide for the AMS/EPA regulatory model

– AERMOD. 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

. Jeffries, B

. - U

. Kim, M

. Webster
, and M

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

- 13. http://files.harc.edu/Projects/AirQuality/Projects/H013.2003/H13FinalReport.pdf
8 . US EPA, 2004. Air Toxics Risk Assessment Reference Library, Volume 1. EPA

- 453

- K

- 04

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

- assessment

- and

- modeling

- air

- toxics

- risk

- assessment

reference

- library
9

. US EPA, 2005. T able 1. Prioritize d Chronic Dose

- Response Values

. Office of Air Quality Planning and Standards. http://www2.epa.gov/fer a/dose

- response

- assessment

assessing

- health

- risks

- associated

- exposure

- hazardous

- air

- pollutants

10 . US EPA, 2005. 1999 National Air Toxics Risk Assessment. http://www.epa.gov/ttn/atw/nata1999
11

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

. 12

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

.

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

50 13

. US EPA, 1994

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

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

14

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

15

. CA Office of Environmental Health Hazard Assessment , 2005. Chronic Reference Exposure Levels Adopted by OEHHA as of December 2008. http://oehha.ca.gov/air/hot_spots/rels_dec2008.html

16

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

. http://www.oehha.ca.gov/air/hot _spots/pdf/may2005hotspots.pdf
17 . US EPA, 2006. Approach for modeling POM. Technical support information for the 1999 National Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf

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

- Response Values for Screening Risk Assessments ( 12/19/2011 ). Office of Air Quality Planning and Standards. http://www2.epa.gov/fera/dose

response

- assessment

- assessing

- health

- risks

- associated

- exposure

- hazardous

- air

- pollutants
19

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

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

- 09/061, 2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003
20 . CA Office of Environmental Health Hazard Assessment, 2000. All Acute Reference Exposure Levels developed by OEHHA as of May 2000. http://www.oehha.ca.gov/air/pdf/acuterel.pdf
21

. American Industrial Hygien e Association , 20 14 . Current AIHA ERPG Values. http://www.aiha.org/get

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

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

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

- B

- 00

- 002.
24 . US EPA, 2002. EPA’s Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by the Environmental Protection Agency. EPA Office of Environmental Information. EPA/260R

- 02

- 008

. http://www.epa.gov/quality/informationguidelines/document s/EPA_InfoQualityGuidelines.pdf
25

. US EPA, 2006. Performing risk assessments that include carcinogens described in the Supplemental Guidance as having a mutagenic mode of action. Science Policy Council Cancer Guidelines Implementation Workgroup Communica tion II: Memo from W.H. Farland dated

### Residual Risk Assessment for the Off

- Site Waste and Recovery O perations Source Category

-- February 2015 FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE

51 14 June 2006. http://www.epa.gov/osa/spc/pdfs/CGIWGCommunication_II.pdf
26

. US EPA, 2005. Supplemental guidance for assessing early

- life exposur e to carcinogens. EPA/630/R

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

.
27

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

28

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

- 630

- R

- 98

- 002. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=2256 7
29

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

- 630/R

- 00

- 002. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=20533
30

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

- 03/001F, 2005. http://www2.epa.gov/sites/production/files/2013

- 09/documents/cancer_guidelines_final_3

- 25

- 05.pdf
31

. US EPA, 2000

. Risk Characterization Handbook. EPA 100

- B

- 00

- 002.
32

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

. 33

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

. 26, p. 825

– 830.
34

. US EPA. 1993. Reference Dose (RfD): Description and Use in Health Risk Assessments. http://www.epa.gov/iris/rfd.htm
35

. US EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and Applic ation of Inhala

tion Dosimetry. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=71993

. 36

. US EPA. 2002. A Review of the Reference Dose and Reference Concentration Processes. http://www2.epa.gov/sites/production/files/2014

- 12/documents/rfd

- final.pdf

### Appendix 1 T echnical Support Document for HE M-AERMOD Modeling

# Modeling for the Residual Risk and Technology Review

Using the Human Exposure Model 3

– AERMOD Version

Updated 11/2 6 /2013

Technical Support Document

Prepared for:
U. S. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Planning and Standards Health and Environmental Impacts Division Air Toxics Assessment Group Research Triangle Park, NC 27711

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

### iii 

## Disclaimer
The research described in this document has been funded by the United States Environmental Protection Agency contracts 68-D-01 -076 , EP -D06 -119 , and currently EP-W-12 -011 to EC/R Incorporated. Although it has been subject to the Agency's review, it does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.

### iv 

# Contents 1. Introduction ............................................................................................................................................ 1

2. Overview of the HEM-3

– AERMOD System ...................................................................................... 2 2.1 Preparation of Dispersion Modeling Inputs ..................................................................................... 3 2.1.1 Compiling Emission Source Data ................................................................ ............................. 3 2.1.2 Defining the Modeling Domain and Receptors ........................................................................ 4 Treatment of Nearby Census Blocks and Screening for Overlapping Blocks ................................ 4 Polar receptor network .................................................................................................................... 5 Elevations and hill heights for model receptors .............................................................................. 5 2.1.3 Selection of Meteorological Data ................................................................ ............................. 6 2.2 Running of AERMOD ..................................................................................................................... 6 2.2.1 AERMOD Dispersion Options Used by HEM-3

...................................................................... 6 2.2.2 Use of Dilution Factors ................................

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

............................................................................. 7 2.3.1 Calculation of Impacts at Modeled Receptors .......................................................................... 8 2.3.2 Interpolation of Impacts at Outer Census Blocks ................................................................... 10 2.3.3 Calculation of Population Exposures and Incidence ................................ .............................. 10 2.3.4 Model Outputs ................................................................................................ ........................ 11 2.4 Data Libraries Used in HEM-3

................................ ...................................................................... 13 2.4.1 Chemical Health Effects Information ................................................................ ..................... 13 2.4.2 Census Block Locations and Elevation Data .......................................................................... 13 2.4.3 Meteorological Data................................

................................................................................ 14 2.4.4 Gaseous Deposition Parameters ................................................................ .............................. 14
3. Modeling for the Residual Risk Technology Review .......................................................................... 15 3.1 Emission Source Inputs.................................................................................................................. 15 3.2 Pollutant Cross-Referencing ................................

.......................................................................... 15 3.3 Meteorological Data................................................................................................ ....................... 16 3.4 Model Options Selected ................................................................................................................. 1 8 3.4.1 Urban or Rural Dispersion Characteristics ............................................................................. 1 8 3.4.2 Deposition and Plume Depletion ............................................................................................ 1 8 3.4.3 Cutoff Distance for Modeling of Individual Blocks ............................................................... 19 3.4.4 Facility Boundary Assumptions .............................................................................................. 19 3.5 Modeling of Multiple Facilities ..................................................................................................... 2 1

4. Quality Assurance

................................................................................................................................ 2 2 4.1 Engineering Review ....................................................................................................................... 2 2 4.2 Geographic Pre-Modeling Checks ................................................................................................. 2 2 4.3 Geographic Post-Modeling Checks ............................................................................................... 2 3

5. Uncertainties ........................................................................................................................................ 2 5

6. References ............................................................................................................................................ 3 2

### v 

# Figures

Figure 3-1. AERMOD Meteorological Stations. ..........................................................................1 7



# Tables

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

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

Review Assessments ....................................................................................................2 0

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

Risk Assessments .........................................................................................................

. 2 7

### 1 

# 1. Introduction

This document describes the modeling approach used to estimate the risks to human populations in support of the Residual Risk and Technology Review (RTR) currently being carried out by the U.S. Environmental Protection Agency (EPA). The model used in these risk assessments is the Human Exposure Model, Vers ion 3 (HEM

- 3). HEM

- 3 incorporates AERMOD, a state of the art air dispersion model developed under the direction of the American Meteorological Society / Environmental Protection Agency Regulatory Model Improvement Committee (AERMIC).

Section 2 of this report provides an overview of the HEM

- 3

- AERMOD system; and Section 3 describes inputs and choices made in implementing the model for the RTR program. Quality assurance efforts undertaken in the modeling effort are discussed in Section 4, and uncertainties associated with the modeling effort are discussed in Section 5

.

### 2



# 2. Overview of the HEM

- 3

– AERMOD System

HEM-3 performs three main operations: dispersion modeling, estimation of population exposure, and estimation of human health risks. The state-of-the-art American Meteorological Society (AMS) EPA Regulatory Model (AERMOD) 1 , 2 

### is used for dispersion modeling. AERMOD can handle a wide range of different source types which may be associated with an industrial source complex, including stack (point) sources, area and polygon sources, and volume

sources. To prepare dispersion modeling inputs and carry out risk calculations, HEM-3 draws on four data libraries, which are provided with the model. The first is a library of meteorological data for over 824 stations, which are used for dispersion calculations. A second library of Census block (“centroid”) internal point locations and populations provides the basis of human exposure calculations. The Census library also includes the elevations of every Census block, which are used in the dispersion calculations for the RTR assessments. A third library of pollutant unit risk estimates and reference concentrations is used to calculate population risks. These unit risk estimates and reference concentrations are based on the latest values recommended by EPA for hazardous air pollutants (HAP) and other toxic air pollutants. The fourth data library, which provides deposition parameters for gaseous pollutants, is used only when the user opts to compute deposition and plume depletion (not computed for the RTR assessments to date).
HEM-3 has been implemented in two versions: a single facility version, and a multiple facility version (“Multi HEM

- 3”). Multi HEM -3 is used in the RTR risk assessment modeling. Both versions operate under the same general principles. In essence, Multi HEM-3 provides a platform for running the single facility version multiple times. In both versions, source location and emissions data are inpu t through a set of ExcelTM spreadsheets. The main difference is in the user interface for other model inputs. Single HEM-3 includes a graphical user interface (GUI) for the selection of various dispersion modeling options. In Multi HEM-3, a control file replaces many of these GUI inputs.

The model estimates cancer risks and noncancer adverse health effects due to inhalation exposure at Census block internal point locations (or “centroids”), at concentric rings surrounding the facility center, and at other receptor locations that can be specified by the user. Cancer risks are computed using EPA’s recommended unit risk estimates for Hazardous Air Pollutants (HAP) and other toxic air pollutants. The resulting estimates reflect the excess cancer risk for an individual breathing the ambient air at a given receptor site 24-hours per day over a 70 -year lifetime. The model estimates the numbers of people exposed to various cancer risk levels. In addition, HEM-3 estimates the total incremental cancer risks for people living within different distances of the modeled emission sources.
Potential noncancer health effects due to chronic exposures are quantified using hazard quotients and hazard indices for various target organs. The “hazard quotient” (HQ) for a given chemical and receptor site is the ratio of the ambient concentration of the chemical to the reference concentration. The “hazard index” (HI) for a given organ is the sum of hazard quotients for substances that affect that organ. HEM-3 computes target-organ-specific hazard

### 3

indices (TOSHI) for HAPs and other toxic air pollutants, and estimates the numbers of people exposed to different hazard index levels. In addition, maximum short term (“acute”) concentrations are computed for all pollutants, and concentrations are compared with threshold levels for acute health effects.
The following sections outline the methodologies used in the HEM-3

– AERMOD system. Section 2.1 describes the preparation of dispersion modeling inputs, Section 2.2 describes the running of AERMOD, Section 2.3 describes calculations performed by HEM-3 to calculate risks and exposures, and Section 2.4 details the sources and methods used to produce HEM- 3’s data libraries. The HEM- 3 User’s Manuals – for single HEM -3 and Multi HEM -3

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

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

## 2.1 Preparation of Dispersion Modeling Inputs

HEM-3 compiles data that will be needed for dispersion modeling, and prepares an input file suitable for running AERMOD. The dispersion modeling inputs can be divided into three main components: emission source data, information on the modeling domain and receptors for which impacts will be computed, and meteorological data.
2.1.1 Compiling Emission Source Data
A series of ExcelTM spreadsheet files are used to specify the emissions and configuration of the facility to be modeled. At a minimum, two files are needed: a pollutant emission file, and an emission location file. The emission file includes an emission source identification code for each emission source at the facility, the names of pollutants emitted by each source, and the emission rate for each pollutant. In addition, if the model run is to incorporate deposition or plume depletion, the emission file must also specify the percentage of each pollutant that is in the form of particulate matter. The balance is assumed to be in vapor form.

The emission location file includes the coordinates of each source, as well as information on the configuration and other characteristics of the source. HEM-3 can analyze point sources,
area and polygon sources, and volume sources - configurations that are described in AERMOD's documentation

. 1, 2 

### For stack ( point ) sources, the location file must provide the stack height, stack diameter, emission velocity, and emission temperature. The file must also provide dimensions for each area or volume source, as well as the height of the source above the ground. For area sources, the angle of rotation from north can also be specified. The user can also provide the terrain elevation at the base of each source. (The controlling hill height is also used in AERMOD’s flow calculations. C alculation of the controlling hill height by HEM-3 is discussed in Section 2.4.2.) If the terrain elevations are not provided by the user, HEM-3 will calculate elevations and controlling hill heights based on elevations and hill heights provided by the Census database for the Census blocks nearest to the facility.

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

a third input file to specify the particle size distribution . This input file must include the average particle diameter, the mass fraction percentage, and the average particle density for each size range emitted. Another optional file can be used to specify building dimensions if building wake effects are to be modeled.

### 4

2.1.2 Defining the Modeling Domain and Receptors
HEM-3 defines a modeling domain for each facility that is analyzed based on parameters specified by the model user or calculated by the model. These parameters are summarized in Table 2-1. The modeling domain is circular, and is centered on the facility, with a radius specified by the user. For the RTR analysis, the radius of the modeling domain is 50 kilometers (km). HEM-3 identifies all of the Census block locations in the modeling domain from its Census database, and divides the blocks into two groups based on their distance from the facility. For the inner group of Census blocks (closest to the facility), each block location is modeled as a separate receptor in AERMOD. The cutoff distance for modeling individual Census blocks is generally set to 3 km for the RTR assessments, although it can be set differently by the model user. The model user can also provide an ExcelTM spreadsheet specifying additional locations to be included as model receptors in AERMOD. These additional discrete “user receptors” may include facility boundary locations, monitoring sites, individual residences, schools, or other locations of interest.
For Census blocks in the outer group, beyond this modeling cutoff distance, emission impacts are interpolated based on modeling results for a polar receptor network. The user also specifies an “overlap” distance, within which Census block coordinates will be considered to be on facility property. The following paragraphs provide more details on the treatment of blocks near the facility, on the polar receptor network, and on the determination of receptor elevations and controlling hill heights to be used in AERMOD.

Treatment of Nearby Census Blocks and Screening for Overlapping Blocks
Census block locations near the facility are modeled as separate receptors within AERMOD. The cutoff distance for modeling of individual Census blocks may be chosen by the user, but is typically 3000 meters for the RTR assessments. This distance is not measured from the center of the facility, but is the minimum distance from any source at the facility. Therefore, any Census block location that is within the cutoff distance from any emission source is treated as a discrete AERMOD receptor. Table 2

- 1. Parameters Used to Del ineate the Modeling Domain in HEM

- 3

Parameter Typical value Modeling domain size

– maximum radial distance to be modeled from

facility center 50 km

Cutoff distance for modeling of individual blocks 1 3,000 m
Overlap distance

–

where receptors are considered on facility property 1 30 m

Polar receptor network:
Distance to the innermost ring 2 ≥ 100 m Number of concentric rings 13
Number of radial directions 16

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

2 Generally model-calculated to encompass all emission sources but not less than 100 meters from the facility center

### 5

HEM-3 checks Census blocks that are very close to the facility in order to assess whether they overlap any point, area or volume emission sources. In addition, the user can specify an overlap distance, within which receptors will be considered to be on facility property. The default value for the overlap distance is 30 meters, or approximately equal to the width of a

narrow buffer and a roadway. HEM-3 tests each nearby receptor to determine whether it is within this distance from any stack or from the perimeter of any area or volume source. If a receptor falls within this distance, HEM-3 will not calculate risks based on the location of that receptor, but will instead assume that the risks associated with the receptor are the same as the highest predicted value for an y receptor that is not overlapping. The location for calculating the default impact may be either another Census block, one of the polar grid receptors, or one of the additional discrete user-specified receptor locations.

Polar r eceptor network
The polar receptor network used in HEM-3 serves three functions. First, it is used to estimate default impacts if one or more Census locations are inside the overlap cutoff distance used to represent the facility boundary. Second, it is used to evaluate potential acute effects that may occur due to short-term exposures in locations outside the facility boundary. Third, the polar receptor network is used to interpolate long-term and short-term impacts at Census block locations that are outside the cutoff distance for modeling of individual blocks.
Generally, the model calculates the inner radius (or f irst ring dist ance ) for the polar receptor network to be just outside the emission source loc ations, but not less than 100 meters fr om the facility c enter

. However, the user can override the d efault dist ance c alculated by the model to fit the size a nd shape of the f acility p roperties to be modele d. Likewise, the model will also use de fault va lues for the number of concentric r ings to be a nalyzed (13 rings by de fault), and the number of ra dial directions (16 radials by default), although these d efault va lues can also be c hanged by the user to mee t the ne eds of a spe cific modeling stud y. The inner r adius of the polar ne twork shoul d be t he mi nimum distance f rom the f acility center th at is generally outsi de of f acility prop erty

. (Fo r complex f acility shap es, it is sometimes useful to specify a n inner ring that encroaches on facility property in some directions.) HEM-3 will dist ribute the radia l directions evenly a round the facility

. For th e c oncentric r ings, the model will generate a logarithmic progression of distances starting at the inner ring radium and ending at the outer radiu m of the modeling domain.

Elev ations and hill heights for model receptors
HEM-3 includes terrain elevations by default for the RTR assessments, but the user can choose to exclude terrain effects when running AERMOD. If the default terrain option is used, H EM -3 obtains elevations and controlling hill heights for Census block receptors from its internal Census location library. Section 2.4.2 describes the derivation of these elevations and hill heights.
Elevations and controlling hill heights for the polar grid receptors are also estimated based on values from the Census library. HEM-3 divides the modeling domain into sectors based on the polar receptor network, with each Census block assigned to the sector corresponding to

### 6

the closest polar grid receptor. Each polar grid receptor is then assigned an elevation based on the highest elevation for any Census block in its sector. The controlling hill height is also set to the maximum hill height within the sector. If a sector does not contain any blocks, the model defaults to the elevation and controlling hill height of the nearest block outside the sector.
2.1.3 Selection of Meteorological Data
In addition to source and receptor information, AERMOD requires surface and upper air meteorological observations in a prescribed format. The model user can select a meteorological station from the HEM-3 meteorological data library, or add new files to the library if site-specific data are available. If the user does not specify a meteorological station, HEM-3 will select the closest station to the center of the modeling domain, as is generally done for the RTR assessments. 

## 2.2 Running of AERMOD

Based on the user input data and other data described in the previous section, HEM-3 produces an input file suitable for AERMOD. HEM-3 then runs AERMOD as a compiled executable program. No changes have been made from the version of AERMOD released to the public by EPA. The following sections give additional information on how AERMOD is used within HEM-3.
2.2.1 AERMOD Dispersion Options Used by HEM-3
AERMOD provides a wide array of options for controlling dispersion modeling calculations. In general, HEM-3 uses the regulatory default options when running AERMOD. 1 

### These options include the following:

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

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

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

 Use the “calms processing” routines;

 Use upper-bound concentration estimates for sources influenced by building downwash from super-squat buildings;

 Use default wind profile exponents;

 Use low wind speed threshold;

 Use default vertical potential temperature gradients;

 Use of missing-data processing routines; and

 Consider terrain effects.
The following additional AERMOD options are available to the HEM-3 user:

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

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

 Consideration of building wake effects;

### 7

 Calculation of short term (acute) impacts; and

 Use of the FASTALL option, which conserves model runtime by simplifying the AERMOD algorithms used to represent meander of the pollutant plume.
As noted in Section 2.1, the calculation of deposition or depletion and the consideration of building wake effects require additional user inputs.

The u ser can opt to analyze short term impacts on a number of different time scales (i.e., 1 hour, 6 hours, 8 hours, or 24 hours) however only one short term time scale can be selected per run. If the user chooses to analyze short term impacts, a multiplier must be specified to reflect the ratio between the maximum short term emission rate and the long term average emission rate. The default multiplier for short term emissions is a factor of 10. This means that in the default case the maximum short term emission rate is assumed to be 10 times the long term average emission rate. The multiplier can be set to 1.0 if emissions from the facility are known to be constant. For RTR assessments, acute impacts are generally included in the modeling and the default multiplier of 10 is used, unless more source-specific information is available upon which to base the acute factor for the source category being modeled.
2.2.2 Use of Dilution Factors
To save computer run time when analyzing the impacts of multiple pollutants, HEM-3 does not model each pollutant separately. Instead, AERMOD is used to compute a series of dilution factors, specific to each emission source and receptor. The dilution factor for a particular emission source and receptor is defined as the predicted ambient impact from the given source and at the given receptor, divided by the emission rate from the given source.
If the user chooses not to analyze deposition and plume depletion, the dilution factor does

n ot vary from pollutant to pollutant. If deposition and depletion is chosen as a model option, separate dilution coefficients must be computed for each gaseous pollutant. In addition, separate dilution factors must be computed for different components of particulate matter if the components do not have the same particle size distribution. In the current version of HEM-3, this can be done by creating a separate emission record for each pollutant emitted by from each source. (Common location data and source configurations can be used for different pollutant records representing the same emission source.) 

## 2.3 Postprocessing of AERMOD Results in HEM -3

HEM-3 estimates total excess cancer risks and potential chronic noncancer health effects for all Census block locations in the modeling domain, all user-defined receptors, and all points in the polar receptor network. Potential chronic noncancer health effects are expressed in terms of TOSHI. Based on the results for Census blocks and other receptors, HEM-3 estimates the maximum individual risk (MIR) and maximum TOSHI for populated receptors, and determines the locations of these maximum impacts. The model also determines the concentrations of different pollutants at the sites of the maximum risks, and the contributions of different emission sources to these maximum estimated risks. It should be noted that the locations may differ for the maximum individual cancer risk and for the hazard indices for different target organs.

### 8

For acute impacts, HEM-3 calculates the maximum short term concentrations for all pollutants emitted by the facility. These maximum short term concentrations are compared with various threshold levels for acute health effects (e.g., the Ca lifornia EPA reference exposure level [REL] for no adverse effects).
At the option of the model user, HEM-3 will also compute the long term and short term predicted ambient concentrations of all pollutants emitted by the facility at all of the receptors in the modeling domain. In addition, pollutant contributions from each emission source at the facility are computed under this option.

Section 2.3.1. describes methods used to calculate cancer risks and hazard indices for receptors that are explicitly modeled using AERMOD. Section 2.3.2 describes the interpolation approach used to estimate cancer risks and hazard indices at Census blocks that are not explicitly modeled.

2.3.1 Calculation of Impacts at Modeled Receptors
As noted in Section 2.2.2, HEM-3 does not model each pollutant separately unless deposition or depletion is being analyzed. Instead, AERMOD is used to compute a series of dilution factors, specific to each emission source and receptor. HEM-3 also conserves computer memory by computing cancer risks and hazard indices directly, without recording the concentration of each pollutant at each receptor. The following algorithms are used to compute cancer risks and TOSHI for chronic noncancer health effects.

For cancer risk: CR T 

### =  i, j 

### CR i, j



### CR i, j 

### = DF i, j 

### × CF ×  k 

### [E i, k 

### × URE k 

### ]

For TOSHI: TOSHI T 

### = 

###  i, j 

### TOSHI i, j



### TOSHI i, j 

### = DF i, j 

###  CF  

###  k 

## [ E i, k 

###  RfC k 

## ] where: CR T 

### = total cancer risk at a given receptor (probability for one person)  i, j 

### = the sum over all sources i and pollutant types j (particulate or gas) CR i, j 

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

### = dilution factor [(  g/m 3 

### ) / (g/sec)] at the given receptor for source i and pollutant type j CF = conversion factor, 0.02877 [(g/sec) / (ton/year)]  k 

### = sum over all pollutants k within pollutant type j (particulate or gas) E i, k 

### = emis sions of pollutant k from source i and in pollutant type j

### 9

The above equations are equivalent to the following simpler equations:

CR T 

### =  i, k 

### AC i, k 

### × URE k



### HI T 

### =  i, k 

### AC i, k 

###  RC k 

### where: AC i, k 

### = ambient concentration (  g/m 3 

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

same as [E i, k 

### × DF i, j 

### × CF]
However, use of these simpler equations would require modeling all pollutants individually in AERMOD, and performing separate risk calculations for each pollutant.
If the cancer unit risk estimate is not available for a given chemical, then that chemical is not included in the calculation of cancer risk. Likewise, if the noncancer reference concentration is not available for a given chemical, that chemical is not included in the calculation of hazard indices. Note also that separate reference concentrations are used for acute and chronic hazard indices.
HEM-3 computes short term concentrations and records the highest short term concentration for each pollutant. In addition, the user can opt to compute and record the short term and long concentrations at each receptor. Concentrations are computed as follows.
Long term concentrations: AC T, k 

### = 

##  i 

### AC i, k



### AC i, k 

### = E i, k 

###  DF i, j 

###  CF

Short term concentrations: AC T 

### = 

##  i 

### AC i, k



### AC i, k 

### = E i, k 

###  DF i, j 

###  CF  M where: URE k 

### = cancer unit risk factor for pollutant k TOS HI T 

### = total target

- organ

- specific hazard index at a given receptor TOS HI i, j 

### = target

- organ

- specific hazard index at the given receptor for sou rce i and pollutant

type j R f C k 

### = non

- cancer health effect reference concentration for pollutant k

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 pollutant k at the given receptor as a result of emissions from source i (  g/m 3 

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

### 10

2.3.2 Interpolation of Impacts at Outer Census Blocks
For Census blocks outside of the cutoff distance for individual block modeling, HEM-3 estimates cancer risks and hazard indices by interpolation from the polar receptor network. Impacts at the polar grid receptors are estimated using AERMOD modeling results and the algorithms described in Section 2.3.1. If terrain elevation is part of the modeling, then an elevation is estimated for each polar receptor. HEM-3 estimates elevations and controlling hill heights for the polar grid receptors based on values from the census library. HEM-3 divides the modeling domain into sectors based on the polar grid receptor network, with each census block assigned to the sector corresponding to the closest polar grid receptor.
HEM-3 then assigns each polar grid receptor an elevation based on the highest elevation for any census block in its sector. The controlling hill height is also set to the maximum hill height within the sector. If a sector does not contain any blocks, the model defaults to the elevation and controlling hill height of the nearest block outside the sector.
The impacts at each outer Census block are interpolated from the four nearest polar grid receptors. The interpolation is linear in the angular direction, and logarithmic in the radial direction, as summarized in the following equations:

I a, r 

### = I A1, r 

### + (I A2, r 

### –

I A1, r 

### ) × (a

– A1)  (A2

– A1)

I A1, r 

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

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

### )

– ln(I A1,R1 

### )] × [(ln r)

– ln(R1)]  [ln(R2)

– ln(R1)]} I A2, r 

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

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

### )

– ln(I A2,R1 

### )] × [(ln r)

– ln(R1)]  [ln(R2)

– ln(R1)]}

where:

2.3.3 Calculation of Population Exposures and Incidence
Using the predicted impacts for Census blocks, HEM-3 estimates the numbers of people exposed to various cancer risk levels and TOSHI levels. This is done by adding up the populations for receptors that have predicted cancer risks or TOSHI above the given threshold.
The model also estimates the total annual excess cancer risk (incidence) for the entire modeling region. The following equation is used: I a,r 

### = the impact (cancer risk
, hazard index , or concentration ) at an angle, a, from north, and radius, r, from the center of the modeling domain

a = the angle of the target receptor, from north

r = the radius of the target receptor, from the center of the modeling domain A1 = the angle of the polar network receptors immediately counterclockwise from the target receptor A2 = the angle of the polar network rec eptors immediately clockwise from the target receptor R1 = the radius of the polar network receptors immediately inside the target receptor R2 = the radius of the polar network receptors immediately outside the target receptor

### 11 TCR = 

##  m 

## [ 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
Furthermore, HEM-3 estimates the contributions of different chemicals and emission sources to total annual cancer incidence for the overall modeling domain using the following equations:

TCR i, j 

### = 

###  m 

## [(  k 

### E i, k 

### × URE k 

### ) × DF i, j, m 

### × CF  LT]

TCR i, k 

### = TCR i, j 

### × E i, k 

### × URE k 

##  (  k 

### E i, k 

### × URE k 

### ) where:

2.3.4 Model Outputs
The following is a summary of the outputs produced by HEM-3 . These are written to a collection of files in ExcelTM and dBaseTM format (dbf).

! Maximum long term impacts at populated locations

" maximum lifetime individual cancer risk (MIR)
" maximum TOSHI for the following health effects - respiratory system effects - liver effects - neurological system effects TCR i, j 

### = the estimated total annual cancer incidence (cancers/year) to the population in the

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

##  m 

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

##  k = 

### the sum over all pollutant k, within pollutant type j E i, k 

### = emissions of pollutant k from source i (tons/year) URE k 

### = u nit risk factor for pollutant k DF i, j, m 

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

pollutant k), from source i CF = conversion factor, 0.02877 [(g/sec) / (ton/year)] TCR i, k 

### = the estimated annual cancer inciden ce (cancers/year) of the population in the

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

### 12 - developmental effects - reproductive system effects - kidney effects - ocular system effects - endocrine system effects - hematological system effects - immunological system effects - skeletal system effects - spleen effects - thyroid effects - whole body effects
" locations of the maximum cancer risk and TOSHI
" Census block identification codes for the maximum cancer risk and TOSHI, and number of people in the Census block
" contributions of different chemicals and emission sources to the maximum risk and TOSHI

! Maximum acute impacts
" maximum short term ambient concentration for each chemical
" threshold levels for acute health effects of each chemical (compared with the maximum short term concentrations)
" locations of the maximum impacts for different chemicals (often polar receptors)
" Census block identification codes at the locations of maximum concentration, and number of people in the block
" contribution of each emission source at the facility to the maximum short term concentration of each chemical

! Outputs for all receptors
" maximum individual cancer risk and TOSHI (all target organs) for each Census block and each user-specified discrete receptor (monitoring sites, etc.)
" maximum individual cancer risk and TOSHI (all target organs) for each polar grid receptor
" estimated deposition (optional)
" predicted ambient concentration resulting from each emission source at each Census block and polar grid receptor (optional)

! Population exposures and total cancer risk, or incidence
" estimated numbers of people exposed to different levels of lifetime individual cancer risk (1 in a million, 1 in 100,000, etc.)
" estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.)
" total cancer risk, or incidence, in estimated cancer deaths per year, over the entire modeling domain, and for each pollutant and source combination

### 13



## 2.4 Data Libraries Used in HEM -3
2.4.1 Chemical Health Effects Information

HEM-3 includes a library of available health effects data for HAPs. For each pollutant, the library includes the following parameters, where available:

! unit risk estimate (URE) for cancer
! reference concentration (RfC) for chro nic noncancer health effects
! reference concentrations for acute health effects
! target organs affected by the chemical for chronic noncancer health effects

Unit risk estimates and reference concentrations included in the HEM-3 chemical library have been taken from EPA’s database of recommended dose -response factors for HAPs, which is updated periodically, consistent with continued research on these parameters. 4 

### The URE represents the upper-bound excess lifetime cancer risk estimated to result from continuous exposure to an agent at a concentration of 1 microgram per cubic meter (μg/m 3 

### ) in air (e.g., if the URE = 1.5 x 10 -6 

### per μg/m 3 

### , 1.5 excess tumors are expected to develop per 1 million people if all 1 million people were exposed daily for a lifetime to 1 microgram of the chemical in 1 cubic meter of air). 5


### The RfC is a concentration estimate of a continuous inhalation exposure to the human population that is likely to be without an appreciable “risk” of deleterious non -cancer health effects during a lifetime. No adverse effects are expected as a result of exposure if the ratio of the potential exposure concentration to the RfC, defined as the hazard quotient (HQ), is less than 1. 5



### The reference benchmark concentration for acute health effects, similar to the chronic RfC, is the concentration below which no adverse health effects are anticipated when an individual is exposed to the benchmark concentration for 1 hour (or 8 hours, depending on the specific acute benchmark used and the formulation of that benchmark). Target organs are those organs (e.g., kidney) or organ systems (e.g., respiratory) which may be impacted with chronic

non -cancer health effects by exposure to the chemical in question. A more in-depth discussion of the development and use of these parameters for estimating cancer risk and non-cancer hazard may be found in the EPA’s Air Toxics Risk Assessment Library

. 6


### The model user can add pollutants and associated health effects to HEM- 3’s chemical health effects (dose-response) library, as needed.

2.4.2 Census Block Locations and Elevation Data

The HEM-3 Census library includes Census block identification codes, locations, populations, elevations, and controlling hill heights for all of the over 6 million Census blocks identified in the 2010 Census and the over 5 million Census blocks identified in the 2000 Census. The model user may choose to use either Census database according to their modeling needs. The location coordinates reflect the internal “centroid” of the block, which is a point selected by the Census to be roughly in the center of the block. For complex shapes, the internal

### 14

point may not be in the geographic center of the block. Locations and population data for Ce nsus blocks in the 50 states and Puerto Rico were extracted from the LandView® database For the 2000 Census 7 

### and from the U.S. Census Bureau website for the 2010 Census. 8 

### Locations and populations for blocks in the Virgin Islands were obtained from the U. S. Census Bureau website.
U.S. Geological Survey data was used to estimate the elevation of each census block in the continental U.S. and Hawaii. The data used for the 2000 Census elevations have a resolution of 3 arc seconds, or about 90 meters. 9 

### The data used for the 2010 Census elevations have a resolution of 1/3 of an arc second, or about 10 meters. 10 

### Using analysis tools (ArcGIS® 9.1 software application for the 2000 Census, and ArcGIS® 10 for the 2010 Census), elevation was estimated for each census block in Alaska and the U.S. Virgin Islands. The point locations of the census blocks in Alaska and the U.S. Virgin Islands were overlaid with a raster layer of North American Digital Elevation Model (DEM) elevations (in meters). 9 

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

An algorithm used in AERMAP, the AERMOD terrain processor , is used to determine controlling hill heights. 11 , 12 

### These values are used for flow calculations within AERMOD. To
save r un time and resources, the HEM- 3 census block elevation database i s substituted for the DEM da ta generally used in AERMAP. As noted a bove, the census bloc k elevations were originally de rived from the DEM database. To determine the controlling hil l height for each census block, a cone is projected away f rom the block centroid location, representing a 10% elevati on grade. The c ontrolling hil l height is selected based on the highe st elevation above th at 10% grade (in accordance with the A ERMAP methodology). The dist ance cutoff f or thi s calculation is 100 km. (This corresponds to a n elevation difference at a 10% gra de o f 10,000 m, which considerably exceeds the max imum elevation difference in North America.)
2.4.3 Meteorological Data

HEM-3 includes an extensive li brary of meteorological data to support the AERMOD dispersion model. Currently 824 meteorological stations have been preprocessed for AERMOD as part of the RTR effort. Section 3.3 includes a depiction of these meteorological stations and Appendix 2 discuss es the preparation of meteorological data for the RTR in more detail.

2.4.4 Gaseou s Deposition Parameters

HEM-3 provides options to compute the deposition of air pollutants, and to take into account the impacts of plume depletion due to deposition of gaseous and particulate pollutants. If the deposition and depletion option is selected by the model user for gaseous pollutants, a number of pollutant properties are required by AERMOD. (These include the diffusivity of the pollutant in air, the diffusivity of the pollutant in water, the Henry’s Law constant, and a parameter reflecting the cuticular resistance to uptake of the pollutant by leaves r CL 

### ). 13 

### HEM-3 includes a library of these parameters for most gaseous HAPs. This library is based on a compendium of gaseous deposition parameters developed by Argonne National Laboratories. 14 

### It should be noted, however, that the deposition and depletion option of HEM-3 and AERMOD have not been used to date for the RTR assessments.

### 15



# 3. Modeling for the Residual Risk Technology Review

This section discusses the general approach used to implement the HEM-3 AERMOD system for the RTR modeling analyses. Separate reports have been prepared for all of the emission source categories analyzed to date. These reports provide information on the emissions inputs and results for specific emission categories. 

## 3.1 Emission Source Inputs

HEM-3 and AERMOD require detailed data on emissions from each emission source included in the modeling analysis. These data include:

! pollutants emitted;
! emission rate for each pollutant;
! emission source coordinates;
! stack height (or emission height for fugitive and other area sources);
! stack diameter (or configuration of fugitive and other area sources);
! emission velocity; and
! emission temperature.
Currently, emissions data for the RTR assessments are taken from the 2005 National Emissions Inventory (NEI). 15 

### These data include HAP emission rates, emission source coordinates, stack heights, stack diameters, flow rates, and exit temperatures. EPA performs an engineering review of the NEI data. In cases where new or better data were known to exist for a particular source category, that information is integrated into the data used in modeling for that category. For each source category, the emissions are summarized in the source category specific report. Detailed computer files containing all emission and release characteristics are available in the docket prepared for the specific RTR source category proposed or final rule.
As noted in the previous section, industrial emission sources can be characterized in AERMOD as point, area, polygon, or volume sources. Fugitive emissions are generally characterized as low point sources with minimal exit velocities. For some categories, additional information was available on the configuration of fugitive emission sources. This information was incorporated into the emissions database as part of the engineering review. Thus, fugitive emission sources were characterized as area or volume sources when sufficient configuration information was available. 

## 3.2 Pollutant Cross-Referencing

Because the NEI is developed from a number of different data sources, a single chemical may be listed in the inventory under different names (i.e. a “common name” and one or more structure-based names). In addition, pollutant groupings such as polycyclic organic matter (POM), can be listed in the NEI under the names of individual member compounds, and under different synonyms (e.g. polynuclear aromatic hydrocarbons). HEM-3 requires an exact match in the

### 16

chemical name in order to link emissions to the appropriate dose-response factors. The model will not process any pollutant that is not specifically listed in the chemical library (see Table 2-1). Therefore, all of the HAP names used in the NEI were linked to the appropriate chemical names in the HEM-3 reference file.
Pollutant-specific dose response values are used in the HEM-3 modeling whenever available, including when modeling POM pollutants. Pollutant groupings, such as POM groupings, are used for POMs without a chemical-specific unit URE’s. These POMs are assigned a URE associated with various POM compounds having similar characteristics. “An Overview of Methods for EPA’s National

- Scale Air Toxics Assessment” 2011 document 16 

### provides more details regarding POM modeling, including:
[S]ome emissions of POM were reported in [the] NEI as “7

- PAH” or “16

- PAH,” representing subsets of certain POM, or simply as “total PAH” or “polycyclic organic matter.” In other cases, individual POM compounds are reported for which no quantitative cancer dose-response value has been published in the sources used for NATA. As a result, simplifying assumptions that characterize emissions reported as POM are applied so that cancer risk can be quantitatively evaluated for these chemicals without substantially under- or overestimating risk (which can occur if all reported emissions of POM are assigned the same URE). To accomplish this, POM emissions as reported in NEI are grouped into categories. EPA assigns dose-response values based on the known or estimated toxicity for POM within each group and on information for the POM speciation of emission sources, such as wood fires and industrial processes involving combustion.
Emissions of metal compounds a re a lso adjusted using a lgorithms developed for the Emissions Modeling S ystem for Ha za rdous Air Pollutants (EMS-HAP) under the Na tional-scale Air Toxics Assessment (NATA). A ma ss adjustment fac tor was applied to the emissions of metal compounds to account for a pa rticular portion (e.g., the lead portion of le ad su lfate) or to partition them among mul tiple pollutant categories (e.g. chromium arsenate into chromium VI c ompounds and arsenic c ompounds ). I n addition, where no specific compound information was available, metals were sp eciated into appropriate oxidation states (e.g. chromium c ompounds int o chrome VI and chr ome III) based on f actors that have be en developed for sp ecific source c ategor ies and average factors that have been developed for the inventory as a whole. The adjustment factors and speciation factors were taken from the HAP Table module of EMS-HAP. 17 , 18



## 3.3 M eteorological Data

Nationwide meteorological data files are accessed by HEM-3 and used for the RTR modeling. The current HEM-3 AERMOD Meteorological Library includes over 800 nationwide locations, depicted in Figure 3-1. This library contains surface and upper air meteorological data from National Weather Service (NWS) observation stations , which are named beginning with the state abbreviation for the state in which the station is located. AERMOD requires surface and upper air meteorological data that meet specific format requirements. 19, 20 

### Appendix 2 discusses the preprocessing performed on the meteorological data used by AERMOD and includes a detailed listing of the 824 meteorological station pairs.

### 1 7 Figur e 3-1 . AERMOD Meteorological Stations

### 1 8



## 3.4 M odel Options Selected

HEM-3 presents a number of options for characterizing the modeling domain and data sources. As many sources are generally modeled in RTR assessment s, established defaults and common practices are relied on to make these choices. The choices available to a HEM-3 user and the selec tions that are made in most RTR assessments are presented in Table 3-1 . Some of the key selections are discussed in more detail in the paragraphs below.

It should be note d that although routine emissions are not expected to vary significantly with time, nonroutine ( upset) e missions can be si gnificant relative to routine e missions. Upset emissions occur durin g p eriods of star tup, shutdown, and malfunction. Upset emissions a re not likely f or equipment or storage tanks, but do result f rom malfunctioning control devices and le aks in cooling towe r heat exchangers. There is some limited data on upset emissions a vailable, 21 

### but no facility-specific a nalyses of these da ta we re pe rformed to characterize s hort-term emissions from these e mission sources, and upse t emissi ons are generally not modeled for the RTR r isk assessments. 3.4.1 Urban or Rural Dispersion Characteristics

Current RTR source category assessments which use the 2010 Census are based on either urban or rural dispersion characteristics, depending on the land characteristics surrounding each modeled facility. The EPA provides guidance on whether to select urban or rural dispersion coefficients in its Guideline on Air Quality Models. 22 

### In general, the urban option is used if (1) the land use is classified as urban for more than 50% of the land within a 3-kilometer radius of the emission source, or (2) the population density within a 3-kilometer radius is greater than 750 people per square kilometer. Of these two criteria, the land use criterion is more definitive.
Using the 2010 Census, the HEM-3 model determines, by default, whether to use rural or urban dispersion characteristics. HEM-3 will find the nearest census block to the facility center and determine whether that census block is in an urban area, as designated by the 2010 Census. 23 

### The population of the designated urban area will be used to specify the population input for AERMOD's urban mode. (Alternatively, a user may select the rural or urban option to override determination by the model. If a user selects an urban dispersion environment, then the user must pro vide the urban population as well.)
For the 2008 and prior screening-level RTR assessments of 51 source categories, the rural option was chosen to be most conservative (i.e., more likely to overestimate risk results). The rural option is also chosen by default by the HEM-3 model whenever the 2000 Census is selected by the

user. 3.4.2 Deposition and Plume Depletion

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

conc entrations in the plume due to wet or dry d eposition, although HEM-3 c an model deposition

### 19

a nd depletion using AERMOD. In addition, reactivity and decay have not been considered. It is possible that this approach may overestimate air concentrations and therefore risk. However, one of the main metrics used by EPA in the residual risk program is the risk to the individual most exposed (the maximum individual risk , or MIR). Because the maximum risk usually occurs at a receptor very close to the emission source, it is unlikely to be influenced by altered plume dispersion characteristics of this type. For more refined, multipathway assessments, EPA may consider deposition and depletion.
3.4.3 Cutoff Distance for Modeling of Individual Blocks
The cutoff distance for modeling individual Census blocks is initially set to 3 km by default. This distance generally ensures that the maximum individual cancer risk and the maximum TOSHI are modeled explicitly and not interpolated. Following a modeling run, the results for each facility are checked to determine whether the maximum impacts are located inside the modeling cutoff distance. If the maximum impacts are outside the cutoff distance, and if any of the impacts are significant, then HEM-3 is rerun for the facility with a cutoff distance greater than 3 km. In general, this is done if the cancer risk exceeds 1 in 1 million or any TOSHI exceeds 1. However, the risks for such facilities are generally very low, since the maximum impacts are in most cases only interpolated when the nearest Census block is more than 3 km from the facility (i.e., in sparsely populated areas).
3.4.4 Facility Boundary Assumptions

The main input mechanisms for incorporating facility boundary information in HEM-3 are the overlap distance, the distance to the innermost polar receptor ring, and user-specified receptor locations. The NEI does not provide information on facility boundaries. However, satellite/aerial images are used to locate residential populations that are closer to a facility than the Census block centroid. User-specified receptor locations are used in such assessments to avoid underestimating risk. Conservative default assumptions are used for the overlap distance and the innermost polar receptor ring. However, these are adjusted for some categories where facility sites are known to be large. In addition, satellite imagery is used to check the facility boundary assumptions for facilities with large projected impacts. These checks are discussed further in the section on Quality Assurance (Section 4).

### 2 0

Table 3

- 1

. HEM

- 3 Domain and Set

- Up Options As Used in the Residual Risk and Technology Review Assessments Option Selection Dispersion model AERMO D Census database: 2010 or 2000 Based on date of analysis Type of analysis: chronic, acute, or both Both Averaging time for short term impacts 1

- hour Multiplier for short term emissions 10 generally, although source type-specific factors are used if available
D ispersion characteristics : u rban or rural
, as determined by model
, based on closest 2010 Census block to each facility (when using 2010 Census)

. Rural by default, when using the 2000 Census. Urban or Rural based on facility location ; ( Rural fo r 2000 Census) Include terrain impacts Yes Include building wake effects No Calculate deposition (wet, dry, or both) & include impacts of plume depletion No
User

- specified receptor locations (for residential population locations, facility boundary site s , or other sites of interest) Yes, for some facilities Modeling domain size

– maximum distance to be modeled 50 km Cutoff distance for modeling of individual blocks 3 km a Overlap distance where receptors are considered to be on facility property

– m easured from each source 30 m b

Polar receptor network specifications : Distance from the facility center to the innermost ring ≥ 100 m c Number of rings 13 Number of directions 16 Meteorology data Closest site

a The individual block modeling cutoff is increased for categories and for some facilities to ensure that the maximum individual risk values are not interpolated. b The overlap distance is adjusted for some facilities to avoid modeling locations that are on facility property (see section 4.2).

c HEM

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

### 2 1



## 3 .5 Modeling of Multiple Facilities

HEM-3 models one facility at a time. However, clusters of nearby facilities may impact the same people, resulting in higher risk to those people. To account for this situation, risks are summed at each Census block for all facilities affecting the Census block.
As described earlier (Section 2.3.4), HEM-3 produces detailed output tables containing the risk and population for every Census block in the modeling domain. These detailed tables are combined for all facilities in a source category and the risk for each Census block is summed, using the RTR Summary Program add-on module to the Multi HEM-3 model. 3 

### Thus , the effect of multiple facilities in the same source category on the same receptor are estimated. The resulting “combined facility” or “cluster

- effect” Census block risks are used to calculate population exposure to different cancer risk levels, noncancer hazard indices, and source category incidence.

### 2 2 

# 4. Quality Assurance

The National Emissions Inventory (NEI ) is subject to an extensive program of quality assurance (QA) and quality control (QC). The QA/QC program for the point source component of the NEI is documented in a separate report, available from the NEI website. 24 

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

## 4.1 Engineering Review

In addition to the standardized QA steps taken for the entire NEI, EPA performs an engineering review of NEI data for the emission source categories included in the RTR analysis. This engineering review includes two main components. The first component addresses the list of facilities included in each source category. EPA engineers review independent sources of information to identify all sources in the category that are included in the NEI. In addition, EPA reviews the list of sources represented as part of each category in the NEI to make sure that the facilities actually manufacture products characteristic of the source category.
The second component of the engineering review focuses on the appropriateness of facility emissions. EPA reviews the list of HAPs reportedly emitted by each facility to make sure that the pollutants are appropriate to the source category. In addition, EPA engineers review the magnitude of those HAP emissions. In cases where new or better data are known to exist for a particular source category, that information is integrated into the data used in modeling for that category. In these cases, the source category specific documents provide additional details on the emissions inputs used. 

## 4.2 Geographic Pre-Modeling Checks

The NEI QA process includes some basic checks on location data for point sources. The coordinates for each source are checked to ensure that they are in the county that has been specified for the source. If this is not the case, or if no geographic coordinates are available for the emission source, then the coordinates are set to a default location based on the nature of the emission source category. 25 

### In addition, coordinates for all emission sources at a given facility are checked to ensure that they are within 3 km of one another. These QA checks happen prior to HEM-3 modeling and the results of such checks are reflected in the HEM-3 input files.
Another pre-modeling geographic QA check regards the location of the census block receptors. As noted above, to estimate ambient concentrations for evaluating long-term exposures, the HEM-3 model uses the census block centroids as dispersion model receptors. The census block

c entroids are often good surrogates for where people live within a census block. A census block generally encompasses about 40 people or 10-15 households. However, in cases where a block centroid is located on industrial facility property, or where a census block is large and the centroid less likely to be representative of the block’s residential locations, the block centroid may not be an appropriate surrogate.

### 2 3

Ce nsus block centroids that are on facility property can sometimes be identified by their proximity to emission sources. In cases where a census block centroid is within 300 meters of any emission source, aerial images of the facility are reviewed to determine whether the block centroid is likely located on facility property. The selection of the 300-meter distance reflects a compromise between too few and too many blocks identified as being potentially on facility property. Distances smaller than 300 meters would identify only block centroids very near the emission sources and could exclude some block centroids that are still within facility boundaries, particularly for large facilities. Distances significantly larger than 300 meters would identify many block centroids that are outside facility boundaries, particularly for small facilities. Block centroids confirmed to be located on facility property are moved to a location that best represents the residential locations in the block.
In addition, census block centroids for blocks with large areas may not be representative of residential locations. Risk estimates based on such centroids can be understated if there are residences nearer to a facility than the centroid, and overstated if the residences are farther from the facility than the centroid. To avoid understating the maximum individual risk associated with a facility, block centroids are relocated in some cases, or additional user-specified receptors are added to a block. Aerial images of all large census blocks within one kilometer of any emission

source are examined. Experience from previous risks characterizations show that in most cases the MIR is generally located within 1 km of the facility boundary. If the block centroid does not represent the residential locations, it is relocated in the H EM -3 input files to better represent them. If residential locations c an not be represented by a single receptor (that is, the residences are spread out over the block), additional user-specified receptors are included in the HEM-3 input files to represent residences nearer to the facility than the centroid. 

## 4.3 Geographic Post-Modeling Checks

As part of the RTR modeling analysis, additional geographical QA checks are made for some facilities, after initial HEM-3 modeling results are reviewed. Facilities subjected to these addit ional checks include:

! cases where the initial estimates of maximum risks are particularly high
" maximum individual cancer risk of over 1 in 10,000
" any maximum TOSHI above 10
! cases where no Census blocks are identified by the model within 3 km of the facility

HEM-3 produces a detailed Google EarthTM map of the modeled point and area emissionsources and surrounding receptors (including Census block centroids, polar receptors and userspecified receptors) overlaying Google EarthTM’s satellite imagery. This map allows a QA check of the specific source locations, as well as an approximate check of the facility boundaries. The emission source coordinates are reviewed for each of these facilities and compared with the address reported for the facility. If the address and the coordinates represent the same location, then the coordinates are taken to be correct. For more recent modeling of source categories, theemission coordinates initially modeled by HEM-3 tend to be correct, as they undergo premodeling scrutiny and QA checks (as discussed in Section 4.2).

### 2 4

More r arely, the modeled emission coordinates will be determined post initial modeling not to be located on facility property. If the facility and emission coordinate locations are different, then the satellite imagery for the address and the coordinate location are reviewed to determine whether either photograph includes an industrial facility. Generally for the 2008 and prior screening-level RTR a ssessments of 51 source categories, where the two locations were different, the facility address was found to be correct (and the emission source coordinates required correction). In some cases, this comparison could not be made because the reported address was a Post Office box or a headquarters address. Where this occurred, the aerial photograph for the coordinate location was reviewed to determine whether an industrial facility was located at or near the location. If emission source coordinates are found to be incorrect, HEM-3 is rerun using corrected coordinates. These changes are described in the source category documents.
For the high-risk facilities, the coordinates used to represent the most impacted Census blocks are also reviewed. This review draws on detailed Census block boundary maps and satellite imagery. Large industrial facilities will frequently occupy one or more entire Census blocks. However, these blocks may also include one or more residences on the periphery of the industrial land. Generally, the centroid coordinates listed for a Census block are near the center of the block. In these cases of mixed industrial and residential blocks, the coordinates may be on facility property.
In general, block coordinates are considered to be on facility property if they are located between the different emission source locations listed for the facility. In these situations, HEM-3 is rerun with an expanded overlap distance, in order to exclude the Census block coordinates that appear to be located on facility property. The distance to the innermost polar receptor ring is also adjusted to ensure that this ring is not on facility property, but as close to the apparent facility boundaries as possible.

### 2 5 

# 5. Uncertainties

Th e RTR risk assessments using HEM-3 and AERMOD are subject to a number of uncertainties. For instance, model verification studies for AERMOD show predicted maximum annual concentrations ranging from 0.3 to 1.6 times measured values, with an average of 0.9. Predicted maximum short term (1 to 24 hours) concentrations were 0.25 to 2.5 times measured values, with an average of 1.0. 25


### In addition, a number of simplifying assumptions are made in these modeling analyses. First, the coordinates reported by the Department of Census for Census block internal points (“centroids”) have been used as a surrogate for long-term population exposures. Locations of actual residences have not been modeled. In addition, the current version of HEM-3 does not take into account the movement of people from one Census block to another during the course of their lives, or commuting patterns during a given day. Nor does the model take into account the attenuation of pollutant from outside emission sources in indoor air. Ideally, risks to individuals would be modeled as they move through their communities and undertake different activities. However, such modeling is time- and resource-intensive and can only capture a portion of theuncertainty associated with the full range of human activities. In general, it is expected that longterm exposures will be overstated for high-end estimates (as most individuals will not spend all their time at their highly affected residences), but may understate the total population exposed (as some individuals living outside the modeled area may regularly commute into the area for work or school).
When considering long-term or lifetime exposures, it should be noted that relatively few people in the United States reside in one place for their entire lives. For the purposes of this assessment, cancer risk estimates are based on a lifetime exposure at the Census-identified place of residence. While it is impossible to know how this assumption affects the risk experiences by a particular individual (as people can move into higher- or lower-risk areas), it is likely that this

a ssumption will overstate the exposure to those most exposed (i.e., people already living in high exposure areas are unlikely to move to yet higher exposure areas). However, this assumption will also tend to underestimate the total number of people exposed and population risk (i.e., incidence) because population levels are generally increasing.
In the current analyses, only direct inhalation is modeled. Other pathways such as the deposition of pollutants to drinking water, and to bioaccumulation of deposited pollutants in the food supply may be a significant source of exposure for persistent and bioaccumulative hazardous air pollutants (PB HAP). Screening level evaluations of the potential human health risks associated with emissions of PB HAP from the modeled facilities are used to determine if additional analyses are needed, but these analyses are outside the scope of this document. Because th e HEM-3 AERMOD analyse s are re stricted to the inhalation pathway, the impacts of plume depletion due to deposition are not taken into account. Thus, inhalation impacts may be overestimated for some pollutants, but exposures through other pathways would be underestimated.

### 2 6

A number of o ther simplifications are made in the dispersion modeling analyses, as noted in Table 3-1. For instance, building wake effects are not considered. In addition, meteorological observations are based on the closest station in the HEM-3 meteorological library (see Figure 3 -1).Alternative meteorological stations may be more appropriate for some facilities. Ideally, facilityspecific meteorological observations would be used. A single year of meteorological data (2011 ) is currently used for AERMOD’s dispersion modeling. (The 2008 and prior screening-level RTR assessments of 51 source categories used meteorological data based on the year 1991.) When considering off-site meteorological data most site specific dispersion modeling efforts will employ up to five years of data to capture variability in weather patterns from year to year. However, because of the large number of facilities in the analyses and the extent of the dispersion modeling analysis (national scale), it is not practical to model five years of data. Other national studies such as NATA also consider only a single year of meteorological data. A sensitivity analyses performed by the NATA assessment found that variability attributable to the selection of the meteorology location/time (both temporal and spatial) resulted in a 17-84% variation in predicted concentrations at a given station. 26


### Finally, risk and exposure factors are also subject to uncertainty. Not all individuals experience the same degree of exposure or internal dose of a given pollutant due to individualspecific parameters such as weight, age, and gender. While the health benchmarks used in the analyses crudely account for sensitive populations, a prototypical human (e.g., body weight, ventilation rate) is used to define the benchmark. Because of the variability of these parameters in the population, this factor will result in a degree of uncertainty in the resulting risk estimate.
Table 5-1 summarizes the general sources of uncertainty for the RTR modeling analyses. The table also gives a qualitative indication of the potential direction of bias on risk estimates. The sources of uncertainty in Table 5-1 are divided into four categories, based on the major components of the analyse s:

! emissions inventory;
! fate and transport modeling;
! exposure assessment; and
! toxicity assessment.
It must also be noted that individual source categories may be subject to additional uncertainties. These are discussed in separate reports which are prepared for each emission source category included in the RTR assess ments.

### 2 7

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

Parameter Assumption Uncertaint y/Variability Discussion Potential Direction of Bias on Risk Estimates Emissions Inventory

Individual HAP emissions rates and facility characteristics ( stack parameters
, property boundaries) Emissions and facility characteristics from the NEI provide an accurate characterization of actual source emissions. Our current emissions inventory is based on the 200 5 NEI, our internal review, and public comments received. The degree to which the data in our inventory represents actual emissions i s likely to vary a cross sources. For the 2008 screening level assessments, n early half of the sources in a given source categor y submitted a review of their emissions and facility characteristics data. Some detailed data, such as property boundary information is not availab le for most facilit ies

. This is an important consideration in determining acute impacts. Unbiased overall, magnitude variable

Multiplier for short

- term emission rates Generally, m aximum short term emission rates are estimated by applying a simple multipli er (a factor of 10) to average annual emissions. The ratio between short

- term and long

- term average emission rates may vary among the different emission sources at a facility. In addition, the use of a simple multiplier means that impacts of maximum short term emissions are modeled for all meteorological conditions, including the worst

- case conditions for population exposure. Potential overestimate due to the fact that worst

- case emissions are assumed to occasionally coincide with worst

- case meteorology.

O verestimate due to lack of actual information on short

term emission rates. Fate and Transport Modeling

Atmospheric dispersion model choice AERMOD is EPA's recommended dispersion model for assessing pollutant concentrations from industrial facilities Fie ld testing of dispersion models , including AERMOD, have shown results to generally be within a factor of 2 of measured concentrations. Unbiased overall

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

2 8 Parameter Assumption Uncertaint y/Variability Discussion Potential Direction of Bias on Risk Estimates

Building downwash Not included in assessment s Use of this algorithm in AERMOD could improve the dispersion calculations at individual facilities. However, data are not readily available to utilize this option. Potential underestimate of maximum risks near facility. No effect on risks further out.

Plume dep osition and depl etion Not included in assessment s Igno ring these impacts for pollutants that deposit minimally
, and whose risks derive predominantly from inhalation
, should have minimal effect on risk estimates. Unbiased or minimal overestimate.

Meteorology One year of meteorological data from the nearest we ather station (selected from 824 nationwide) is representative of long

- term weather conditions at the facility. The use of one year of data rather than the five or more adds uncertainty based on whether that year is representative of each location’s climat ology. Use of weather station data rather than on

- site data can add to uncertainty. Additionally, the use of default surface parameters in the generation of the meteorological datasets imparts uncertainty to the results from any individual facility. Minim al underestimate or overestimate.

Reactivity Not included in the assessment s Chemical reactions and transformations of individual HAP into other compounds due to solar radiation and reactions with other chemicals happens in the atmosphere. However, in gen eral, the HAP in this assessment do not react quickly enough for these transformations to be important near the sources, where the highest individual risks are estimated. Further, most of the HAP do not react quickly enough for these transformations to be important to risk estimates in the entire modeled domain (i.e., within 50 km of the source). No impact on maximum risk estimates. Minimal impact on population risks and incidence.

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

29 Parameter Assumption Uncertaint y/Variability Discussion Potential Direction of Bias on Risk Estimates

Maximum modeling distance 50 kilometers from center of facility This distan ce is considered to be the maximum downwind distance for a Gaussian plume model such as AERMOD. This is because, in general, winds cannot be considered to follow straight line trajectories beyond this distance. No effect on maximum individual risks. Minima l underestimation of incidence.
Exposure Assessment

Locations and short

term movements of individuals Ambient concentration at centroid of each off

- site census block is equal to the exposure concentration for all people living in that census block.

Effe ct of human activity patterns on exposures is not included in the assessment. People live at different areas within block that may have higher or lower exposures than at the centroid. Individuals also move from outdoors to indoors and from home to school/w ork to recreation, etc., and this can affect their total exposure from these sources. Unbiased across population for most pollutants and individuals, likely overestimate for most exposed and underestimate for least exposed persons.

Long

- term movements of individuals MIR individual is exposed continuously to the highest exposure concentration for a 70

- year lifetime. M aximum individual risk (MIR ) is defined in this way to be a maximum theoretical risk at a point where a person can actually reside. Unbiased f or most individuals, likely overestimate for the actual individual most exposed and likely underestimate for the least exposed. Incidence remains unbiased unless population around facilities increases or decreases over 70 years.

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

3 0 Parameter Assumption Uncertaint y/Variability Discussion Potential Direction of Bias on Risk Estimates Toxicity Assessment

Reference concentrations (RfC) Consistent with EPA guidance, RfCs are developed including uncertainty factors to be protective of sensitive subpopulations. Additionally, RfCs are developed based on the level producing an effect in the most sensitive targe t organ or system. While other organ systems may be impacted at concentrations above the RfC, these are not included in the calculation of target organ

specific hazard indices. In general, EPA der ives RfCs using procedures whose goal is to avoid underestim ating risks in light of uncertainty and variability. The greater the

uncer tainties , the greater the potential for overestimating risks.

Unit Risk Estimate (URE) Use of unit risk estimates developed from dose

- response models such as linear low

- dose extrapo lation. Uncertainty in extrapolating the impacts from short

- duration, high

- dose animal or work

- related exposures to longer duration, lower

- dose environmental impacts. Overestimate of risks for nonlinear carcinogens and for linear carcinogens with sparse he alth effects data

. In general, EPA derives URE values using procedures aimed at overestimating risks in light of uncertaint y and variabilit y

.

Toxicity of mixtures Cancer risks and noncancer hazard quotients were calculated for each HAP individually and th en summed into a total risk or hazard index (assumption of additivity). Concurrent exposures to multiple chemicals may result in either increased or decreased toxicity due to chemical interactions but the data needed to quantify these effects are generally not available. Unbiased overall. Some mixtures may have underestimated risks, some overestimated, and some correctly estimated.

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

3 1 Parameter Assumption Uncertaint y/Variability Discussion Potential Direction of Bias on Risk Estimates

Surrogate dose

response values for HAPs without values In the case of groups of HAPs such as glycol ethers, the most

conserv ative dose

- response value of the chemical group was used as a surrogate for missing dose

response values in the group. For others, such as unspeciated metals, we have applied speciation profiles appropriate to the source category to develop a composite dos e

- response value for the group.
For HAP which are not in a group and for which no URE’s or RfC’s are available from credible sources, no assessment of risk is made. Rather than neglecting the assessment of risks from some HAPs lacking dose response values
, conservative assumptions allow the examination of whether these HAPs may pose an unacceptable risk and require further examination, or whether the conservative level examination with surrogates screens out the HAPs from further assessment. Overestimate w here most conservative values used. Unbiased where category

specific profiles applied.

There is the potential to underestimate risks for pollutants which are not included in the assessment.

### 3 2 

# 6. R eferences

1 . EPA. 2004. AERMOD: Description of Model Formulation . EPA-454/R-03 -004, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf

2 . EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD . EPA-454/B03 -001, U.S. Environmental Protection Agency, Research Triangle Park, NC. www.epa.gov/scram001/dispersion_prefrec.htm#aermod

3 . EPA. 2013. The Human Exposure Model (HEM): Single-facility HEM-3 Use r’s Guide; and Multi-facility HEM- 3 and RTR Summary Programs User’s Guide. Prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC, by EC/R Inc., Chapel Hill, NC. February 2013. http://www.epa.gov/ttn/fera/human_hem.html

4 . EPA. 2012. Dose-Response Assessment for Assessing Health Risks Associated With Exposure to Hazardous Air Pollutants. U.S. Environmental Protection Agency, Research Triangle Park, NC. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

5

. 

### EPA. 2011. Glossary of Key Terms . Technology Transfer Network Air Toxics, 2005 National-Scale Air Toxics Assessment. U.S. Environmental Protection Agency. http://www.epa.gov/ttn/atw/natamain/gloss1.html

.
6

. EPA. 2004. Air Toxics Risk Assessment Reference Library, EPA-453 -K- 04 -001A, U.S. Environmental Protection Agency, Research Triangle Park, NC. April 2004. www.epa.gov/ttn/fera/risk_atra_vol1.html

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

8 . Census. 2010. Census Summary File 1

– Un ited States: http://www2.census.gov/census_2010/04-Summary_File_1/ prepared by the U.S. Census Bureau, Washington, DC, 2011. See also Technical Documentation for the 2010 Census Summary File 1.

9 . USGS. 2006. US GeoData Digital Elevation Models

– Fact Sheet 040-00 (April 2000)

. U.S. Department of the Interior - U.S. Geological Survey, Washington, DC. http://data.geocomm.com/sdts/fs04000.pdf

10 . USGS. 2010. USGS Seamless Data Warehouse. U.S. Department of the Interior - U.S. Geological Survey, Washington, DC. http://nationalmap.gov/viewer.html

### 3 3

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

12

. EPA. 2011. Addendum to the User’s Guide for the AERMOD Terrain Preprocessor (AERMAP) (EPA-454/B-03 -003, October 2004), U.S. Environmental Protection Agency, Research Triangle Park, NC. March 2011. http://www.epa.gov/scram001/dispersion_related.htm#aermap

13

. EPA. 2012. Addendum: User's Guide for the AMS/EPA Regulatory Mode l

– AERMOD ( EPA-454/B-03-001, September 2004) (Section 2.2 related to deposition). U.S. Environmental Protection Agency, Research Triangle Park, NC. December 2012. http:// www.epa.gov/scram001/dispersion_prefrec.htm#aermod

. See also the AERMOD Deposition Algorithms

– Science Document (Revised Draft)” at http://www.epa.gov/ttn/scram/7thconf/aermod/aer_scid.pdf

14 . Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for the Industrial Source Complex (ISC3) Model

. ANL/ER/TR

– 01/003, Argonne National Laboratory, Argonne, Illinois 60439. See “AERMOD Deposition Parameterizations Document” pdf link under “Model Supporting Documents” section of TTN’s Preferred/Recommended Models webpage at http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

15 . EPA. 2012. 2005 National Emissions Inventory Data & Documentation . U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/chief/net/2005inventory.html#inventorydata

16

. EPA. 2011. An Overview of Methods for EPA’s National -Scale Air Toxics Assessment. Prep ared for EPA’s Office of Air Quality, Planning, and Standards, Research Triangle Park, NC, by ICF International , Durham, NC. January 2011, p 55. http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf

. 17

. EPA. 200 4

. EMS

- HAP Modeling System

. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/dispersion_related.htm#ems-h ap
18 . Strum, Madeleine, Richard Mason, and James Thurman. 2004. User's Guide for the Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 3.0

. EPA-454/B-03 -006, Environmental Protection Agency, Research Triangle Park, NC. August 2004. http://www.epa.gov/ttn/scram/userg/other/emshapv3ug.pdf

19

. EPA. 2004. User’s Guide for the AERMOD Meteorological Preprocessor (AERMET), EPA-454/B-03 -002, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/7thconf/aermod/aermetugb.pdf

### 3 4

20 . EPA. 2012. Addendum to the User’s Guide for the AERMOD Meteorological Preprocessor (AERMET) (EPA-454/B-03-002, November 2004), U.S. Environmental Protection Agency, Research Triangle Park, NC. December 2012. http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet

21 . Texas Commission on Environmental Quality. 2006. Reports on Air Emission Events. http://www.tceq.texas.gov/field/eventreporting

22 . EPA. 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions. Appendix W of 40 CFR Part 51. http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf

23 . Federal Register, 2012. Qualifying Urban Areas for the 2010 Census. FR 77:59 (27 March 2012). p.18652. http://www.gpo.gov/fdsys/pkg/FR-2012-03 -27/pdf/2012-6903.pdf

24 . EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources . U.S. Environmental Protection Agency, Emission Inventory Group, RTP, NC. http://ww w.epa.gov/ttn/atw/nata2005/05pdf/2002nei_qa_augmentation_report0206.pdf

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

26 . EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01 -003; EPA 454/B-03 -003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/atw/nata/peer.html

### Appendix 2 Meteorological Data for HEM 3 Modeling

### 2 

## DRAFT

METEOROLOGICAL DATA PROCESSING USING AERMET FOR HEM3Meteorological Data Processing using AERMET December 2 8 , 2012 For HEM3



### 1 BACKGROUND

The AERMOD meteorological processor, AERMET, and its supporting modeling system (AERSURFACE and AERMINUTE) w ere used to process one year of meteorological data for over 800 observation stations across the continental United States, Alaska, Hawaii, and Puerto Rico.

METEOROLOGICAL DATA
To estimate the boundary layer parameters required by AERMOD, AERMET requires hourly surface weather observations ( which may includ e hourly v alues calculated from 1

- minute data) and the full (i.e., meteorological variables reported at all levels) twice

- daily upper air soundings. The surface and upper air stations are paired to produce the required input data for AERMOD.
USEPA meteorologists obtained calendar year 2011 Integrated Surface Hourly Data (ISHD) for over 800 Automated Surface Observation System (ASOS) stations spanning the entire US
, as well as Puerto Rico and the US Virgin Islands
, from the National Climatic Data Center (NCDC). To support AERMET, ASOS 1 -minute d ata for each surface station w ere also obtained from NCDC in a DSI 6405 format.
Further, upper air sounding data for the same time period for over 80 observation sites were obtained from the “ NOAA/ESRL ” online Radiosonde Database

. These datasets were produced by ESRL in Forecast Systems Laboratory (FSL) format. Appendix 1 lists the surface stations
, as well as the location, ground elevation, and anemometer height for each station. Figure 1 is a map that shows the locations of all the surface stations. Appendix 2 lists the upper air stations and their locations

.

AERMET PROCESSING
Utilizing the AERMET meteorological data preprocessor , and the ASOS surface and FSL upper air stations, surface and profile files for input in to AERMOD were generated nationwide. The surface stations were paired with representative upper air stations by taking the upper air station closest to each surface station. The AERSURFACE tool wa s used to estimate the surface characteristics for input in to AERMET utilizing land cover data surrounding the surface station

. In addition, the AERMINUTE preprocessor was
u sed to process 1

- minute ASOS wind data for input in to AERMET. Table 1 outlines th e approach/inputs each of the data preprocessors and tools used to generate the AERMOD meteorological data.Meteorological Data Processing using AERMET December 2 8 , 2012 For HEM3



### 1 Figure 1 . Surface StationsMeteorological Data Processing using AERMET December 2 8 , 2012 For HEM3



### 1 Table 1

. AERMET Processing Options AERMET Options Version 11059 ASOS Site Yes Surface Data Format NCDC TD

- 3505 (ISHD) Upper Air Data Format FSL, all levels, tenths m/s AERMINUTE Options Version 11325 Include 1 minute ASOS Data Yes, where available TD

- 6405 format AERSURFACE Options Version 08009 Landcover data USGS NLCD92 GeoTIFF format (except Alaska and Hawaii which used 20 0 1 landcover data) Radius for Surface Roughness Calculations 1 km

Site Characteristics Airport Site (where applicable) Arid Regions for all sites in AZ, NM, UT only Temporal resolution Monthly, 12 sectors Site Surface Moisture Average Snow Cover Late Winter/Winter without continuous snow cover – all states (except Alaska where we used continuous snow)

RESULTS

To assure that each s urface and profile file would run properly, USEPA meteorologists

ran AERMOD using a model plant

. Further
, the surface files were examined for completenes s. If more than 10 percent of the data w ere missing, the station was not considered suitable for the HEM

- 3 meteorological database. In all, 824 met station pair s ran successfully and will be included in the HEM

- 3 meteorological library

. A summary of the se stations, along with the missing and calm hours for each
, are presented in Appendix 31 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) AK ANNETTE 25308 55.04333 -131.57000 33 10.05 AK JUNEAU 25309 58.35500 -134.57500 7 10.05 AK HAINES 25323 59.24500 -135.52000 7 10.05 AK KETCHIKAN 25325 55.35667 -131.71167 26 10.05 AK PALMER 25331 61.59611 -149.09167 70 10.05 AK SITKA 25333 57.04833 -135.36000 5 10.05 AK SKAGWAY 25335 59.46000 -135.31333 6 10.05 AK YAKUTAT 25339 59.51028 -139.62778 12 10.05 AK KALWOCK 25367 55.58000 -133.07500 20 10.05 AK KODIAK 25501 57.75000 -152.49167 21 10.05 AK KING SALMON 25503 58.68361 -156.65389 14 10.05 AK ILIAMNA 25506 59.75333 -154.91500 52 10.05 AK HOMER 25507 59.64667 -151.47667 17 10.05 AK SELDOVIA 25516 59.44333 -151.70167 9 10.05 AK COLD BAY 25624 55.20667 -162.72167 24 10.05 AK ST PAUL ISLAND 25713 57.16333 -170.22000 11 10.05 AK ANCHORAGE 26409 61.21694 -149.85500 42 10.05 AK CORDOVA 26410 60.48889 -145.45111 13 10.05 AK FAIRBANKS 26411 64.81667 -147.85500 132 10.05 AK NORTHWAY 26412 62.96139 -141.94583 522 10.05

AK DELTA JUNCTION/FT GREELY 26415 63.99500 -145.71833 391 10.05 AK EAGLE 26422 64.77667 -141.14833 273 10.05 AK GULKANA 26425 62.16028 -145.45750 479 10.05 AK NENANA 26435 64.55000 -149.07167 109 10.05 AK SEWARD 26438 60.12833 -149.41667 4 10.05 AK ANCHORAGE 26451 61.17500 -149.99333 37 10.05 AK ANCHORAGE 26491 61.17861 -149.96139 27 10.05 AK PORTAGE GLACIER 26492 60.78500 -148.83889 31 10.05 AK KALTAG 26502 64.32667 -158.74167 45 10.05 AK MC GRATH 26510 62.95333 -155.60333 101 10.05 AK KENAI 26523 60.57972 -151.23917 28 10.05 AK TALKEETNA 26528 62.32167 -150.09167 107 10.05 AK TANANA 26529 65.17444 -152.10694 67 10.05 AK BETTLES 26533 66.91611 -151.50889 196 10.05 AK KOTZEBUE 26616 66.88500 -162.59667 2 10.05 AK NOME 26617 64.51333 -165.44333 6 10.05 AK KIVALINA 26642 67.73167 -164.54833 4 10.05 AK DEADHORSE 27406 70.19167 -148.47722 16 10.05 AK BARROW 27502 71.28667 -156.76333 12 10.05 AK WAINWRIGHT 27503 70.63917 -159.99500 8 10.05 AK NUIQSUT 27515 70.21167 -151.00167 16 10.05 AL HUNTSVILLE 03856 34.64361 -86.78556 190 10.05 AL TROY 03878 31.86056 -86.01222 117 10.05 Appendix 1 ASOS Surface Stations used for AERMET (2011)2 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) AL MOBILE 13838 30.62639 -88.06806 5 10.05 AL DOTHAN 13839 31.32139 -85.44972 113 10.05 AL ANNISTON 13871 33.58806 -85.85806 182 10.05 AL BIRMINGHAM 13876 33.56389 -86.75444 187 10.05 AL MOBILE 13894 30.68833 -88.24556 66 10.05 AL MONTGOMERY 13895 32.30056 -86.39417 38 10.05 AL MUSCLE SHOALS 13896 34.74528 -87.61028 166 10.05 AL EVERGREEN 53820 31.41556 -87.04417 77 10.05 AL ALABASTER 53864 33.17833 -86.78167 172 10.05 AL TUSCALOOSA 93806 33.21194 -87.61583 48 10.05 AR JONESBORO 03953 35.83111 -90.64639 79 10.05 AR HOT SPRINGS 03962 34.47806 -93.09611 155 10.05 AR LITTLE ROCK 13963 34.74667 -92.23306 77 10.05 AR FORT SMITH 13964 35.33361 -94.36500 136 10.05 AR HARRISON 13971 36.26139 -93.15472 415 10.05 AR TEXARKANA 13977 33.45361 -94.00750 116 10.05 AR BLYTHEVILLE 53869 35.94028 -89.83083 77 10.05 AR MOUNTAIN HOME 53918 36.36889 -92.47028 281 10.05 AR MONTICELLO 53919 33.63833 -91.75083 85 10.05 AR RUSSELLVILLE 53920 35.25778 -93.09472 117 10.05 AR FAYETTEVILLE/SPRINGDALE 53922 36.28167 -94.30667 388 10.05 AR De QUEEN 53925 34.04694 -94.39944 105 10.05 AR WEST MEMPHIS 53959 35.13500 -90.23444 73 10.05 AR PINE BLUFF 93988 34.17500 -91.93472 62 10.05 AR EL DORADO 93992 33.22083 -92.81333 80 10.05 AR FAYETTEVILLE 93993 36.00500 -94.17000 379 10.05 AZ WINDOW ROCK 03029 35.65750 -109.06139 2052 7.92 AZ FLAGSTAFF 03103 35.14028 -111.67222 2135 10.05 AZ PAGE 03162 36.92611 -111.44778 1308 10.05 AZ PHOENIX 03184 33.68833 -112.08167 453 10.05 AZ SCOTTSDALE 03192 33.62278 -111.91056 449 10.05 AZ GRAND CANYON 03195 35.94611 -112.15472 1991 10.05 AZ NOGALES 03196 31.42083 -110.84583 1191 10.05 AZ TUCSON 23160 32.13139 -110.95528 777 10.05 AZ PHOENIX 23183 33.44306 -111.99028 337 10.05 AZ PRESCOTT 23184 34.65167 -112.42083 1525 10.05 AZ WINSLOW 23194 35.02194 -110.72194 1489 10.05 AZ DOUGLAS BISBEE 93026 31.46917 -109.60361 1251 10.05 AZ ST. JOHNS 93027 34.51833 -109.37917 1744 10.05 AZ SAFFORD 93084 32.85472 -109.63528 966 10.05 AZ KINGMAN 93167 35.25944 -113.93722 1042 10.05 CA ONTARIO 03102 34.05611 -117.60028 281 10.05 CA PALM SPRINGS 03104 33.62778 -116.16000 -37 10.05 CA SAN DIEGO 03131 32.81583 -117.13944 128 10.05 CA IMPERIAL 03144 32.83417 -115.57861 -18 10.05 CA LANCASTER 03159 34.74083 -118.21889 713 10.05 CA FULLERTON 03166 33.87194 -117.97889 26 10.05 CA HAWTHORNE 03167 33.92278 -118.33417 18 10.053 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) CA RIVERSIDE 03171 33.95194 -117.43861 229 10.05 CA CARLSBAD 03177 33.12806 -117.27944 94 10.05 CA SAN DIEGO 03178 32.57222 -116.97944 158 10.05 CA CHINO 03179 33.97528 -117.63611 193 10.05 CA LONG BEACH 23129 33.82833 -118.16306 9 10.05 CA VAN NUYS 23130 34.20972 -118.48917 239 10.05 CA CAMARILLO 23136 34.21667 -119.08333 84 10.05 CA BURBANK 23152 34.20056 -118.35861 222 10.05 CA BAKERSFIELD 23155 35.43361 -119.05583 149 10.05 CA BISHOP 23157 37.37306 -118.36278 1250 10.05 CA BLYTHE 23158 33.61917 -114.71694 120 10.05 CA DAGGETT 23161 34.85361 -116.78667 584 10.05 CA LOS ANGELES 23174 33.93806 -118.40556 34 10.05 CA NEEDLES 23179 34.76611 -114.62333 271 10.05 CA PALMDALE 23182 34.62944 -118.08361 764 10.05 CA SANDBERG 23187 34.74361 -118.72444 1375 10.05 CA SAN DIEGO 23188 32.73472 -117.16861 4 10.05 CA SANTA BARBARA 23190 34.42611 -119.84361 3 10.05 CA AVALON 23191 33.40500 -118.41583 487 10.05 CA SANTA ROSA 23213 38.50917 -122.81167 36 10.05 CA EMIGRANT GAP 23225 39.29167 -120.70833 1612 10.05 CA OAKLAND 23230 37.75472 -122.22083 1 10.05 CA SACRAMENTO 23232 38.51250 -121.49250 5 10.05 CA SALINAS 23233 36.66333 -121.60528 23 10.05 CA SAN FRANCISCO 23234 37.61972 -122.39806 2 10.05 CA STOCKTON 23237 37.89417 -121.23722 8 10.05 CA MOUNTAIN VIEW 23244 37.41472 -122.04750 10 10.05 CA CONCORD 23254 37.99167 -122.05194 5 10.05 CA MERCED 23257 37.28472 -120.51278 46 10.05 CA MODESTO 23258 37.62583 -120.95333 22 10.05 CA MONTEREY 23259 36.58806 -121.84528 50 10.05 CA UKIAH 23275 39.12583 -123.20083 184 10.05 CA WATSONVILLE 23277 36.93583 -121.78861 49 10.05 CA LIVERMORE 23285 37.69389 -121.81722 120 10.05 CA SAN JOSE 23293 37.36167 -121.92750 16 10.05 CA MOUNT SHASTA 24215 41.33250 -122.33278 1077 10.05 CA RED BLUFF 24216 40.15194 -122.25361 108 10.05 CA REDDING 24257 40.51500 -122.31333 148 10.05 CA MONTAGUE 24259 41.78139 -122.46806 803 10.05 CA ARCATA/EUREKA 24283 40.97806 -124.10861 61 10.05 CA CRESCENT CITY 24286 41.78028 -124.23667 17 10.05 CA HANFORD 53119 36.31861 -119.62889 74 10.05 CA RAMONA 53120 33.03333 -116.91667 423 10.05 CA OCEANSIDE 53121 33.21944 -117.34944 7 10.05 CA OXNARD 93110 34.20083 -119.20639 11 10.05 CA LOS ANGELES 93134 34.02778 -118.29583 54 5.79 CA PALM SPRINGS 93138 33.82806 -116.50528 124 10.05 CA SANTA ANA 93184 33.68000 -117.86639 12 10.054 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) CA FRESNO 93193 36.78000 -119.71944 101 10.05 CA SANTA MONICA 93197 34.01583 -118.45139 43 10.05 CA MARYSVILLE 93205 39.09778 -121.56972 19 10.05 CA SAN LUIS OBISPO 93206 35.23722 -120.64139 61 10.05 CA PASO ROBLES 93209 35.67278 -120.62694 247 10.05 CA OROVILLE 93210 39.49000 -121.61833 55 10.05 CA SACRAMENTO 93225 38.69556 -121.58972 7 10.05 CA NAPA 93227 38.21333 -122.27972 4 10.05 CA HAYWARD 93228 37.65944 -122.12139 10 10.05 CA SOUTH LAKE TAHOE 93230 38.89389 -119.99528 1908 10.05 CA VACAVILLE 93241 38.37694 -121.96139 33 10.05 CA MADERA 93242 36.98778 -120.11056 75 10.05 CA ALTURAS 94299 41.48333 -120.56667 1336 10.05 CO LAMAR 03013 38.07000 -102.68806 1123 10.05 CO DENVER 03017 39.83278 -104.65750 1657 10.05 CO BURLINGTON 03026 39.24472 -102.28417 1278 10.05 CO ALAMOSA 23061 37.43611 -105.86556 2296 10.05 CO GRAND JUNCTION 23066 39.13417 -108.53750 1469 10.05 CO LA JUNTA 23067 38.05139 -103.52694 1280 10.05 CO TRINIDAD 23070 37.25917 -104.34056 1749 10.05 CO CRAIG 24046 40.49528 -107.52111 1886 10.05 CO DURANGO 93005 37.14306 -107.75972 2017 10.05 CO LEADVILLE 93009 39.22806 -106.31611 3027 10.05 CO LIMON 93010 39.18944 -103.71583 1631 10.05 CO MONTROSE 93013 38.50500 -107.89750 1740 10.05 CO COLORADO SPRINGS 93037 38.81194 -104.71111 1884 10.05 CO PUEBLO 93058 38.29000 -104.49833 1424 10.05 CO DENVER 93067 39.57028 -104.84889 1788 10.05 CO CORTEZ 93069 37.30306 -108.62750 1796 10.05 CO ASPEN 93073 39.22306 -106.86833 2341 10.05 CO MEEKER 94050 40.04889 -107.88528 1939 10.05 CT GROTON NEW LONDON 14707 41.32750 -72.04944 2 10.05 CT WINDSOR LOCKS 14740 41.93806 -72.68250 52 10.05 CT HARTFORD 14752 41.73611 -72.65056 4 10.05 CT NEW HAVEN 14758 41.26389 -72.88722 2 10.05 CT DANBURY 54734 41.37139 -73.48278 139 10.05 CT WILLIMANTIC 54767 41.74194 -72.18361 74 10.05 CT MERIDEN 54788 41.50972 -72.82778 32 10.05 CT BRIDGEPORT 94702 41.17500 -73.14556 2 10.05 DC WASHINGTON 13743 38.86500 -77.03417 3 10.05 DC WASHINGTON 93738 38.93472 -77.44750 88 10.05 DE GEORGETOWN 13764 38.68917 -75.35917 16 10.05 DE WILMINGTON 13781 39.67278 -75.60083 23 10.05 FL MARIANNA 03818 30.83556 -85.18389 33 10.05 FL PUNTA GORDA 12812 26.91722 -81.99139 7 10.05 FL ORLANDO 12815 28.43389 -81.32500 27 10.05 FL GAINESVILLE 12816 29.68972 -82.27194 41 10.05 FL BROOKSVILLE 12818 28.47361 -82.45444 20 10.055 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) FL LEESBURG 12819 28.82083 -81.80972 23 10.05 FL APALACHICOLA 12832 29.73333 -85.03333 6 10.05 FL DAYTONA BEACH 12834 29.17722 -81.06000 9 10.05 FL FORT MYERS 12835 26.58639 -81.86361 4 10.05 FL KEY WEST 12836 24.55333 -81.75361 2 10.05 FL MELBOURNE 12838 28.10278 -80.64583 8 10.05 FL MIAMI 12839 25.82389 -80.29972 2 10.05 FL ORLANDO 12841 28.54528 -81.33306 33 10.05 FL TAMPA 12842 27.96139 -82.54028 2 10.05 FL VERO BEACH 12843 27.65556 -80.41806 6 10.05 FL WEST PALM BEACH 12844 26.68472 -80.09944 5 10.05 FL FORT LAUDERDALE 12849 26.07194 -80.15361 3 10.05 FL ORLANDO 12854 28.77972 -81.24361 16 10.05 FL SARASOTA/BRADENTON 12871 27.40139 -82.55861 6 10.05

FL ST PETERSBURG/ CLEARWATER 12873 27.91056 -82.68750 1 10.05 FL WINTER HAVEN 12876 28.06222 -81.75417 44 10.05 FL MIAMI 12882 25.90694 -80.28028 2 10.05 FL FORT LAUDERDALE 12885 26.19694 -80.17083 3 10.05 FL MIAMI 12888 25.64750 -80.43306 2 10.05 FL FORT MYERS 12894 26.53611 -81.75500 8 10.05 FL FORT PIERCE 12895 27.49806 -80.37667 7 10.05 FL MARATHON 12896 24.72583 -81.05167 2 10.05 FL NAPLES 12897 26.15250 -81.77500 2 10.05 FL CRESTVIEW 13884 30.77972 -86.52250 49 10.05 FL JACKSONVILLE 13889 30.49444 -81.69333 8 10.05 FL PENSACOLA 13899 30.47306 -87.18750 34 10.05 FL DESTIN 53853 30.40000 -86.47167 6 7.92 FL JACKSONVILLE 53860 30.33611 -81.51472 14 10.05 FL POMPANO BEACH 92805 26.25000 -80.10833 5 10.05 FL ST PETERSBURG 92806 27.76472 -82.62750 2 10.05 FL HOLLYWOOD 92809 25.99889 -80.24111 3 10.05 FL TALLAHASSEE 93805 30.39306 -84.35333 17 10.05 GA MACON 03813 32.68778 -83.65444 104 10.05 GA AUGUSTA 03820 33.36972 -81.96472 40 10.05 GA SAVANNAH 03822 32.11889 -81.20222 8 10.05 GA ATLANTA 03888 33.77917 -84.52139 244 10.05 GA AUGUSTA 13837 33.46694 -82.03861 126 10.05 GA ALBANY 13869 31.53556 -84.19444 58 10.05 GA ALMA 13870 31.53611 -82.50667 59 10.05 GA ATHENS 13873 33.94833 -83.32667 244 10.05 GA ATLANTA 13874 33.64028 -84.42694 304 10.05 GA BRUNSWICK 13878 31.25167 -81.39139 6 10.05 GA ATLANTA 53819 33.35528 -84.56694 243 10.05 GA GAINESVILLE 53838 34.27194 -83.83028 386 10.05 GA ATLANTA 53863 33.87500 -84.30222 298 10.05 GA CARTERSVILLE 53873 34.12306 -84.84861 230 10.05 GA ROME 93801 34.34778 -85.16111 211 10.056 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) GA COLUMBUS 93842 32.51611 -84.94222 119 10.05 GA VALDOSTA 93845 30.78250 -83.27667 60 10.05 HI HILO 21504 19.72333 -155.05139 9 10.05 HI KAILUA/KONA 21510 19.73556 -156.04889 12 10.05 HI KAHULUI 22516 20.90194 -156.43306 16 10.05 HI HONOLULU 22521 21.32750 -157.94306 2 10.05 HI KAUNAKAKAI 22534 21.15722 -157.09861 134 10.05 HI KAPOLEI 22551 21.31667 -158.06667 7 10.05 IA BURLINGTON 14931 40.78333 -91.12528 207 10.05 IA DES MOINES 14933 41.53778 -93.66611 277 10.05 IA IOWA CITY 14937 41.63278 -91.54306 198 10.05 IA MASON CITY 14940 43.15778 -93.33139 362 10.05 IA SIOUX CITY 14943 42.39139 -96.37917 332 10.05 IA OTTUMWA 14950 41.10667 -92.44806 255 10.05 IA SPENCER 14972 43.16444 -95.20167 408 10.05 IA WATERLOO 94910 42.55444 -92.40111 264 10.05 IA ESTHERVILLE 94971 43.40750 -94.74611 401 10.05 IA DAVENPORT 94982 41.61389 -90.59139 226 10.05 IA MARSHALLTOWN 94988 42.11278 -92.91750 296 10.05 IA LAMONI 94991 40.63306 -93.90194 84 10.05 ID JEROME 04110 42.72667 -114.45639 1224 10.05 ID CHALLIS 04114 41.52278 -114.21500 1536 10.05 ID BOISE 24131 43.56500 -116.22000 858 10.05 ID BURLEY 24133 42.54250 -113.77167 1261 10.05 ID IDAHO FALLS 24145 43.51639 -112.06722 1442 10.05 ID LEWISTON 24149 46.37472 -117.01444 434 10.05 ID MULLAN PASS 24154 47.45694 -115.64500 1833 7.92 ID POCATELLO 24156 42.92028 -112.57111 1353 10.05 ID TWIN FALLS 94178 42.48194 -114.48694 1267 10.05 ID McCALL 94182 44.88889 -116.10167 1526 10.05 ID REXBURG 94194 43.83389 -111.88111 1481 10.05 IL DECATUR 03887 39.98444 -88.86556 205 10.05 IL CAHOKIA/ST.LOUIS 03960 38.57139 -90.15722 124 10.05 IL CHICAGO/AURORA 04808 41.77000 -88.48139 214 10.05

IL CHICAGO/PROSPECT HEIGHTS/WHEELING 04838 42.12083 -87.90472 194 10.05 IL LAWRENCEVILLE 13809 38.76417 -87.60556 131 10.05 IL CHICAGO 14819 41.78611 -87.75222 187 10.05 IL PEORIA 14842 40.66750 -89.68389 199 10.05 IL CHICAGO/WAUKEGAN 14880 42.42194 -87.86778 217 10.05 IL MOLINE 14923 41.46528 -90.52333 180 10.05 IL MATTOON/CHARLESTON 53802 39.47806 -88.28028 216 10.05 IL BLOOMINGTON/NORMAL 54831 40.47778 -88.91583 259 10.05

IL CARBONDALE/MURPHYBORO 93810 37.77972 -89.24972 124 10.05 IL SPRINGFIELD 93822 39.84528 -89.68389 180 10.05 IL QUINCY 93989 39.94250 -91.19444 231 10.05 IL ROCKFORD 94822 42.19611 -89.09250 223 10.057 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) IL CHICAGO 94846 41.98611 -87.91417 202 10.05 IL CHAMPAIGN/URBANA 94870 40.03972 -88.27778 226 10.05 IL CHICAGO/WEST CHICAGO 94892 41.91444 -88.24639 228 10.05 IN TERRE HAUTE 03868 39.45194 -87.30889 174 10.05 IN BLOOMINGTON 03893 39.14444 -86.61667 257 10.05 IN VALPARAISO 04846 41.45250 -87.00583 235 10.05 IN FORT WAYNE 14827 41.00611 -85.20556 252 7.92 IN GOSHEN 14829 41.52722 -85.79222 251 10.05 IN LAFAYETTE 14835 40.41222 -86.93694 182 10.05 IN SOUTH BEND 14848 41.70722 -86.33306 237 10.05 IN INDIANAPOLIS 53842 39.82500 -86.29583 249 10.05 IN SHELBYVILLE 53866 39.57806 -85.80333 245 10.05 IN EVANSVILLE 93817 38.04306 -87.53694 122 10.05 IN INDIANAPOLIS 93819 39.71000 -86.27222 241 10.05 IN MUNCIE 94895 40.23417 -85.39361 285 10.05 KS SALINA 03919 38.81333 -97.66083 387 10.05 KS WICHITA 03928 37.64722 -97.42944 402 10.05 KS MANHATTAN 03936 39.13528 -96.67778 321 10.05 KS OLATHE 03967 38.85000 -94.73917 329 10.05 KS WICHITA 03974 37.74972 -97.21889 430 10.05 KS LAWRENCE 03997 39.00833 -95.21167 252 10.05 KS PARSONS 03998 37.32778 -95.50417 265 10.05 KS TOPEKA 13920 38.95028 -95.66389 315 10.05 KS CHANUTE 13981 37.67028 -95.48417 300 10.05 KS CONCORDIA 13984 39.54917 -97.65194 448 10.05 KS DODGE CITY 13985 37.77278 -99.96972 785 10.05 KS HUTCHINSON 13986 38.06806 -97.86056 464 7.92 KS EMPORIA 13989 38.33056 -96.18972 367 10.05 KS TOPEKA 13996 39.07250 -95.62583 269 7.92 KS GARDEN CITY 23064 37.92722 -100.72472 877 10.05 KS GOODLAND 23065 39.36750 -101.69306 1112 10.05 KS OLATHE 93909 38.83167 -94.88972 325 10.05 KS COFFEYVILLE 93967 37.09111 -95.56639 228 7.92 KS HILL CITY 93990 39.37556 -99.82972 669 10.05 KS RUSSELL 93997 38.87222 -98.82806 568 10.05 KY PADUCAH 03816 37.05639 -88.77389 123 10.05 KY LONDON 03849 37.08722 -84.07694 360 10.05 KY JACKSON 03889 37.59139 -83.31444 405 10.05 KY LOUISVILLE 13810 38.22806 -85.66361 158 10.05 KY FRANKFORT 53841 38.18472 -84.90333 236 10.05 KY BOWLING GREEN 93808 36.98111 -86.43639 160 10.05 KY LEXINGTON 93820 38.04083 -84.60583 294 10.05 KY LOUISVILLE 93821 38.17722 -85.72972 146 10.05 LA LAKE CHARLES 03937 30.12472 -93.22833 3 10.05 LA BOOTHVILLE 12884 29.34972 -89.40750 0 10.05 LA NEW ORLEANS 12916 29.99278 -90.25083 0 10.05 LA ALEXANDRIA 13935 31.39472 -92.29556 28 10.05 LA MONROE 13942 32.51083 -92.03750 23 10.058 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) LA SHREVEPORT 13957 32.44694 -93.82417 70 10.05 LA BATON ROUGE 13970 30.53722 -91.14694 20 10.05 LA LAFAYETTE 13976 30.20500 -91.98750 12 7.92 LA SLIDELL 53865 30.34500 -89.82083 8 10.05 LA SHREVEPORT 53905 32.53972 -93.74444 53 10.05 LA NEW IBERIA 53915 30.05278 -91.88750 5 10.05 LA NEW ORLEANS 53917 30.04250 -90.02806 2 7.92 LA ALEXANDRIA 93915 31.33472 -92.55861 24 10.05 MA FITCHBURG 04780 42.55194 -71.75583 102 10.05 MA BEDFORD 14702 42.47000 -71.28944 41 10.05 MA BOSTON 14739 42.36056 -71.01056 6 10.05 MA NANTUCKET 14756 41.25306 -70.06083 12 10.05 MA PITTSFIELD 14763 42.42722 -73.28917 349 10.05 MA WESTFIELD/SPRINGFIELD 14775 42.15778 -72.71611 81 10.05 MA NORWOOD 54704 42.19083 -71.17361 14 10.05 MA BEVERLY 54733 42.58417 -70.91750 26 10.05 MA ORANGE 54756 42.57000 -72.29111 168 10.05 MA NORTH ADAMS 54768 42.69622 -73.17021 198 1.21 MA PLYMOUTH 54769 41.90972 -70.72944 44 10.05 MA TAUNTON 54777 41.87556 -71.02111 8 10.05 MA CHATHAM 94624 41.68750 -69.99333 19 10.05 MA HYANNIS 94720 41.66861 -70.28000 12 10.05 MA LAWRENCE 94723 42.71722 -71.12389 41 10.05 MA VINEYARD HAVEN 94724 41.39306 -70.61500 18 10.05 MA NEW BEDFORD 94726 41.67639 -70.95833 21 7.92 MA WORCESTER 94746 42.26722 -71.87611 306 10.05 MD HAGERSTOWN 93706 39.70778 -77.72972 211 10.05 MD SALISBURY 93720 38.34056 -75.51028 15 10.05 MD BALTIMORE 93721 39.17222 -76.68389 44 10.05 MD OCEAN CITY 93786 38.30833 -75.12389 2 10.05 ME FRENCHVILLE 04836 47.28556 -68.30722 299 10.05 ME AUGUSTA 14605 44.32056 -69.79722 107 10.05 ME BANGOR 14606 44.80750 -68.82417 45 10.05 ME CARIBOU 14607 46.86694 -68.03278 189 10.05 ME HOULTON 14609 46.12306 -67.79194 145 10.05 ME MILLINOCKET 14610 45.64778 -68.68611 123 10.05 ME PORTLAND 14764 43.64222 -70.30444 15 10.05 ME FRYEBURG 54772 43.99056 -70.94750 137 10.05 ME WISCASSET 94623 43.96361 -69.71167 16 10.05 MI HOLLAND 04839 42.74611 -86.09667 207 10.05 MI ADRIAN 04847 41.86778 -84.07944 243 7.92 MI GAYLORD 04854 45.01333 -84.70139 406 10.05 MI BATTLE CREEK 14815 42.30750 -85.25111 283 10.05 MI DETROIT 14822 42.40917 -83.01000 190 10.05 MI FLINT 14826 42.96667 -83.74944 235 10.05 MI JACKSON 14833 42.25972 -84.45944 302 10.05 MI LANSING 14836 42.78028 -84.57889 262 10.05 MI MUSKEGON 14840 43.17111 -86.23667 191 10.059 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) MI PELLSTON 14841 45.57083 -84.79611 215 10.05 MI SAGINAW 14845 43.53306 -84.07972 201 10.05 MI SAULT STE MARIE 14847 46.46667 -84.36667 220 10.05 MI TRAVERSE CITY 14850 44.74083 -85.58250 186 10.05 MI DETROIT 14853 42.23667 -85.52611 216 10.05 MI HANCOCK 14858 47.16861 -88.50556 323 7.92 MI HOUGHTON LAKE 94814 44.36778 -84.69083 351 10.05 MI KALAMAZOO 94815 42.23472 -85.55194 262 10.05 MI PONTIAC 94817 42.66500 -83.41806 296 10.05 MI DETROIT 94847 42.21528 -83.34861 192 10.05 MI GRAND RAPTIDS 94860 42.88222 -85.52306 241 10.05 MI BENTON HARBOR 94871 42.12917 -86.42222 191 10.05 MI ANN ARBOR 94889 42.22300 -83.74400 253 10.05

MI IRON MOUNTAIN/KINGSFORD 94893 45.81833 -88.11444 343 10.05 MN ALEXANDRIA 14910 45.88306 -95.39306 433 10.05 MN DULUTH 14913 46.84389 -92.19417 433 10.05 MN INTERNATIONAL FALLS 14918 48.56639 -93.40306 361 10.05 MN MINNEAPOLIS 14922 44.88306 -93.22889 248 10.05 MN ROCHESTER 14925 43.90417 -92.49167 397 10.05 MN ST CLOUD 14926 45.54472 -94.05194 310 10.05 MN ST PAUL 14927 44.93028 -93.04806 214 10.05 MN REDWOOD FALLS 14992 44.54722 -95.08222 312 10.05 MN HIBBING 94931 47.38667 -92.83889 408 10.05 MN BRAINERD 94938 46.40472 -94.13083 372 10.05 MN MINNEAPOLIS 94960 45.06250 -93.35083 264 10.05 MN BAUDETTE 94961 48.72750 -94.61028 330 7.92 MN MINNEAPOLIS 94963 44.83222 -93.47028 276 10.05 MN PARK RAPIDS 94967 46.90056 -95.06778 438 10.05 MO CAPE GIRARDEAU 03935 37.22528 -89.57056 102 10.05 MO COLUMBIA 03945 38.81694 -92.21833 272 10.05 MO KANSAS CITY 03947 39.29917 -94.71778 297 10.05 MO JEFFERSON CITY 03963 38.59111 -92.15583 168 10.05 MO ST LOUIS 03966 38.65722 -90.65583 140 10.05 MO POPLAR BLUFF 03975 36.77250 -90.32472 100 10.05 MO SEDALIA 03994 38.70417 -93.18333 273 10.05 MO JOPLIN 13987 37.14944 -94.49833 296 10.05 MO KANSAS CITY 13988 39.12333 -94.59250 226 7.92 MO ST LOUIS 13994 38.75250 -90.37361 199 10.05 MO SPRINGFIELD 13995 37.23972 -93.38972 385 10.05 MO ROLLA/VICHY 13997 38.12750 -91.76944 336 10.05 MO KIRKSVILLE 14938 40.09722 -92.54333 293 10.05 MO LEE'S SUMMIT 53879 38.95972 -94.37139 304 10.05 MO WEST PLAINS 53901 36.87806 -91.90250 375 10.05 MO ST CHARLES 53904 38.92861 -90.42806 134 10.05 MS JACKSON 03940 32.31972 -90.07750 91 10.05 MS TALLULAH/VICKSBURG 03996 32.34806 -91.03000 31 10.05 MS HATTIESBURG 13833 31.26500 -89.25306 45 10.0510 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) MS MERIDIAN 13865 32.33306 -88.75111 88 10.05 MS JACKSON 13927 32.33472 -90.22250 103 10.05 MS GREENVILLE 13939 33.48278 -90.98556 38 10.05 MS GREENWOOD 13978 33.49556 -90.08417 45 10.05 MS PASCAGOULA 53858 30.46361 -88.53194 4 10.05 MS TUPELO 93862 34.26083 -88.77111 104 10.05 MS GULFPORT 93874 30.40722 -89.07000 7 10.05 MS MCCOMB 93919 31.17833 -90.47194 126 10.05 MT BILLINGS 24033 45.80806 -108.54306 1091 10.05 MT LEWISTOWN 24036 47.04917 -109.46667 1254 10.05 MT MILES 24037 46.42806 -105.88611 800 10.05 MT BOZEMAN 24132 45.79361 -111.15222 1349 10.05 MT BUTTE 24135 45.95333 -112.51250 1678 10.05 MT CUT BANK 24137 48.60833 -112.37611 1170 10.05 MT DILLON 24138 45.25500 -112.55167 1585 10.05 MT GREAT FALLS 24143 47.47333 -111.38222 1117 10.05 MT HELENA 24144 46.60556 -111.96361 1167 10.05 MT KALISPELL 24146 48.30417 -114.26361 901 10.05 MT LIVINGSTON 24150 45.69944 -110.44833 1415 10.05 MT MISSOULA 24153 46.92083 -114.09250 973 10.05 MT GLASGOW 94008 48.21389 -106.62139 696 10.05 MT HAVRE 94012 48.55944 -109.78000 788 10.05 MT WOLF POINT 94017 48.09444 -105.57444 604 10.05 MT BAKER 94055 46.35833 -104.25000 903 10.05 NC HICKORY 03810 35.74111 -81.38972 357 7.92 NC ASHEVILLE 03812 35.43194 -82.53750 645 10.05 NC RALEIGH/DURHAM 13722 35.87056 -78.78639 121 10.05 NC GREENSBORO 13723 36.09750 -79.94361 277 10.05 NC WILMINGTON 13748 34.26833 -77.90611 7 7.92 NC LUMBERTON 13776 34.61000 -79.05944 37 10.05 NC ELIZABETH CITY 13786 36.26056 -76.17500 2 7.92 NC CHARLOTTE 13881 35.21444 -80.94361 213 10.05 NC GASTONIA 53870 35.19667 -81.15583 241 10.05 NC MONROE 53872 35.01694 -80.62056 204 10.05 NC NEW BERN 93719 35.06750 -77.04722 3 10.05 NC CAPE HATTERAS 93729 35.23222 -75.62250 3 7.92 NC FAYETTEVILLE 93740 34.99139 -78.88028 55 7.92 NC ROCKY MOUNT 93759 35.85500 -77.89306 46 10.05 NC BEAUFORT 93765 34.73361 -76.66056 2 10.05 NC MAXTON 93782 34.79167 -79.36611 65 10.05 NC BURLINGTON 93783 36.04667 -79.47694 183 7.92 NC CHAPEL HILL 93785 35.93333 -79.06417 153 10.05 NC WINSTON SALEM 93807 36.13361 -80.22222 290 10.05 ND FARGO 14914 46.92528 -96.81111 273 10.05 ND GRAND FORKS 14916 47.94917 -97.17583 256 10.05 ND JAMESTOWN 14919 46.92972 -98.67833 455 10.05 ND BISMARCK 24011 46.77417 -100.74750 503 10.05 ND DICKINSON 24012 46.79722 -102.80194 786 10.0511 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) ND MINOT 24013 48.25944 -101.28111 505 10.05 ND WILLISTON 94014 48.19472 -103.64194 580 10.05 ND HETTINGER 94038 46.01389 -102.65472 822 7.92 NE GRAND ISLAND 14935 40.95833 -98.31250 564 7.92 NE LINCOLN 14939 40.83111 -96.76444 357 10.05 NE NORFOLK 14941 41.98056 -97.43694 476 10.05 NE OMAHA 14942 41.31028 -95.89917 299 10.05 NE CHADRON 24017 42.83750 -103.09528 1001 10.05 NE NORTH PLATTE 24023 41.12194 -100.66833 842 7.92 NE SCOTTSBLUFF 24028 41.87417 -103.59528 1203 7.92 NE SIDNEY 24030 41.10139 -102.98472 1306 10.05 NE VALENTINE 24032 42.85861 -100.55139 789 7.92 NE ALLIANCE 24044 42.05722 -102.80000 1196 10.05 NE IMPERIAL 24091 40.51000 -101.62000 996 10.05 NE MC COOK 94040 40.20639 -100.59139 780 10.05 NE BROKEN BOW 94946 41.43667 -99.63889 770 10.05 NE HASTINGS 94949 40.60444 -98.42722 590 10.05 NE FALLS CITY 94957 40.07889 -95.59194 299 10.05 NE ORD 94958 41.62361 -98.95167 628 10.05 NE TEKAMAH 94978 41.76361 -96.17778 312 10.05 NH MANCHESTER 14710 42.93333 -71.43833 69 10.05 NH CONCORD 14745 43.19528 -71.50111 104 10.05 NH WHITEFIELD 54728 44.36750 -71.54500 318 7.92 NH JAFFREY 54770 42.80500 -72.00361 309 7.92 NH ROCHESTER 54791 43.27806 -70.92222 101 10.05 NH BERLIN 94700 44.57611 -71.17861 343 10.05 NH LEBANON 94765 43.62639 -72.30472 84 10.05 NJ MILLVILLE 13735 39.36611 -75.07833 18 7.92 NJ NEWARK 14734 40.71583 -74.16944 2 10.05 NJ TRENTON 14792 40.27667 -74.81389 58 7.92 NJ CALDWELL 54743 40.87639 -74.28306 53 10.05 NJ SOMERVILLE 54785 40.62389 -74.66944 84 10.05 NJ ATLANTIC CITY 93730 39.45750 -74.45667 20 7.92 NJ MOUNT HOLLY 93780 39.94056 -74.84111 15 10.05 NJ TETERBORO 94741 40.85000 -74.06139 2 7.92 NM CLINES CORNERS 03027 35.00278 -105.66278 2160 10.05 NM ROSWELL 23009 33.30806 -104.54111 1105 7.92 NM TUCUMCARI 23048 35.18222 -103.60306 1230 10.05 NM SANTA FE 23049 35.61694 -106.08889 1915 7.92 NM ALBUQUERQUE 23050 35.04194 -106.61556 1618 10.05 NM CLAYTON 23051 36.44583 -103.15417 1512 10.05 NM RATON 23052 36.74139 -104.50167 1935 10.05 NM LAS VEGAS 23054 35.65417 -105.14250 2093 10.05 NM DEMING 23078 32.26222 -107.72056 1312 10.05 NM GALLUP 23081 35.51111 -108.78944 1970 10.05 NM FARMINGTON 23090 36.74361 -108.22917 1675 10.05 NM CARLSBAD 93033 32.33750 -104.26333 992 10.05 NM TRUTH OR CONSEQUENCES 93045 33.23667 -107.26806 1469 7.9212 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) NV MERCURY 03160 36.62056 -116.02778 985 10.05 NV TONOPAH 23153 38.06028 -117.08722 1644 10.05 NV ELY 23154 39.29500 -114.84528 1904 10.05 NV LAS VEGAS 23169 36.07889 -115.15528 648 10.05 NV RENO 23185 39.48389 -119.77111 1344 7.92 NV ELKO 24121 40.82500 -115.79167 1539 7.92 NV WINNEMUCCA 24128 40.90194 -117.80722 1309 10.05 NV LOVELOCK 24172 40.06639 -118.56528 1189 10.05 NV LAS VAGAS 53123 36.21167 -115.19583 666 10.05 NY NIAGARA FALLS 04724 43.10722 -78.94528 178 10.05 NY BINGHAMTON 04725 42.20778 -75.98139 486 10.05 NY ISLIP 04781 40.79389 -73.10167 26 10.05 NY MONTGOMERY 04789 41.50917 -74.26500 108 10.05 NY WESTHAMPTON BEACH 14719 40.84361 -72.63222 13 10.05 NY NEW YORK 14732 40.77889 -73.88083 3 10.05 NY BUFFALO 14733 42.94083 -78.73583 216 10.05 NY ALBANY 14735 42.74806 -73.80333 85 10.05 NY DUNKIRK 14747 42.49333 -79.27222 203 7.92 NY ELMIRA/CORNING 14748 42.15944 -76.89194 285 7.92 NY GLEN FALLS 14750 43.34111 -73.61028 98 7.92 NY POUGHKEEPSIE 14757 41.62667 -73.88417 48 7.92 NY ROCHESTER 14768 43.11667 -77.67667 164 10.05 NY SYRACUSE 14771 43.10917 -76.10333 125 10.05 NY WELLSVILLE 54757 42.10944 -77.99194 636 10.05 NY FULTON 54773 43.34972 -76.38472 143 7.92 NY PENN YAN 54778 42.64250 -77.05639 268 10.05 NY FARMINGDALE 54787 40.73417 -73.41694 23 10.05 NY SHIRLEY 54790 40.82167 -72.86889 18 10.05 NY DANSVILLE 94704 42.57083 -77.71306 197 10.05 NY MASSENA 94725 44.93583 -74.84556 62 7.92 NY NEW YORK 94728 40.78333 -73.96667 48 10.05 NY SARANAC LAKE 94740 44.38528 -74.20667 505 10.05 NY WHITE PLAINS 94745 41.06694 -73.70750 116 10.05 NY NEW YORK 94789 40.65528 -73.79556 3 10.05 NY WATERTOWN 94790 43.99222 -76.02167 94 7.92 OH WOOSTER 04842 40.87472 -81.88694 338 10.05 OH TOLEDO 04848 41.56306 -83.47639 189 10.05 OH LORAIN/ELYRIA 04849 41.17944 -82.17944 241 7.92 OH LIMA 04850 40.70833 -84.02667 297 7.92 OH DEFIANCE 04851 41.33750 -84.42889 214 10.05 OH NEW PHILADELPHIA 04852 40.47000 -81.42000 271 10.05 OH CLEVELAND 04853 41.51750 -81.68361 177 10.05 OH MARION 04855 40.61611 -83.06361 300 10.05 OH ASHTABULA 04857 41.77806 -80.69583 280 7.92 OH NEWARK 04858 40.02278 -82.46250 268 7.92 OH WILMINGTON 13841 39.42028 -83.82167 322 10.05 OH AKRON 14813 41.03750 -81.46417 318 10.05 OH CLEVELAND 14820 41.40500 -81.85278 237 10.0513 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) OH COLUMBUS 14821 39.99139 -82.88083 247 10.05 OH FINDLAY 14825 41.01361 -83.66861 247 10.05 OH YOUNGSTOWN/WARREN 14852 41.25444 -80.67389 357 10.05 OH MANSFIELD 14891 40.82028 -82.51778 394 10.05 OH AKRON 14895 40.91806 -81.44250 368 10.05 OH LANCASTER 53844 39.75556 -82.65722 258 10.05 OH HAMILTON 53855 39.36444 -84.52472 185 10.05 OH DAYTON 53859 39.59361 -84.22639 290 7.92 OH CINCINNATI 93812 39.10333 -84.41889 145 10.05 OH COVINGTON/CINCINNATI 93814 39.04306 -84.67167 262 10.05 OH DAYTON 93815 39.90611 -84.21861 303 10.05 OH ZANESVILLE 93824 39.94444 -81.89222 269 10.05 OH TOLEDO 94830 41.58861 -83.80139 205 10.05 OK GUYMON 03030 36.68167 -101.50528 949 7.92 OK CLINTON 03932 35.33972 -99.20028 586 10.05 OK LAWTON 03950 34.56778 -98.41639 328 10.05 OK OKLAHOMA CITY 03954 35.53417 -97.64694 390 7.92 OK STILLWATER 03965 36.16028 -97.08583 294 10.05 OK FREDERICK 03981 34.35222 -98.98417 380 10.05 OK OKLAHOMA CITY 13967 35.38861 -97.60028 388 10.05 OK TULSA 13968 36.19750 -95.88639 195 10.05 OK PONCA CITY 13969 36.73056 -97.09972 307 10.05 OK GAGE 13975 36.29667 -99.77472 668 10.05 OK TULSA 53908 36.03944 -95.98444 190 10.05 OK GUTHRIE 53913 35.85028 -97.41556 328 10.05 OK MC ALESTER 93950 34.89889 -95.78333 233 10.05 OK MUSKOGEE 93953 35.65667 -95.36139 185 7.92 OK HOBART 93986 35.00806 -99.05111 475 7.92 OR HERMISTON 04113 45.82583 -119.26111 193 10.05 OR SCAPPOOSE 04201 45.77278 -122.86111 13 10.05 OR BAKER CITY 24130 44.83806 -117.80972 1024 10.05 OR PENDLETON 24155 45.69833 -118.83417 451 10.05 OR ONTARIO 24162 44.02056 -117.01278 666 10.05 OR THE DALLES 24219 45.61861 -121.16722 72 10.05 OR EUGENE 24221 44.13333 -123.21444 108 7.92 OR MEDFORD 24225 42.38917 -122.87139 398 10.05 OR PORTLAND 24229 45.59083 -122.60028 6 10.05 OR REDMOND 24230 44.25417 -121.15000 928 10.05 OR ROSEBURG 24231 43.23889 -123.35472 151 10.05 OR SALEM 24232 44.90778 -122.99500 62 7.92 OR SEXTON SUMMIT 24235 42.61694 -123.38083 1168 10.05 OR PORTLAND 24242 45.54944 -122.40000 7 10.05 OR BURNS 94185 43.59222 -118.95389 1262 10.05 OR ASTORIA 94224 46.15806 -123.87750 3 10.05 OR KLAMATH FALLS 94236 42.14694 -121.72417 1244 7.92 OR PORTLAND 94261 45.54056 -122.94861 59 7.92 OR MC MINNVILLE 94273 45.19472 -123.13389 47 7.92 OR AURORA 94281 45.24861 -122.76861 59 7.9214 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) PA JOHNSTOWN 04726 40.30139 -78.83389 694 7.92 PA BRADFORD 04751 41.80306 -78.64028 643 7.92 PA DU BOIS 04787 41.17833 -78.89889 551 10.05 PA MEADVILLE 04843 41.62639 -80.21500 429 10.05 PA PHILADELPHIA 13739 39.86833 -75.23111 2 10.05 PA HARRISBURG 14711 40.19361 -76.76333 91 7.92 PA READING 14712 40.37333 -75.95944 101 7.92 PA ALTOONA 14736 40.30000 -78.31694 447 10.05 PA ALLENTOWN 14737 40.65083 -75.44917 117 7.92 PA HARRISBURG 14751 40.21722 -76.85139 102 7.92 PA PITTSBURGH 14762 40.35472 -79.92167 378 7.92 PA SELINSGROVE 14770 40.82056 -76.86417 137 10.05 PA WILKES-BARRE/SCRANTON 14777 41.33889 -75.72667 290 10.05 PA WILLIAMSPORT 14778 41.24333 -76.92167 160 7.92 PA ERIE 14860 42.08000 -80.18250 222 7.92 PA LANCASTER 54737 40.12028 -76.29444 122 7.92 PA POTTSTOWN 54782 40.23833 -75.55722 89 7.92 PA DOYLESTOWN 54786 40.33000 -75.12250 116 10.05 PA MOUNT POCONO 54789 41.13889 -75.37944 577 10.05 PA CLEARFIELD 54792 41.04667 -78.41167 461 10.05 PA YORK 93778 39.91806 -76.87417 144 10.05 PA PHILADELPHIA 94732 40.08194 -75.01111 31 7.92 PA PITTSBURGH 94823 40.50139 -80.23111 341 10.05 PR SAN JUAN 11641 18.43000 -66.00000 2 5.79 RI PROVIDENCE 14765 41.72194 -71.43250 16 10.05 RI NEWPORT 14787 41.53000 -71.28361 44 7.92 RI WESTERLY 14794 41.34972 -71.79889 20 10.05 SC GREER 03870 34.89944 -82.21917 285 7.92 SC FLORENCE 13744 34.18778 -79.73083 43 7.92 SC CHARLESTON 13880 32.89861 -80.04083 12 10.05 SC COLUMBIA 13883 33.94194 -81.11806 69 7.92 SC GREENVILLE 13886 34.84611 -82.34611 307 10.05 SC CLEMSON 53850 34.67194 -82.88639 271 7.92 SC ORANGEBURG 53854 33.46167 -80.85806 60 7.92 SC COLUMBIA 53867 33.97056 -80.99583 55 7.92 SC ROCK HILL 53871 34.98694 -81.05750 196 7.92 SC GREENWOOD 53874 34.24861 -82.15917 192 10.05 SC NORTH MYRTLE BEACH 93718 33.81556 -78.72056 8 7.92 SC ANDERSON 93846 34.49500 -82.70917 234 10.05 SD ABERDEEN 14929 45.44972 -98.42139 395 10.05 SD HURON 14936 44.38528 -98.22889 391 10.05 SD SIOUX FALLS 14944 43.57694 -96.75361 433 10.05 SD WATERTOWN 14946 44.93083 -97.15444 532 7.92 SD PHILIP 24024 44.05111 -101.60111 673 7.92 SD PIERRE 24025 44.38278 -100.28583 524 7.92 SD RAPID CITY 24090 44.04556 -103.05389 963 10.05 SD CUSTER 94032 43.73056 -103.62806 1699 7.92 SD PINE RIDGE 94039 43.02056 -102.51833 1003 7.9215 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) SD MOBRIDGE 94052 45.54639 -100.40778 515 7.92 SD MITCHELL 94950 43.77694 -98.03750 375 7.92 SD WINNER 94990 43.39056 -99.84222 617 10.05 TN JACKSON 03811 35.59306 -88.91667 131 7.92 TN CROSSVILLE 03847 35.95139 -85.08500 566 7.92

TN BRISTOL/JOHNSON CITY/KINGSPORT 13877 36.47972 -82.39889 457 10.05 TN CHATTANOOGA 13882 35.03333 -85.20000 205 7.92 TN KNOXVILLE 13891 35.81806 -83.98583 293 7.92 TN MEMPHIS 13893 35.06111 -89.98500 93 7.92 TN NASHVILLE 13897 36.11889 -86.68917 183 10.05 TX BORGER 03024 35.70000 -101.39361 927 10.05 TX LONGVIEW 03901 32.38472 -94.71139 106 10.05 TX COLLEGE STATION 03904 30.58833 -96.36361 93 7.92 TX DALLAS-FT WORTH 03927 32.89639 -97.04111 167 7.92 TX DALLAS 03971 32.68083 -96.86806 198 7.92 TX DENTON 03991 33.20611 -97.19889 198 10.05 TX BURNET 03999 30.74056 -98.23528 389 10.05 TX HARLINGEN 12904 26.22806 -97.65417 10 7.92 TX VICTORIA 12912 28.86250 -96.92972 34 7.92 TX BEAUMONT/PORT ARTHUR 12917 29.95056 -94.02056 4 7.92 TX HOUSTON 12918 29.64528 -95.27861 13 10.05 TX BROWNSVILLE 12919 25.90639 -97.42556 6 10.05 TX SAN ANTONIO 12921 29.53278 -98.46361 236 10.05 TX GALVESTON 12923 29.26500 -94.86028 2 7.92 TX CORPUS CHRISTI 12924 27.77306 -97.51278 12 10.05 TX ALICE 12932 27.74083 -98.02722 52 7.92 TX PALACIOS 12935 28.72750 -96.25083 5 7.92 TX COTULLA 12947 28.45806 -99.22000 140 10.05 TX PORT ISABEL 12957 26.16583 -97.34583 4 7.92 TX MC ALLEN 12959 26.17528 -98.23833 30 7.92 TX HOUSTON 12960 29.99250 -95.36389 29 7.92 TX HONDO 12962 29.35944 -99.17417 280 10.05 TX SAN ANTONIO 12970 29.33667 -98.47083 174 7.92 TX NEW BRAUNFELS 12971 29.70861 -98.04528 197 7.92 TX ROCKPORT 12972 28.08361 -97.04639 8 7.92 TX HOUSTON 12975 29.52111 -95.24028 12 7.92 TX ANGLETON/LAKE JACKSON 12976 29.10972 -95.46194 7 7.92 TX HOUSTON 12977 29.62222 -95.65639 23 10.05 TX AUSTIN/BERGSTROM 13904 30.17944 -97.68056 148 7.92 TX AUSTIN/CITY 13958 30.31667 -97.76667 200 10.05 TX WACO 13959 31.61139 -97.22861 152 7.92 TX DALLAS 13960 32.84694 -96.85139 145 7.92 TX FORT WORTH 13961 32.81917 -97.36139 187 7.92 TX ABILENE 13962 32.41111 -99.68167 544 7.92 TX WICHITA FALLS 13966 33.97861 -98.49278 308 7.92 TX TYLER 13972 32.35417 -95.40222 161 7.92 TX JUNCTION 13973 30.51083 -99.76639 522 7.9216 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) TX DEL RIO 22010 29.36694 -100.92167 307 7.92 TX CHILDRESS 23007 34.43361 -100.28778 593 10.05 TX MIDLAND 23023 31.93222 -102.20806 872 7.92 TX SAN ANGELO 23034 31.35139 -100.49389 582 7.92 TX WINK 23040 31.77972 -103.20139 858 7.92 TX LUBBOCK 23042 33.66750 -101.82139 989 10.05 TX EL PASO 23044 31.81111 -106.37583 1200 7.92 TX AMARILLO 23047 35.21944 -101.70556 1095 7.92 TX GUADALUPE PASS 23055 31.83111 -104.80889 1661 5.79 TX FORT STOCKTON 23091 30.91500 -102.91194 917 10.05 TX CONROE 53902 30.35167 -95.41417 72 7.92 TX HUNTSVILLE 53903 30.74667 -95.58694 104 7.92 TX ARLINGTON 53907 32.66361 -97.09389 187 7.92 TX FORT WORTH 53909 32.97333 -97.31806 201 7.92 TX HOUSTON 53910 30.06167 -95.55250 47 7.92 TX TERRELL 53911 32.71000 -96.26722 143 7.92 TX CORSICANA 53912 32.02722 -96.39778 135 7.92 TX MC KINNEY 53914 33.18028 -96.59028 169 10.05 TX DALHART 93042 36.02333 -102.54722 1216 7.92 TX MINERAL WELLS 93985 32.78222 -98.06111 287 10.05 TX LUFKIN 93987 31.23389 -94.75000 86 7.92 UT BRYCE CANYON 23159 37.70639 -112.14556 2312 10.05 UT MILFORD 23176 38.44333 -113.02833 1532 7.92 UT OGDEN 24126 41.19611 -112.01139 1355 10.05 UT SALT LAKE CITY 24127 40.78694 -111.96806 1286 10.05 UT MOAB 93075 38.75500 -109.75417 1390 7.92 UT CEDAR CITY 93129 37.70167 -113.09722 1703 7.92 UT PRICE 93141 39.54528 -110.74972 1777 7.92 UT VERNAL 94030 40.44111 -109.50917 1603 10.05 UT LOGAN 94128 41.78722 -111.85333 1355 10.05 VA DANVILLE 13728 36.57278 -79.33611 169 10.05 VA LYNCHBURG 13733 37.33750 -79.20667 273 10.05 VA NORFOLK 13737 36.90361 -76.19194 4 7.92 VA RICHMOND 13740 37.51111 -77.32333 50 10.05 VA ROANOKE 13741 37.31694 -79.97417 346 10.05 VA CHARLOTTESVILLE 93736 38.13861 -78.45306 187 7.92 VA WALLOPS ISLAND 93739 37.94056 -75.49639 11 7.92 VA NEWPORT NEWS 93741 37.13194 -76.49306 12 7.92 VA WAKEFIELD 93773 36.98389 -77.00722 32 7.92 VA RICHMOND/ASHLAND 93775 37.70806 -77.43444 62 10.05 VT BURLINGTON 14742 44.46806 -73.15028 101 7.92 VT SPRINGFIELD 54740 43.34361 -72.51778 175 7.92 VT MORRISVILLE 54771 44.53444 -72.61444 225 7.92 VT BENNINGTON 54781 42.89139 -73.24694 244 10.05 VT BARRE/MONTPELIER 94705 44.20333 -72.57944 336 7.92 WA MOSES LAKE 24110 47.20778 -119.31917 355 7.92 WA EPHRATA 24141 47.30444 -119.51361 381 7.92 WA SPOKANE 24157 47.62139 -117.52778 717 10.0517 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) WA WALLA WALLA 24160 46.09472 -118.28694 356 7.92 WA PASCO 24163 46.26472 -119.11806 122 7.92 WA BELLINGHAM 24217 48.79389 -122.53722 47 7.92 WA ELLENSBURG 24220 47.03389 -120.53028 531 7.92 WA EVERETT 24222 47.90778 -122.28028 166 7.92 WA OLYMPIA 24227 46.97333 -122.90333 57 7.92 WA SEATTLE 24233 47.46139 -122.31361 113 7.62 WA SEATTLE 24234 47.53028 -122.30083 5 7.92 WA YAKIMA 24243 46.56417 -120.53361 320 7.92 WA DEER PARK 94119 47.96861 -117.42139 670 10.05 WA PULLMAN/MOSCOW 94129 46.74389 -117.10861 769 1.21 WA SPOKANE 94176 47.68306 -117.32139 591 10.05 WA OMAK 94197 48.46444 -119.51694 394 7.92 WA HOQUIAM 94225 46.97111 -123.93667 4 7.92 WA SHELTON 94227 47.23806 -123.14750 83 7.92 WA WENATCHEE 94239 47.39889 -120.20667 375 7.92 WA QUILLAYUTE 94240 47.93417 -124.56083 56 10.05 WA RENTON 94248 47.49333 -122.21444 6 7.92 WA PORT ANGELES 94266 48.12028 -123.49833 79 7.92 WA TACOMA 94274 47.26750 -122.57611 88 7.92 WA FRIDAY HARBOR 94276 48.52222 -123.02306 33 7.92 WA VANCOUVER 94298 45.62083 -122.65722 8 7.92 WI RHINELANDER 04803 45.63083 -89.46528 493 7.92 WI WISCONSIN RAPTIDS 04826 44.35917 -89.83694 309 7.92 WI FOND DU LAC 04840 43.77000 -88.48639 240 7.92 WI SHEBOYGAN 04841 43.76944 -87.85139 226 7.92 WI KENOSHA 04845 42.59500 -87.93806 223 7.92 WI MADISON 14837 43.14056 -89.34528 262 7.92 WI MILWAUKEE 14839 42.94667 -87.89694 206 7.92 WI WAUSAU 14897 44.92861 -89.62667 366 7.92 WI GREEN BAY 14898 44.51278 -88.12000 210 7.92 WI LA CROSSE 14920 43.87600 -91.25890 199 7.92 WI LONE ROCK 14921 43.21194 -90.18139 218 7.92 WI EAU CLAIRE 14991 44.86528 -91.48500 270 7.92 WI RACINE 94818 42.76111 -87.81361 203 5.79 WI OSHKOSH 94855 43.98444 -88.55694 246 10.05 WI ASHLAND 94929 46.54861 -90.91889 251 7.92 WI HAYWARD 94973 46.02611 -91.44417 367 10.05 WI MARSHFIELD 94985 44.63694 -90.18917 383 7.92 WI B0SCOBEL 94994 43.15611 -90.67750 205 10.05 WV CLARKSBURG 03802 39.29556 -80.22889 360 7.92 WV PARKERSBURG 03804 39.34500 -81.43917 247 10.05 WV BLUEFIELD 03859 37.29583 -81.20778 873 7.92 WV HUNTINGTON 03860 38.38167 -82.55500 251 7.92 WV BECKLEY 03872 37.79500 -81.12472 757 7.92 WV ELKINS 13729 38.88528 -79.85278 603 7.92 WV MARTINSBURG 13734 39.40194 -77.98444 164 7.92 WV MORGANTOWN 13736 39.64278 -79.91639 372 7.9218 of 18 State City WBAN # Latitude Longitude Base Elevation (M) Anemometer Ht (M) WV CHARLESTON 13866 38.37944 -81.59139 277 7.92 WV WHEELING 14894 40.17639 -80.64722 366 7.92 WY EVANSTON 04111 41.27306 -111.03056 2176 7.92 WY CHEYENNE 24018 41.15778 -104.80694 1864 7.92 WY LANDER 24021 42.81667 -108.73333 1704 10.05 WY LARAMIE 24022 41.31250 -105.67444 2217 10.05 WY ROCK SPRINGS 24027 41.59417 -109.06528 2055 4.87 WY SHERIDAN 24029 44.77389 -106.97639 1209 7.92 WY GREYBULL 24048 44.51694 -108.08222 1191 7.92 WY RAWLINS 24057 41.80639 -107.20000 2065 7.92 WY RIVERTON 24061 43.06417 -108.45889 1660 7.92 WY WORLAND 24062 43.96583 -107.95083 1269 7.92 WY CASPER 24089 42.89750 -106.47306 1621 7.92 WY BIG PINEY 24164 42.58444 -110.10750 2116 7.92 WY GILLETTE 94023 44.33944 -105.54194 1327 7.92 WY TORRINGTON 94053 42.06472 -104.15278 1279 10.05 WY BUFFALO 94054 44.38139 -106.72111 1502 10.05 WY DOUGLAS 94057 42.79722 -105.38556 1500 7.921 of 2 State City WBAN # Latitude Longitude AK ANNETTE ISLAND 25308 55.03000 -131.57000 AK YAKUTAT 25339 59.52000 -139.67000 AK ANCHORAGE IAP/PT. CAMPBE 26409 61.17000 -150.02000 AK KODIAK 25501 57.75000 -152.48000 AK KING SALMON 25503 58.68000 -156.65000 AK COLD BAY 25624 55.20000 -162.72000 AK ST PAUL ISLAND 25713 57.15000 -170.22000 AK FAIRBANKS 26411 64.82000 -147.87000 AK MCGRATH 26510 62.97000 -155.62000 AK KOTZEBUE 26616 66.87000 -162.63000 AK NOME AP 26617 64.50000 -165.43000 AK POINT BARROW 27502 71.30000 -156.78000 AL BIRMINGHAM (SHELBY APT) 53823 34.60000 -86.62000 LA SLIDELL 53813 30.33000 -89.82000 FL TALLAHASEE 93805 30.52000 -86.58000 AR N LITTLE ROCK 03952 34.83000 -92.27000 MO SPRINGFIELD REGIONAL AP 13995 37.23000 -93.40000 LA SHREVEPORT REGIONAL AP 13957 32.45000 -93.83000 NM ALBUQUERQUE 23050 35.05000 -106.62000 AZ FLAGSTAFF/BELLEMT (ARMY) 53103 35.23000 -111.82000 AZ TUCSON 23160 32.23000 -110.96000 NV LAS VEGAS 03120 36.05000 -115.18000 CA MIRAMAR NAS 03190 32.87000 -117.15000 CA VANDENBERG 93214 34.75000 -120.57000 NV RENO 03198 39.57000 -119.80000 CA OAKLAND INT AP 23230 37.75000 -122.22000 OR MEDFORD 24225 42.37000 -122.87000 KS DODGE CITY 13985 37.77000 -99.97000 CO DENVER/STAPLETON ARPT 23062 39.77000 -104.88000 CO GRAND JUNCTION 23066 39.12000 -108.53000 NY BROOKHAVEN 94703 40.87000 -72.87000 VA STERLING(WASH DULLES) 93734 38.98000 -77.47000 VA WALLOPS ISLAND 93739 37.93000 -75.48000 FL TAMPA BAY/RUSKIN 12842 27.70000 -82.40000 FL JACKSONVILLE 13889 30.43000 -81.70000 FL KEY WEST INT AP 12836 24.55000 -81.75000 FL MIAMI/FL INTL UNIV 92803 25.75000 -80.38000 GA PEACHTREE CITY 53819 33.35000 -84.56000 SC CHARLESTON 13880 32.90000 -80.03000 HI HILO 21504 19.72000 -155.07000 HI LIHUE/KAUAI 22536 21.98000 -159.35000 IA DAVENPORT MUNICIPAL AP 94982 41.60000 -90.57000 NE OMAHA/VALLEY 94980 41.32000 -96.37000 MN MINNEAPOLIS 94983 44.83000 -93.55000 ID BOISE 24131 43.57000 -116.22000 Appendix 2 Upper Air Stations used for AERMET (2011)2 of 2 State City WBAN # Latitude Longitude NV ELKO 04105 40.87000 -115.73000 WY RIVERTON 24061 43.06000 -108.47000 WA SPOKANE INTNL APT 04106 47.68000 -117.63000 UT SALT LAKE CITY 24127 40.77000 -111.97000 IL LINCOLN-LOGAN COUNTY AP 04833 40.15000 -89.33000 WI GREEN BAY 14898 44.48000 -88.13000 OH WILMINGTON 13841 39.42000 -83.82000 MI DETROIT/PONTIAC 04830 42.70000 -83.47000 TN NASHVILLE 13897 34.60000 -86.62000 KS TOPEKA 13996 39.07000 -95.62000 NE NORTH PLATTE 24023 41.13000 -100.68000 LA LAKE CHARLES 03937 30.12000 -93.22000 NY ALBANY 54775 42.69000 -73.83000 MA CHATHAM 14684 41.67000 -69.97000 ME GRAY 54762 43.89000 -70.25000 ME CARIBOU 14607 46.87000 -68.02000 MI GAYLORD / ALPENA 04837 44.55000 -84.43000 MN INTERNATIONAL FALLS 14918 48.57000 -93.38000 MS JACKSON/THOMPSON FLD 03940 32.32000 -90.07000 MT GREAT FALLS 04102 47.45000 -111.38000 MT GLASGOW 94008 48.20000 -106.62000 SD RAPID CITY 94043 44.07000 -103.21000 NC GREENSBORO 13723 36.08000 -79.95000 NC MOREHEAD CITY/NEWPORT 93768 34.70000 -76.80000 SD ABERDEEN 14929 45.45000 -98.42000 ND BISMARCK 24011 46.77000 -100.75000 NM SANTA TERESA 03020 31.90000 -106.70000 TX AMARILLO 23047 35.23000 -101.70000 TX MIDLAND 23023 31.93000 -102.20000 NY BUFFALO/GRTR ARPT 14733 42.93000 -78.73000 PA PITTSBURGH/MOON TOWNSHIP 94823 40.53000 -80.23000 OK NORMAN 03948 35.23000 -97.47000 OR SALEM 24232 44.92000 -123.02000 PR SAN JUAN 11641 18.43000 -66.00000 VA ROANOKE/BLACKSBURG 53829 37.20000 -80.41000 TX FT WORTH 03990 32.80000 -97.30000 TX BROWNSVILLE 12919 25.90000 -97.43000 TX CORPUS CHRISTI 12924 27.77000 -97.50000 TX DEL RIO 22010 29.37000 -100.92000 WA QUILLAYUTE 94240 47.95000 -124.550001 of 21 State City WBAN # State City WBAN # % Missing % Calms AK ANNETTE 25308 AK ANNETTE ISLAND 25308 1.05 11 AK JUNEAU 25309 AK YAKUTAT 25339 0.29 15 AK HAINES 25323 AK YAKUTAT 25339 0.24 4 AK KETCHIKAN 25325 AK ANNETTE ISLAND 25308 0.19 8

AK PALMER 25331 AK ANCHORAGE IAP/PT. CAMPBE 26409 0.30 11 AK SITKA 25333 AK ANNETTE ISLAND 25308 0.10 5 AK SKAGWAY 25335 AK YAKUTAT 25339 0.19 11 AK YAKUTAT 25339 AK YAKUTAT 25339 0.41 17 AK KALWOCK 25367 AK ANNETTE ISLAND 25308 2.32 17 AK KODIAK 25501 AK KODIAK 25501 0.11 3 AK KING SALMON 25503 AK KING SALMON 25503 0.14 4 AK ILIAMNA 25506 AK KING SALMON 25503 0.18 3

AK HOMER 25507 AK ANCHORAGE IAP/PT. CAMPBE 26409 0.38 9 AK SELDOVIA 25516 AK KODIAK 25501 1.29 32 AK COLD BAY 25624 AK COLD BAY 25624 0.18 2 AK ST PAUL ISLAND 25713 AK ST PAUL ISLAND 25713 0.26 2

AK ANCHORAGE 26409 AK ANCHORAGE IAP/PT. CAMPBE 26409 0.42 15

AK CORDOVA 26410 AK ANCHORAGE IAP/PT. CAMPBE 26409 1.02 30 AK FAIRBANKS 26411 AK FAIRBANKS 26411 0.22 23 AK NORTHWAY 26412 AK FAIRBANKS 26411 0.25 33

AK DELTA JUNCTION/FT GREELY 26415 AK FAIRBANKS 26411 0.22 6 AK EAGLE 26422 AK FAIRBANKS 26411 2.16 40

AK GULKANA 26425 AK ANCHORAGE IAP/PT. CAMPBE 26409 0.23 15 AK NENANA 26435 AK FAIRBANKS 26411 0.39 18

AK SEWARD 26438 AK ANCHORAGE IAP/PT. CAMPBE 26409 0.27 10

AK ANCHORAGE 26451 AK ANCHORAGE IAP/PT. CAMPBE 26409 0.41 6

AK ANCHORAGE 26491 AK ANCHORAGE IAP/PT. CAMPBE 26409 0.50 12

AK PORTAGE GLACIER 26492 AK ANCHORAGE IAP/PT. CAMPBE 26409 2.58 13 AK KALTAG 26502 AK MCGRATH 26510 1.54 23 AK MC GRATH 26510 AK MCGRATH 26510 0.94 28

AK KENAI 26523 AK ANCHORAGE IAP/PT. CAMPBE 26409 0.15 7

AK TALKEETNA 26528 AK ANCHORAGE IAP/PT. CAMPBE 26409 0.42 25 Surface Upper Air Appendix 3 Processed Stations used for AERMET (2011)

Processed2 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

AK TANANA 26529 AK FAIRBANKS 26411 0.63 12 AK BETTLES 26533 AK FAIRBANKS 26411 1.08 15 AK KOTZEBUE 26616 AK KOTZEBUE 26616 0.14 3 AK NOME 26617 AK NOME AP 26617 0.22 8 AK KIVALINA 26642 AK KOTZEBUE 26616 0.15 2 AK DEADHORSE 27406 AK POINT BARROW 27502 0.10 2 AK BARROW 27502 AK POINT BARROW 27502 0.10 1 AK WAINWRIGHT 27503 AK POINT BARROW 27502 0.26 2 AK NUIQSUT 27515 AK POINT BARROW 27502 2.08 3

AL HUNTSVILLE 03856 AL BIRMINGHAM (SHELBY APT) 53823 0.25 7

AL TROY 03878 AL BIRMINGHAM (SHELBY APT) 53823 0.38 25 AL MOBILE 13838 LA SLIDELL 53813 1.34 5 AL DOTHAN 13839 FL TALLAHASEE 93805 0.27 5

AL ANNISTON 13871 AL BIRMINGHAM (SHELBY APT) 53823 0.71 16

AL BIRMINGHAM 13876 AL BIRMINGHAM (SHELBY APT) 53823 0.25 18 AL MOBILE 13894 LA SLIDELL 53813 0.35 7

AL MONTGOMERY 13895 AL BIRMINGHAM (SHELBY APT) 53823 0.32 11

AL MUSCLE SHOALS 13896 AL BIRMINGHAM (SHELBY APT) 53823 0.38 14

AL EVERGREEN 53820 AL BIRMINGHAM (SHELBY APT) 53823 0.81 27

AL ALABASTER 53864 AL BIRMINGHAM (SHELBY APT) 53823 0.46 27

AL TUSCALOOSA 93806 AL BIRMINGHAM (SHELBY APT) 53823 0.26 20 AR JONESBORO 03953 AR N LITTLE ROCK 03952 0.29 8 AR HOT SPRINGS 03962 AR N LITTLE ROCK 03952 0.64 12 AR LITTLE ROCK 13963 AR N LITTLE ROCK 03952 0.15 7 AR FORT SMITH 13964 AR N LITTLE ROCK 03952 0.13 6

AR HARRISON 13971 MO SPRINGFIELD REGIONAL AP 13995 0.31 4

AR TEXARKANA 13977 LA SHREVEPORT REGIONAL AP 13957 0.19 4 AR BLYTHEVILLE 53869 AR N LITTLE ROCK 03952 0.81 27

AR MOUNTAIN HOME 53918 MO SPRINGFIELD REGIONAL AP 13995 0.57 8 AR MONTICELLO 53919 AR N LITTLE ROCK 03952 2.63 12 AR RUSSELLVILLE 53920 AR N LITTLE ROCK 03952 0.65 27

AR FAYETTEVILLE/SPRING DALE 53922 MO SPRINGFIELD REGIONAL AP 13995 0.56 6

AR De QUEEN 53925 LA SHREVEPORT REGIONAL AP 13957 0.68 19 AR WEST MEMPHIS 53959 AR N LITTLE ROCK 03952 2.34 7 AR PINE BLUFF 93988 AR N LITTLE ROCK 03952 0.99 103 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

AR EL DORADO 93992 LA SHREVEPORT REGIONAL AP 13957 0.78 15

AR FAYETTEVILLE 93993 MO SPRINGFIELD REGIONAL AP 13995 0.53 16 AZ WINDOW ROCK 03029 NM ALBUQUERQUE 23050 1.32 24

AZ FLAGSTAFF 03103 AZ FLAGSTAFF/BELLEMT (ARMY) 53103 0.76 24

AZ PAGE 03162 AZ FLAGSTAFF/BELLEMT (ARMY) 53103 0.86 10 AZ PHOENIX 03184 AZ TUCSON 23160 0.59 9 AZ SCOTTSDALE 03192 AZ TUCSON 23160 0.32 21

AZ GRAND CANYON 03195 AZ FLAGSTAFF/BELLEMT (ARMY) 53103 0.59 17 AZ NOGALES 03196 AZ TUCSON 23160 1.03 11 AZ TUCSON 23160 AZ TUCSON 23160 0.26 2 AZ PHOENIX 23183 AZ TUCSON 23160 0.31 4

AZ PRESCOTT 23184 AZ FLAGSTAFF/BELLEMT (ARMY) 53103 0.24 3

AZ WINSLOW 23194 AZ FLAGSTAFF/BELLEMT (ARMY) 53103 0.39 3 AZ DOUGLAS BISBEE 93026 AZ TUCSON 23160 3.77 10

AZ ST. JOHNS 93027 AZ FLAGSTAFF/BELLEMT (ARMY) 53103 0.53 8 AZ SAFFORD 93084 AZ TUCSON 23160 0.24 3 AZ KINGMAN 93167 NV LAS VEGAS 03120 0.24 4 CA ONTARIO 03102 CA MIRAMAR NAS 03190 0.61 13 CA PALM SPRINGS 03104 CA MIRAMAR NAS 03190 0.31 3 CA SAN DIEGO 03131 CA MIRAMAR NAS 03190 0.39 11 CA IMPERIAL 03144 CA MIRAMAR NAS 03190 0.40 5 CA LANCASTER 03159 CA VANDENBERG 93214 0.25 6 CA FULLERTON 03166 CA MIRAMAR NAS 03190 1.03 21 CA HAWTHORNE 03167 CA MIRAMAR NAS 03190 0.43 26 CA RIVERSIDE 03171 CA MIRAMAR NAS 03190 0.89 15 CA CARLSBAD 03177 CA MIRAMAR NAS 03190 0.78 14 CA SAN DIEGO 03178 CA MIRAMAR NAS 03190 0.39 2 CA CHINO 03179 CA MIRAMAR NAS 03190 0.51 18 CA LONG BEACH 23129 CA MIRAMAR NAS 03190 0.58 15 CA VAN NUYS 23130 CA MIRAMAR NAS 03190 0.18 16 CA CAMARILLO 23136 CA VANDENBERG 93214 0.79 11 CA BURBANK 23152 CA MIRAMAR NAS 03190 0.47 11 CA BAKERSFIELD 23155 CA VANDENBERG 93214 0.41 8 CA BISHOP 23157 NV RENO 03198 1.05 2 CA BLYTHE 23158 CA MIRAMAR NAS 03190 0.75 5 CA DAGGETT 23161 NV LAS VEGAS 03120 0.41 2 CA LOS ANGELES 23174 CA MIRAMAR NAS 03190 0.35 4 CA NEEDLES 23179 NV LAS VEGAS 03120 0.63 3 CA PALMDALE 23182 CA MIRAMAR NAS 03190 0.33 4 CA SANDBERG 23187 CA VANDENBERG 93214 3.34 1 CA SAN DIEGO 23188 CA MIRAMAR NAS 03190 0.09 94 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

CA SANTA BARBARA 23190 CA VANDENBERG 93214 0.23 16 CA AVALON 23191 CA MIRAMAR NAS 03190 0.50 4 CA SANTA ROSA 23213 CA OAKLAND INT AP 23230 0.27 18 CA EMIGRANT GAP 23225 NV RENO 03198 1.00 6 CA OAKLAND 23230 CA OAKLAND INT AP 23230 0.13 4 CA SACRAMENTO 23232 CA OAKLAND INT AP 23230 0.41 13 CA SALINAS 23233 CA OAKLAND INT AP 23230 0.08 5 CA SAN FRANCISCO 23234 CA OAKLAND INT AP 23230 0.07 3 CA STOCKTON 23237 CA OAKLAND INT AP 23230 0.19 6 CA MOUNTAIN VIEW 23244 CA OAKLAND INT AP 23230 0.46 16 CA CONCORD 23254 CA OAKLAND INT AP 23230 0.73 11 CA MERCED 23257 CA OAKLAND INT AP 23230 0.37 4 CA MODESTO 23258 CA OAKLAND INT AP 23230 0.46 11 CA MONTEREY 23259 CA OAKLAND INT AP 23230 0.32 6 CA UKIAH 23275 CA OAKLAND INT AP 23230 0.27 37 CA WATSONVILLE 23277 CA OAKLAND INT AP 23230 0.32 14 CA LIVERMORE 23285 CA OAKLAND INT AP 23230 0.38 14 CA SAN JOSE 23293 CA OAKLAND INT AP 23230 0.14 8 CA MOUNT SHASTA 24215 OR MEDFORD 24225 2.73 24 CA RED BLUFF 24216 NV RENO 03198 0.23 4 CA REDDING 24257 OR MEDFORD 24225 0.56 15 CA MONTAGUE 24259 OR MEDFORD 24225 0.30 12 CA ARCATA/EUREKA 24283 OR MEDFORD 24225 0.21 9 CA CRESCENT CITY 24286 OR MEDFORD 24225 0.40 6 CA HANFORD 53119 CA VANDENBERG 93214 0.79 11 CA RAMONA 53120 CA MIRAMAR NAS 03190 0.96 25 CA OCEANSIDE 53121 CA MIRAMAR NAS 03190 1.22 24 CA OXNARD 93110 CA VANDENBERG 93214 0.50 8 CA LOS ANGELES 93134 CA MIRAMAR NAS 03190 2.84 47 CA PALM SPRINGS 93138 CA MIRAMAR NAS 03190 0.42 6 CA SANTA ANA 93184 CA MIRAMAR NAS 03190 0.62 14 CA FRESNO 93193 CA VANDENBERG 93214 0.32 10 CA SANTA MONICA 93197 CA MIRAMAR NAS 03190 0.19 10 CA MARYSVILLE 93205 CA OAKLAND INT AP 23230 0.40 9 CA SAN LUIS OBISPO 93206 CA VANDENBERG 93214 0.38 10 CA PASO ROBLES 93209 CA VANDENBERG 93214 0.55 11 CA OROVILLE 93210 NV RENO 03198 0.50 9 CA SACRAMENTO 93225 CA OAKLAND INT AP 23230 0.14 6 CA NAPA 93227 CA OAKLAND INT AP 23230 0.42 7 CA HAYWARD 93228 CA OAKLAND INT AP 23230 0.57 6

CA SOUTH LAKE TAHOE 93230 NV RENO 03198 0.81 15 CA VACAVILLE 93241 CA OAKLAND INT AP 23230 0.70 16 CA MADERA 93242 CA OAKLAND INT AP 23230 2.49 8 CA ALTURAS 94299 OR MEDFORD 24225 0.80 22 CO LAMAR 03013 KS DODGE CITY 13985 0.87 3

CO DENVER 03017 CO DENVER/STAPLETON ARPT 23062 0.26 15 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

CO BURLINGTON 03026 CO DENVER/STAPLETON ARPT 23062 0.94 1

CO ALAMOSA 23061 CO DENVER/STAPLETON ARPT 23062 0.58 6 CO GRAND JUNCTION 23066 CO GRAND JUNCTION 23066 1.22 4

CO LA JUNTA 23067 CO DENVER/STAPLETON ARPT 23062 0.37 1

CO TRINIDAD 23070 CO DENVER/STAPLETON ARPT 23062 0.24 1 CO CRAIG 24046 CO GRAND JUNCTION 23066 0.27 18 CO DURANGO 93005 CO GRAND JUNCTION 23066 0.65 8

CO LEADVILLE 93009 CO DENVER/STAPLETON ARPT 23062 0.48 5

CO LIMON 93010 CO DENVER/STAPLETON ARPT 23062 0.34 3 CO MONTROSE 93013 CO GRAND JUNCTION 23066 0.27 4

CO COLORADO SPRINGS 93037 CO DENVER/STAPLETON ARPT 23062 0.24 1

CO PUEBLO 93058 CO DENVER/STAPLETON ARPT 23062 0.31 4

CO DENVER 93067 CO DENVER/STAPLETON ARPT 23062 0.30 2 CO CORTEZ 93069 CO GRAND JUNCTION 23066 1.22 4 CO ASPEN 93073 CO GRAND JUNCTION 23066 0.43 6 CO MEEKER 94050 CO GRAND JUNCTION 23066 1.48 13

CT GROTON NEW LONDON 14707 NY BROOKHAVEN 94703 0.29 5 CT WINDSOR LOCKS 14740 NY BROOKHAVEN 94703 0.34 7 CT HARTFORD 14752 NY BROOKHAVEN 94703 0.53 13 CT NEW HAVEN 14758 NY BROOKHAVEN 94703 0.32 10 CT DANBURY 54734 NY BROOKHAVEN 94703 0.92 15 CT WILLIMANTIC 54767 NY BROOKHAVEN 94703 0.64 21 CT MERIDEN 54788 NY BROOKHAVEN 94703 0.29 24 CT BRIDGEPORT 94702 NY BROOKHAVEN 94703 0.30 5

DC WASHINGTON 13743 VA STERLING(WASH DULLES) 93734 0.34 5

DC WASHINGTON 93738 VA STERLING(WASH DULLES) 93734 0.29 9 DE GEORGETOWN 13764 VA WALLOPS ISLAND 93739 0.43 13

DE WILMINGTON 13781 VA STERLING(WASH DULLES) 93734 0.29 4 FL MARIANNA 03818 FL TALLAHASEE 93805 1.58 17 FL PUNTA GORDA 12812 FL TAMPA BAY/RUSKIN 12842 0.82 4 FL ORLANDO 12815 FL TAMPA BAY/RUSKIN 12842 0.34 6 FL GAINESVILLE 12816 FL JACKSONVILLE 13889 0.42 16 FL BROOKSVILLE 12818 FL TAMPA BAY/RUSKIN 12842 0.90 16 FL LEESBURG 12819 FL TAMPA BAY/RUSKIN 12842 0.34 6 FL APALACHICOLA 12832 FL TALLAHASEE 93805 0.22 11 FL DAYTONA BEACH 12834 FL JACKSONVILLE 13889 0.27 116 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

FL FORT MYERS 12835 FL TAMPA BAY/RUSKIN 12842 0.19 7 FL KEY WEST 12836 FL KEY WEST INT AP 12836 0.39 2 FL MELBOURNE 12838 FL TAMPA BAY/RUSKIN 12842 0.14 3 FL MIAMI 12839 FL MIAMI/FL INTL UNIV 92803 0.27 7 FL ORLANDO 12841 FL TAMPA BAY/RUSKIN 12842 0.54 5 FL TAMPA 12842 FL TAMPA BAY/RUSKIN 12842 0.18 4 FL VERO BEACH 12843 FL TAMPA BAY/RUSKIN 12842 0.08 6

FL WEST PALM BEACH 12844 FL MIAMI/FL INTL UNIV 92803 0.15 4

FL FORT LAUDERDALE 12849 FL MIAMI/FL INTL UNIV 92803 0.13 4 FL ORLANDO 12854 FL TAMPA BAY/RUSKIN 12842 0.16 4

FL SARASOTA/BRADENTO N 12871 FL TAMPA BAY/RUSKIN 12842 0.30 2

FL ST PETERSBURG/ CLEARWATER 12873 FL TAMPA BAY/RUSKIN 12842 0.18 3 FL WINTER HAVEN 12876 FL TAMPA BAY/RUSKIN 12842 4.29 14 FL MIAMI 12882 FL MIAMI/FL INTL UNIV 92803 0.25 6

FL FORT LAUDERDALE 12885 FL MIAMI/FL INTL UNIV 92803 0.53 7 FL MIAMI 12888 FL MIAMI/FL INTL UNIV 92803 0.19 10 FL FORT MYERS 12894 FL TAMPA BAY/RUSKIN 12842 0.43 6 FL FORT PIERCE 12895 FL TAMPA BAY/RUSKIN 12842 0.21 11 FL MARATHON 12896 FL KEY WEST INT AP 12836 0.37 3 FL NAPLES 12897 FL MIAMI/FL INTL UNIV 92803 0.25 5 FL CRESTVIEW 13884 FL TALLAHASEE 93805 0.32 23 FL JACKSONVILLE 13889 FL JACKSONVILLE 13889 0.26 13 FL PENSACOLA 13899 LA SLIDELL 53813 0.26 3 FL DESTIN 53853 FL TALLAHASEE 93805 1.70 9 FL JACKSONVILLE 53860 FL JACKSONVILLE 13889 0.22 15 FL POMPANO BEACH 92805 FL MIAMI/FL INTL UNIV 92803 0.13 3 FL ST PETERSBURG 92806 FL TAMPA BAY/RUSKIN 12842 0.21 2 FL HOLLYWOOD 92809 FL MIAMI/FL INTL UNIV 92803 0.19 8 FL TALLAHASSEE 93805 FL TALLAHASEE 93805 0.10 19 GA MACON 03813 GA PEACHTREE CITY 53819 0.35 12 GA AUGUSTA 03820 SC CHARLESTON 13880 0.18 21 GA SAVANNAH 03822 SC CHARLESTON 13880 0.24 8 GA ATLANTA 03888 GA PEACHTREE CITY 53819 0.32 21 GA AUGUSTA 13837 SC CHARLESTON 13880 0.67 6 GA ALBANY 13869 FL TALLAHASEE 93805 0.32 17 GA ALMA 13870 FL JACKSONVILLE 13889 0.51 17 GA ATHENS 13873 GA PEACHTREE CITY 53819 0.40 8 GA ATLANTA 13874 GA PEACHTREE CITY 53819 0.10 2 GA BRUNSWICK 13878 FL JACKSONVILLE 13889 1.08 6 GA ATLANTA 53819 GA PEACHTREE CITY 53819 0.32 32 GA GAINESVILLE 53838 GA PEACHTREE CITY 53819 0.65 6 GA ATLANTA 53863 GA PEACHTREE CITY 53819 0.29 14 GA CARTERSVILLE 53873 GA PEACHTREE CITY 53819 0.53 35 GA ROME 93801 GA PEACHTREE CITY 53819 1.78 267 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

GA COLUMBUS 93842 GA PEACHTREE CITY 53819 0.30 15 GA VALDOSTA 93845 FL TALLAHASEE 93805 0.47 21 HI HILO 21504 HI HILO 21504 0.19 2 HI KAILUA/KONA 21510 HI HILO 21504 0.18 1 HI KAHULUI 22516 HI HILO 21504 0.14 1 HI HONOLULU 22521 HI LIHUE/KAUAI 22536 0.14 1 HI KAUNAKAKAI 22534 HI LIHUE/KAUAI 22536 1.50 2 HI KAPOLEI 22551 HI LIHUE/KAUAI 22536 0.14 1

IA BURLINGTON 14931 IA DAVENPORT MUNICIPAL AP 94982 0.22 3 IA DES MOINES 14933 NE OMAHA/VALLEY 94980 0.15 2

IA IOWA CITY 14937 IA DAVENPORT MUNICIPAL AP 94982 0.39 8 IA MASON CITY 14940 MN MINNEAPOLIS 94983 0.10 3 IA SIOUX CITY 14943 NE OMAHA/VALLEY 94980 0.08 3

IA OTTUMWA 14950 IA DAVENPORT MUNICIPAL AP 94982 0.14 1 IA SPENCER 14972 NE OMAHA/VALLEY 94980 0.07 2

IA WATERLOO 94910 IA DAVENPORT MUNICIPAL AP 94982 0.21 5 IA ESTHERVILLE 94971 MN MINNEAPOLIS 94983 0.40 1

IA DAVENPORT 94982 IA DAVENPORT MUNICIPAL AP 94982 0.40 2

IA MARSHALLTOWN 94988 IA DAVENPORT MUNICIPAL AP 94982 1.13 2 IA LAMONI 94991 NE OMAHA/VALLEY 94980 0.26 1 ID JEROME 04110 ID BOISE 24131 0.53 1 ID CHALLIS 04114 NV ELKO 04105 0.38 14 ID BOISE 24131 ID BOISE 24131 0.15 5 ID BURLEY 24133 ID BOISE 24131 0.16 4 ID IDAHO FALLS 24145 WY RIVERTON 24061 0.26 3 ID LEWISTON 24149 WA SPOKANE INTNL APT 04106 0.42 6 ID MULLAN PASS 24154 WA SPOKANE INTNL APT 04106 3.38 2 ID POCATELLO 24156 UT SALT LAKE CITY 24127 0.15 3 ID TWIN FALLS 94178 ID BOISE 24131 0.06 1 ID McCALL 94182 ID BOISE 24131 0.22 22 ID REXBURG 94194 WY RIVERTON 24061 0.09 5

IL DECATUR 03887 IL LINCOLN-LOGAN COUNTY AP 04833 0.27 4

IL CAHOKIA/ST.LOUIS 03960 IL LINCOLN-LOGAN COUNTY AP 04833 0.27 9

IL CHICAGO/AURORA 04808 IA DAVENPORT MUNICIPAL AP 94982 0.10 6

IL CHICAGO/PROSPECT HEIGHTS/WHEELING 04838 IA DAVENPORT MUNICIPAL AP 94982 0.07 9

IL LAWRENCEVILLE 13809 IL LINCOLN-LOGAN COUNTY AP 04833 0.68 58 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

IL CHICAGO 14819 IL LINCOLN-LOGAN COUNTY AP 04833 0.11 2

IL PEORIA 14842 IL LINCOLN-LOGAN COUNTY AP 04833 0.30 6

IL CHICAGO/WAUKEGAN 14880 WI GREEN BAY 14898 1.11 5

IL MOLINE 14923 IA DAVENPORT MUNICIPAL AP 94982 0.35 8

IL MATTOON/CHARLEST ON 53802 IL LINCOLN-LOGAN COUNTY AP 04833 0.23 4

IL BLOOMINGTON/NORM AL 54831 IL LINCOLN-LOGAN COUNTY AP 04833 1.88 2

IL CARBONDALE/MURPH YBORO 93810 IL LINCOLN-LOGAN COUNTY AP 04833 0.16 9

IL SPRINGFIELD 93822 IL LINCOLN-LOGAN COUNTY AP 04833 0.06 4

IL QUINCY 93989 IL LINCOLN-LOGAN COUNTY AP 04833 0.13 1

IL ROCKFORD 94822 IA DAVENPORT MUNICIPAL AP 94982 0.08 5

IL CHICAGO 94846 IA DAVENPORT MUNICIPAL AP 94982 0.15 2

IL CHAMPAIGN/URBANA 94870 IL LINCOLN-LOGAN COUNTY AP 04833 0.14 2

IL CHICAGO/WEST CHICAGO 94892 IA DAVENPORT MUNICIPAL AP 94982 0.40 6

IN TERRE HAUTE 03868 IL LINCOLN-LOGAN COUNTY AP 04833 0.23 9 IN BLOOMINGTON 03893 OH WILMINGTON 13841 0.16 13

IN VALPARAISO 04846 IL LINCOLN-LOGAN COUNTY AP 04833 0.19 5 IN FORT WAYNE 14827 OH WILMINGTON 13841 0.14 5 IN GOSHEN 14829 MI DETROIT/PONTIAC 04830 0.19 6

IN LAFAYETTE 14835 IL LINCOLN-LOGAN COUNTY AP 04833 0.74 9 IN SOUTH BEND 14848 MI DETROIT/PONTIAC 04830 0.07 5 IN INDIANAPOLIS 53842 OH WILMINGTON 13841 1.56 9 IN SHELBYVILLE 53866 OH WILMINGTON 13841 0.13 3 IN EVANSVILLE 93817 TN NASHVILLE 13897 0.32 11 IN INDIANAPOLIS 93819 OH WILMINGTON 13841 0.15 2 IN MUNCIE 94895 OH WILMINGTON 13841 0.31 5 KS SALINA 03919 KS TOPEKA 13996 0.05 2 KS WICHITA 03928 KS TOPEKA 13996 0.11 2 KS MANHATTAN 03936 KS TOPEKA 13996 0.49 12 KS OLATHE 03967 KS TOPEKA 13996 0.32 2 KS WICHITA 03974 KS TOPEKA 13996 0.24 1 KS LAWRENCE 03997 KS TOPEKA 13996 0.35 5

KS PARSONS 03998 MO SPRINGFIELD REGIONAL AP 13995 0.81 109 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

KS TOPEKA 13920 KS TOPEKA 13996 0.53 3 KS CHANUTE 13981 KS TOPEKA 13996 0.38 2 KS CONCORDIA 13984 KS TOPEKA 13996 0.48 2 KS DODGE CITY 13985 KS DODGE CITY 13985 0.34 0 KS HUTCHINSON 13986 KS DODGE CITY 13985 0.37 4 KS EMPORIA 13989 KS TOPEKA 13996 0.84 1 KS TOPEKA 13996 KS TOPEKA 13996 0.37 9 KS GARDEN CITY 23064 KS DODGE CITY 13985 0.22 0 KS GOODLAND 23065 NE NORTH PLATTE 24023 0.38 1 KS OLATHE 93909 KS TOPEKA 13996 0.17 2

KS COFFEYVILLE 93967 MO SPRINGFIELD REGIONAL AP 13995 0.14 3 KS HILL CITY 93990 KS DODGE CITY 13985 0.26 2 KS RUSSELL 93997 KS DODGE CITY 13985 0.49 0 KY PADUCAH 03816 TN NASHVILLE 13897 0.33 10 KY LONDON 03849 TN NASHVILLE 13897 0.38 23 KY JACKSON 03889 OH WILMINGTON 13841 1.67 19 KY LOUISVILLE 13810 OH WILMINGTON 13841 0.34 14 KY FRANKFORT 53841 OH WILMINGTON 13841 0.26 12 KY BOWLING GREEN 93808 TN NASHVILLE 13897 0.21 15 KY LEXINGTON 93820 OH WILMINGTON 13841 1.48 11 KY LOUISVILLE 93821 OH WILMINGTON 13841 0.09 4 LA LAKE CHARLES 03937 LA LAKE CHARLES 03937 0.46 7 LA BOOTHVILLE 12884 LA SLIDELL 53813 0.64 4 LA NEW ORLEANS 12916 LA SLIDELL 53813 0.19 5 LA ALEXANDRIA 13935 LA LAKE CHARLES 03937 1.53 24

LA MONROE 13942 LA SHREVEPORT REGIONAL AP 13957 0.66 12

LA SHREVEPORT 13957 LA SHREVEPORT REGIONAL AP 13957 0.07 6 LA BATON ROUGE 13970 LA SLIDELL 53813 0.50 11 LA LAFAYETTE 13976 LA LAKE CHARLES 03937 0.66 13 LA SLIDELL 53865 LA SLIDELL 53813 0.41 23

LA SHREVEPORT 53905 LA SHREVEPORT REGIONAL AP 13957 0.22 8 LA NEW IBERIA 53915 LA LAKE CHARLES 03937 0.21 8 LA NEW ORLEANS 53917 LA SLIDELL 53813 2.03 2 LA ALEXANDRIA 93915 LA LAKE CHARLES 03937 0.58 13 MA FITCHBURG 04780 NY ALBANY 54775 0.55 14 MA BEDFORD 14702 MA CHATHAM 14684 0.29 13 MA BOSTON 14739 MA CHATHAM 14684 0.08 1 MA NANTUCKET 14756 MA CHATHAM 14684 0.09 1 MA PITTSFIELD 14763 NY ALBANY 54775 0.68 19

MA WESTFIELD/SPRINGFIE LD 14775 NY ALBANY 54775 0.99 14 MA NORWOOD 54704 MA CHATHAM 14684 0.59 21 MA BEVERLY 54733 MA CHATHAM 14684 0.17 7 MA ORANGE 54756 NY ALBANY 54775 0.59 28 MA NORTH ADAMS 54768 NY ALBANY 54775 1.14 2310 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

MA PLYMOUTH 54769 MA CHATHAM 14684 0.88 11 MA TAUNTON 54777 MA CHATHAM 14684 0.84 19 MA CHATHAM 94624 MA CHATHAM 14684 1.66 5 MA HYANNIS 94720 MA CHATHAM 14684 0.23 10 MA LAWRENCE 94723 ME GRAY 54762 1.72 9 MA VINEYARD HAVEN 94724 MA CHATHAM 14684 0.23 4 MA NEW BEDFORD 94726 MA CHATHAM 14684 0.83 13 MA WORCESTER 94746 NY ALBANY 54775 0.41 1

MD HAGERSTOWN 93706 VA STERLING(WASH DULLES) 93734 0.22 6 MD SALISBURY 93720 VA WALLOPS ISLAND 93739 0.25 12

MD BALTIMORE 93721 VA STERLING(WASH DULLES) 93734 0.21 11 MD OCEAN CITY 93786 VA WALLOPS ISLAND 93739 1.66 8 ME FRENCHVILLE 04836 ME CARIBOU 14607 0.71 1 ME AUGUSTA 14605 ME GRAY 54762 0.47 5 ME BANGOR 14606 ME GRAY 54762 0.19 10 ME CARIBOU 14607 ME CARIBOU 14607 0.11 5 ME HOULTON 14609 ME CARIBOU 14607 0.70 13 ME MILLINOCKET 14610 ME CARIBOU 14607 1.26 18 ME PORTLAND 14764 ME GRAY 54762 0.19 3 ME FRYEBURG 54772 ME GRAY 54762 2.32 28 ME WISCASSET 94623 ME GRAY 54762 1.84 31 MI HOLLAND 04839 MI DETROIT/PONTIAC 04830 0.42 7 MI ADRIAN 04847 MI DETROIT/PONTIAC 04830 0.14 7 MI GAYLORD 04854 MI GAYLORD / ALPENA 04837 0.78 10 MI BATTLE CREEK 14815 MI DETROIT/PONTIAC 04830 0.15 4 MI DETROIT 14822 MI DETROIT/PONTIAC 04830 0.51 5 MI FLINT 14826 MI DETROIT/PONTIAC 04830 0.26 5 MI JACKSON 14833 MI DETROIT/PONTIAC 04830 0.22 8 MI LANSING 14836 MI DETROIT/PONTIAC 04830 0.16 7 MI MUSKEGON 14840 WI GREEN BAY 14898 0.26 6 MI PELLSTON 14841 MI GAYLORD / ALPENA 04837 0.19 16 MI SAGINAW 14845 MI DETROIT/PONTIAC 04830 0.17 2 MI SAULT STE MARIE 14847 MI GAYLORD / ALPENA 04837 1.18 7 MI TRAVERSE CITY 14850 MI GAYLORD / ALPENA 04837 0.19 13 MI DETROIT 14853 MI DETROIT/PONTIAC 04830 0.27 5 MI HANCOCK 14858 WI GREEN BAY 14898 0.19 3 MI HOUGHTON LAKE 94814 MI GAYLORD / ALPENA 04837 0.24 8 MI KALAMAZOO 94815 MI DETROIT/PONTIAC 04830 0.51 7 MI PONTIAC 94817 MI DETROIT/PONTIAC 04830 0.15 6 MI DETROIT 94847 MI DETROIT/PONTIAC 04830 0.16 4 MI GRAND RAPTIDS 94860 MI DETROIT/PONTIAC 04830 0.11 3 MI BENTON HARBOR 94871 MI DETROIT/PONTIAC 04830 1.21 9 MI ANN ARBOR 94889 MI DETROIT/PONTIAC 04830 0.33 9

MI IRON MOUNTAIN/KINGSFOR D 94893 WI GREEN BAY 14898 0.54 12 MN ALEXANDRIA 14910 MN MINNEAPOLIS 94983 3.12 211 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

MN DULUTH 14913 MN INTERNATIONAL FALLS 14918 0.13 3

MN INTERNATIONAL FALLS 14918 MN INTERNATIONAL FALLS 14918 0.31 11 MN MINNEAPOLIS 14922 MN MINNEAPOLIS 94983 0.17 3 MN ROCHESTER 14925 MN MINNEAPOLIS 94983 0.05 1 MN ST CLOUD 14926 MN MINNEAPOLIS 94983 0.25 7 MN ST PAUL 14927 MN MINNEAPOLIS 94983 0.15 6 MN REDWOOD FALLS 14992 MN MINNEAPOLIS 94983 0.13 2

MN HIBBING 94931 MN INTERNATIONAL FALLS 14918 0.43 15 MN BRAINERD 94938 MN MINNEAPOLIS 94983 0.32 8 MN MINNEAPOLIS 94960 MN MINNEAPOLIS 94983 0.15 10

MN BAUDETTE 94961 MN INTERNATIONAL FALLS 14918 0.64 8 MN MINNEAPOLIS 94963 MN MINNEAPOLIS 94983 0.17 3

MN PARK RAPIDS 94967 MN INTERNATIONAL FALLS 14918 0.34 5 MO CAPE GIRARDEAU 03935 TN NASHVILLE 13897 0.25 8

MO COLUMBIA 03945 MO SPRINGFIELD REGIONAL AP 13995 0.67 1 MO KANSAS CITY 03947 KS TOPEKA 13996 0.17 1

MO JEFFERSON CITY 03963 MO SPRINGFIELD REGIONAL AP 13995 0.29 8

MO ST LOUIS 03966 IL LINCOLN-LOGAN COUNTY AP 04833 0.11 14

MO POPLAR BLUFF 03975 MO SPRINGFIELD REGIONAL AP 13995 0.55 12

MO SEDALIA 03994 MO SPRINGFIELD REGIONAL AP 13995 0.65 1

MO JOPLIN 13987 MO SPRINGFIELD REGIONAL AP 13995 0.23 2 MO KANSAS CITY 13988 KS TOPEKA 13996 0.39 5

MO ST LOUIS 13994 IL LINCOLN-LOGAN COUNTY AP 04833 0.07 3

MO SPRINGFIELD 13995 MO SPRINGFIELD REGIONAL AP 13995 0.02 2

MO ROLLA/VICHY 13997 MO SPRINGFIELD REGIONAL AP 13995 1.14 2

MO KIRKSVILLE 14938 IA DAVENPORT MUNICIPAL AP 94982 0.22 3 MO LEE'S SUMMIT 53879 KS TOPEKA 13996 0.79 1

MO WEST PLAINS 53901 MO SPRINGFIELD REGIONAL AP 13995 0.22 6

MO ST CHARLES 53904 IL LINCOLN-LOGAN COUNTY AP 04833 0.16 6

MS JACKSON 03940 MS JACKSON/THOMPSON FLD 03940 0.70 1712 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

MS TALLULAH/VICKSBURG 03996 MS JACKSON/THOMPSON FLD 03940 0.82 8 MS HATTIESBURG 13833 LA SLIDELL 53813 1.00 23

MS MERIDIAN 13865 MS JACKSON/THOMPSON FLD 03940 0.38 17

MS JACKSON 13927 MS JACKSON/THOMPSON FLD 03940 1.16 13

MS GREENVILLE 13939 MS JACKSON/THOMPSON FLD 03940 0.18 4

MS GREENWOOD 13978 MS JACKSON/THOMPSON FLD 03940 0.51 10 MS PASCAGOULA 53858 LA SLIDELL 53813 1.52 28

MS TUPELO 93862 AL BIRMINGHAM (SHELBY APT) 53823 0.25 10 MS GULFPORT 93874 LA SLIDELL 53813 0.51 13 MS MCCOMB 93919 LA SLIDELL 53813 2.75 17 MT BILLINGS 24033 MT GREAT FALLS 04102 0.21 1 MT LEWISTOWN 24036 MT GREAT FALLS 04102 0.59 2 MT MILES 24037 MT GLASGOW 94008 0.35 1 MT BOZEMAN 24132 MT GREAT FALLS 04102 0.55 6 MT BUTTE 24135 MT GREAT FALLS 04102 0.89 9 MT CUT BANK 24137 MT GREAT FALLS 04102 0.30 2 MT DILLON 24138 MT GREAT FALLS 04102 0.34 1 MT GREAT FALLS 24143 MT GREAT FALLS 04102 0.55 2 MT HELENA 24144 MT GREAT FALLS 04102 0.68 10 MT KALISPELL 24146 MT GREAT FALLS 04102 0.68 19 MT LIVINGSTON 24150 MT GREAT FALLS 04102 1.54 2 MT MISSOULA 24153 MT GREAT FALLS 04102 0.45 17 MT GLASGOW 94008 MT GLASGOW 94008 0.23 2 MT HAVRE 94012 MT GREAT FALLS 04102 0.27 3 MT WOLF POINT 94017 MT GLASGOW 94008 0.41 8 MT BAKER 94055 SD RAPID CITY 94043 0.38 5 NC HICKORY 03810 NC GREENSBORO 13723 0.86 17 NC ASHEVILLE 03812 NC GREENSBORO 13723 0.33 17 NC RALEIGH/DURHAM 13722 NC GREENSBORO 13723 0.18 12 NC GREENSBORO 13723 NC GREENSBORO 13723 0.18 6

NC WILMINGTON 13748 NC MOREHEAD CITY/NEWPORT 93768 0.22 10 NC LUMBERTON 13776 NC GREENSBORO 13723 1.11 15

NC ELIZABETH CITY 13786 NC MOREHEAD CITY/NEWPORT 93768 0.38 3 NC CHARLOTTE 13881 NC GREENSBORO 13723 0.31 5 NC GASTONIA 53870 NC GREENSBORO 13723 1.61 22 NC MONROE 53872 NC GREENSBORO 13723 0.94 13

NC NEW BERN 93719 NC MOREHEAD CITY/NEWPORT 93768 0.30 9

NC CAPE HATTERAS 93729 NC MOREHEAD CITY/NEWPORT 93768 0.32 1 NC FAYETTEVILLE 93740 NC GREENSBORO 13723 0.19 413 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

NC ROCKY MOUNT 93759 NC MOREHEAD CITY/NEWPORT 93768 0.95 16

NC BEAUFORT 93765 NC MOREHEAD CITY/NEWPORT 93768 1.24 4 NC MAXTON 93782 NC GREENSBORO 13723 0.17 11 NC BURLINGTON 93783 NC GREENSBORO 13723 0.19 15 NC CHAPEL HILL 93785 NC GREENSBORO 13723 1.92 24 NC WINSTON SALEM 93807 NC GREENSBORO 13723 0.62 14 ND FARGO 14914 SD ABERDEEN 14929 0.71 2

ND GRAND FORKS 14916 MN INTERNATIONAL FALLS 14918 0.17 2 ND JAMESTOWN 14919 ND BISMARCK 24011 0.15 2 ND BISMARCK 24011 ND BISMARCK 24011 0.11 4 ND DICKINSON 24012 ND BISMARCK 24011 0.11 1 ND MINOT 24013 ND BISMARCK 24011 0.13 2 ND WILLISTON 94014 MT GLASGOW 94008 0.42 5 ND HETTINGER 94038 ND BISMARCK 24011 0.40 6 NE GRAND ISLAND 14935 NE OMAHA/VALLEY 94980 0.19 1 NE LINCOLN 14939 NE OMAHA/VALLEY 94980 0.10 5 NE NORFOLK 14941 NE OMAHA/VALLEY 94980 0.18 4 NE OMAHA 14942 NE OMAHA/VALLEY 94980 0.18 3 NE CHADRON 24017 SD RAPID CITY 94043 0.35 4 NE NORTH PLATTE 24023 NE NORTH PLATTE 24023 0.19 4 NE SCOTTSBLUFF 24028 SD RAPID CITY 94043 0.22 1 NE SIDNEY 24030 NE NORTH PLATTE 24023 0.29 0 NE VALENTINE 24032 NE NORTH PLATTE 24023 0.14 2 NE ALLIANCE 24044 NE NORTH PLATTE 24023 0.18 1 NE IMPERIAL 24091 NE NORTH PLATTE 24023 0.51 2 NE MC COOK 94040 NE NORTH PLATTE 24023 0.61 2 NE BROKEN BOW 94946 NE NORTH PLATTE 24023 0.16 7 NE HASTINGS 94949 NE OMAHA/VALLEY 94980 0.16 3 NE FALLS CITY 94957 KS TOPEKA 13996 1.50 4 NE ORD 94958 NE NORTH PLATTE 24023 0.16 7 NE TEKAMAH 94978 NE OMAHA/VALLEY 94980 0.82 2 NH MANCHESTER 14710 ME GRAY 54762 0.29 11 NH CONCORD 14745 ME GRAY 54762 0.30 21 NH WHITEFIELD 54728 ME GRAY 54762 0.96 37 NH JAFFREY 54770 NY ALBANY 54775 0.64 19 NH ROCHESTER 54791 ME GRAY 54762 1.38 14 NH BERLIN 94700 ME GRAY 54762 0.49 37 NH LEBANON 94765 NY ALBANY 54775 0.53 28 NJ MILLVILLE 13735 VA WALLOPS ISLAND 93739 1.19 18 NJ NEWARK 14734 NY BROOKHAVEN 94703 0.45 3 NJ TRENTON 14792 NY BROOKHAVEN 94703 0.32 8 NJ CALDWELL 54743 NY BROOKHAVEN 94703 0.57 26 NJ SOMERVILLE 54785 NY BROOKHAVEN 94703 1.07 31 NJ ATLANTIC CITY 93730 VA WALLOPS ISLAND 93739 0.22 7 NJ MOUNT HOLLY 93780 NY BROOKHAVEN 94703 0.50 18 NJ TETERBORO 94741 NY BROOKHAVEN 94703 0.22 914 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

NM CLINES CORNERS 03027 NM ALBUQUERQUE 23050 2.47 2 NM ROSWELL 23009 NM SANTA TERESA 03020 0.39 3 NM TUCUMCARI 23048 TX AMARILLO 23047 0.99 1 NM SANTA FE 23049 NM ALBUQUERQUE 23050 0.22 1 NM ALBUQUERQUE 23050 NM ALBUQUERQUE 23050 0.15 3 NM CLAYTON 23051 TX AMARILLO 23047 1.55 1 NM RATON 23052 NM ALBUQUERQUE 23050 0.11 1 NM LAS VEGAS 23054 NM ALBUQUERQUE 23050 0.15 1 NM DEMING 23078 NM SANTA TERESA 03020 0.16 2 NM GALLUP 23081 NM ALBUQUERQUE 23050 0.46 16 NM FARMINGTON 23090 NM ALBUQUERQUE 23050 0.63 2 NM CARLSBAD 93033 TX MIDLAND 23023 0.61 2

NM TRUTH OR CONSEQUENCES 93045 NM SANTA TERESA 03020 0.21 2 NV MERCURY 03160 NV LAS VEGAS 03120 1.16 5 NV TONOPAH 23153 NV LAS VEGAS 03120 0.95 1 NV ELY 23154 NV ELKO 04105 0.54 2 NV LAS VEGAS 23169 NV LAS VEGAS 03120 0.31 1 NV RENO 23185 NV RENO 03198 0.19 13 NV ELKO 24121 NV ELKO 04105 0.32 11 NV WINNEMUCCA 24128 NV ELKO 04105 0.80 4 NV LOVELOCK 24172 NV RENO 03198 1.06 10 NV LAS VAGAS 53123 NV LAS VEGAS 03120 0.43 4 NY NIAGARA FALLS 04724 NY BUFFALO/GRTR ARPT 14733 0.13 4 NY BINGHAMTON 04725 NY ALBANY 54775 0.18 2 NY ISLIP 04781 NY BROOKHAVEN 94703 0.11 6 NY MONTGOMERY 04789 NY ALBANY 54775 1.71 18

NY WESTHAMPTON BEACH 14719 NY BROOKHAVEN 94703 2.93 12 NY NEW YORK 14732 NY BROOKHAVEN 94703 0.15 1 NY BUFFALO 14733 NY BUFFALO/GRTR ARPT 14733 0.09 1 NY ALBANY 14735 NY ALBANY 54775 0.13 12 NY DUNKIRK 14747 NY BUFFALO/GRTR ARPT 14733 0.68 4 NY ELMIRA/CORNING 14748 NY BUFFALO/GRTR ARPT 14733 0.58 19 NY GLEN FALLS 14750 NY ALBANY 54775 0.62 23 NY POUGHKEEPSIE 14757 NY ALBANY 54775 0.54 27 NY ROCHESTER 14768 NY BUFFALO/GRTR ARPT 14733 0.11 4 NY SYRACUSE 14771 NY ALBANY 54775 0.11 6 NY WELLSVILLE 54757 NY BUFFALO/GRTR ARPT 14733 0.34 4 NY FULTON 54773 NY BUFFALO/GRTR ARPT 14733 1.46 11 NY PENN YAN 54778 NY BUFFALO/GRTR ARPT 14733 0.75 7 NY FARMINGDALE 54787 NY BROOKHAVEN 94703 0.74 5 NY SHIRLEY 54790 NY BROOKHAVEN 94703 0.53 11 NY DANSVILLE 94704 NY BUFFALO/GRTR ARPT 14733 0.15 7 NY MASSENA 94725 NY ALBANY 54775 0.34 13 NY NEW YORK 94728 NY BROOKHAVEN 94703 3.48 5 NY SARANAC LAKE 94740 NY ALBANY 54775 0.48 21 NY WHITE PLAINS 94745 NY BROOKHAVEN 94703 0.33 7 NY NEW YORK 94789 NY BROOKHAVEN 94703 0.11 115 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

NY WATERTOWN 94790 NY ALBANY 54775 0.80 7

OH WOOSTER 04842 PA PITTSBURGH/MOON TOWNSHIP 94823 0.17 9 OH TOLEDO 04848 MI DETROIT/PONTIAC 04830 0.10 4

OH LORAIN/ELYRIA 04849 PA PITTSBURGH/MOON TOWNSHIP 94823 0.58 4 OH LIMA 04850 OH WILMINGTON 13841 0.49 4 OH DEFIANCE 04851 MI DETROIT/PONTIAC 04830 0.78 11

OH NEW PHILADELPHIA 04852 PA PITTSBURGH/MOON TOWNSHIP 94823 0.50 18

OH CLEVELAND 04853 PA PITTSBURGH/MOON TOWNSHIP 94823 0.11 2 OH MARION 04855 OH WILMINGTON 13841 0.46 3

OH ASHTABULA 04857 PA PITTSBURGH/MOON TOWNSHIP 94823 0.71 10 OH NEWARK 04858 OH WILMINGTON 13841 0.21 19 OH WILMINGTON 13841 OH WILMINGTON 13841 0.35 4

OH AKRON 14813 PA PITTSBURGH/MOON TOWNSHIP 94823 0.47 9

OH CLEVELAND 14820 PA PITTSBURGH/MOON TOWNSHIP 94823 0.10 2 OH COLUMBUS 14821 OH WILMINGTON 13841 0.07 5 OH FINDLAY 14825 OH WILMINGTON 13841 0.11 2

OH YOUNGSTOWN/WARR EN 14852 PA PITTSBURGH/MOON TOWNSHIP 94823 0.10 3 OH MANSFIELD 14891 OH WILMINGTON 13841 0.23 2

OH AKRON 14895 PA PITTSBURGH/MOON TOWNSHIP 94823 0.07 2 OH LANCASTER 53844 OH WILMINGTON 13841 0.42 2 OH HAMILTON 53855 OH WILMINGTON 13841 1.12 19 OH DAYTON 53859 OH WILMINGTON 13841 0.21 4 OH CINCINNATI 93812 OH WILMINGTON 13841 0.56 2

OH COVINGTON/CINCINNA TI 93814 OH WILMINGTON 13841 0.13 4 OH DAYTON 93815 OH WILMINGTON 13841 0.10 2

OH ZANESVILLE 93824 PA PITTSBURGH/MOON TOWNSHIP 94823 0.13 13 OH TOLEDO 94830 MI DETROIT/PONTIAC 04830 0.94 7 OK GUYMON 03030 TX AMARILLO 23047 0.18 1 OK CLINTON 03932 OK NORMAN 03948 0.42 0 OK LAWTON 03950 OK NORMAN 03948 0.65 6 OK OKLAHOMA CITY 03954 OK NORMAN 03948 0.14 1 OK STILLWATER 03965 OK NORMAN 03948 0.16 3 OK FREDERICK 03981 OK NORMAN 03948 0.92 1 OK OKLAHOMA CITY 13967 OK NORMAN 03948 0.26 1 OK TULSA 13968 OK NORMAN 03948 0.10 3 OK PONCA CITY 13969 OK NORMAN 03948 0.47 5 OK GAGE 13975 KS DODGE CITY 13985 1.10 2 OK TULSA 53908 OK NORMAN 03948 0.43 1516 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

OK GUTHRIE 53913 OK NORMAN 03948 2.92 2 OK MC ALESTER 93950 OK NORMAN 03948 2.16 8 OK MUSKOGEE 93953 OK NORMAN 03948 0.55 2 OK HOBART 93986 OK NORMAN 03948 2.32 0 OR HERMISTON 04113 WA SPOKANE INTNL APT 04106 0.41 5 OR SCAPPOOSE 04201 OR SALEM 24232 0.88 19 OR BAKER CITY 24130 ID BOISE 24131 1.12 11 OR PENDLETON 24155 WA SPOKANE INTNL APT 04106 0.30 3 OR ONTARIO 24162 ID BOISE 24131 0.78 10 OR THE DALLES 24219 OR SALEM 24232 0.24 11 OR EUGENE 24221 OR SALEM 24232 0.25 7 OR MEDFORD 24225 OR MEDFORD 24225 0.45 23 OR PORTLAND 24229 OR SALEM 24232 0.13 7 OR REDMOND 24230 OR SALEM 24232 1.24 10 OR ROSEBURG 24231 OR MEDFORD 24225 0.42 32 OR SALEM 24232 OR SALEM 24232 0.34 13 OR SEXTON SUMMIT 24235 OR MEDFORD 24225 0.98 3 OR PORTLAND 24242 OR SALEM 24232 0.43 14 OR BURNS 94185 ID BOISE 24131 0.74 7 OR ASTORIA 94224 OR SALEM 24232 0.26 5 OR KLAMATH FALLS 94236 OR MEDFORD 24225 0.42 18 OR PORTLAND 94261 OR SALEM 24232 0.34 20 OR MC MINNVILLE 94273 OR SALEM 24232 0.66 14 OR AURORA 94281 OR SALEM 24232 0.64 20

PA JOHNSTOWN 04726 PA PITTSBURGH/MOON TOWNSHIP 94823 0.16 2 PA BRADFORD 04751 NY BUFFALO/GRTR ARPT 14733 1.83 12

PA DU BOIS 04787 PA PITTSBURGH/MOON TOWNSHIP 94823 0.35 4

PA MEADVILLE 04843 PA PITTSBURGH/MOON TOWNSHIP 94823 1.31 5

PA PHILADELPHIA 13739 VA STERLING(WASH DULLES) 93734 0.15 3

PA HARRISBURG 14711 VA STERLING(WASH DULLES) 93734 0.34 12

PA READING 14712 VA STERLING(WASH DULLES) 93734 0.80 15

PA ALTOONA 14736 VA STERLING(WASH DULLES) 93734 0.24 10 PA ALLENTOWN 14737 NY BROOKHAVEN 94703 0.22 9

PA HARRISBURG 14751 VA STERLING(WASH DULLES) 93734 0.23 11

PA PITTSBURGH 14762 PA PITTSBURGH/MOON TOWNSHIP 94823 0.65 2

PA SELINSGROVE 14770 VA STERLING(WASH DULLES) 93734 1.03 29

PA WILKESBARRE/SCRANTON 14777 NY ALBANY 54775 0.30 817 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

PA WILLIAMSPORT 14778 NY BUFFALO/GRTR ARPT 14733 0.46 16 PA ERIE 14860 NY BUFFALO/GRTR ARPT 14733 0.21 3

PA LANCASTER 54737 VA STERLING(WASH DULLES) 93734 0.22 8

PA POTTSTOWN 54782 VA STERLING(WASH DULLES) 93734 1.42 23 PA DOYLESTOWN 54786 NY BROOKHAVEN 94703 1.21 18 PA MOUNT POCONO 54789 NY BROOKHAVEN 94703 0.35 5

PA CLEARFIELD 54792 PA PITTSBURGH/MOON TOWNSHIP 94823 0.29 8

PA YORK 93778 VA STERLING(WASH DULLES) 93734 0.43 23 PA PHILADELPHIA 94732 NY BROOKHAVEN 94703 0.23 8

PA PITTSBURGH 94823 PA PITTSBURGH/MOON TOWNSHIP 94823 0.13 9 PR SAN JUAN 11641 PR SAN JUAN 11641 0.62 14 RI PROVIDENCE 14765 MA CHATHAM 14684 0.02 3 RI NEWPORT 14787 MA CHATHAM 14684 0.74 6 RI WESTERLY 14794 NY BROOKHAVEN 94703 0.29 12 SC GREER 03870 NC GREENSBORO 13723 0.31 9 SC FLORENCE 13744 SC CHARLESTON 13880 0.95 8 SC CHARLESTON 13880 SC CHARLESTON 13880 0.15 6 SC COLUMBIA 13883 SC CHARLESTON 13880 0.81 11 SC GREENVILLE 13886 NC GREENSBORO 13723 0.27 8 SC CLEMSON 53850 GA PEACHTREE CITY 53819 0.37 10 SC ORANGEBURG 53854 SC CHARLESTON 13880 0.54 16 SC COLUMBIA 53867 SC CHARLESTON 13880 0.81 31 SC ROCK HILL 53871 NC GREENSBORO 13723 0.27 17 SC GREENWOOD 53874 GA PEACHTREE CITY 53819 1.64 13

SC NORTH MYRTLE BEACH 93718 SC CHARLESTON 13880 0.22 8 SC ANDERSON 93846 GA PEACHTREE CITY 53819 1.82 14 SD ABERDEEN 14929 SD ABERDEEN 14929 0.13 2 SD HURON 14936 SD ABERDEEN 14929 0.33 4 SD SIOUX FALLS 14944 SD ABERDEEN 14929 0.11 5 SD WATERTOWN 14946 SD ABERDEEN 14929 0.14 3 SD PHILIP 24024 SD RAPID CITY 94043 0.19 3 SD PIERRE 24025 SD ABERDEEN 14929 0.22 2 SD RAPID CITY 24090 SD RAPID CITY 94043 0.09 5 SD CUSTER 94032 SD RAPID CITY 94043 1.38 5 SD PINE RIDGE 94039 SD RAPID CITY 94043 0.18 6 SD MOBRIDGE 94052 ND BISMARCK 24011 0.23 3 SD MITCHELL 94950 SD ABERDEEN 14929 0.48 2 SD WINNER 94990 SD ABERDEEN 14929 0.82 1 TN JACKSON 03811 TN NASHVILLE 13897 0.86 13 TN CROSSVILLE 03847 TN NASHVILLE 13897 0.76 16

TN BRISTOL/JOHNSON CITY/KINGSPORT 13877 VA ROANOKE/BLACKSBURG 53829 0.26 2818 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

TN CHATTANOOGA 13882 TN NASHVILLE 13897 0.22 24 TN KNOXVILLE 13891 TN NASHVILLE 13897 0.19 9 TN MEMPHIS 13893 AR N LITTLE ROCK 03952 0.18 4 TN NASHVILLE 13897 TN NASHVILLE 13897 3.57 19 TX BORGER 03024 TX AMARILLO 23047 0.47 1

TX LONGVIEW 03901 LA SHREVEPORT REGIONAL AP 13957 0.41 6 TX COLLEGE STATION 03904 TX FT WORTH 03990 0.10 4

TX DALLAS-FT WORTH 03927 TX FT WORTH 03990 0.10 2 TX DALLAS 03971 TX FT WORTH 03990 0.53 1 TX DENTON 03991 TX FT WORTH 03990 0.33 3 TX BURNET 03999 TX FT WORTH 03990 0.99 6 TX HARLINGEN 12904 TX BROWNSVILLE 12919 0.30 1 TX VICTORIA 12912 TX CORPUS CHRISTI 12924 0.26 2

TX BEAUMONT/PORT ARTHUR 12917 LA LAKE CHARLES 03937 0.11 4 TX HOUSTON 12918 LA LAKE CHARLES 03937 0.09 5 TX BROWNSVILLE 12919 TX BROWNSVILLE 12919 0.08 3 TX SAN ANTONIO 12921 TX CORPUS CHRISTI 12924 0.07 2 TX GALVESTON 12923 LA LAKE CHARLES 03937 2.19 3 TX CORPUS CHRISTI 12924 TX CORPUS CHRISTI 12924 0.15 1 TX ALICE 12932 TX CORPUS CHRISTI 12924 0.15 2 TX PALACIOS 12935 TX CORPUS CHRISTI 12924 0.16 1 TX COTULLA 12947 TX CORPUS CHRISTI 12924 1.34 2 TX PORT ISABEL 12957 TX BROWNSVILLE 12919 0.10 1 TX MC ALLEN 12959 TX BROWNSVILLE 12919 0.13 2 TX HOUSTON 12960 LA LAKE CHARLES 03937 0.16 6 TX HONDO 12962 TX DEL RIO 22010 0.29 1 TX SAN ANTONIO 12970 TX CORPUS CHRISTI 12924 0.47 8 TX NEW BRAUNFELS 12971 TX CORPUS CHRISTI 12924 0.11 2 TX ROCKPORT 12972 TX CORPUS CHRISTI 12924 0.07 1 TX HOUSTON 12975 LA LAKE CHARLES 03937 0.34 13

TX ANGLETON/LAKE JACKSON 12976 LA LAKE CHARLES 03937 0.38 10 TX HOUSTON 12977 LA LAKE CHARLES 03937 0.08 6

TX AUSTIN/BERGSTROM 13904 TX CORPUS CHRISTI 12924 0.19 7 TX AUSTIN/CITY 13958 TX FT WORTH 03990 4.25 11 TX WACO 13959 TX FT WORTH 03990 0.13 2 TX DALLAS 13960 TX FT WORTH 03990 0.31 4 TX FORT WORTH 13961 TX FT WORTH 03990 0.13 2 TX ABILENE 13962 TX FT WORTH 03990 0.99 1 TX WICHITA FALLS 13966 OK NORMAN 03948 0.10 1

TX TYLER 13972 LA SHREVEPORT REGIONAL AP 13957 0.08 3 TX JUNCTION 13973 TX DEL RIO 22010 0.46 8 TX DEL RIO 22010 TX DEL RIO 22010 0.23 3 TX CHILDRESS 23007 TX AMARILLO 23047 1.00 119 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

TX MIDLAND 23023 TX MIDLAND 23023 0.31 1 TX SAN ANGELO 23034 TX MIDLAND 23023 0.27 2 TX WINK 23040 TX MIDLAND 23023 0.57 1 TX LUBBOCK 23042 TX AMARILLO 23047 0.14 1 TX EL PASO 23044 NM SANTA TERESA 03020 0.18 3 TX AMARILLO 23047 TX AMARILLO 23047 0.10 0 TX GUADALUPE PASS 23055 NM SANTA TERESA 03020 4.93 0 TX FORT STOCKTON 23091 TX MIDLAND 23023 0.33 1 TX CONROE 53902 LA LAKE CHARLES 03937 0.18 18 TX HUNTSVILLE 53903 LA LAKE CHARLES 03937 0.42 11 TX ARLINGTON 53907 TX FT WORTH 03990 0.15 2 TX FORT WORTH 53909 TX FT WORTH 03990 0.13 7 TX HOUSTON 53910 LA LAKE CHARLES 03937 0.37 10 TX TERRELL 53911 TX FT WORTH 03990 0.13 6 TX CORSICANA 53912 TX FT WORTH 03990 0.25 1 TX MC KINNEY 53914 TX FT WORTH 03990 0.46 7 TX DALHART 93042 TX AMARILLO 23047 0.26 1 TX MINERAL WELLS 93985 TX FT WORTH 03990 0.41 3

TX LUFKIN 93987 LA SHREVEPORT REGIONAL AP 13957 0.19 9

UT BRYCE CANYON 23159 AZ FLAGSTAFF/BELLEMT (ARMY) 53103 0.35 2 UT MILFORD 23176 UT SALT LAKE CITY 24127 0.47 2 UT OGDEN 24126 UT SALT LAKE CITY 24127 0.61 4 UT SALT LAKE CITY 24127 UT SALT LAKE CITY 24127 0.06 3 UT MOAB 93075 CO GRAND JUNCTION 23066 0.32 14 UT CEDAR CITY 93129 NV LAS VEGAS 03120 0.21 6 UT PRICE 93141 UT SALT LAKE CITY 24127 0.30 5 UT VERNAL 94030 CO GRAND JUNCTION 23066 0.82 8 UT LOGAN 94128 UT SALT LAKE CITY 24127 0.56 15 VA DANVILLE 13728 NC GREENSBORO 13723 0.50 11

VA LYNCHBURG 13733 VA ROANOKE/BLACKSBURG 53829 2.60 22 VA NORFOLK 13737 VA WALLOPS ISLAND 93739 0.22 5

VA RICHMOND 13740 VA STERLING(WASH DULLES) 93734 0.09 6

VA ROANOKE 13741 VA ROANOKE/BLACKSBURG 53829 0.17 12

VA CHARLOTTESVILLE 93736 VA STERLING(WASH DULLES) 93734 0.53 27 VA WALLOPS ISLAND 93739 VA WALLOPS ISLAND 93739 0.51 5 VA NEWPORT NEWS 93741 VA WALLOPS ISLAND 93739 0.15 10 VA WAKEFIELD 93773 VA WALLOPS ISLAND 93739 1.82 29

VA RICHMOND/ASHLAND 93775 VA STERLING(WASH DULLES) 93734 1.12 22 VT BURLINGTON 14742 NY ALBANY 54775 0.08 7 VT SPRINGFIELD 54740 NY ALBANY 54775 0.32 36 VT MORRISVILLE 54771 ME GRAY 54762 1.66 33 VT BENNINGTON 54781 NY ALBANY 54775 1.47 2220 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

VT BARRE/MONTPELIER 94705 ME GRAY 54762 0.97 20 WA MOSES LAKE 24110 WA SPOKANE INTNL APT 04106 0.35 4 WA EPHRATA 24141 WA SPOKANE INTNL APT 04106 0.29 3 WA SPOKANE 24157 WA SPOKANE INTNL APT 04106 0.24 4 WA WALLA WALLA 24160 WA SPOKANE INTNL APT 04106 0.43 3 WA PASCO 24163 WA SPOKANE INTNL APT 04106 0.19 8 WA BELLINGHAM 24217 WA QUILLAYUTE 94240 0.41 13 WA ELLENSBURG 24220 WA SPOKANE INTNL APT 04106 0.50 9 WA EVERETT 24222 WA QUILLAYUTE 94240 1.52 6 WA OLYMPIA 24227 WA QUILLAYUTE 94240 0.86 18 WA SEATTLE 24233 WA QUILLAYUTE 94240 0.08 3 WA SEATTLE 24234 WA QUILLAYUTE 94240 0.55 12 WA YAKIMA 24243 WA SPOKANE INTNL APT 04106 0.59 7 WA DEER PARK 94119 WA SPOKANE INTNL APT 04106 0.45 13

WA PULLMAN/MOSCOW 94129 WA SPOKANE INTNL APT 04106 0.64 14 WA SPOKANE 94176 WA SPOKANE INTNL APT 04106 0.25 16 WA OMAK 94197 WA SPOKANE INTNL APT 04106 1.02 9 WA HOQUIAM 94225 WA QUILLAYUTE 94240 0.48 5 WA SHELTON 94227 WA QUILLAYUTE 94240 0.17 18 WA WENATCHEE 94239 WA SPOKANE INTNL APT 04106 0.43 7 WA QUILLAYUTE 94240 WA QUILLAYUTE 94240 4.29 13 WA RENTON 94248 WA QUILLAYUTE 94240 0.42 10 WA PORT ANGELES 94266 WA QUILLAYUTE 94240 1.54 8 WA TACOMA 94274 WA QUILLAYUTE 94240 0.21 11 WA FRIDAY HARBOR 94276 WA QUILLAYUTE 94240 0.40 19 WA VANCOUVER 94298 OR SALEM 24232 0.51 20 WI RHINELANDER 04803 WI GREEN BAY 14898 0.26 9

WI WISCONSIN RAPTIDS 04826 WI GREEN BAY 14898 0.23 11 WI FOND DU LAC 04840 WI GREEN BAY 14898 0.19 5 WI SHEBOYGAN 04841 WI GREEN BAY 14898 0.32 6 WI KENOSHA 04845 WI GREEN BAY 14898 0.11 2 WI MADISON 14837 WI GREEN BAY 14898 0.38 10 WI MILWAUKEE 14839 WI GREEN BAY 14898 0.11 4 WI WAUSAU 14897 WI GREEN BAY 14898 0.26 6 WI GREEN BAY 14898 WI GREEN BAY 14898 0.50 6 WI LA CROSSE 14920 MN MINNEAPOLIS 94983 0.24 5

WI LONE ROCK 14921 IA DAVENPORT MUNICIPAL AP 94982 0.24 14 WI EAU CLAIRE 14991 MN MINNEAPOLIS 94983 0.39 11 WI RACINE 94818 WI GREEN BAY 14898 0.27 3 WI OSHKOSH 94855 WI GREEN BAY 14898 0.24 5 WI ASHLAND 94929 MN MINNEAPOLIS 94983 0.13 6 WI HAYWARD 94973 MN MINNEAPOLIS 94983 0.89 22 WI MARSHFIELD 94985 WI GREEN BAY 14898 0.61 5

WI B0SCOBEL 94994 IA DAVENPORT MUNICIPAL AP 94982 1.26 2121 of 21 State City WBAN # State City WBAN # % Missing % Calms Surface Upper Air Processed

WV CLARKSBURG 03802 PA PITTSBURGH/MOON TOWNSHIP 94823 0.17 21

WV PARKERSBURG 03804 PA PITTSBURGH/MOON TOWNSHIP 94823 2.05 18

WV BLUEFIELD 03859 VA ROANOKE/BLACKSBURG 53829 0.35 4 WV HUNTINGTON 03860 OH WILMINGTON 13841 0.54 11

WV BECKLEY 03872 VA ROANOKE/BLACKSBURG 53829 0.16 7

WV ELKINS 13729 PA PITTSBURGH/MOON TOWNSHIP 94823 0.32 34

WV MARTINSBURG 13734 VA STERLING(WASH DULLES) 93734 0.26 17

WV MORGANTOWN 13736 PA PITTSBURGH/MOON TOWNSHIP 94823 0.68 14

WV CHARLESTON 13866 VA ROANOKE/BLACKSBURG 53829 0.26 24

WV WHEELING 14894 PA PITTSBURGH/MOON TOWNSHIP 94823 0.68 5 WY EVANSTON 04111 UT SALT LAKE CITY 24127 0.78 1

WY CHEYENNE 24018 CO DENVER/STAPLETON ARPT 23062 0.30 1 WY LANDER 24021 WY RIVERTON 24061 0.56 4

WY LARAMIE 24022 CO DENVER/STAPLETON ARPT 23062 0.63 1 WY ROCK SPRINGS 24027 WY RIVERTON 24061 0.42 2 WY SHERIDAN 24029 WY RIVERTON 24061 0.11 8 WY GREYBULL 24048 WY RIVERTON 24061 0.33 8 WY RAWLINS 24057 WY RIVERTON 24061 0.92 4 WY RIVERTON 24061 WY RIVERTON 24061 0.32 2 WY WORLAND 24062 WY RIVERTON 24061 0.57 6 WY CASPER 24089 WY RIVERTON 24061 0.21 1 WY BIG PINEY 24164 WY RIVERTON 24061 0.40 6 WY GILLETTE 94023 SD RAPID CITY 94043 0.54 3 WY TORRINGTON 94053 SD RAPID CITY 94043 3.93 1 WY BUFFALO 94054 WY RIVERTON 24061 0.37 2 WY DOUGLAS 94057 SD RAPID CITY 94043 1.00 4

### 1 

## Appendix 3 : Analysis of data on short-term emission rates relative to long-term emission rates

### 2 

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

Ted Palma Roy Smith EPA/OAQPS/SBAG

1. Introduction

1.1. The problem
The process of listing hazardous air pollutants (HAPs) provided by the Clean Air Act (CAA, section 112(b)(2)) explicitly 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

## Technical Support Document for the TRIM

- Based Multipathway Tiered Screening Methodology for RTR

March , 201 3

Prepared For:

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Research Triangle Park, NC 27711

Prepared By:

ICF International 2222 East NC

- 54 Suite 480 Durham, NC 27713[This page intentionally left blank.]TRIM

- Based T iered Screening Methodology for RTR

Overview iii 6

- 29

- 2012 

## C ONTENTS

1. Introduction and Background

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

2. Tier 1

................................ ................................ ................................ ................................ . 2
2.1 Chemicals of Concern

................................ ................................ ............................... 2
2.2 Development of Emission Thresholds

................................ ................................ ........ 4 2.2.1 Modeling Fate and Transport

................................ ................................ ...... 5 2.2.2 Modeling Transfer and Uptake

................................ ................................ .... 6 2.2.3 Estimating Ingestion Exposure

................................ ................................ ... 6 2.2.4 Calculating Lifetime Cancer Risk and Non

- Cancer HQs

............................. 7 2.2.5 Determining Threshold Emission Rates

................................ ...................... 7

3. Tier 2

................................ ................................ ................................ ................................ . 7

4. Refined Multipathway Assessment

................................ ................................ .................... 9

5. References

................................ ................................ ................................ ........................ 9

Attachment A. Tier 1 Screening Methodology

................................ ................................ . A

- 1 A.1 Introduction

................................ ................................ ................................ ............ A

- 6 A.2 Summary of Approach

................................ ................................ ........................... A

- 6 A.2.1 Overview

................................ ................................ ................................ . A

- 6 A.2.2 Chemicals of Potential Concern

................................ ............................... A

- 9 A.2.3 Conceptual Exposure Scenario

................................ ............................. A

- 10 A.2.4 Approach to Development of the Tier 1 Scenario

................................ ... A

- 12 A.2.5 Fate and Transport Modeling (TRIM.FaTE)

................................ ........... A

- 14 A.2.6 Exposure Modeling and Risk Characterization (MIRC)

.......................... A

- 15 A.2.7 Implementation of Risk

- based Emission Scaling Fa ctors for

POM and Dioxin Emissions

................................ ................................ ... A

- 16 A.3 Description of Environmental Modeling Scenario

................................ ................. A

- 20 A.3.1 Chemical Properties

................................ ................................ .............. A

- 20 A.3. 2 Spatial Layout

................................ ................................ ........................ A

- 21 A.3.3 Watershed and Water Body Parameterization

................................ ....... A

- 22 A.3.4 Meteorology

................................ ................................ ........................... A

- 24 A.3.5 Aquati c Food Web

................................ ................................ ................. A

- 26 A.3.6 Using TRIM.FaTE Media Concentrations

................................ .............. A

- 27 A.4 Description of Exposure and Risk Modeling Scenario

................................ .......... A

- 28 A.4.1 Calculating Concentrations in Farm Food Chain Media

......................... A

- 29 A.4.2 Ingestion Exposure

................................ ................................ ................ A

- 29 A.4.3 Calculating Risk

................................ ................................ ..................... A

- 32 A.4.4 Summary of Tier 1 Assumptions

................................ ............................ A

- 33 A.5 Evaluation of Screening Scenario

................................ ................................ ........ A

- 37 A.5.1 Introduction

................................ ................................ ............................ A

- 37 A.5.2 Cadmium Compounds

................................ ................................ ........... A

- 37 A.5.3 Mercury Compounds

................................ ................................ ............. A

- 38 A.5.4 Dioxins A

- 40 A.5.5 Polycyclic Aromatic Hydrocarbons

................................ ......................... A

- 42 A.5.6 Summary

................................ ................................ ............................... A

- 44A.6 References

................................ ................................ ................................ .......... A

- 45

Addendum 1. TRIM .FaTE Inputs

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

- 1

Addendum 2. Description of Multimedia Ingestion Risk Calculator (MIRC) Used for RTR Exposure and Risk Estimates

................................ ................................ ... 2

- 1

Addendum 3. Dermal Risk Screening

................................ ................................ ............ 3

- 1

Attachment B. Tier 2 Screening Methodology

................................ ................................ . B

- 1 B.1 Overview of Approach

................................ ................................ ........................... B

- 5 B.2 Selection of Site

- Specific Characteristics to Include in the Tier 2 Analysis

................................ ................................ ................................ ................. B

- 7 B.3 Estimation of Adjustment Factors for Selected Site

- Specific Parameters

................................ ................................ ................................ ............ B

- 8 B.3.1 Selection Values for Variables of Interest

................................ ................ B

- 9 B.3.2 Estimation of Adjustment Factors

................................ .......................... B

- 12 B.4 Preparing National Databases of Lake and Meteorological Data

......................... B

- 16 B.4.1 Processing Lake Data for Tier 2 Analysis

................................ .............. B

- 16 B.4.2 Processing Meteorological Data for Tier 2 Analysis

............................... B

- 19 B.5 Implementation of Tier 2 Analysis

................................ ................................ ........ B

- 23 B.5.1 Facility List for Tier 2 Screen (Step 3)

................................ .................... B

- 26 B.5.2 Facility/Lake Distance Table (Step 4)

................................ .................... B

- 26 B.5.3 Finding the Closest Meteorological Station (Step 5)

.............................. B

- 27 B.5.4 Assembling Threshold Adjustment Factors (Step 6)

.............................. B

- 29 B.5.5 Finalizing Threshold Adjustment Factor s and Assembling

Screening Results (Step 7)

................................ ................................ ... B

- 30 B.5.6 Assembling Results (Step 8)

................................ ................................ . B

- 30 B.6 References

................................ ................................ ................................ .......... B

- 33

Addendum 1. Summary of TRIM.FaTE Parameters Considered for

Inclusion in Tier 2 Analysis

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

- 1

Addendum 2. Analysis of Lake Size and Sustainable F ish Population

.......................... 2

- 1[This page intentionally left blank.]TRIM

- Based T iered Screening Methodology for RTR

Overview 1 6

- 29

- 2012 

## 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) f ollowing the implementation of maximum achievable control technology (MACT) standards for emission sources. This risk assessment is a major component of EPA’s Risk and Technology Review (RTR) program . As part of this program , EPA must consider additional emission standards for a source category if the current emission standards

— with MACT regulations in place

— do not provide an “ample margin of safety” for human health. One aspect of human health that EPA must consider under RTR is the potential for health effects resulting from exposures to persistent and bioaccumulative HAPs (PB

- HAPs) via non

- inhalation pathways , namely ingestion and dermal exposure

. EPA’s a ssessment for RTR focuses on specific PB

- HAPs that the Office of Air Quality Planning and Standard s (OAQPS) has identifi ed as candidates for multipathway risk assessments (selection of the PB

- HAPs is discussed in Section 5.A.2.2 ). These non
- inhalation human heal th risk s 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 t o be significantly higher than any dermal exposures that might occur as a result of the same emissions (see below and Addendum 3 ). Consequently, a methodology has been developed to evaluate ingestion exposure and risk for PB

- HAPs efficiently in the context of EPA’s RTR program.

T o evaluate ingestion exposures and human health risks for RTR on a source category basis
, an iterative approach was developed that enables EPA to confidently screen out PB

- HAP emissions unlikely to pose health risks a bove levels of concern (i.e., a cancer risk of 1 in 1 million or a noncancer hazard of 1.0 ) and to focus additional resources on sources of greater
concern within the category

. To estimate exposure and risk, two models are used : the Fate, Transport, and Ecological Exposure module of EPA’s Total Risk Integrated Methodology (TRIM.FaTE) to model the fate and transport of pollutants released to the environment and the Multimedia Ingestion Risk Calculator (MIRC) to estimate transfer and uptake into the food ch ain 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 sour ce 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 grow n 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
scena rio

. For a facility, if the emission rate of each PB

- HAP is less than the Tier 1 threshold emission rate, risks are assumed to be low and no additional multipathway screening is done

. If, however, the emission rate of any PB

- HAP exceeds the Tier 1 thresho ld emission rate, the facility c an be evaluated further in Tier 2.

 In Tier 2, the actual location of the facility emitting PB

- HAPs is used to refine some of the assumptions associated with the environmental scenario while maintaining the Tier 1 ingestion exposure scenario assumptions . Th e environmental scenario assumptions are refined by incorporating binned site

- specific meteorological data and and location s of fishable lakes near the facility (see below)

. T he risk

- based threshold for each PB

- HAP isTRIM

- Based T iered Screening Methodology for RTR

Overview 2 6

- 29

- 2012 the n adjusted for that facility based on an understanding of how exposure concentrations estimated for the screening scenario change with meteorology and lake location. PB

HAP emissions that do not exceed the adjusted threshold are assumed to pose risks belo w levels of concern and no additional multipathway assessment for RTR is required. F acilities having emissions that exceed the adjusted thresholds for Tier 2 may require additional analysis

.

 For facilities emitting PB

- HAPs at levels that cannot be ruled out as being above levels of concern based on the screening analyses , a refined
, site

- specific, multipathway risk assessment can be conducted . Such an assessment would incorporate location

- or facility

- specific characteristics regarding the environment to which PB

- HAPs are emitted, relevant exposure pathways, ingestion rates or other exposure factors, and other parameters

. A range of exposure scenarios could be evaluated as part of a Tier 4 assessment, resulting in a range of risk estimates.
The key processes and decisions that make up t his approach are summarized in Exhibit 1

. In the remainder of this overview , each of the tiers in the multipathway assessment a pproach is described in additional detail. A ttachments to this appendix provide a comprehensive record of t he characteristics and methods associated with Tier 1 ( Attachment A ) and Tier 2 ( Attachment B )

. If a site

- specific analysis is conducted
, a separate report detailing that analysis will be prepared.


## 2. Tier 1

The methods used in Tier 1 are intended to enable EPA to evaluate PB

- HAP emissions from multiple sources in a particular category quickly and efficiently and to remove from consideration those that are unlikely to pose risks above levels of concern , while also minimi zing 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 avoi d underestimating exposures to PB

- HAPS that might be encountered for any location throughout the United States. The scenario also is intended to avoid grossly overestimating risk to the point where no emissions screen out (i.e., overprotective , resulting in too many “false positives”).



### 2.1 Chemicals of Concern

The assessment of risk from multipathway exposures begins with a review of data for sources in a particular category to determine if emissions of any of the following PB

- HAPs are reported:

 Cadmium compounds
,

 Chlorinated dibenzodioxins and furans (dioxins)
,

 Mercury compounds
, and

 Polycyclic organic matter (POM).TRIM

- Based T iered Screening Methodology for RTR

Overview 3 6

- 29

- 2012 Exhibit 1

. Conceptual Decision Tree for Evaluati ng Non

- Inhalation Exposures for PB

- HAPsTRIM

- Based T iered Screening Methodology for RTR

Overview 4 6

- 29

- 2012 Based on current emissions , bioaccumulation potential, and toxicity considerations, emissions of these four PB

- HAPs are expected to pose the majority of the non

- inhalation risks to humans from air emissions at sources subject to residual risk provisions of the CAA 1 Thus, although EPA has identified nine other PB

- HAPs that should be evaluated as part of residual risk assessments, the methods for multipathway assessment described here encompass only these four at this time

. If emissions of any of the four PB

- HAPs listed ab ove are reported for a facility, the emission rate for that PB

- HAP is compared to the threshold emissions rate derived for that chemical using the hypothetical TRIM

- based screening scenario. This threshold is the emission rate that, when input to the mode ls used in evaluating multipathway risk for RTR, results in a specified cancer risk or non

- cancer HQ threshold level of concern. For the screening scenario, threshold emission rate s were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0, depe nding on the more sensitive health effect of the PB

- HAP. However, due to the hypothetical nature of the screening scenario, exceeding the threshold emissions rate by 60 times, for PAH for example, does not imply a resulting cancer risk of 60 in 1 million. Rather, exceeding the threshold emissions rate by 60 times for PAH implies that it is highly unlikely that the actual risk would exceed 60 in 1 million

. 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 among the various congeners

. The details of the methods for assessing dioxins and POM are provided in Attachment A

— Technical Support Document for TRIM

- Based Multipathway Tier 1 Scenario for RTR

— to this appendix

. 

### 2.2 Development of Emission Thresholds Generally, t he approach used to assess ingestion exposure s and resulting risk s for RTR has four components ( Exhibit 2 ) :
1. F ate and transport modeling of PB

- HAPs emitted to air that partition into soil, water, and other environmental media (including fish uptake );
2. M odeling of uptake of PB

- HAPs by farm food chain media f rom soil and air; 3. E stimating ingestion exposures in terms of average daily dose for consumption of farm food items by a hypothetical exposed human; and
4. C alculating lifetime cancer risk estimates or chronic non

- cancer HQs for each HAP and corresponding screening threshold emission rates

. The TRIM.FaTE model is used in the first component , and the MIRC model is used to conduct calculations for the other three components. 2 To derive the emission thresholds used in Tier 1, these models are used to estimate the emission rate corresponding to a cancer risk of 1 in

1 Potential impacts on human health from non

- inhalation exposures to lead are evaluated for RTR using the National Ambient Air Quality Standard for lead, which takes into account multipathway risks.
2 EPA’s TRIM methodology was conceived as a comprehensive modeling framework for evaluating risks from air toxics . It w as designed to address each of the four steps in screening ingestion risk ; however, only the fate and transport module currently is ava ilable use . For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC) was constructed to complete the calculations required for estimating PB

- HAP concentrations in farm food chain media, average daily ingestion doses, and cancer risk s and chronic non

- cancer HQs. The framework is conceptually identical to the ingestion exposure and risk analyses that TRIM intended to cover. Information about the current status of TRIM modules and documentation of modules developed thus far can be accessed on EPA’s Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis website (http://www.epa.gov/ttn/fera/).TRIM

- Based T iered Screening Methodology for RTR

Overview 5 6

- 29

- 2012 1 million or a noncancer HQ of 1 (depending on the PB

- HAP) for each PB

- HAP within the hypothetical environmental scenario, as described in more detail in the following sections.

Exhibit 2

. Overview of Ingest ion Exposure and Risk Screening Evaluation Method

2.2.1 Modeling Fate and Transport To model chemical fate and transport in the environment when deriving emission thresholds for Tier 1, the TRIM.FaTE module of the TRIM system was used

. 3 The two main components of the fate and transport modeling are (1) the modeled domain, including the meteorological data and (2) the environmental and chemical

- specific properties associated with fate and transport through the environment. The hypothetica l modeled domain includes a farm homestead and a fishable lake near (i.e., 2 km) an emissions source, which are assumed to be the primary food sources for exposed individuals. The spatial layout and other physical aspects of the modeled domain configurati on are designed to be health

- protective, which results in an ingestion exposure situation that is unlikely to be exceeded at any actual facility evaluated under the RTR program. The environmental and chemical

- specific properties governing fate and transpo rt 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 positives

. (See Attachment A and A ddenda 1 and 2 for additional discussion on parameter values and their selection.) 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 assumpt ions about these two components of fate and transport modeling are refined with relatively more site

- specific data in subsequent tiers. The spatial

3 http://www.epa.gov/ttn/fera/trim_fate.htmlTRIM

- Based T iered Screening Methodology for RTR

Overview 6 6

- 29

- 2012 layout used to develop the threshold emission levels in Tier 1 and other details of the Tier 1 methodology are presented in Attachment A to this appendix.

2.2.2 Modeling Transfer and Uptake MIRC was developed to conduct the required calculations involving farm food chain tra nsfer, 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 both particle and vapor phases
,

 PB

- HAP concentrations in fish tissue for fish cons umed
, 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 algorithms based on those included in EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combust ion Facilities (EPA 2005) and biotransfer factors (e.g., soil

- to

- plant factors, which are the ratios of the concentrations in plants to concentrations in soil). The outputs of MIRC are PB

- HAP concentrat ions in contaminated food items.

2.2.3 Estimating Ingestion Exposure

MIRC is also used to estimate exposure in terms of average daily doses (ADDs)
, normalized to body weight for the following 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 old; dioxins only ) 4

Chemicals are modeled sep arately to evaluate the potential for risks, with exposures (in terms of ADD ) for each PB

- HAP summed across all ingestion exposure pathways

. For the screening scenario used in Tiers 1 and 2 of this analysis , exposure characteristics were selected that
res ult in a highly health protective estimate of total exposure

. The ingestion rate for each exposure pathway listed above was set (as feasible) equal to an upper percentile of the distribution of national data (99 th percentile for fish and 90 th percentile for all other food types) for consumers of that food type recommended by EPA in the Exposure Factors Handbook ( U.S. EPA 2011 a ) or in other sources as appropriate

. All media were assumed to be obtained from a location impacted by the modeled so urce

. T his approach results in an overestimate of total chemical exposure for a hypothetical exposure scenario

. F or example, the resulting total food ingestion rate is extremely h igh for a hypothetical consumer, with ingestion rates at the 99 th percentil e 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).

4 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown
no t to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section A.4.2.3 of Attachment A and Section 3.4 of Addendum 2 for full discussions of infant exposures via breast milk ingestion.TRIM

- Based T iered Screening Methodology for RTR

Overview 7 6

- 29

- 2012 D ermal absorption of chemicals that are originally airborne is generally relatively minor and this pathway was not included in the scenario used to calculate Tier 1 emission thresholds (this topic is discussed further in Attachment A and Addendum 3 )

. 2.2.4 Calculating Lifetime Cancer Risk and Non

- Cancer HQs Lifetime cancer risks and the potential for chronic non

- cancer effects are estimated using chemical

- specific oral can cer 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 Addendum 2, ag e
- 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 Addendum 2, the HQ for the most sensitive age group is used to determine the screening threshold emission rate.
2.2.5 Determining Threshold Emission Rates

Tier 1 emission 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 mitting these PB

- HAPs. 

## 3. Tier 2
The Tier 1 screening approach is, by design, generic and health protective

. It was constructed for quick application to a large number of facilities in a source category with the least chance of returning false negatives for risk. O nce the initial screen is complete, however, facilities that “fail” for any PB

- HAPs can be scrutinized further . Based on screening analyses conducted for RTR to date, many facilities could “fail” the Tier 1 screen for some source cat egories. However, conducting a full site

- specific analysis of all facilities that cannot be screened out in Tier 1 would not be practical

. S ite

- specific values for some influential variables , however, can be determined without intensive effort during the assessment

. The use of these site

- specific values instead of the generic values used in Tier 1 ca n be used to justify adjusting the screening threshold for a given PB

HAP at that facility, potentially eliminating the facility from concern while maintaining a high degree of confidence that risks above levels of concern have not been overlooked

. Specifically, for Tier 2, location

- specific data on two types of variables are taken into account :

 M eteorological characteristics, including the fraction of time the wind blows toward the farm and lake ( using wind direction), win d speed, precipitation rate, and mixing height ; and

 Location of the nearest fishable lake(s) relative to the facility 5 (including the absence of a fishable lake)

. These variables affect the PB

- HAP concentrations in environmental media estimated by TRIM.FaTE, but they are not related to specific exposure assumptions. The exposure

5 The lake size also was changed for each new facility lake distance , which allowed for the simulations to maintain a
c onstant ratio between watershed and erosion area compared with lake area.TRIM

- Based T iered Screening Methodology for RTR

Overview 8 6

- 29

- 2012 assumptions, such as ingestion rate and fraction of diet derived from the lake and farm remain at fixed, health

- protective values in Tier 2. In selecting the fate and t ransport variables to include in Tier 2, a balance was struck between the degree of impact on the risk estimate, the ease of implementation in TRIM.FaTE, and the ease of obtaining relatively certain site

- specific values for all facilities that might be eva luated under the RTR program

. For efficient Tier 2 evaluation of the impacts these parameters could have on specific facilities, a series of TRIM.FaTE simulations was performed that systematically varied the values used in the screening scenario for four o f the five selected variables (lake location, wind speed, precipitation rate, and mixing height). Wind direction affects only whether the chemical mass advects toward the farm and lake, so the effect of site

- specific wind directions can be evaluated outsid e TRIM.FaTE simulations. The values of each of the four variables were changed, independent of any other changes. The values (four to six for each variable, including the original screening scenario values) were selected using statistics on U.S. meteorolo gical data or professional judgment to capture the expected range in the facility data. Four to six values were selected to result in a total number of runs that was reasonable. This set of values was used to develop “bins” for each variable.

Based on t he TRIM.FaTE results of these simulations (and the subsequent exposure and risk characterization, conducted using MIRC)
, threshold adjustment factors were calculated for each unique combination of the five parameters, for each PB

- HAP. These adjustment fac tors represent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the baseline Tier 1 screening scenario and the risk metric obtained from the adjusted run. For a given facility and PB

- HAP , an adjusted Tier 2 emission threshold can be estimated by multiplying the Tier 1 emission threshold by the adjustment factor that best corresponds to the meteorological conditions present at the site and the presence and location of lakes at the site

. To facilitate the implementation of this appr oach without requiring facility

- specific data searches for each new source category evaluated, databases of the relevant U.S. meteorological and lake data were created that could be accessed readily during a Tier 2 evaluation. These databases are describe d in more detail in Attachment B. The meteorological database includes annual

- average summary statistics on wind direction, wind speed, and precipitation for more than 1,500 surface stations located throughout the United States (data available from the Nat ional Climatic Data Center). The mixing heights for each surface station were estimated using the data from the upper

- air station closest to the surface station. The mixing height data are available for more than 50 stations (data available from EPA). Th e lake database, based on U.S. Geological Survey (USGS) data and including location and size information, consisted of hundreds of thousands of water bodies classified as “Lake/Pond” or “Reservoir” but not designated for disposal, evaporation, or treatment . To focus on lakes that can support angling of upper trophic level fish, only lakes greater than 25 acres were included. These databases are described in more detail in Attachment B. When the Tier 2 screening is conducted, three additional processing steps are completed for each facility and PB

- HAP that will be analyzed in Tier 2 before looking up the appropriate adjustment factors. First, using GIS software, each relevant lake witnin a 50

- km of the facility is identified and matched to its respective directional “octant” relative to the facility. For the purposes of Tier 2, a “relevant” lake meets the size and designation criteria discussed in the previous paragraph. Second, the lake s are manually screened to remove lakes whose names indicate uses related to disposal, evaporation, or treatment (sometimes the name indicates one of these uses while the USGS designations do not). Third, the lakes around the facility that remain after the first two processing steps are screened to include only the closest lake for each octant.TRIM

- Based T iered Screening Methodology for RTR

Overview 9 6

- 29

- 2012 To access these databases, a Microsoft® Excel tool was created that merges the TRIM.FaTE Tier 2 adjustment factors with the lake and meteorology information relevan t to a specific facility from the databases. In the tool, each facility is matched with the closest meteorological station, and the values for the four relevant meteorological parameters at that station are recorded. The distances from the facility to th e nearest lakes estimated using GIS are also imported. These five values become the set of facility

- specific parameters. Then, the adjustment factors for each chemical for the combination of these five variables are determined. As described above, the T ier 1 screening emission threshold is then multiplied by the appropriate adjustment factor to obtain an updated, Tier 2 emission threshold for that PB

- HAP. More information about Tier 2 assessment methods can be found in Attachment B.


## 4. Refined Multipathwa y Assessment

If , based on results of the screening analyses, a risk assessor concludes that there is a reasonable probability that individuals could be adversely affected by the facility emissions, a refined site

- specific multipathway analysis might be per formed. Examples of recent refined multipthway analyses include assessments of two coal

- fired electric utility units conducted in support of EPA’s utility rule ( U.S. EPA 2011 c ), residual risk assessments of two secondary lead smelting facilities ( U.S. EPA 2011 b ), and a case study evaluation of a portland c ement facility included with other RTR materials presented to the Science Advisory Board for review (Appendix I of U.S. EPA 2009). Whereas a Tier 2 analysis incorporates some binned site

- specific and reg ional information on meteorology and water bodies , a refined multipathway analysis uses detailed site

- specific data to parameterize more accurately (to the extent possible) each important parameter that affects pollutant fate and transport. These site

- spe c ific properties are incorporated into model scenarios configured in TRIM.FaTE and MIRC. Important site

- specific properties likely would include emission release height and plume buoyancy, hourly meteorology (e.g., wind flow, temperature, mixing height, and precipitation)
, surface compartments based on watershed and terrain data, local farms and water bodies, land use, soil, erosion, runoff, surface water and sediment, water transfer, and aquatic ecosystems. The outputs from the site

- specific run of TRIM.FaTE (i.e., chemical concentrations in environmental media and fish) are used in MIRC to produce estimates of exposure and health risk (i.e., risk and/or HQ values). Additional analyses of t he media concentra tions, exposure estimates, and risk estimates for the various ingested media using a range of ingestion rates for each modeled PB

- HAP allows the risk assessor to understand, based on TRIM.FaTE and MIRC, the sources and pathways of possible human health ri sk from emissions of PB

- HAPs. 

## 5. References

U.S. EPA (U.S. Environmental Protection Agency) (2005 ) Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities

. EPA Office of Solid Waste. EPA

530/R

- 05

- 006. 09/2005

. U.S. EPA . (2009) Risk an d Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies

– MACT I Petroleum Refining Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and Standards. EPA

- 452/R

- 09

- 006. 06/2009.
U.S. EPA. (2011a) Exposure Factors Handbook: 2011 edition. National Center for Environmental Assessment, Washington, DC; EPA/600/R

- 09/052F. Available from theTRIM

- Based T iered Screening Methodology for RTR

Overview 10 6

- 29

- 2012 National Technical Information Service, Springfield, VA, and online at http://www.e pa.gov/ncea/efh.
U.S. EPA. (2011 b ) Human Health Multipathway Residual Risk Assessment for the Secondary Lead Smelting Source Category. Draft Report. Prepared by ICF International for EPA Risk and Exposure Assessment Group. 02/15/2011.
U.S. EPA. (2011 c ) Technical Support Document: Case Study Analyses of Potential Local

- scale Human Health Risks Associated with Mercury Emissions from Electric Utility Steam

generating Units. Draft. Prepared by ICF International for EPA Office of Air Quality Planning and Standards. 02/17/2011.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 1 6

- 29

- 2012



## Attachment A. Tier 1 Screening Methodology[ This pag e intentionally left blank. ]TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 3 6

- 29

- 2012 

## CONTENTS , ATTACHMENT A

Attachment A. Tier 1 Screening Methodology

................................ ................................ . A

- 1

A.1 Introduction

................................ ................................ ................................ ..................... A

- 6

A.2 Summary of Approach

................................ ................................ ................................ .... A

- 6 A.2.1 Overview

................................ ................................ ................................ ............... A

- 6 A.2.2 Chemicals of Potential Concern

................................ ................................ ............. A

- 9 A.2.3 Conceptual Exposure Scenario

................................ ................................ ............ A

- 10 A.2.4 Approach to Development of the Tier 1 Scenario

................................ ................. A

- 12 A.2.4.1 Modeling Framework

................................ ................................ ............. A

- 13 A.2.4.2 Model Configuration and Parameterization

................................ ............ A

- 14 A.2.5 Fate and Transport Modeling (TRIM.FaTE)

................................ ......................... A

- 14 A.2.6 Exposure Modeling and Risk Characterization (MIRC)

................................ ........ A

- 15 A.2.7 Implementation of Risk

- based Emission Scaling Factors for POM and Dioxin Emissions

................................ ................................ ................................ . A

- 16 A.2.7.1 Calculation of Scaling Factors for POM Congeners

............................... A

- 17 A.2.7.2 Calculation of Scaling Factors for Dioxin Congeners

............................. A

- 19

A.3 Description of Environmental Modeling Scenario

................................ .......................... A

- 20 A.3.1 Chemical Properties

................................ ................................ ............................ A

- 20 A.3.2 Spatial Layout

................................ ................................ ................................ ...... A

- 21 A.3.3 Watershed and Water Body Parameterization

................................ ..................... A

- 22 A.3.3.1 Water Balance

................................ ................................ ....................... A

- 22 A.3.3.2 Sediment Balance

................................ ................................ ................. A

- 23 A.3. 4 Meteorology

................................ ................................ ................................ ......... A

- 24 A.3.5 Aquatic Food Web

................................ ................................ ............................... A

- 25 A.3.6 Using TRIM.FaTE Media Concentrations

................................ ............................. A

- 27

A.4 Description of Exposure and Risk Modeling Scenario

................................ ................... A

- 28 A.4.1 Calculating Concentrations in Farm Food Chain Media

................................ ....... A

- 29 A.4.2 Ingestion Exposure

................................ ................................ .............................. A

- 2 9 A.4.2.1 Exposure Scenarios and Corresponding Inputs

................................ ..... A

- 29 A.4.2.2 Calculating Average Daily Doses

................................ ........................... A

- 32 A.4.2.3 Infant Ingestion of Breast Milk

................................ ................................ A

- 32 A.4.3 Calculating Risk

................................ ................................ ................................ ... A

- 32 A.4.4 Summary of Tier 1 Assumptions

................................ ................................ .......... A

- 33

A.5 Evaluation of Screening Scenario

................................ ................................ ................. A

- 37 A.5.1 Introduction

................................ ................................ ................................ .......... A

- 37 A.5.2 Cadmium Compounds

................................ ................................ ......................... A

- 37 A.5.2.1 Behavior in the Environment

................................ ................................ .. A

- 37TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 4 6

- 29

- 2012

A.5.2.2 Concentrations in Ingestible Products

................................ .................... A

- 37 A.5.2.3 Average Daily Dose (ADD)

................................ ................................ .... A

- 38 A.5.3 Mercury Compounds

................................ ................................ ........................... A

- 38 A.5.3.1 Behavior in the Environment

................................ ................................ .. A

- 38 A.5.3.2 Concentrations in Ingestible Products

................................ .................... A

- 40 A.5.3.3 Average Daily Dose

................................ ................................ ............... A

- 40 A.5.4 Dioxins

................................ ................................ ................................ ................. A

- 40 A.5.4.1 Behavior in the Environment

................................ ................................ .. A

- 40 A.5.4.2 Concentrations in Ingestible Products

................................ .................... A

- 42 A.5.4.3 Lifetime Average Daily Dose (LADD)

................................ ..................... A

- 42 A.5.5 Polycyclic Aromatic Hydrocarbons

................................ ................................ ....... A

- 42 A.5.5.1 Behavior in the Environment

................................ ................................ .. A

- 43 A.5.5.2 Concentrations in Ingestible Products

................................ .................... A

- 44 A.5.5.3 Lifetime Average Daily Dose

................................ ................................ . A

- 44 A.5.6 Summary

................................ ................................ ................................ ............. A

- 44

A.6 References

................................ ................................ ................................ ................... A

- 45

Addendum 1. TRIM.FaTE Inputs

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

- 1

Ad dendum 2. Description of Multimedia Ingestion Risk Calculator (MIRC) Used for RTR Exposure and Risk Estimates

................................ ................................ ... 2

- 1

Addendum 3. Dermal Risk Screening

................................ ................................ ............ 3

- 1TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 5 6

- 29

- 2012 EXHIBITS , ATTACHMENT A
Exhibit_Att A

- 1. Conceptual Decision Tree for Evaluatio n of Non

- Inhalation

Exposures of PB

- HAPs

................................ ................................ ..................... A

- 7 Exhibit_Att A

- 2. Emission Thresholds for Screening of Multipathway Exposures Error! Bookmark not defined. Exhibit_Att A

- 3. OAQPS PB

- HAP Compounds

................................ ................................ ..... A

- 10 Exhibit_Att A

- 4. Overview of Ingestion Exposure and Risk Screening Evaluation

Method

................................ ................................ ................................ ............ A

- 13 Exhibit_Att A

- 5. Overview of Process Carried Out in the Multimedia Ingestion

Risk Calculator

................................ ................................ ................................ A

- 16 Exhibit_Att A

- 6. Exposure, Toxicity, and Risk Equivalency Fac tors Relative to

BaP for Modeled POM Congeners

................................ ................................ .. A

- 18 Exhibit_Att A

- 7. Relationship between Exposure and Kow for POM Congeners

................... A

- 19 Exhibit_Att A

- 8. Exposure and Toxicity Equivalency Factors Relative to TCDD

for Modeled Dioxin Congeners

................................ ................................ ........ A

- 20 Exhibit_Att A

- 9. TRIM.FaTE Surface Parcel Layout

................................ ............................. A

- 21 Exhibit_Att A

- 10. Summary of Key Meteorological Inp uts

................................ ..................... A

- 25 Exhibit_Att A

- 11. Aquatic Biota Parameters for the TRIM.FaTE Screening

Scenario

................................ ................................ ................................ ......... A

- 27 Exhibit_Att A

- 12. Spatial Considerations

– TRIM.FaTE Results Selected for

Calculating Farm Food Chain Media Concentrations and Receptor Exposures

................................ ................................ ................................ ....... A

- 28 Exhibit_Att A

- 13. Summary of Ingestion Exposure Pathways and Routes of

Uptake

................................ ................................ ................................ ............ A

- 30 Exhibit_Att A

- 14. Overview of Exposure Factors Used for RTR Multipathway

Screening a,b

................................ ................................ ................................ .... A

- 31 Exhibit_Att A

- 15. Dose

- response Values for PB

- HAPs Addressed by the

Screening Scenario

................................ ................................ ......................... A

- 33 Exhibit_ Att A

- 16. Summary of RTR Screening Scenario Assumptions

................................ . A

- 33 Exhibit_Att A

- 17. Estimated Contributions of Modeled Food Types to C admium

Ingestion Exposures and Hazard Quotients

................................ .................... A

- 39 Exhibit_Att A

- 18. Estimated Contributions of Modeled Food Types to Methyl

Me rcury Ingestion Exposures

................................ ................................ .......... A

- 41 Exhibit_Att A

- 19. Estimated Contributions of Modeled Food Types to Dioxin

Ingestion Exposures

................................ ................................ ....................... A

- 43 Exhibit_Att A

- 20. Estimated Contributions of Modeled Food Types to PAH

Ingestion Exposures

................................ ................................ ....................... A

- 45TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 6 6

- 29

- 2012 

## A.1 Introduction As discussed in the Overview Document, the U.S. Environmental Protection Agency (EPA) will implement a tiered approach to evaluate multipathway exposures and human health ri sks for the Risk and Technology Review (RTR) program

. EPA’s a ssessment for RTR focuses on persistent and bioaccumulative hazardous air pollutants ( PB

- HAPs ) that the Office of Air Quality Planning and Standards (OAQPS) has identifi ed as candidates for multipathway risk assessments (selection of the PB

- HAPs is discussed in Section A.2.2 ). In the first tier, a screen is conducted that focuses on the identity and m agnitude of emissions of PB

- HAPs ,from a given facility to determine whether a facility passes certain human health risk

- based criteria. Sources that are “screened out” in the Tier 1 analysis are assumed to pose no risks to human health above levels of con cern and and are not considered in further analyses. For sources that do not pass the Tier 1 screen, more refined assessments, up to and including site

- specific multipathway assessments, can be conducted as appropriate.

This document describes the tec hnical basis for the first, screening

- level tier of EPA’s multipathway human health evaluation of PB

- HAP emissions from RTR sources. Specifically, the scenarios, models, configurations, and inputs used to derive screening threshold emission rates in the f irst tier of the approach are described in detail in the following sections.

 Section A.2 presents an overview of how screening is conducted in Tier 1, the chemicals and exposure scenario evaluated in Tier 1, and the models and methods used to conduct the screen.

 Sections A.3 and A.4 present technical descriptions of the hypothetical environmental setting and the exposure modeling scenario used in Tier 1 as well as the models used in the screen.

 Section A.5 provides a brief discussion of the screening threshold emissions for each of the chemicals assessed. References cited in this report are listed in Section A.6

. More refined multipathway risk screening methods, may be used for facilities that do not screen out of the Tier 1 analysis. The Tier 2 screen is discussed separately in Attachment B

. 

## A.2 Summary of Approach

A.2.1 Overview The Tier 1 approach for evaluating non

- inhalation, multipathway exposures to PB

- HAPs for RTR is diagrammed in Exhibit_Att A

- 1 . Air toxics emitted by a source under consideration are reviewed to determine, first, whether emissions are reported for any of the four PB

- HAPs of concern for non

- inhalation pathways. If such emissions are reported, the emission rates are compared to Tier 1 threshold

- screening emission rates that have been derived using the TRIM

based Tier 1 scenario described in this documen t (see Exhibit_Att A

- 2 for threshold screening emission rates )

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 7 6

- 29

- 2012 Exhibit_Att A

- 1

. Conceptual Decision Tree for E valuation of Non

- Inhalation Exposures of PB

- HAPsTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 8 6

- 29

- 2012 The TRIM

- based multipathway modeling configuration, referred to in this document as the Tier 1 scenario, forms the technical basis for determining the Tier 1 emission thresholds. The term
, Tier 1 scena rio
, is used to refer collectively to the specific TRIM.FaTE and exposure modeling configuration described here in , including the set of assumptions and input values associated with a hypothetical watershed and the exposure and risk scenarios evaluated for this watershed

. The Tier 1 scenario is a static configuration , and its primary purpose is as a modeling tool to calculate the Tier 1 emission rate thresholds for PB

- HAPs of concern

. The two potential outcomes of the Tier 1 evaluation are :

 Non

- inhalation exposures are unlikely to pose a human health problem (i.e., the emissions evaluated “pass” the screen); or

 Risks above the levels of concern from n on
- inhalation exposure s cannot be ruled out

. An ideal screening approach strikes a balance between being health

- protective

— to ensure that risks above levels of concern are identified
, and being accurate

— to minimize results suggesting that additional analysis is required when in fact the actual risk is low . Typically, gains in accuracy in envir onmental modeling are accompanied by additional resource requirements. Stated another way, a suitable approach minimizes both false negatives and false positives. False negatives (i.e., results that suggest that the risk is acceptable when in fact the ac tual risk is high) can lead to inappropriate and non

- protective health or environmental policy decisions. False positives (i.e., results that suggest more assessment is required when in fact the actual risk is low) can result in wasted resources by leadin g to additional, unnecessary analysis. For the evaluation of multipathway human health exposures to PB

- HAPs, the methods for screening described in this document are intended to achieve this balance.

Because the Tier 1 evaluation enables EPA to confidently eliminate from consideration those facilities where risks from non

- inhalation exposures are projected to be minimal
, resources can be target ed toward th os e facilities that do not pass the screening test . For facilities that do not pass the Tier 1 screening, in additional tiers of analysis, some of the Tier 1 parameters are reassessed, and if appropriate, are changed to more accurately reflect site

- specific characteristics. With each successive tier of the assessment, additional Tier 1 a ssumptions are evaluated and refined to better reflect site

- specific characteristics of the facility being modeled. The Tier 1 screening evaluation for RTR compares reported air emission rates of PB

- HAPs (summed by PB

- HAP for each facility) to screening threshold emission rates derived using the Tier 1 scenario. A threshold emission rate is the level that, when input to a risk model us ing emissions as a parameter, corresponds to a specified cancer risk or non

- cancer hazard quotient ( HQ ) that, for the purposes of the evaluation being conducted, is assumed to be below a level of
concern. Tier 1 t hreshold emission rates were calculated for a cancer risk of 1 in 1 million or an HQ of 1.0 and are presented in Exhibit_Att A

- 2

. 6 Conceptually, a threshold level for the RTR multipathway screening evaluation could be obtained by back

- calculating the emission rate that results in the specified cancer risk or HQ level, taking into account the exposure and fate and transport calculations included in the model. Because the models used in this assessment are not designed to run “backwards,” the rates instead were derived from regression equations established fol lowing a series of TRIM.FaTE and exposure/risk model runs spanning a wide

6 For chemicals known to cause both cancer and chronic non

- cancer impacts, and for which acceptable quantitative dose

- response values are available for both cancer and non

- cancer endp oints, the endpoint that results in the lower threshold emission level is used for screening (i.e., the threshold will be based on the effect that occurs at the lower exposure level). For the set of PB

- HAPs for which screening threshold levels have been d erived, only chlorinated dibenzo

- dioxins and

- furans meet both criteria. Because the cancer dose

- response value is lower than that for
non
- cancer effects, the screening threshold value is based on the cancer endpoint.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 9 6

- 29

- 2012 range of emission rates for each chemical. The estimated screening

- level emission rates are verified by performing model runs using the estimated threshold emission rate to confirm that the emission rates result in a cancer risk of 1 in 1 million or an HQ of 1.0.
Exhibit_Att A

- 2

. Emission Thresholds for Screening of Multipathway Exposures

Chemical Screening Threshold Emission Ra te (TPY) Basis of Threshold

(Type of Health Endpoint)

POM (as b enzo(a)pyrene equivalents) a 3.09E

− 03 Cancer Dioxins (as 2,3,7,8

- TCDD equivalents) a 6.38E

- 09 Cancer Cadmium 7

. 00 E

− 0 2 Non

- c ancer Divalent m ercury 1.94E

− 03 Non

- c ancer T PY = U.S. short tons per year a See Section A.2.7 for a discussion on the derivation of equivalent emissions. The more probable risk for each emission rate would be lower than the level corresponding to the screening threshold risk quantitie s 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 for the simulated Tier 1 scenario at equivalent emission rates. This is because the Tier 1 scenario assumes

– for each risk influential parameter in the model

– parameter values that result in high

- end risk estimates

. I n the real world, the probability of such risk

- maximizing conditions prevailing across multiple parameters is very low. For example, the Tier 1 scenario assumes a fishable lake approximately 2 km from any given facility, when in reality, a lake may be greater than 50 km away. Additional conservative assumptions used in the Tier 1 screen are described in Section A.4.4 of this Attachment.

Tier 1 emission thresholds were developed individually for elemental and divalent mercury. Both were based on the lower of the thresholds associated with multipathway exposures to divalent mercury and methyl mercury. 7 Only speciated emissions of divalent mercury are screened because the sum of elemental mercury emissions across all Na tional Emission Inventory (NEI) facilities is less than the elemental mercury screening threshold level. See Section A.5.3 for a detailed discussion of mercury.



### A.2.2 Chemicals of Potential Concern EPA’s a ssessment of multipathway human exposures for RTR focuses on PB

- HAPs 8 that the Office of Air Quality Planning and Standards (OAQPS) has identifi ed as candidates for multipathway risk assessments

. OAQPS developed a li st of 14 chemicals and chemical groups

7 Note that TRIM.FaTE models the trans formation of mercury within the environment; thus, emissions of both divalent and elemental mercury will result in multipathway exposures to elemental mercury, methyl mercury, and divalent mercury. 8 Although POM (polycyclic organic matter) is the name of the HAP listed in the Clean Air Act, the term “polycyclic aromatic hydrocarbons” or PAHs is used in many cases. Much of the literature regarding toxicity and fate and transport of this chemical group refers to PAHs rather than POM. In addition, the in dividual POM species that are of concern with respect to health risk for RTR evaluations are all PAHs (i.e., there are no POM species explicitly evaluated for RTR that do not include an aromatic ring). The terms are used interchangeably throughout this te xt.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 10 6

- 29

- 2012 that are PB

- HAPs based on a two

- step process taking into account the following (U.S. EPA 2004a):

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

 a semiquantitative ranking of toxicity and bioaccumulation potential of the entire list of HAPs.

The list’s development and utility in hazard identification for multipathway risk assessment are explained further in Chapter 14 and Appendix D of Volume I of EPA’s Air Toxics Risk Assessment (ATRA) Reference Library ( U.S. EPA 2004a )

. Exhibit_Att A

- 3 presents t he 14 chemicals and groups that are PB

- HAPs

. Exhibit_Att A

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

a Source of list: U.S. EPA (2004a). Compounds in bold text can be evaluated using the current version of the TRIM

based screening scenario. The screening scenario described in this document is not configured for evaluating the risk potential for all 14 PB

- HAPs on the list

. Currently, the scenario can only be used to estimate exposures and risks quantitatively for 4 of the 14 PB

- HAPs ( as indicated in Exhibit_Att A

- 3 )

. These four PB

- HAPs are the focus of the current scenario because, based on current emissions , bioaccumulation potential, and toxicity considerations, they are expected to pose the vast majority of the non

- inhalation risks to humans from air emissions at sources subject to residual risk provisions of the Clean Air Act

. 9



### A.2.3 Conceptual Exposure Scenario A conceptual exp osure scenario was developed that encompasses the specific exposure routes and pathways of interest for the four PB

- HAPs that are assessed in the Tier 1 analysis.

9 Potential impacts on human health from non

- inhalation exposures to lead are evaluated for RTR using the National Ambient Air Quality Standard for lead, which takes into account multipathway risks. Non

- inhalation exposures to the other nine PB

- HAPs not addressed by the modeling scenario discussed in this report will be evaluated on an individual facility or source category basis as appropriate

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 11 6

- 29

- 2012 Exposure routes and pathways describe the movement of air toxics from the point of release t o the point where exposure occurs and generally consist of the following elements:

 Release to the environment (i.e., emissions);

 A retention medium, or a transport mechanism and subsequent retention medium in cases involving media transfer of chemicals;

 A point of potential human contact with the contaminated medium; and

 An exposure route. An exposure route is the particular means of entry into the body. Receptors are exposed to chemicals emitted to the atmosphere via two primary routes: either directly via inhalation, or indirectly via ingestion or dermal contact with various media that have been contaminated with the emitted PB

- HAPs. (Inhalation pathways are assessed separately and are not considered in the Tier 1 assessment presented here.)

PB

- HAPs c an persist in the environment for long periods of time and also build up in soil and in the food chain, including fish, fruits and vegetables, and animal products (e.g., meat, dairy, eggs). For this reason, ingestion of foods grown within an area impacted by RTR sources can be an important source of exposure to PB

- HAPs.
To assess risks from hazardous waste combustion facilities, EPA identifie d several hypothetical receptor scenarios, noting that these scenarios are considered appropriate for a broad range of situations, rather than to represent any actual scenario. These scenarios are described in EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP ( U.S. EPA 2005a) . In this document, EPA recommends assessment o f the following hypothetical receptors: a Farmer, Farmer Child, Resident, Resident Child, Fisher
, Fisher Child, Acute Receptor, and Nursing Infant. These receptors are distinguished by their pathways of exposures. EPA further notes in HHRAP that some exp osure settings might warrant including additional exposure pathways; such as including exposure through fish ingestion for the farmer receptor. For the RTR screening scenario, risks are assessed for a single hypothetical receptor. Based on the guidance p rovided in HHRAP, a health protective exposure scenario was developed whereby the hypothetical receptor receives ingestion exposure via both the farm food chain and the fish ingestion pathways. The exposure scenario for the RTR Tier 1 analysis includes th e following ingestion pathways:

 Incidental ingestion of soil,

 Ingestion of homegrown fruits and vegetables,

 Ingestion of homegrown beef,

 Ingestion of dairy products from homegrown cows,

 Ingestion of homegrown poultry and eggs,

 Ingestion of homegrown pork,

 Ingestion of locally caught fish, and

 Ingestion of breast milk (for children less than 1 year old and for dioxins only ). 10

10 Breast milk ingestion is an important exposure pathway for lipophilic compounds like dioxins and has been shown not to contribute meaningfully for exposures to mercury, cadmium, and POM. See Section A.4.2.3 of this attachment and Section 3.4 of Addendum 2 to this attachment for full discussions of infant exposures via breas t milk ingestion.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 12 6

- 29

- 2012 As discussed in detail in Section A.4.2 , exposure via these pathways is assessed for adults, various age categories for children, and nursing infants (for dioxins only).
Other n on
- inhalation exposure routes of possible concern for PB

- HAPs discussed in HHRAP include the use of surface waters as a drinking water source and dermal exposure. These exposure routes, however, are not evaluated in the current assessment

. The drinking water exposure pathway is not likely for the modeling s cenario developed for th is analysis 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 ( U.S. EPA 2006
, Cal / EPA 2000) . Preliminary calculations of estimated dermal exposure and risk of PB

- HAP s , presented in Addendum 3 to this attachment
, showed that the dermal exposure route is not a significant risk pathway relative to ingestion exposures.



### A.2.4 Approach to Development of the Tier 1 Scenario The TRIM

- based Tier 1 scenario described in this document is used to provide a means to qualitatively estimate the potential for non

- inhalation risks above the levels of concern for PB

HAPs emissions from facilities in the context of residual risk assessment s conducted as part of RTR. The Tier 1 scenario used to derive the threshold emission rates is not intended to be representative of any particular situation

. Rather, it was developed for the purpose of RTR to portray a hypothetical exposure scenario 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 p otential long

- term exposure levels for a given geographic region would be reasonably unlikely to exceed those of the Tier 1 configuration

. These criteria were met by constructing a hypothetical scenario that would be health

- protective in key aspects, incl uding spatial orientation, meteorology, 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 latte r 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 ( U.S. EPA 1999) ; Volumes I and II of the Air Toxics Risk Assessment Reference Library ( U.S. EPA 2004 a, 2005 a ) ; and other EPA publications

. The scenario described in this appendix is the culmination of analyses 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 pe rform Tier 2 and Tier 3 analyses, as described in Attachment B and Error! Reference source not found. , respectively. Nevertheless, this scenario should not be considered “final” but rather a product that can continue to evolve based on feedback from the scientific community and Agency review ers, lessons learned as the scenario is further applied for RTR, variations in EPA’s needs and requirements, and other factors.



### A.2.4.1 Modeling Framework The approach for multipathway risk screening and evaluation for RTR can be divided into four steps :

11 An exception to this generality would be reservoirs used for drinking water supplies. This situation might be worthy of additional analysis, if warranted by the characteristics of a given assessment (e.g., to estimate PB

- HAP concentrati ons in treated drinking water derived from reservoirs).TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 13 6

- 29

- 2012 1. Fate and transport modeling of PB

- HAPs emitted to air by the source that partition into soil, water, and other environmental media (including 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;
3. Estimating exposures from ingestion of selected media and estimating average daily ingestion doses for a hypothetical human receptor; and
4. Calculating lifetime cancer risk estimates or chronic non

- cancer HQs
, as appropriate
, for each PB

- HAP and comparing these to selected evaluation criteria

. The relationship among these four processes is shown in Exhibit_Att A

- 4

.

As shown in Exhibit_Att A

- 4 , two models are used to evaluate the four steps outlined above. EPA’s TRIM methodology was conceived as a comprehensive modeling framework for evaluat ing risks from air toxics, and the TRIM system was designed to address each of the four steps involved in screening ingestion risk. 13 Currently , however, only one component corresponding to the first step included in Exhibit_Att A

- 4

— the fate and transport module

— is available for application in an ingestion risk assessment

. EPA has completed some development activities for TRIM.Expo

- Ingestion and TRIM.Risk

- Human Health , two additional modules that cover the other three steps. M odeling software , however, is not currently available for these modules

. For the RTR screening scenario, the Multimedia Ingestion Risk Calculator (MIRC), a Microsoft Excel

- based comput ing framew ork
, was constructed to complete the calculations required for estimating PB

- HAP concentrations in farm food chain media, average daily ingestion doses, and cancer risks and chronic non

- cancer HQs

. This framework is

12 As discussed below, concentrations in fish calculated by the TRIM.FaTE model were used to estimate ingestion exposures for humans consuming fish. Modeling of fish concentrations is therefore discuss ed herein as part of the fate and transport modeling. Uptake of PB

- HAPs into all other biotic media assumed to be ingested is modeled in the second step of the modeling framework.
13 Information about the current status of TRIM modules and comprehensive documentation of modules developed thus far can be accessed on EPA’s Technology Transfer Network (TTN) on the Fate, Exposure, and Risk Analysis website ( http://www.epa.gov/ttn/fera/ ).

Exhibit_Att A

- 4 . Overview of Ingestion Exposure and

Risk Screening Evaluation MethodTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 14 6

- 29

- 2012 conceptually identical to the ingestion exposure and risk analyses that TRIM is intended to
cover.


### A.2.4.2 Model Configuration and Parameterization Th e Tier 1 scenario is intended to reduce the possibility that EPA would underestimate potential multipathway human health risks

. Although the he alth protective approach likely overestimates risk, EPA determined that this approach is appropriate for the purposes of an initial multipathway screening assessment

. As was done with the preliminary multipathway screening for RTR conducted in 2006 (U.S. EPA 2006) , exposures were modeled for a hypothetical farm homestead and fishable lake located adjacent to an emissions source

. The hypothetical individual for which exposures were calculated was assumed to derive all potentially contaminated foodstuffs fr om 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 screeni ng scenario was parameterized with two types of values. One type is typical values, such as national averages. The second type is health

- protective values, or values that would tend to overestimate concentrations in media driving ingestion exposures for humans, based on knowledge of exposure patterns. In general, the spatial and temporal aspects of the scenario and the components of the scenario that influence air concentrations and deposition rates (which in turn affect all other exposures) were defined to be health protective. Chemical

- specific and non

- chemical

- specific properties of the environmental media were parameterized with either typical or health protective values; properties having greater uncertainty were assigned a greater level of health p rotective bias. The spatial layout of the Tier 1 scenario and the meteorological data (or a combination of these two factors) are generally more influential than physical/chemical parameters in dictating the screening model outcomes , taking into account th e 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 i n soil, water, and biota . In contrast, relatively large change s in soil characteristics within the range of possible values (e.g., organic carbon content, water content) typically result in relatively small changes in media concentrations.

The mix of health protective and central

- tendency assumptions and parameterization is expected to result in a scenario configuration that, on average, is likely to overpredict environmental concentrations of PB

- HAPs in media of interest for this evaluation

. Given th e 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 incl usion of central

- tendency values where warranted is intended to minimize the number of false positives, some false positives are to be expected from a screening scenario . False positives are addressed in subsequent tiers of the screening evaluation for a particular source

. 

### A.2.5 Fate and Transport Modeling (TRIM.FaTE) The fate and transport modeling step depicted in the first box in Exhibit_Att A

- 4 is implemented for RTR us ing 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 TRIM.FaTE was used to model the fate and transport of emitted PB

- HAPs and to estimate concentrations in

14 TRIM.FaTE is a spatially e xplicit, compartmental mass balance model that describes movement and transformation of pollutants over time, through a user

- defined, bounded system that includes both biotic and abiotic compartments

. Outputs include pollutant concentrations in multiple e nvironmental media and biota

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 15 6

- 29

- 2012 r elevant media

. Additional information about TRIM.FaTE, including support documentation, software, and the TRIM.FaTE public reference library, is available at http://www.epa.gov/ttn/fera/

. The algorithms used to model mercury species and polyaromatic hydrocarbons (PAHs) are described in Volume II of the TRIM.FaTE Technical Support Document (U.S. EPA 2002a). A comprehensive evaluation of the performance of TRIM.FaTE for modeling mercury was documented in Volumes I and II of the T RIM.FaTE Evaluation Report (U.S. EPA 2002b, 2005b). Algorithms specific to the fate and transport of 14 chlorinated dibenzo

- dioxin and

- furan congeners were added following the addition of those for mercury and PAHs. Documentation of the application of TRIM.FaTE for dioxin emissions is contained in the third volume of the TRIM.FaTE Evaluation Report (U.S. EPA 2004b). More recently (largely as part of this current project), the TRIM.FaTE public reference library was updated to include information on modeling for cadmium

. In general, many of the algorithms and properties included in the public reference library that are used to model mercury ( except for the mercury transformation algorithms) are also applicable to cad mium . 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) are documented in Addendum 1 to this attachment . Based on a thorough 2011 evaluation of TRIM.FaTE performance in modeling mercury’s fate, transport, and transformation in the aquatic fo od 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 Secti on A.3

. 

### A.2.6 Exposure Modeling and Risk Characterization (MIRC) The algorithms included in MIRC that calculate chemical concentrations in farm food chain media and inges tion exposures for hypothetical individuals were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or HHRAP ( U.S. EPA 2005a). 15 These algorithms, and the required exposure factors and other parameter values, were compiled into a database. An overview of the computational processes this tool carrie s out and the types of input data it require s is presented in Exhibit_Att A

- 5

. 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 risk calculations carried out using MIRC. Additional discussion of exposure and risk calculations for the Tier 1 scenario is presented in Section A.4 and Addendum 2 to this attachment , and all inputs required by these calculations are documented in Addendum 2

.

15 The farm food chain calculations and ingestion exposure equations to be included in the TRIM.Expo software are expected to be very similar to those included in HHRAP.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 16 6

- 29

- 2012

Exhibit_Att A

- 5

. Overview of Process Carried Out in the

Multimedia Ingestion Risk Calculator



### A.2.7 Implementation of Risk

- based Emission Scaling Factors for POM and Dioxin Emissions Two of the four PB

- HAPs for which screening emission thresholds have been developed for RTR

— POM and dioxins

— are chemical groups comprising numerous individual entities. The members of these categories reported in NEI include both specific chemicals and groups containing multiple chemicals. For example, for POM, emissions reported in NEI include various species, such as benz[a]anthracene, 2

- methylnaphthalene, and chrysene, as well as
non
- specific entries, such as “PAH, total.” The constituents included in the POM and dioxin PB

- HAP categories are grouped together not only because they are types of the “same” HAP, but also because members of these groups are assumed to have similar characteristics with respect to toxicity and 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 for POM and dioxins are benzo[a]pyrene and 2,3,7,8

- TCDD, respectively . T hese compounds were selected because they are relatively well

studied among the members of the two groups and are also among the most toxic species within each group.

Derivation of a ppropriate scaling factors begins with an evaluation of the basic relationship used to characterize health risk:

Risk 

# ∝ ( Exposure Concentration) × (Toxicity)

For a given air pollutant, the incremental exposure concentration is directly proportional to the emissions of that substance. That is, as the emissions increase, so too does the exposure toTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 17 6

- 29

- 2012 that substance. Furthermore, toxicity is assumed to increase linearly with concentration. Consequently, emissions of one substance (e.g., c hrysene) can be scaled proportional to a reference compound (e.g., benzo(a)pyrene or BaP ) by applying weighting factors corresponding to the relative differences in exposure behavior and toxicity. Using the POM group as an example and BaP as the reference compound, this scaling can be expressed through an equation as follows:

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)
E miss PAHi = Emission rate 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 route 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 fo r a given exposure scenario. The TEF for each PAH and dioxin species can be calculated on the basis of relative toxicities. Toxicities were not evaluated separately for RTR but are based on analyses conducted by EPA elsewhere. For PAHs, oral toxicity values for individual species ha ve been derived following the same approach used to develop inhalation toxicity values. For dioxins, TEFs are based on the relative toxicities developed by EPA recently and are ultimately based on the values developed by the World Health Organization (van der Berg et al. 2006). Refer to Attachment B for more information on these values.

The EEFs can be calculated directly for each individual chemical that can be modeled in TRIM.FaTE and MIRC. TRIM.FaTE is configured for 14 POM 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 th is 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 l ifetime average daily dose. The specific calculations for EEFs and exposure surrogates for each chemical group are discussed in the sections that follow.



### A.2.7.1 Calculation of Scaling Factors for POM C ongeners The calculated EEFs , TEFs, and total REFs for the 14 POM congeners that are configured in TRIM.FaTE , plus 15 others not configured in TRIM.FaTE, are shown in Exhibit_Att A

- 6

. To determine appropriate exposure surrogat es for chemicals not parameterized in TRIM.FaTE, EPA evaluated the relationships between chemical

- specific properties (e.g., K ow and Henry’s law constant, k h ) and intermediate modeled values (e.g., deposition , soil concentration ) and exposure in terms of l ifetime average daily dose (LADDs) where the average daily doses (ADDs) for the youngest two age groups were adjusted by the age

- dependent adjustment factors (ADAFs) to account for the mutagenic mode of action of PAHs. The correlation between K ow and expo sure is stronger than for any other chemical

- specificTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 18 6

- 29

- 2012 property. Exposure surrogates were thus identified for each congener by calculating Total Lifetime Average Daily Dose (Age Adjusted) for each based on the congener’s K ow and the power regression of th e modeled PAHs. Exhibit_Att A

- 7 shows that as K ow increases, so too does exposure.
Exhibit_Att A

- 6

. Exposure, Toxicity, and Risk Equi valency Factors Relative to BaP for POM Congeners Currently Evaluated in Tiers 1 and 2 Analyses

Chemical Fully Parameterized in TRIM.FaTE? Exposure

Equivalency Factor (EEF) Toxicity

Equivalency Factor (TEF) a Risk

Equivale ncy Factor (REF) Dibenzo(a,i)pyrene n 22.06 16.44 362.61 7,12

- Dimethylbenz(a)anthracene Y 1.91 34.25 65.31 3

- Methylcholanthrene n 2.75 3.01 8.28 Dibenz(a,h)anthracene Y 4.56 0.56 2.56 Benzo(k)fluoranthene Y 7.00 0.16 1.15 Benzo(a)pyrene Y 1 1 1 Benzo(b)flouranthene Y 4.38 0.16 0.72 Indeno(1,2,3

- cd)pyrene Y 3.10 0.16 0.51 PAH, total n 3.34 0.068 0.23 Polycyclic Organic Matter n 3.34 0.068 0.23 Benzo(g,h,i)perylene Y 2.91 0.068 0.20 Benzo(e)pyrene n 2.88 0.068 0.20 Retene n 2.32 0.068 0.16 Dibenzo(a,j)acridine n 0.41 0.16 0.067 Perylene n 0.64 0.068 0.044 Benzo(a)anthracene Y 0.075 0.16 0.012 Chrysene Y 0.18 0.016 0.0030 2

- Acetylaminofluorene n 0.0014 1.37 0.0019 Fluoranthene Y 0.019 0.068 0.0013 Acenaphthylene Y 0.012 0.068 0.00083 2

- Chloronaphthalene n 0.011 0.068 0.00075 Fluorene Y 0.010 0.068 0.00067 Acenaphthene Y 0.0087 0.068 0.00060 1

- Methylnaphthalene n 0.0057 0.068 0.00039 2

- Methylnaphthalene Y 0.0052 0.068 0.00036 Carbazole n 0.0040 0.0027 0.000011 Anthracene n 0.023 0 0 Phenanthrene n 0.024 0 0 Pyrene n 0.066 0 0

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 Addendum 2 to this attachment

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 19 6

- 29

- 2012 For POMs reported as unspeciated groups (i.e., “PAH, total” and “Polycyclic Organic Matter”) EPA assigned surrogates with 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 predi ct exposure

.

Exhibit_Att A

- 7 . Relationship between Exposure and Kow

for POM Congeners



### A.2.7.2 Calculation of Scaling Factors for Dioxin Congeners The calculated EEFs, TEFs, and REFs for the 17 dioxin congeners that are configured in TRIM.FaTE are presented in Exhibit_Att A

- 8

. Although there are many dioxins rep orted 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 surrog ate 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 TCDD. This approach could be improved by obtaining information on the speciation of dioxin emissions for each facility or an average speciation profile that could be assumed to apply to all facilities in a source category.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 20 6

- 29

- 2012 Exhibit_Att A

- 8 . Exposure and Toxicity Equivalency Factors Relative to TCDD for Modeled Dioxin Congeners

Chemical Exposure Equivalency Factor (EEF) Toxicity

Equivalency Factor (TEF) a Risk

Equivalency Factor (REF) PentaCDD, 1,2,3,7,8

- 3.96 1.0 3.96 TetraCDD, 2,3,7,8

- 1.0 1.0 1.0 Dioxins, Total, w/o Indiv. Isomers Rptd. 1.0 1.0 1.0
Dioxins 1.0 1.0 1.0 2,3,7,8

- TCDD TEQ 1.0 1.0 1.0 HexaCDD, 1,2,3,4,7,8

- 1.65 0.10 0.17 PentaCDF, 2,3,4,7,8

- 0.50 0.30 0.15 HexaCDD, 1,2,3,6,7,8

- 1.1 0 0.1 0.11 HexaCDF, 1,2,3,7,8,9

- 0.64 0.1 0.064 HexaCDF, 1,2,3,6,7,8

- 0.6 2 0.10 0.06 2 HexaCDD, 1,2,3,7,8,9

- 1.1 0 0.041 0.046 HexaCDF, 1,2,3,4,7,8

- 0.42 0.1 0.042 HexaCDF, 2,3,4,6,7,8

- 0. 58 0.1 0. 0 58 PentaCDF, 1,2,3,7,8

- 0.61 0.03 0.018 TetraCDF, 2,3,7,8

- 0.16 0.1 0.016 HeptaCDD, 1,2,3,4,6,7,8

- 1.1 9 0.01 0.012 HeptaCDF, 1,2,3,4,6,7,8

- 0.33 0.01 0.0033 HeptaCDF, 1,2,3,4,7,8,9

- 0.30 0.01 0.0030 OctaCDD, 1,2,3,4,6,7,8,9

- 0.45 0.0003 0.00014 OctaCDF, 1,2,3,4,6,7,8,9

- 0.37 0.0003 0.00011

a Values from Van den Berg et al. (2006), except for 1,2,3,7,8,9

- hexaCDD, which is calculated based on the ratio of the IRIS

based CSF for 1,2,3,7,8,9

- hexaCDD to the IRIS

- based CSF for 2,3,7,8

- TCDD. Dose response values, including CSFs, that are used in the screening assessment are discussed in Section 4 of Addendum 2 to this a ttachment

. 

## A.3 Description of Environmental Modeling Scenario As described in Section A.2.4.2 , the physical configuration of the RTR Screening Scenario was designed to encompass the upper end of potential long

- term PB

- HAP exposures, and the environmental and chemical

- specific properti es were parameterized with either health protective or central

- tendency values

. Information regarding the scenario configuration and important aspects of the parameterization process, justifications for selecting particular property values, and model uncertainties is presented in the sections that follow

. Comprehensive documentation of TRIM.FaTE property values for this scenario is provided in Addendum 1 to th is attachment

. 

### A.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 chemica l

- specific properties are related more specifically toTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 21 6

- 29

- 2012 a particular abiotic or biotic compartment type; these properties are discussed generally in the sections that follow and are documented in Addendum 1 to this attachment

. 

### A.3.2 Spatial Layout For the purpose of estimating media concentrations, the TRIM.FaTE scenario is intended to represent a farm homestead and a fishable lake (and its surrounding watershed) located near the emissions source of interest

. A diagram of the surface parcel layout is presented in Exhibit_Att A

- 9

. The source parcel is parameterized as a square with s ides 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 direction toward the east, the modeled layout is generally symmetric about an east

- west line and is we dge

- shaped to reflect Gaussian dispersion of the emission plume.
A lateral, downwind distance of 10 km was established for the watershed included in the scenario. Based on the results of dispersion modeling
, the location of the maximum air concentration and deposition rate is expected to occur relatively close to the facility (probably within a few hundred meters, with the exact location varying with stack height and other parameters) and well within a 10

- km radius. Additionally, deposition rates for the PB

- HAPs for which this screening scenario is applicable is expected to decrease by about two orders of magnitude relative to the predicted maximum rate within a 10

- km radius. Extending the modeling layout be yond 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 additio nal deposited mass on ingestion exposures is expected to be negligible. 16 Given these conditions, a downwind length of 10 km was determined to be appropriate for the screening scenario.
Exhibit_Att A

- 9

. TRIM.FaTE Surface Parcel Layout

16 M ass deposited at the outer edge of the watershed is expe cted 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 transpor t of chemical mass by erosion and runoff, dampening the effect of including additional deposition beyond 10 km. (Other exposure pathways largely would be unaffected; the soil concentrations used to calculate exposures for the farm food scenario are derive d from soil parcels located close to the source and unaffected by deposition to the far reaches of the watershed.)TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 22 6

- 29

- 2012 The north

- south width of the wedge

- shaped watershed was set based on the observed behavior of chemicals emitted to the ambient air. If meteorological stability is known or can be assumed, the lateral spread of the plume (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. T herefore, the plume σ was set at 3 times 550 m, or approximately 1.75 km from the centerline at a distance of 10 km. The plume width for these conditions is expected to be about twice this distance, or 3.5 km. These dimensions were used to define the dim ensions 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 conditi ons and the other (Parcel N7) untilled to represent pasture. The depth of the surface soil compartments was set to 1 cm , except for Parcel N6, for which the depth was set to 20 cm to simulate the effect of tillage

. Characteristics of the soil layers (e. g., organic carbon content, air and water content , and subsoil depth ) generally were set to represent typical or national averages as summarized by McKone et al. (2001) , for example. The overall shape and boundaries of the air parcel layout mirror 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).


### A.3.3 Watershed and Water Body Parameterization Properties associated with the watershed soil and lake determine how pollutants in the system are transported through and accumulate in various media compartments

. These properties describe the physical characteristics of the environmenta l media included in the modeled region, as well as the assumed connections and relationships between media types and modeled spatial components th at in turn affect chemical transport via water runoff, ground infiltration
, deposition of suspended sediments in the water column, and other processes

. This section presents the justification for setting the key properties of the soil, water, and sediment compartments

. Also discussed are some of the chemical properties related to watershed and waterbody processe s (chemical

- specific compartment properties in TRIM.FaTE) and the configuration of terrestrial plants included in the scenario.


### A.3.3.1 Water Balance Water

- related properties of the lake and related watershed characteristics (e.g., runoff rates from each surface s oil compartment) were set s o that a simplified water balance is achieved

. Although TRIM.FaTE maintains a chemical mass balance
, the model does not calculate or maintain media mass balance s (e.g., for water) except where specified in certain formula s. For the Tier 1 scenario, the parameter s were set to satisfy two equations relating water volume

. The first equation maintains a balance of water entering and leaving the terrestrial portion of the scenario:

17 http://www.epa.gov/scram001/userg/regmod/isc3v2.pdfTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 23 6

- 29

- 2012

[ total precipitation] = [evapotranspiration] + [total runoff]
In this 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] + [dir ect 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 characteri stics assumed for the 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.

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

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

 40 percent of total precipitation contributes to ov erland runoff.

 25 percent of total precipitation infiltrates into the groundwater and eventually flows into the lake.
For these calculations, the source parcel was considered to be outside the watershed and therefore was not included in the water balance

. The evaporation rate from the lake was assumed to be 700 mm/yr based on data reported by Morton (1986) for various lakes

. The runoff rate was defined to be spatially constant and temporally constant (i.e., it is not linked to precipitation events) thro ughout 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 characte ristics TRIM.FaTE uses are listed in Addendum 1 to this attachment

. 

### A.3.3.2 Sediment Balance A simplified balance of sediment transfers between the watershed and the lake was also maintained for the screening scenario via the parameterization of sediment

- related properties

. As with water, the model does not internally balance sediment mass; these calculations were performed externally for the purposes of setting param eter values. The sediment balance maintained is described by the following equation, where terms represent mass of sediment:

[total surface soil transfers to the lake via erosion] = [removal of sediment from the water column via outflow] + [sediment bur ial] where the second term (removal of sediment from the water column via outflow) is accounted for in TRIM.FaTE by lake flushing rate and the third term (sediment burial) is the transfer of sediment from the unconsolidated benthic sediment compartment to the consolidated sediment layer.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 24 6

- 29

- 2012 To maintain the sediment balance, erosion rates were calculated for each surface soil compartment using the universal soil loss equation (USLE, Wischmeier and Smith 1978)
, assuming a relatively high rate of erosion

. The total suspended sediment concentration is assumed to remain constant in TRIM.FaTE
, and the 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 difference between these sediment fluxes was taken to be the sediment burial rate

. The sediment burial rate is the rate at which sediment particles in the unconsolidated benthic sediment layer are transported to the consolidated s ediment, 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 transport ed to the sink cannot be returned to the modeling domain

. The burial rate is a formula property calculated by the model according to the difference between user
- specified values for sediment deposition velocity (from the water column to the benthic sedime nt) and sediment resuspension velocity (back into the water column from the benthic sediments)

. These formula properties assume a constant volume of particles in the sediment layer (because the densities for benthic and suspended sediment particles were d efined to the same value, the mass of particles in the sediment is also constant)

. For the Tier 1 scenario described here, the average sediment delivery rate (i.e., transfer of sediment mass from watershed surface soil to the lake due to erosion ) for the entire watershed was estimated to be about 0.0036 kg/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 ( U.S

. EPA 2005a)

. For the Tier 1 scenario, however, this approach was considered to be appropriate in that health protective assumptions are a goal of the screening scenario. 18 S urface soil compartments adjacent to the lake are linked directly to the lake for the purposes of estimating erosion and runoff transfers (see layout in Exhibit_Att A

- 9 )

. Erosion and runoff from the source parcel are linked directly to a sink and do not enter the Tier 1 scenario lake. The transport of sediment to the lake via overland is thus assumed to be efficient

. Note that erosion from parcels not directly adjacent to the lake is assumed to be somewhat attenuated, effected by using a lower sediment delivery ratio in the USLE. Using the calculated surface soil erosion rates for the scenario, the total average daily sediment load to the lake from the watershed is about 16,60 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 rem ainder of the sediment input to the lake transferred to the sediment burial sink.


### A.3.4 Meteorology Meteorological properties used in TRIM.FaTE algorithms include air temperature, mixing height, wind speed and direction, and precipitation rate

. These properties, which can vary significantly among geographic locations
, and seasonally and hourly for a single location, greatly influence the chemical concentrations predicted in media of interest

. Because the screening scenario is intended to be gene rally applicable for any U.S

. location, and to minimize the frequency of false negatives, a health protective configuration was used

. The meteorology of the screening scenario was defined to ensure that (when used in combination with the selected spatial layout)

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 Methodology for RTR

Attachment A

– Tier 1 A

- 25 6

- 29

- 2012 the maximum exposures that might be encountered for the scenarios of interest would be encompassed (i.e., consumption of home

- grown farm foodstuffs and self

- caught fish, with all farm foods and fish obtained from locations impacted by chemicals emi tted from the local 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 meteor ological 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 u sed were based on actual data trends for U.S

. locations as specified in Exhibit_Att A

- 10 but an artificial data set was compiled for this analysis (for example, temporally variable meteorological parameters were made to vary only on a daily basis). This simplified approach allow ed for greater control (relative to selecting a data set for an actual location) so that desired trends or outcomes could be specified. Also, using a meteorological data set with values varying on a daily basis rather than a shorter period (such as hourly , which is the typical temporal interval for meteorological measurements) reduced required model run time. Meteorological inputs are summarized in Exhibit_Att A

- 10

. The sensitivity of modeled PB

- HAPs to changes in these meteorological variables was tested. Lower wind speeds and mixing heights affected concentrations the most. This sensitivity is not unexpected because lower wind speeds should increase pollutant de position 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 a nnual average wind speed is approximately 4 m/s in the contiguous United States). The mixing height (mean heights from 4 states) used was 710 m (the 5 th percentile of annual averages among all 40 states in the SCRAM database).

Exhibit_Att A

- 10 . Summary of Key Meteorological Inputs Meteorologica l Property Selected Value Justification Air temperature Constant at 298 Kelvin Recommended default value listed in HHRAP (U.S. EPA 2005a). Value is similar to the mean daily June temperature in much of the U.S. Southern Plains and Southeast. a Mixing height Constant at 710 m Value is 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 a ccumulating in the location of interest. For much of the U.S. mid

- Atlantic and western regions, the wind tends to favor the eastward direction. a Among the NCDC 1981

– 2010 normalized wind vector data, the average wind direction had a strong eastward compon ent 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 w ind direction data compiled by the National Weather Service (NCDC 1995). This pattern was approximated in the RTR scenario with a configuration in which the modeled domain is downwind of the source 3 out of 7 days

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 26 6

- 29

- 2012 Meteorologica l Property Selected Value Justification

Horizontal wind speed Constant at 2.8 m /sec Set to 5 th percentile of annual average speed for 239 stations across the contiguous United States (about 50 years of data per station). Value is similar to the annual average wind speeds of many areas of the U.S. east coast and west coast. a Precipi tation frequency Precipitation occurs 3

days per week; wind direction blows into domain 2 of these days This value was selected so that two

- thirds of the total precipitation occurs when the domain is downwind of the modeled source. This pattern approximates that for rainy U.S. locations, where precipitation occurs 35

– 40% of the time (Holzworth 1972). These locations include parts of the U.S. Northeast and Northwest. a Total Precipitation 1.5 m/yr Assumed to represent rainy conditions for the Uni ted 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 condition s in many areas along the U.S. east coast and in the Midwest and Plains. a

a National Climatic Data Center CliMaps (NCDC

- CliMaps) (2007). http://cdo.ncdc.noaa.gov/cgi

- bin/climaps/climaps.pl

. b Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/ttn/scram/

. c National Climatic Data Center 1981

- 2010 Climate Normals; http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html


### A.3.5 Aquatic Food Web The aquatic food web is an important part of the screening scenario because the chemical concentrations modeled in fish are used to calculate human ingestion exposure and risks associated with eating contaminated local fish

. A biokinetic approach to modeling bioaccumulation in fish is used in the RTR screening scenario. The primary producers (first trophic level) in the TRIM.FaTE aquatic ecosystems are algae and macrophytes in the water column and detritus in the sediments (the latter simulated as sediment particles). The scenario includes a benthic invertebrate compart ment to represent the primary invertebrate consumers (second trophic level) in the benthic environment, and the fish compartments represent the higher tropic levels in the aquatic system

. For TRIM.FaTE to provide reasonable predictions of the distribution of a chemical across biotic and abiotic compartments in aquatic systems, the biomass of the aquatic biotic compartments must represent all biota in the system and the distribution of biomass among the trophic levels and groups must be as realistic as poss ible

. To support the development of a relatively generic freshwater aquatic ecosystem in which to model bioaccumulation in fish, a literature search, review, and analysis was conducted in support of developing and parameterizing aquatic biotic compartmen ts for TRIM.FaTE (ICF 2005). This research demonstrated that the diversity of species and food webs across U.S. aquatic ecosystems is substantial, reflecting the wide range of sizes, locations, and physical/chemical attributes of both flowing (rivers, st reams) and low

- flow water bodies (ponds, lakes, reservoirs). In general, lentic bodies of water (lakes and ponds) can accumulate higher levels of contaminants in both sediments and biota than lotic systems (rivers, streams). Also, the previous research ( ICF 2005) suggest s that a lake of at least 60 hectares (ha) or 150 acres could support higher trophic level predatory fish, with some fraction of the ir diet comprising smaller fish

. The RTR Tier 1 scenario includes a generic aquatic ecosystem with a 47

- h ectare ( 116

- acre) lake

. Although slightly smaller than the size suggested by the previous review (ICF 2005)
, a 47

- ha lake is large enough to support higher trophic level fish given appropriate conditions (e.g., high productivity given a sufficient nutrien t base and temperature). Also, t his size was compatible with the overall size of the defined watershed in the screening scenario. The fish types, biomass, diet fractions, and body weights recommended for fish compartments for theTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 27 6

- 29

- 2012 Tier 1 scenario are list ed in Exhibit_Att A

- 11

. B iomass is based on an assumption that the total fish biomass (wet

- weight) for the aquatic ecosystem is 5.7 g rams per square meter (g w /m 2

, IC F 2005). In general, the food web implemented in the Tier 1 scenario is consistent with aquatic food webs that support trophic level 4 fish (to maximize bioaccumulation), and is intended to be generally health protective.
Exhibit_Att A

- 11 . Aquatic Biota Parameters for the TRIM.FaTE Screening Scenario

TRIM.FaTE Compartment Type Organisms

Represented by Compartment Biomass
Diet Average Body Weight (kg) Areal density (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 sucker, carp 0.5 8.8% 100% water column planktivore 0.25

Water column carnivore largemouth bass,

walleye 0.2 3.5% 100% water column omnivore 2.0

Benthic invertebrate aquatic insect

larvae, crustaceans, mollusks b 20

– detritus in sediments 0.000255

Benthic omnivore small catfish,

rock bass 2.0 35.1% 100% benthic invert. 0.25

Benthic carnivore large catfish,

sculpins 1.0 17.5% 50% benthic invert. 50% benthic omniv. 2.0
Total Fish Biomass c 5.7

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

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


### A.3.6 Using TRIM.FaTE Media Concentrations The Tier 1 scenario outputs include average PB

- HAP concentrations and deposition rates for each year and for each parcel of the model scenario

. In each surface parcel, deposition rates to the soil are provided as are soil concentrations for the surface, root, and vadose zones and grass or leaf concent rations as appropriate for the plants

. For each air parcel, air concentrations are provided

. For the lake, surface water concentrations and concentrations in the various levels of the aquatic food chain are provided. For the ingestion exposure calculati ons, some concentrations are used to calculate direct exposure (e.g., soil ingestion)
, and some are used to perform the farm food chain concentration calculations in the various media that humans can ingest (see Exhibit_Att A

- 4 )

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 28 6

- 29

- 2012 The locations that determine exposures were selected to be health protective. Decisions regarding which TRIM.FaTE outputs to use in calculating exposures for the Tier 1 scenario
assume e xposure at locations very close to the modeled source. These locations are predicted to have amongst the highest media concentrations consistent with the specified spatial layout, thereby resulting in higher exposures to the emitted chemicals. These ass umptions are summarized in Exhibit_Att A

- 12

. Exhibit_Att A

- 12 . Spatial Considerations

– TRIM.FaTE Results Selec ted for Calculating

Farm Food Chain Media Concentrations and Receptor Exposures TRIM.FaTE Output Used in Exposure

Calculations Representative Compartment Concentration in air, for uptake by plants via vapor transfer Air compartment in air Parcel N2 (air over tilled soil)
Deposition rates, for uptake by farm produce Deposition to surface soil compartment in surface Parcel N6 (tilled soil) Concentration in surface soil, for incidental ingestion by humans and farm animals Surface soil compartment in surface Parcel N1 (untilled soil, closest to facility) 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 angler Water column carnivore compartment in lake (50% of fish consumed) and benthic carnivore in lake (50% of fish consumed)
TRIM.FaTE can output instantaneous chemical concentrations for a user

- specified time step and also can be configured to calculate temporal averages (e.g., ann ual 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 a veraged to obtain annual average concentrations. The default assumption is annual average concentrations for media during the fiftieth year of emissions. For the chemicals modeled in this scenario, long

- term concentrations in environmental media will be relatively constant at 50 years

. A side from mercury, chemicals modeled for RTR approach steady state before 50 years . And, a lthough m ercury concentrations do not achieve steady state after 50 years in the modeled screening scenario configuration, the rat e of change in mercury concentrations shows a decreasing trend )

. 

## A.4 Description of Exposure and Risk Modeling Scenario This section describes the approach for modeling chemical concentrations in farm food chain (FFC) media (Section A.4.1 ); estimating human exposures associated with ingestion of FFC media, incidental ingestion of soil, ingestion of fish, and infant consumption of breast milk (Section A.4.2 ); and calculating human health screening risk metrics associated with these exposure pathways (Section A.4.3 ). All of these calculations are conducted using MIRC. For this multipathway screening evaluation
, partitioning of PB

- HAPs into FFC media is modeled with MIRC
, not as a part of the TRIM.FaTE modeling

. Consequently, processes and inputs related to estimating chemical levels in FFC media are summarized in this section and discussed in detail in Addendum 2 to this a ttachment

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 29 6

- 29

- 2012 

### A.4.1 Calculating Concentrations in Farm Food Chain Media As was shown in Exhibit_Att A

- 5 , MIRC was compiled to calculate concentrations of PB

- HAPs in foodstuffs that are part of the farm food chain . The FFC media included in this screening scenario include :

 exposed and protected fruit,

 exposed and protected vegetables,

 root vegetables,

 beef,

 dairy products,

 pork, and

 poultry and eggs.
The algorithms used in MIR C were obtained from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (HHRAP; U.S. EPA 2005a ). These algorithms model the transfer of concentrations of PB

- HAP s in FFC media using biotransfer factors. Environmental media concentrations (i.e., the chemical source terms in these algorithms) are obtained from TRIM.FaTE . As noted in Section 2.2.2 , th e TRIM.FaTE outputs included as inputs to MIRC are the following:

 PB

- HAP concentrations in air;

 air

- to

- surface deposition rates for PB

- HAPs in both particle and vapor phases;

 PB

- HAP concentrations in fish tissue for water column carnivores and benthic
carn ivores; and

 PB

- HAP concentrations in surface soil and root zone soil

. In general, 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 ( U.S. EPA 2005a)

. Addendum 2 to this attachment provides parameter values used in MIRC for the Tier 1 assessment.



### A.4.2 Ingestion Exposure MIRC was used to e stimate ingestion rates as ADDs
, normalized to body weight for a range of exposure pathways

. Exposure pathways included are incidental ingestion of soil
, consumption of fish, produce, farm animals and related products , and ingestion of breast milk by infants

. The ingestion exposure pathways included in the screening evaluation and the environmental media through which these exposures occur are summarize d in Exhibit_Att A

- 13

. 

### A.4.2.1 Exposure Scenarios and Corresponding Inputs Specific exposure scenarios are developed by defining the ingestion activity patterns (i.e., estimating how much of each medium is consumed and the fraction of the consumed medium that is grown in or obtained from contaminated areas) and the characteristics of the hypothetical human exposed (e.g., age and body weight). MIRC computes exposure doses and risks for each ingestion pathway separately, enabling the pathway(s) of interest for each PB

- HAP to b e determined. Data related to exposure factors and receptor characteristics were obtained primarily from EPA’s Exposure Factors Handbook (U.S. EPA 2011).TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 30 6

- 29

- 2012 Exhibit_Att A

- 13 . Summary of Ingestion Exposure Pathways and Routes of Uptake

Ingestion Exposure Pathway Medium Ingested Intermediate Exposure Pathway

– Farm Animals a Environmental Uptake Route

Medium Process b

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

Consumption of fish Fish from local water

body N/A Fish tissue Direct uptake from water and consumption of food compartments modeled in TRIM.FaTE c Consumption of breast milk d Breast milk N/A Breast milk Contaminant i ngested by mother partition s to breast milk

Consumption of produce Aboveground

produce, exposed fruits and vegetables N/A Air Air Soil Deposition to leaves/plants

Vapor transfer Root uptake Aboveground produce, protected fruits and vegetables N/A Soil Root uptake
Belowground produce N/A Soil Root uptake

Consumption of farm animals and related food products Beef Ingestion of forage Air Air Soil Direct deposition to plant

Vapor transfer to plant Root uptake Ingestion of silage Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

Dairy (milk) Ingestion of forage Air Air Soil Direct deposition to plant

Vapor transfer to plant Root uptake Ingestion of silage Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface

Pork Ingestion of silage Air Air Soil Direct deposition to plant

Vapor transfer to plant Root uptake Ingestion of grain Soil Root uptake Ingestion of soil Soil Ingestion from surface
Poultry Ingestion of grain Soil Root uptake

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

Ingestion of soil Soil Ingestion from surface

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

c Modeled in TRIM.FaTE. d The consumption of breast milk exposure scenario is discussed in Section A.4.2.3

. For the Tier 1 scenario described here, exposure characteristics that would result in a highly health protective estimate of total exposure were selected. The ingestion rate for each medium was set at high

- end values (equal to the 90 th percentile of the distribution of nationalTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 31 6

- 29

- 2012 data for for all food types except for fish, which was set at the 99 th percentile)

. All media were assumed to be obtained from locations impacted by the modeled source

. Although this approach results in an overestimate of total chemical exposure for a hypothetical exposure scenario (for example, note that the total food ingestion rate that results is extremely high for a hypothetical consumer with ingesti on rates in the upper percentile for every farm food type), it was selected to avoid underestimating exposure for any single farm food type

. The exposure characteristics selected for the Tier 1 scenario are summarized in Exhibit_Att A

- 14

. Exhibit_Att A

- 14 . Overview of Exposure Factors Used for RTR Multipathway Screening a,b

Exposure Factor Selection for Screening Assessment Age group evaluated Infants under 1 year (breast milk only) Children 1

– 2 years of age Children 3

– 5 years of age Children 6

– 11 years of age Children 12

– 19 years of age Adult (20

– 70 years) Body weight (BW; varies by age) Weighted mean of national distribution Intake rate and ingestion rate (IR) for farm produce and animal products other than fish (varies by age and media consumed) 90 th percentile of distribution of consumers who produce own food
Ingestion rate for fish 105.1 g/day ( repre sents the 9 9 th percentile of fish consumption in the adult U.S. general population including consumers and non

- consumers c ; this estimate is co n sidered within the range of average v alue s for subsistence anglers d ); lower values were assumed for younger age g roups, also based on the 99 th percentile of the U.S. general population. Value reflects “as prepared” consumption rate so cooking and preparation losses did not need to be considered. Exposure frequency (EF) 365 days/year Exposure duration Lifetime, fo r estimating cancer risk; varies by chemical for chronic non

- cancer evaluation Fraction contaminated (FC) (varies by media consumed) e 1
Cooking loss f Assumed to be “typical” ; varies depending on food product (see Addendum 2 to this attachment )

. Cooking losses were not considered for fish consumption because intake rates represent “as prepared” values.

a Data for exposure characteristics are presented in Addendum 2 to this attachment

. Exposure parameter values were based on data obtained from the Expos ure Factors Handbook ( U.S. EPA 2011 )

. See Addendum 2 to this attachment for details. b Exposure factor inputs are used in calculating ADD estimate s for each exposure pathway

. ADD equations for each pathway evaluated in this screening assessment are provided in Addendum 2

. c Fish consumption ra tes for adult and child consumers were derived based on Estimated Per Capita Fish Consumption in the United States (U.S. EPA 2002c). Consumption rates were normalized to body weights that are representative of the age groups used in the screening scenario

. d The relationship equating the 99 th percentile of fish consumption in the general population with the range of average fish consumption rates for subsistence anglers was obtained from Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (U.S. EPA 2000)

. The 105. 1

- g/day consumption rate is similar to the “default” value of 142.4 g/day EPA’s Office of Water recommends for use in estimating exposures to subsistence fishers.

e Fraction contaminated represents the fr action of food product that is derived from the environment included in the screening scenario (e.g., produce grown on soil impacted by PB

- HAPs). This parameter is defined separately for each FFC medium; however, for the Tier 1 scenario, all ingested medi a are assumed to be impacted. f 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.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 32 6

- 29

- 2012 

### A.4.2.2 Calculating Average Daily Doses MIRC calculates chemical

- specific ADDs normalized to body weight (mg PB

- HAP per kg of body weight per day)

. Equations used to calculate ADDs were adapted from the algorithms provided in the technical documentation of EPA’s Multimedia, Multipathway, and Multir eceptor Risk Assessment (3MRA) Modeling System ( U.S. EPA 2003), which derived much of its input data from the Exposure Factors Handbook ( U.S. EPA 2011 )

. The ingestion exposure modeling approach embodied by 3MRA is conceptually similar to that presented in HHRAP

. A discussion of exposure dose estimation and the equations to calculate ADDs for each ingestion pathway are provided in Addendum 2 to this attachment

. 

### A.4.2.3 Infant Ingestion of Breast Milk A nursing mother exposed to contaminants by any ingestion pathway described above can pass the contaminants to her infant through breast mi lk (ATSDR 1998). The nursing infant’s exposure can be estimated from the levels of chemical concentrations in the breast milk, which in turn can be estimated based on the mother’s chemical intake. Reports of bioaccumulation of lipophilic compounds such as polychlorinated biphenyls (PCBs), polychlorinated dibenzofurans (PCDFs), and dioxins are prevalent in the scientific literature. Due to their high lipophilicity, these compounds partition almost exclusively to the milk fat of breast milk rather than th e aqueous phase (EPA 1998). PCBs, PCDFs, and PCDDs are the most documented groups of contaminants found in breast milk. Other compounds with lower octanol

- water partition coefficients, such as phenol, benzene, halobenzenes, and PAHs, are found in both th e milk fat and the aqueous phase of breast milk. Heavy metals such as arsenic, lead, cadmium, and mercury have been found in the aqueous phase of the breast milk. However, given their chemical and physical characteristics (and the impact such qualities h ave on partitioning within the body and pharmacokinetics), substances that do not partition as strongly to the lipophilic phase of breast milk tend to be of lower concern with regard to exposures to nursing infants. Because of the greater concern with rega rd to dioxins for this exposure pathway, it is the only PB

- HAP included in the breast milk exposure pathway for RTR at this time. This approach is consistent with the risk assessment procedures included in EPA’s Human Health Risk Assessment Protocol (EPA 2005). Exposure to TCDD via breast milk consumption during the first year of life was estimated to account for approximately 30 percent of an adult’s lifetime ADD. The 30 percent ratio was established by performing multiple modeling iterations using differ ent scenarios. In the absence of congener

- specific data, dioxin congeners were assumed to manifest the same tendency to accumulate in breast milk as TCDD. As a consequence, exposure to all dioxin congeners via breast milk consumption during the first year of life were estimated to account for approximately 30 percent of an adult’s lifetime exposure to those congeners. Exposure via the breast milk pathway during the first year of life was accounted for, in this way, in developing the threshold screening lev el for dioxins.


### A.4.3 Calculating Risk MIRC was used to calculate excess lifetime cancer risk and non

- cancer hazard ( expressed as the hazard quotient or HQ) using the calculated ADDs and ingestion dose

- response values

. Chemical dose

- response data include can cer 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_Att A

- 15 and are discussed in more detail i n Addendum 2 to this attachment

. Equations used to estimate cancer risk and non

- cancer hazard also are provided in Addendum 2 to this attachment

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 33 6

- 29

- 2012

Exhibit_Att A

- 15 . Dose

- response Values for PB

- HAPs Addressed

by the Screening Scenario

PB

- HAP CSF ([mg/kg

- day] - 1 ) Source RfD (mg/kg

- day) Source Inorganics Cadmium c ompounds (as Cd) not available 1E−3 IRIS Elemental mercury not available not available Divalent mercury not available 3E−4 IRIS Methyl m ercury not available 1E−4 IRIS Organics Benzo [a] pyrene a 10 IRIS not available 2,3,7,8

- TCDD 1

. 5 E+5 EPA ORD 7E−10 IRIS
Source: U.S. EPA ( 2007 ). CSF = cancer slope factor; RfD = reference dose; IRIS = Integrated Risk Information System; Cal/EPA = California Environmenta l Protection Agency; EPA ORD = EPA’s Office of Research and Development

a For consistency with the overall approach for dose

- respons e assessment of PAHs, the CSF listed in IRIS for benzo[a]pyrene ([7.3 mg/kg

- day] - 1 ) was adjusted due to its mutagenic mode of action as discussed below (see also Addendum 2 to this attachment )

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

weighted total increase in potency over a life time of 70 years. The cancer potency adjustment for chemicals with a mutagenic mode of action is discussed in Addendum 2 to this attachment

. 

### A.4.4 Summary of Tier 1 Ass umptions As emphasized previously, the screening scenario created for evaluating PB

- HAP emissions from RTR facilities is intended to be health protective to prevent underestimating risk

. The scenario also is intended to avoid grossly overestimating risk t o the point where no emissions
screen. The overall degree to which the scenario is health protective is the sum of the multiple assumptions that affect the outputs of the fate and transport, exposure, and risk modeling. Exhibit_Att A

- 16 summarizes important characteristics that influence exposure and risk estimates for this scenario and indicates the general degree of health protectiveness associated with the values for each assumption. Although this summary does not provide a quantitative estimate of the output uncertainty or the degree to which exposures and risks estimated using the scenario would be overestimated, it does demonstrate qualitatively that the scenar io generally overestimates exposure and thus favors a health

- protective risk output.

Exhibit_Att A

- 16

. Summary of RTR Screening Scenario Assumptions

Characteristic Value Neutral or Health Protective? Comments on Assumptions
General Spatial Attributes Farm location 375 m from

source; generally downwind Health Protective Location dictates soil and air concentrations and deposition rates used to calculate chemical levels in farm produce. Lake location 375 m from source; generally downwind Health Protective Location dictates where impacted fish population is located.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 34 6

- 29

- 2012 Characteristic Value Neutral or Health Protective? Comments on Assumptions

Surface soil properties Typical values or national averages Neutral Based on existing EPA documentation and other references. Size of farm parcel About 4 ha Health Protective Relatively small parcel size results in higher chemical concentration. Size of lake 47 ha; about 3 m average depth Health Protective Lake is large enough to support an aquatic ecosystem with higher trophic

- level fish, but is relatively small and shallow (thus increasing surface area

- to

- volume ratio). Meteorological Inputs Total precipitation 1.5 m/yr Health Protective Intended to represent rainy U.S

. location;

set to highest state

- wide average for the contiguous United States

. Precipitation frequency (with respect to impacted farm/lake) 2/3 of total precipitation fall on farm/lake and watershed Health

Protective Most of total precipitation occurs when the

farm/lake are downwind of the source.

Wind direction Farm/lake are downwind 40% of the time Health

Protective Farm/lake located in the predominantly

downwind direction

. Temporal dominance of wind direction based on data from Yakima, W ashington , wher e wind is predominantly from the west. Wind speed 2.8 m/sec Health Protective Low wind speed (5 th percentile of long

- term

averages for contiguous United States ); increases net deposition to lake/watershed. Air temperature 298 K Neutral Typical for summer temperatures in central and southern United States

. Mixing height 710 m Health Protective Relatively low long

- term average mixing

height (5 th percentile of long

- term averages for contiguous United States ); increases estimated air concentration. Watershed and Water Body Characteristics Evaporation of lake surface water 700 mm/yr Neutral Based on sensitivity analyses, value is not expected to under

- or overestimate concentration in surface water. Surface runoff into lake Equal to 40% of

total precipitation Health

Protective Based on typical water flow in wetter U.S

.

locations; higher runoff results in greater transfer of chemical to lake. Surface water turnover rate in lake About 12 turnovers per year Neutral Consistent with ca lculated 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 precipitation is assumed to flo w into or through lake. Soil erosion from surface soil into lake Varies by parcel; ranges from 0.002 to 0.01 kg/m 2

- day Neutral Erosion rates calculated using the universal soil loss equation (USLE); inputs to USLE were selected to be generally conservative with regard to concentration in the pond (i.e
., higher erosion rates were favored)

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 35 6

- 29

- 2012 Characteristic Value Neutral or Health Protective? Comments on Assumptions

Might underestimate erosion for locations susceptible to high erosion rates

. Note that higher erosion increases concentration in lake (and fish) but decrease s 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/detritivore fish f avor higher concentrations of bioaccumulative chemicals and result in higher concentrations in consumed fish

. Linear food

- chain maximizes concentration of bioaccumulative chemicals in higher trophic

- level fish. Parameters for Estimating Concentrations in Farm Food Chain Media Fraction of plants and soil ingested by farm animals that is contaminated 1.0 (all food and soil from contaminated areas) Health

Protective Assumes livestock feed sources (including

grains and silage) are derived from most highly im pacted locations.

Soil

- and air

- to

- plant transfer factors for produce and related parameters Typical (see Addendum 2 to this attachment for details) Neutral Obtained from peer

- reviewed and standard EPA reference sources.

Biotransfer factors for efficiency of uptake by animal of chemical in food/soil Typica l (see Addendum 2 to this attachment for details) Neutral Obtained from peer

- reviewed and standard EPA reference sources.

Bioavailability of chemicals in soil (for soil ingested by animals) 1.0 (relative to bioavailability of chemical in plant matter) Health

Protective Probably overestimates bioavailability in

soil; many chemicals are less bioavailable in soil than in plants.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 36 6

- 29

- 2012

Exhibit_Att A

- 16. Summary of RTR Screening Scenario Assumptions

Characteristic Value Neutral or Health Protective? Comments on Assumptions

Ingestion Exposure Assumptions Ingestion rates for all farm produce/livestock types Person obtains all food sources from local farm; ingestion rate is 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 1.47 g/ kg

- day for

adult s ; between 1.27 and 2.08 g/ kg

- day for children age s 1

– 19 years Health Protective Rates are based on the 99 th percentile of general population fish consumption from EPA’s 2002 analysis of freshwater and estuarine fish consumption derived from the 1994

– 96 and 1998 USDA Continuing Survey of Food Intake by Individuals ( CSFII; USDA 2002). Consumption rates
were normalized to body weight s that are represent ative of the age groups us ed in the screening scenario. T h ese ingestion rates likely overestimate long

- term fish consumption rates for the general population

. See Addendum 2 to this attac hment for a detailed discussion. Exposure frequency Consumption of contaminated food items occurs 365 days/yr Health Protective All meals from local farm products.

Body weight Mean of national distribution Neutral Note that this does not affect the body

weight

- normalized rates for produce and animal products

. Other Chemical

- Specific Characteristics General chemical properties used in fate and transport modeling (Henry’s law, K ow , etc.) Varies Neutral Obtained from peer

- reviewed sources; intended to be representative of typical behavior and characteristics.

"General" physical properties (plant matter density, aquatic life biomass, algal growth rate, etc.) Varies Neutral Obtained from peer

- reviewed sources; intended to be representative of typical behavior and characteristics.

Dose

- response values Varies Neutral to Health Protective Values used are those determined to be

appropriate for risk assessment by OAQPS; values are developed to be health protective.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 37 6

- 29

- 2012 

## A.5 Evaluation of Screening Scenario

A.5.1 Introduction The screening scenario developed for assessing multipathway human health risk for EPA’s Risk and Technology Review has been subjected to a series of evaluations. The major PB

- HAP categories of concern for this analysis are cadmium compounds (Section A.5.2 ), mercury compounds (Section A.5.3 ), dioxins (Section A.5.4 ), and POM (Section A.5.5 ). The scenario evaluations were focused primarily on assessing the behavior of these HAP categories in the environment, the accumulation of these chemicals in ingestible food products, and the predominant pathways of human exposure.


### A.5.2 Cadmium Compounds 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).



### A.5.2.1 Behavior in the Environment Once emitted into the environment, airborne cadmium particles can be transported over long distances before being d eposited

. Cadmium has been observed to partition prima rily to soil when released to the environment (ATSDR 2008). The mobility of cadmium in soil depends strongly on soil pH, clay content, and availability of organic matter

— factors that determine whether the cadmium is dissolved or sorbed in surface soil

. I n 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 2005a). 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).
Cadmium also enters surface waters , which can occur via atmospheric deposition, runoff and erosion, or wastewate r streams. M ost cadmium compounds entering the water column are quickly removed through adsorption to organic matter in sediment or to other suspended compounds. Cadmium that remains in the water column is expected to exist primarily in the dissolved sta te 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 (Rein felder 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 (BMFs) of less than 1 generally have been reported for fish at higher trophic levels, indicating that cadmium co ncentrations generally biodiminish from lower to higher trophic levels (Chen et al. 2000 ; Mason et al. 2000).

For the RTR screening scenario, the partitioning behavior modeled in TRIM.FaTE was consistent with the behavior of cadmium expected in the natur al environment.


### A.5.2.2 Concentrations in Ingestible Products Most non
- inhalation exposure to cadmium outside of occupational settings is through dietary intake

. Available data indicate that cadmium accumulates in plants, aquatic organisms, and terrestrial animals, offering multiple ingestion exposure pathways (ATSDR 2008) . A ctual cadmium levels in ingestible products , however, varies based on type of food, agricultural andTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 38 6

- 29

- 2012 cultivation practices, atmospheric deposition rates, characteristics of environmenta l media, and presence of other anthropogenic pollutants

. Meat and fish generally contain lower amounts of cadmium overall, but cadmium can be highly concentrated in certain organ meats, such as kidney and liver (ATSDR 2008) . In a study of cadmium concent rations in 14 food groups (including prepared foods), m eat, cheese, and fruits generally contain ed low levels of cadmium (ATSDR 2008). For the RTR screening scenario, the cadmium concentrations output by MIRC were consistent with reported values in all i ngestible media products . The products with higher reported cadmium levels in the literature, including soil, plants, and fish, also contained the higher modeled concentrations.


### A.5.2.3 Average Daily Dose (ADD) To determine the media most relevant to exposure a nd risk, the ingestion exposure factors must be considered in addition to the estimated media concentrations (i.e., a higher concentration for a particular medium does not necessarily mean higher risk). In Exhibit_Att A

- 17 , the 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 dom inant exposure pathway across all age categories, accounting for as much as 98 percent of the ADD for some groups. The combined contribution from all other exposure pathways accounts for less than 5 percent of the total ADD for all age groups. Most of th e additional exposure was from ingestion of fruits and vegetables. The highest ADD corresponds to children aged 1

– 2 years ; thus, the exposure corresponding to this group was used to determine the emission threshold for cadmium. In other words, the threshold emissions rate for cadmium is set at the level where the HQ for this age category is equal to 1.0.



### A.5.3 Mercury Compounds S ome of the largest anthropogenic sources of mercury to air are facilities that process, mine, or smelt mer cury ores; industrial/commercial boilers; fossil fuel combustion activities (primarily coal); cement production facilities ; other urban and industrial facilities; and medical and municipal waste incinerators ( ATSDR 1999) . These facilities can emit a mixtu re of elemental and divalent mercury , mostly in the gaseous phase, with some divalent forms in particle

- bound phases (U.S. EPA 1997 )

. 

### A.5.3.1 Behavior in the Environment Once emitted into the environment, mercury undergoes changes in form and species as it
moves through environmental media. Elemental mercury is the most prevalent species of mercury in the atmosphere. Due to the long residence time of elemental mercury in the atmosphere, this compound is relatively well distributed, even on a global scale.

Dival ent mercury is removed from the atmosphere at a faster rate than elemental mercury, and it can be transferred to the surface near the emission source via wet or dry deposition where it appears to adsorb tightly to soil particles (U.S. EPA 1997) or dissolve d organic carbon. Divalent mercury in soil also can be methylated by microbes or reduced to elemental mercury and revolatilized back into the atmosphere. Most divalent mercury from atmospheric deposition will remain in the soil profile, however, in the f orm of inorganic compounds bound to soil organic matter. Although this complexing behavior with organic matter significantly limits mercury transport, the ability of mercury to form these complexes greatly depends on soil conditions such as pH, temperatur e, and soil humic content. For example, mercury strongly adsorbs to humic materials and sesquioxides in soil at pH > 4 and in soils with high iron and aluminum content (ATSDR 1999). Small amounts of mercury in soil can be transported to surface water via runoff or leaching.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 39 6

- 29

- 2012

Exhibit_Att A

- 17

. Estimated Contribution s of Modeled Food Types to Cadmium Ingestion Exposures and Hazard Quotients

Mercury could also enter the water column through atmospheric fallout

. Once in the water body, divalent mercury can be methylated through microbial activity . In addition, divalent and methyl mercury can be further reduced to elemental mercury, which can volatilize and reenter the atmosphere

. Solid form s of inorganic mercury compounds could adsorb to particulates in the water column or partition to the sediment bed (U.S. EPA 1997 )

. The solubility of mercury in water depends on the species and form of mercury present as well as properties of the water s uch as water pH and chloride ion concentration (ATSDR 1999). Low pH favors the methylation of mercury in the water column, typically performed by sulfur

- reducing bacteria in anaerobic conditions . Methyl mercury is typically of greatest concern because it readily 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 (U.S. EPA 1997). The remaining me rcury is dissolved . Most of the e lemental mercury produced as a result of reduction of divalent mercury volatilizes back into the atmosphere. For the screening scenario, the partitioning behavior modeled in TRIM.FaTE generally was consistent with trends noted in the literature. Divalent mercury was the most prevalent species in modeled surface soil, surface water, and sediment compartments, while methyl mercury was the dominant species in fish

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 40 6

- 29

- 2012 

### A.5.3.2 Concentrations in Ingestible Products Available data indicate that mercury bioaccumulates in plants, aquatic organisms, and terrestrial animals, providing multiple ingestion exposure pathways (U.S. EPA 1997; ATSDR 1999). Low levels of mercury are found in plants, with leafy vegetables contain ing higher concentrations than potatoes, grains, legumes, and other vegetables and fruits (ATSDR 1999; EPA 1997). Cattle demethylate mercury in the rumen and, therefore, store very little of the mercury they ingest by foraging or consuming silage or grain . Thus, mercury content in meat and cow’s milk is low (ATSDR 1999). Concentrations of methyl mercury in fish are generally highest in larger, older specimens at the higher trophic levels (U.S. EPA 1997). Although data on mercury in foods other than fis h are not abundant in the literature, total mercury concentrations output by MIRC were generally consistent with the reported values that were available. The exposure pathways that most influenced the mercury HQs in the model are presented in Exhibit_Att A

- 18 . As shown, the dominant exposure pathway for all age groups is ingestion of fish. R elative to divalent mercury
, methyl mercury concentrations in fish were very high (approximately 95 percent of total mercury).


### A.5.3.3 Average Daily Dose In Exhibit_Att A

- 18 , the 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 dominant exposure pathway across all age categories, accounting for nearly 100 percent of the ADD for each group

. The combined contribution of all other expos ure 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 h ealth 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.


### A.5.4 Dioxins I ncineration and combustion processes are believed to be the primary emission sources for chlorinated dioxins ( ATSDR 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 incine ration, medical waste incineration, hazardous waste kilns from Portland cement manufacturing, secondary aluminum smelting, and biological incineration

. 

### A.5.4.1 Behavior in the Environment Dioxins emitted to the atmosphere can be transported long distances in vap or 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/co mbustion processes are not deposited close to the source (ATSDR 1998). In soil, dioxins strongly adsorb to organic matter and show very little vertical movement, particularly in soils with a high organic carbon content (ATSDR 1998). Most dioxins deposited in soil are expected to remain buried in the soil profile, with erosion of contaminated soil particles the only significant mechanism for transport to water bodies. The dry deposition of dioxins from the atmosphere to water bodies is another important tra nsport 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 bedTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 41 6

- 29

- 2012

Exhibit_Att A

- 18

. Estimat ed Contribution s of Modeled Food Types

to Methyl Mercury Ingestion Exposures

sediment, which appears to be the primary environmental sink for this chemical group (U.S. EPA 2004c). Although dioxins bound to aquatic sediment primarily become buried in t he 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 (U.S. EPA 2004c ). B ecause most dioxins i n the aquatic environment 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 appears to be through uptake of water in contaminated sediment pores , and the primary route of exposure in the higher trophic levels appears to be through food chain transfer. Following inges tion, 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 parti tioning behavior modeled in TRIM.FaTE was consistent with the behavior of 2,3,7,8

- TCDD expected in the natural environment. Also of note is that dioxins readily partition into breast milk due to the lipophilic nature of these compounds.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 42 6

- 29

- 2012 

### A.5.4.2 Concentrations in Ingestible Products The primary source of non

- inhalation exposure to dioxins outside of occupational settings is through dietary intake , which accounts for more than 90 percent of daily dioxin exposure (ATSDR 1998). Available data indicate that dioxins co ncentrate in plants, aquatic organisms, and animals, offering multiple ingestion exposure pathways . A ctual congener levels in ingestible products , however, can vary based on type of food, agricultural and cultivation practices, atmospheric deposition rate s, characteristics of environmental media, and presence of other anthropogenic pollutants . Dioxins appear to enter the terrestrial food chain primarily through vapor

- phase deposition onto surfaces of plant s , which are then consumed by larger animals

. Ano ther major source of exposure to dioxins is through ingestion of contaminated soil by animals

. Observed trends indicate that meat, dairy, and fish consumption are the dominant exposure pathways, comprising 90 percent of dioxin dietary intake (ATSDR 1998). Consistent with the literature, the modeled concentration of 2,3,7,8

- TCDD in the fish compartm ent for the screening scenario was at least one order of magnitude greater than concentrations in the other compartments. Among the compartments with the lowest concentrations were fruits and vegetables, which do not readily accumulate 2,3,7,8

- TCDD. Inge stion of breast milk during infancy and fish ingestion contribute to nearly 94 percent of lifetime dioxin exposure for 2,3,7,8

- TCDD in the screening scenario. Daily intakes of 2,3,7,8

- TCDD from cow’s milk, produce, and fish have been reported in the liter ature to comprise 27 percent, 11 percent, and 10 percent, respectively, of the total daily intake in the general population. Some studies note that specific subpopulations, such as subsistence farmers and anglers, however, might have very different exposu re profiles in which fish, meat, and dairy drive congener exposure (ATSDR 1998). Given the subsistence diet modeled in the RTR screening scenario, the high exposure from consumption of fish is appropriate within the context of this analysis.


### A.5.4.3 Lifetime Aver age 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 Exh ibit_Att A

- 19

. Based on the modeling methodology and assumptions used, exposures via the breast milk pathway consistently account for approximately 30 percent of the lifetime exposure for all congeners, w hile exposure via fish, soil , and the various farm food chain pathways is highly variable across congeners. This variability can be explained in part by differences in the physiochemical properties that drive the environmental transport processes of these congeners ( e.g., K ow , molecular weight). The differences are also likely attributed to differences in the congener
- specific half

- life in abiotic media and the degree to which the congener is metabolized in biotic media.


### A.5.5 Polycyclic Aromatic Hydrocarbons PAHs can enter the atmosphere as a result of a variety of combustion processes, both natural and anthropogenic. 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 burning, other processes such as power generation; incineration; coal tar, coke, and asphalt production; and petroleum catalytic cracking are also major contributors ( ATSDR 1995)

.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 43 6

- 29

- 2012

Exh ibit_Att A

- 19

. Estimate d Contributions of Modeled Food

Types to Dioxin Ingestion Exposures



### A.5.5.1 Behavior in the Environment PAHs emitted to the atmosphere can travel long distances in vapo r form or attach ed to particl es, or they can deposit relatively close to an emission source via wet or dry deposition onto water, soil, and vegetation

. In the atmosphere, PAHs are found primarily in the particle

- bound phase, and atmospheric residence time and transport d istances are highly influenced by climatic conditions and the size of the particles to which they are bound (ATSDR 1995). As a result of sustained input from anthropogenic sources, PAHs are ubiquitous in soil. High molecular weight PAHs, such as benzo( a ) p yrene , 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 sediment and suspended particles, while only small amounts revolatilize back to the atmosphere (ATSDR 1995). Aquatic organisms can accumulate PAHs via uptake from water, sediment, or food. Although fish and other organisms readily take up PAHs from contaminated food (e.g., aquatic insects, other benthic invertegrates, smaller fish)
, biomagnification generally does not occur because many organisms can rapidly metabolize PAHs (ATSDR 1995). As a result, concentrations of PAHs have generally been observed to decrease with increasing trophic levels (ATSDR 1995). Due to this ability to me tabolize PAHs, BAFs in fish are notTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 44 6

- 29

- 2012 expected to be especially high. Sediment

- dwelling organisms can experience increased exposure to PAHs through association (e.g., direct uptake, consumption) with contaminated sediment (ATSDR 1995). For the screening s cenario, the partitioning behavior of benzo(a)pyrene is generally consistent with trends reported in the literature.


### A.5.5.2 Concentrations in Ingestible Products The primary source of n on
- inhalation exposure to benzo(a) pyrene outside of occupational settings is through dietary intake. Exposure can depend on the origin of the food (higher values
are often recorded at contaminated sites) and the method of food preparation (higher values have been reported for food that is smoked or grilled). PAHs have been obser ved to bioaccumulate in aquatic organisms and terrestrial animals through uptake of contaminated water, soil, and food. T hese compounds are readily metabolized by higher trophic level organisms, including humans, however, so biomagnification is not consid ered to be significant ( ATSDR 1995). Plants accumulate PAHs primarily through atmospheric deposition, but chemical concentrations tend to be below detection levels. PAHs in meat have been observed at concentrations below detection levels up to higher con centrations when the meat is smoked. Similar concentrations have been reported for fish, with smoked fish concentrations sometimes quadruple those found in terrestrial animals. Because PAH concentrations are highest in products that are smoked or grilled , most of the available data for benzo ( a ) pyrene in food is for products that have been prepared using these processes. As a result, reported values might be significantly higher than those output by MIRC.

For the RTR screening scenario, concentrations out put 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 measurements and available data that are skewed toward concentrations in highly contami nated products. Considering these mitigating factors, the RTR screening scenario output concentrations are within the range of plausible values for PAHs in ingestible products.


### A.5.5.3 Lifetime Average Daily Dose T he contribution s of ingestion exposure pathways t o the LADD (and thus life time cancer risk) for various PAH s are presented i n Exhibit_Att A

- 20

. As shown
, the variability in the driving exposure pathways across PAHs is significant , with fish, beef, dairy, fruits, and vegetables comprising between 86 and 99 percent of exposure for different PAHs. This variability can be accounted for in part by differences in the physiochemical properties that drive the environmental f ate and transport processes of these PAH s ( e.g., K ow , molecular
we ight, chemical structure), differences in the PAH

- specific half

- life in abiotic media
, and the degree to which the PAHs are metabolized in biotic media. The variability in exposure pathways is consistent with information provided in the literature.


### A.5.6 Summary This analysis 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 Risk and Technology Review Program
, as modeled in TRIM.FaTE. In general, the modeled behavior of the compounds is consistent with data found in the literature. This analysis 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 Methodology for RTR

Attachment A

– Tier 1 A

- 45 6

- 29

- 2012

Exhibit_Att A

- 20

. Estimated Contributions of Modeled Food

Types to PAH Ingestion Exposures



## A.6 References ATSDR ( Agency for Toxic Substances and Disease Registry )

. 1995

. Toxicological profile for polycyclic aromatic hydrocarbons

. Atlanta, GA: U.S

. Department of Health and Human Services, Public Health Service.

ATSDR. 1998. Toxicological profile for chlorinated dibenzo

- p
- dioxins. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.
ATSDR

. 1999

. Toxicological profile for Mercury

. Atlanta, GA: U.S

. Department of Health and Human Services, Public He alth Service.
ATSDR

. 2008

. Toxicological profile for Cadmium

. Atlanta, GA: U.S

. Department of Health and Human Services, Public Health Service. Cal/EPA ( California Environmental Protection Agency ) Office of Environmental Health Hazard Assessment (OEHHA)

. 2 000

. Air Toxics Hot Spots Program Risk Assessment Guidelines; Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document

. Section 6, Dermal Exposure Assessment

. September

. Available at: http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf

. 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.TRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 46 6

- 29

- 2012 Croteau, M., S.N

. Luoma, and A.R

. Stewart

. 2005

. Trophic transfer of metals along freshwater food webs: Evidence of cadmium biomagnification in nature

. Limnology and Oceanography 50(5 ):1511

- 1519. Hazardous Substances Data Bank (HSDB). 2005a. Bethesda, MD: National Library of Medicine, U.S. [Last Revision Date 06/23/2005]. Cadmium Compounds; Hazardous Substances Databank Number: 6922. Available at: http://toxnet.nlm.nih.gov/cgi

- bin/sis/ htmlgen?HSDB
ICF (ICF International)

. 2005

. Memorandum: TRIM.FaTE Screening Scenario: Aquatic Food Web Analysis; submitted to Deirdre Murphy and Terri Hollingsworth, U.S

. EPA, from Margaret McVey and Rebecca Kauffman, ICF Consulting

. October 18.

Mason R

. P

. , J

. Laporte, and S

. Andres

. 2000

. Factors controlling the bioaccumulation of mercury, methylmercury, arsenic, selenium, and cadmium by freshwater invertebrates and fish

. Archives for Environmental Contamination and Toxicology 38(3):283

- 97.
McKone, T.E., A

. Bodnar, and E

. Hertwich

. 2001

. Development and evaluation of state

- specific landscape data sets for multimedia source

- to

- dose models

. University of California at Berkeley

. Supported by U.S

. Environmental Protection Agency (Sustainable Technology Division , National Risk Management Research Laboratory) and Environmental Defense Fund

. July

. LBNL

- 43722.
Morton, F.I

. 1986

. Practical estimates of lake evaporation

. Journal of Climate and Applied Meteorology 25:371

- 387. NCDC ( National Climatic Data Center ). 1995

. Hourly United States Weather Observations (HUSWO) 1990

- 1995.
NCDC. 2007. National Climatic Data Center CliMaps. Available online at: http://cdo.ncdc.noaa.gov/cgi

- bin/climaps/climaps.pl

. Reinfelder, J.R., N.S. Fisher, S.N. Luoma, J.W. Nichols, and W.

- X. Wang. 1998. Trace element trophic transfer in aquatic organisms: A critique of the kinetic model approach. The Science of the Total Environment 219: 117

- 135. Saiki, M.K., D.T. Castleberry, T.W. May, B.A. Martin, and F.N. Bullard. 1995. Copper, cadmium, and zinc concentrations in aquatic food chains from the upper Sacramento River (California) and selected tributaries

. Arch. Environ. Contam. Toxicol. 29:484

- 491.
Stull, R.B

. 1988

. An Introduct ion to Boundary Layer Meteorology

. Kluwer Academic Publishers, The Netherlands

. 452 pp. Turner, D.B. 1970. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999

- AP

- 26. U.S. Department of Health, Education, and Welfare, National Air Pollutio n Control Administration, Cincinnati, Ohio. USDA (U.S. Department of Agriculture ). 2000

. 1994

– 96, 1998 Continuing Survey of Food Intakes by Individuals (CSFII) . CD

- ROM

. Agricultural Research Service, Beltsville Human Nutrition Research Center, Beltsville, MD. Available from the National Technical Information Service, Springfield, VA, Accession Number PB

- 2000500027

. U.S. EPA (United States Environmental Protection Agency) . 1994. Integrated Risk Information System (IRIS) Summary for Benzo[a]pyrene (BaP) (CA SRN 50

- 32

- 8). Office of ResearchTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 47 6

- 29

- 2012 and Development, Washington, DC. Last revised 11/01/1994. Available at: http://www.epa.gov/iris

. U.S. 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

. U.S. EPA. 1998. Methodology f or Assessing Health Risks Associated w ith Multiple Pathways o f Exposure t o Combustor Emissions. National Center for Environmental Assessment. Cincinnati, OH. EPA

- 600/6

- 90/003. December.

U.S. EPA. 1999

. Short Sheet: IEUBK Model Soil/Dust Ingestion Rates

. EPA

- 540

- F

- 00

- 007; OSWER

- 9285.7

- 33

. Washington, D.C.: Office of Solid Waste and Emergency Response; December

. Available at: http://www.epa.gov/superfund/lead/products/ssircolo.pdf

. U.S. EPA. 2000. Methodology for Deriving Ambient Water Quality Criteria for the Prote ction of Human Health. EPA

- 822

- B

- 00

- 004 . October.
U.S

. EPA

. 2002a

. Total Risk Integrated Methodology: TRIM.FaTE Technical Support Document

. Volume II: Description of Chemical Transport and Transformation Algorithms

. EPA

- 453/R

02

- 011b

. Office of Air Quality Planning and Standards: Research Triangle Park, NC

. September.

U.S

. EPA

. 2002b

. Evaluation of TRIM.FaTE, Volume I: Approach and Initial Findings

. EPA

453/R

- 02

- 0012

. Office of Air Quality and Planning Standards: Research Triangle Park, NC

. September.
U.S. EPA. 2002 c. Estimated Per Capita Fish Consumption in the United States

. EPA

- 821

- C

02

- 003

. Office of Water, Office of Science and Technology, Washington, D.C

. August

. U.S

. EP

. 2003

. Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling System, Volume II: Site

- based, Regional, and National Data

. SAB Review Draft

. EP

- 530/D

03

- 001b

. Office of Research and Development, Athens, GA, and Research Triangle Park, NC, and Office of Solid Waste, Washington, DC

. July

. Available at: http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk03.htm

. U.S. EPA

. 2004a

. Air Toxics Risk Assessment Reference Library; Volume 1

– Technical Resource Document, Part III, Human Health Risk Assessment: Multipathway Chapter 14, Overview and Getting Started: Planning and Scoping the Multipathway Risk Assessment

. Office of Air Quality Planning and Standards, Research Triangle Park, NC

. April

. Available at: http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_14.pdf

. U.S. 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/f era/data/trim/eval_rept_vol3_2005.pdf

U.S. 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, N AS Review Draft

. U.S

. Environmental Protection Agency, Washington, D.C., EPA/600/P

- 00/001Cb

. Available at: http://www.epa.gov/ncea/pdfs/dioxin/nas

- review/
U.S

. EPA

. 2005a

. Human Health Risk Asse ssment Protocol for Hazardous Waste Combustion Facilities (including the Hazardous Waste Companion Database of chemical

- specificTRIM

- Based T iered Screening Methodology for RTR

Attachment A

– Tier 1 A

- 48 6

- 29

- 2012 parameter values)

. U.S

. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC

. EPA

- 530

- R

- 05

- 006

. September.
U.S

. EPA

. 2005b

. Evaluation of TRIM.FaTE, Volume II: Model Comparison Focusing on Mercury Test Case

. EPA

- 453/R

- 05

- 002

. Office of Air Quality and Planning Standards: Research Triangle Park, NC

. July.
U.S

. EPA

. 2005c

. Supplemental Guida nce for Assessing Susceptibility from Early

- Life Exposure to Carcinogens

. EPA

- 630/R

- 03

- 003F

. Risk Assessment Forum: Washington, DC

. March.
U.S

. 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

. U.S. EPA. 2007

. Prioritized Chronic Dose

- Response Values for Screening Risk Assess ments (Table 1)

. Office of Air Quality Planning and Standards; June 12, 2007

. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

. U.S. EPA. 2008. Integrated Risk Information Syst em (IRIS) Summary for Toxicological Review of Hexachlorodibenzo

- p
- dioxin (HxCDD), mixture of 1,2,3,6,7,8

- HxCDD and 1,2,3,7,8,9

- HxCDD (CASRN 57653

- 85

- 7 and 19408

- 74

- 3). Office of Research and Development, Washington, DC. Last revised 06/30/2008. Available at: http://www.epa.gov/iris. 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. T uomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health Organiz ation reevaluation of human and mammalian toxic equivalency factors for dioxins and dioxin

- like compounds. Toxicol Sci. 93(2): 223

- 41.
Wischmeier, W.H., and D

. Smith

. 1978

. Predicting Rainfall Erosion Losses: A Guide to Conservation Planning

. USDA

- ARS Agriculture Handbook No

. 537, Washington, D.C

. 58 pp.TRIM

- Based T iered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 1 6

- 29

- 2012 TRIM.FaTE Inputs 

## Addendum 1. TRIM .FaTE Inputs[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 3 6

- 29

- 2012 TRIM.FaTE Inputs Exhibits, Addendum 1
Exhibit_Add A1

- 1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE

Screening Scenario

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

- 5 Exhibit_ Add A1

- 2. Meteorological Inputs for the TRIM.FaTE Screening Scenario

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

- 6 Exhibit_Add A1

- 3. Air Parameters for the TRIM.FaTE Screen ing Scenario

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

- 7 Exhibit_Add A1

- 4. Soil and Groundwater Parameters for the TRIM.FaTE

Screening Scenario

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

- 8 Exhibit_Add A1

- 5. Runoff Assumptions for the TRIM.FaTE Screening Scenario

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

- 10 Exhibit_Add A1

- 6. USLE Erosion Parameters for the TRIM.FaTE Screening

Scenario

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

- 11 Exhibit_Add A1

- 7. Terrestrial Plant Placement for the TRIM.FaTE Screening

Scenario

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

- 12 Exhibit_Add A1

- 8. Terrestrial Plant Parameters for the TRIM.FaTE Screening

Scenario

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

- 13 Exhibit_Add A1

- 9. Surface Water Parameters for the TRIM.FaTE Screening

Scenario

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

- 15 Exhibit_Add A1

- 10. Sediment Parameters for the TRIM.FaTE Screening

Scenario

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

- 16 Exhibit_Add A1

- 11. Aquatic Animals Food Chain, Density, and Mass for the

TRIM.FaTE Screening Scenario

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

- 17 Exhibit_Add A1

- 12. Cadmium Chemical

- Specific Parameters for the TRIM.FaTE

Screening Scenario

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

- 18 Exhibit_Add A1

- 13. Mercury Chemical

- Specific Parameters for the TRIM.FaTE

Screening Scenario

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

- 19 Exhibit_Add A1

- 14. PAH Chemical

- Specific Parameters for the TRIM.FaTE

Screening Scenario

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

- 20 Exhibit_Add A1

- 15. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE

Screening Scenario

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

- 22 Exhibit_Add A1

- 16. Cadmium Chemical

- Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

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

- 24 Exhibit_Add A1

- 17. Mercury Chemical

- Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

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

- 25 Exhibit_Add A1

- 18. PAH Chemical

- Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

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

- 29 Exhibit_Add A1

- 19. Dioxin Chemical

- Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

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

- 3 2 Exhibit_Add A1

- 20. Cadmium Chemical

- Specific Parameters for Plant

Compartments in the TRIM.FaTE Screening Scenario

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

- 36 Exhibit_Add A1

- 21. Mercury Chemical

- Specific Parameters for Plant

Compartments in the TRIM.FaTE Screening Scenario

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

- 37TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 4 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A1

- 22. PAH Chemical

- Specific Parameters for Plant Compartments

in the TRIM.FaTE Screening Scenario

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

- 38 Exhibit_Add A1

- 23. PAH Chemical

- Specific Parameters for Plant Compartments

in the TRIM.FaTE Screening Scenario

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

- 39 Exhibit_Add A1

- 24. Cadmium Chemical

- Specific Parameters for Aquatic

Species in the TRIM.FaTE Screening Scenario

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

- 41 Exhibit_Add A1

- 25. Mercury Chemical

- Specific Parameters for Aquatic Species

in the TRIM.FaTE Screening Scenario

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

- 43 Exhibit_Add A1

- 26. PAH Chemical

- Specific Parameters for Aquatic Species in

the TRIM.FaTE Screening Scenario

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

- 44 Exhibit_Add A1

- 27. Dioxin Chemical

- Specific Parameters for Aquatic Species in

the TRIM.FaTE Screening Scenario

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

- 47TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 5 6

- 29

- 2012 TRIM.FaTE Inputs This attachment provides tables of the model ing inputs for the TRIM.FaTE screening scenario. Exhibit_Add A 1

- 1 presents runtime settings for TRIM.FaTE. Exhibit_Add A 1

- 2 and Exhibit_Add A 1

- 3 present meteorological and air parameters , respectively, entered into the model. Exhibit_Add A 1

- 4 , Exhibit_Add A 1

- 5 , and Exhibit_Add A 1

- 6 present the parameters for soil and groundwater, runoff assumptions, and the USLE (universal soil loss equation) erosion parameters, respectively, for the screening scenario. Exhibit_Add A 1

- 7 and Exhibit_Add A 1

- 8 present terrestrial parameters. Exhibit_Add A 1

- 9 through Exhibit 1

- 1 1 present lake parameters, and Exhibit_Add A 1

- 12 through Exhibit 1

- 2 7 present parameters specific to the chemical s modeled in the scenario.

Exhibit_Add A 1
- 1 . TRIM.FaTE Simulation Parameters for the TRIM.FaTE Screening Scenario
Parameter Name Units Value Used Reference Start of simulation date/time 1/1/1990, midnight Consistent with met data. End of simulation date/time 1/1/2040, midnight Consistent with met data set; selected to provide a 50

- year modeling period. Simulation time step hr 1 Selected value. Output time stepa hr 4 Selected value.

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, Addendum 1 1

- 6 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 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 Professional judgment; vertical wind speed not used by any of the algorithms in the version of the TRIM.FaTE library used for screening. Wind direction degrees clockwise from N (blowing from) 3

- days

- on

4

- days

- off On is defined as time during which wind is blowing into the model domain. A

conservative estimate of time during which wind should blow into the modeling domain was determined by evaluating HUSWO; it was concluded that a conservative estimate would be approximately 42% of the time. Rainfall r ate m 3 [rain]/m 2 [surface area]

- day varies daily 1.5 m/yr is the maximum statewide 30

- year (1971

- 2000) average for the contiguous United States, excluding Rhode Island because of extreme weather conditions on Mt. Washington. Data obtained from the National Cl imatic Data Center at http://www.ncdc.noaa.gov/oa/climate/online/ccd/nrmpcp.txt. The precipitation frequency was 3

- days

- on:4

- days

- off based on data from Holzworth, 1972. Mixing height (used to set air VE property named “top”) m 710 5th percentile annual average mixing heights (calculated from daily morning and afternoon values), for all stations on SCRM (40 state, 70 stations). isDay_SteadyState_forAir unitless

-- Value not used in current dynamic runs (would need to be reevaluated if steady

state runs a re needed). isDay_SteadyState_forOther unitless

--TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 7 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 3

. Air Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Atmospheric dust load kg[dust]/m 3 [air] 6.15E

- 08 Bidleman 1988 Density of air g/cm 3 0.0012 US EPA 1997 b Dust density kg[dust]/m 3 [dust] 1,400 Bidleman 1988 Fraction organic matter
on particulates unitless 0.2 Harner and Bidleman 1998TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 8 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 4

. Soil and Groundwater Parameters for the TRIM. FaTE Screening Scenario

Parameter Name Units Value Used Reference Surface Soil Compar t ment Type Air content volume[air]/volume[compartment] 0.28 McKone et al. 2001. Average vertical velocity of water (percolation) m/day 8.22E

- 04 Assumed to be 0.2 times average precipitation for site. Boundary layer thickness above surface soil m 0.005 Thibodeaux 1996; McKone et al. 2001 (Table 3). Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2600 Default in McKone et al. 2001 (Table 3)

. Thickness

- untilled a m 0.01 McKone et al. 2001 (p. 30). 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 Professional judgment; area assumed rural.

Fraction of area available for runoff m 2 [area available]/m 2 [total] 1 Professional judgment; area assumed rural. Fraction of area available for vertical diffusion m 2 [area available]/m 2 [total] 1 Professional judgment; area assumed rural.

Fraction s and unitless 0.25 Professional judgment. Organic carbon fraction unitless 0.008 U.S. average in McKone et al. 2001 (Table 16 and A

- 3). pH unitless 6.8 Professional judgment. Runoff fraction unitless varies b See Exhibit_Add A 1

- 5

. Total erosion rate kg [soil]/m 2 /day varies b See Exhibit_Add A 1

- 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[compartment] 0.25 McKone et al 2001 (Table 16). Average vertical velocity of water (percolation) m/day 8.22E

- 04 Assumed as 0.2 times average precipitation for New England in McKone et al. 2001. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 McKone et al. 2001 (Table 3). Fraction s and unitless 0.25 Professional judgment.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 9 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A1

- 4 . Soil and Groundwater Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Root Zone Soil Compartment Type , continued Thickness

- 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 Professional judgment. Water content volume[water]/volume[compartment] 0.15 McKone et al. 2001 Vadose Zone Soil Compartment Type Air content volume[air]/volume[compartment] 0.22 McKone et al. 2001 (Table 17). Average vertical velocity of water (percolation) m/day 8.22E

- 04 Assumed as 0.2 times average precipitation for New England in McKone et al. 2001. Density of soil solids (dry weight) kg[soil]/m 3 [soil] 2,600 Default in McKone et al. 2001 (Table 3). Fraction s and unitless 0.35 Professional judgment. 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 Professional judgment. Water content volume[water]/volume[compartment] 0.21 McKone et al. 2001 (Table 17

- national average). Ground w

ater Compartment Type Thickness a m 3 McKone et al. 2001 (Table 3). Fraction s and unitless 0.4 Professional judgment. Organic carbon fraction unitless 0.004 Professional judgment. pH unitless 6.8 Professional judgment. Porosity volume[total pore space]/volume[compartment] 0.2 Default in McKone et al. 2001 (Table 3).
Density of 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_Add A 1

- 5 and Exhibit_Add A 1

- 6 ) for erosion/runoff fractions and total erosion rates.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 10 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 5

. Runoff Assumptions for the TRIM.FaTE Screening Scenario Originating Compartment Destination Compartment Runoff/Erosion F raction SurfSoil_Source SurfSoil_N1 0.0 SurfSoil_S1 0.0 sink 1.0 SurfSoil_N1 SW_Pond 1.0 SurfSoil_Source 0.0 SurfSoil_N6 0.0 SurfSoil_S1 0.0 sink 0.0 SurfSoil_S1 SW_Pond 1.0 SurfSoil_Source 0.0 SurfSoil_N1 0.0 sink 0.0 SurfSoil_N6 SW_Pond 1.0 SurfSoil_N1 0.0 SurfSoil_N7 0.0 sink 0.0 SurfSoil_N7 SW_Pond 1.0 SurfSoil_N6 0.0 SurfSoil_N3 0.0 sink 0.0 SurfSoil_N3 SW_Pond 1.0 SurfSoil_N7 0.0 SurfSoil_N4 0.0 sink 0.0 SurfSoil_N4 SW_Pond 1.0 SurfSoil_N3 0.0 SurfSoil_N5 0.0 SurfSoil_S4 0.0 sink 0.0 SurfSoil_S4 SW_Pond 1.0 SurfSoil_N4 0.0 SurfSoil_S5 0.0 sink 0.0 SurfSoil_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, Addendum 1 1

- 11 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 6

. USLE Erosion Parameters for the TRIM.FaTE Screening Scenario

Soil Parcel Area Rainfall/Erosivity Index Soil

Erodibility Index Length

Slope Factor Land

Use Cover

Mgmt Factor Supporting

Practices Factor Unit Soil Loss Sediment Delivery Ratio a Calculated

(Adjusted) Erosion Rate

Code m 2 R (100 ft

- ton/ac) K (ton/ac/(100 ft

- ton/acre)) LS

(USCS) type C

(USCS) P A (ton/ac/yr) A

(kg/m 2 /d) SDR a kg/m 2 /d

N1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.533 0.005740 N6 4.1E+04 300 0.39 1.5 crops 0.2 1 35.1 0.021557 0.557 0.012014 N7 7.3E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.518 0.005580 N3 3.5E+05 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.385 0.004151 N4 2.0E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.309 0.003331 N5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116 S1 5.8E+04 300 0.39 1.5 grass 0.1 1 17.55 0.010779 0.533 0.005740 S4 2.0E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.309 0.003331 S5 6.7E+06 300 0.39 1.5 forest 0.1 1 17.55 0.010779 0.196 0.002116

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 Methodology for RTR

Attachment A, Addendum 1 1

- 12 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 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 Methodology for RTR

Attachment A, Addendum 1 1

- 13 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 8

. Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Coniferous a Grass/Herb a Value Used Reference Value Used Reference Leaf Compartment Type Allow exchange 1=yes, 0=no 1

- S easonal b

- Average leaf area index m 2 [leaf]/
m 2 [area] 5.0 Harvard Forest, dom. red oak and red maple, CDIAC website 5.0 Mid

- range of 4

- 6 for old fields, R.J. Luxmoore, ORNL. Calculate wet deposition interception fraction ( Boolean ) 1=yes, 0=no 0 Professional judgment. 0 Professional judgment.
Correction exponent, octan o l to lipid unitless 0.76 From roots, Trapp 1995. 0.76 From roots, Trapp 1995.
Degree stomatal opening unitless 1 Set to 1 for daytime based on professional judgment (stomatal diffusion is turned off at night using a different property, IsDay). 1 Set to 1 for daytime based on professional judgment (stomatal diffusion is turned off at night using a different property, IsDay). Density of wet leaf kg/m 3 820 Paterson et al. 1991. 820 Paterson et al. 1991. Leaf wetting factor m 3.00E

- 04 1E

- 04 to 6E

- 04 for different crops and elements, Muller and Prohl 1993. 3.00E

- 04 1E

- 04 to 6E

- 04 for different crops and elements, Muller and Prohl 1993.
Length of leaf m 0.01 Professional judgment. 0.05 Professional judgment. Lipid content kg/kg wet weight 0.00224 European beech, Riederer 1995. 0.00224 European beech, Riederer 1995. Litter fall rate 1/day 0.0021 value assumes 1st

- order relationship and that 99% of leaves fall over 6 years S easonal c

-

Stomatal area normalized effective diffusion path length 1/m 200 Wilmer and Fricker 1996. 200 Wilmer and Fricker 1996.
Vegetation attenuation factor m 2 /kg 2.9 Grass/hay, Baes et al. 1984. 2.9 Grass/hay, Baes et al. 1984. Water content unitless 0.8 Paterson et al. 1991. 0.8 Paterson et al. 1991. Wet deposition interception fraction unitless 0.2 Calculated based on 5 years of local met data, 1987

- 1991. 0.2 Calculated based on 5 years of local met data, 1987

- 1991. Wet mass of leaf per soil area kg[fresh leaf]/m 2 [area] 2.0 Calculated from leaf area index, leaf thickness (Simonich and Hites, 1994), density of wet 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 Methodology for RTR

Attachment A, Addendum 1 1

- 14 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 8 . Terrestrial Plant Parameters for the TRIM.FaTE Screening Scenario

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 Only Allow exchange 1=yes, 0=no S easonal b

- Correction exponent, octanol to lipid unitless 0.76 Trapp 1995.
Lipid content of root kg/kg wet weight 0.011 Calculated. Water content of root kg/kg wet weight 0.8 Professional judgment. Wet density of root kg/m 3 820 Soybean, Paterson et al. 1991. Wet mass per soil area kg/m 2 1.4 Temperate grassland, Jackson et al. 1996. Stem Compartment Type

– Nonwoody Only Allow exchange 1=yes, 0=no S easonal b

- Correction exponent, octanol to lipid unitless 0.76 Trapp 1995

. Density of phloem fluid kg/m 3 1,000 Professional judgment. Density of xylem fluid kg/cm 3 900 Professional judgment. Flow rate of transpired water per leaf area m 3 [water]/m 2 [leaf ] 0.0048 Crank et al. 1981.
Fraction of transpiration flow rate that is phloem rate unitless 0.05 Paterson et al. 1991.
Lipid content of stem kg/kg wet weight 0.00224 Leaves of European beech, Riederer 1995. Water content of stem unitless 0.8 Paterson et al. 1991 Wet density of stem kg/m 3 830 Professional judgment. Wet mass per soil area kg/m 2 0.24 Calculated from leaf and root biomass density based on professional judgment.

a See Exhibit_Add A 1

- 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

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 15 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 9

. Surface Water Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Used Reference Algae carbon content (fraction) unitless 0.465 APHA 1995.
Algae density in water column g[algae]/L[water] 0.0025 Millard et al. 1996 as cited in ICF 2005. Algae growth rate 1/day 0.7 Hudson et al. 1994 as cited in Mason et al. 1995b

. Algae radius um 2.5 Mason et al. 1995b. Algae water content (fraction) unitless 0.9 APHA 1995. Average algae cell density (per vol ume cell, not water) g[algae]/m 3 [algae] 1,000,000 Mason et al. 1995b, Mason et al. 1996. Boundary layer thickness above sediment m 0.02 Cal EPA 1993.

Chloride concentration mg/L 8.0 Kaushal et al. 2005. Chlorophyll concentration mg/L 0.0029 ICF 2005. Depth a m 3.18 WI DNR 200 7

- calculation based on relationship between drainage basin and lake area size. Dimensionless viscous sublayer thickness unitless 4 Ambrose et al. 1995.
Drag coefficient for water body unitless 0.0011 Ambrose et al. 1995.

Flush rate 1/year 12.17 Calculated based on pond dimensions and flow calculations. Fraction Sand unitless 0.25 Professional judgment. Organic carbon fraction in suspended sediments unitless 0.02 Professional judgment.

pH unitless 7.3 Professional judgment. Suspended sediment deposition velocity m/day 2 USEPA 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

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 16 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 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 Professional judgment. Organic carbon fraction unitless 0.02 McKone et al. 2001 (Table 3). Porosity of the sediment
zone volume[total pore space]/volume[sediment compartment] 0.6 USEPA 2005

.

Solid material density in sediment kg[sediment]/m 3 [sediment] 2,600 McKone et al. 2001 (Table 3). pH unitless 7.3 Professional judgment. Sediment resuspension velocity m/day 6.69E

- 05 Calculated from water balance model.

a Set using the volume element properties named "top" and "bottom."TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 17 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 11

. Aquatic Animals Food Chain, Density, and Mass for the TRIM.FaTE Screening Scenario

Aquatic Biota (C onsuming Organism) Fraction Diet

Biomass (kg/m 2 ) Body

Weight (kg) Reference Algae Zooplankto

n Benthic Invertebrate Water Column Herbivore Benthic Omnivore Water Column Omnivore Benthic Carnivore Water Column Carnivore

Benthic Invertebrate 0% 0% 0% 0% 0% 0% 0% 0% 0.020 2.55E

- 04 Professional judgment. Water Column Herbivore 0% 100% 0% 0% 0% 0% 0% 0% 0.002 0.025 Professional judgment. Benthic Omnivore 0% 0% 100% 0% 0% 0% 0% 0% 0.002 2.50E

- 01 Professional judgment. Water Column Omnivore 0% 0% 0% 100% 0% 0% 0% 0% 0.001 0.25 Professional judgment. Benthic Carnivore 0% 0% 50% 0% 50% 0% 0% 0% 0.001 2.0 Professional judgment. Water Column Carnivore 0% 0% 0% 0% 0% 100% 0% 0% 0.0002 2.0 Professional judgment. Zooplankton 100% 0% 0% 0% 0% 0% 0% 0% 0.0064 5.70E

- 08 Professional judgment.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 18 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit_Add A 1
- 12

. Cadmium Chemical

- Specific Parameters

for the TRIM.FaTE Screening Scenario

Parameter Name a Units Value Reference

CAS number b unitless 7440

- 43

- 9

- Diffusion coefficient in pure air m 2 [air]/day 0.71 USEPA 1999 (Table A

- 2

- 35). Diffusion coefficient in pure water m 2 [water]/day 8.16E

- 05 USEPA 1999 (Table A

- 2

- 35). Henry's Law c onstant Pa

- m 3 /mol 1.00E

- 37 USEPA 1999 (Table A

- 2

- 35; assumed to be zero). Melting point degrees K 594 ATSDR 1999. Molecular weight g/mol 112.41 ATSDR 1999. Octanol

- air partition coefficient (K oa ) m 3 [air]/m 3 [octanol]

- -

Octanol

- carbon partition coefficient (K oc ) - -

Octanol

- water partition coefficient (K ow ) L[water]/kg[octanol]

- -

a All parameters in this table are TRIM.FaTE chemical properties. b Th is CAS numbers appl ies to elemental Cd; however, the cations of cadmium are being modeled.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 19 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit_Add A 1
- 13

. Mercury Chemical

- Specific Parameters

for the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) b Hg(2) b MHg b CAS number unitless 7439

- 97

- 6 14302

- 87

- 5 22967

- 92

- 6

- Diffusion coefficient in pure air m 2 [air]/day 0.478 0.478 0.456 USEPA 1997 b

.
Diffusion coefficient in pure water m 2 [water]/day 5.54E

- 05 5.54E

- 05 5.28E

- 05 USEPA 1997 b

. Henry's Law constant Pa

- m 3 /mol 719 7.19E

- 05 0.0477 USEPA 1997 b

. Melting p oint degrees K 234 5.50E+02 443 CARB 1994. Molecular weight g/mol 201 201 216 USEPA 1997 b

. Octanol

- water partition coefficient (K ow ) L[water]/kg[octanol] 4.15 3.33 1.7 Mason et al. 1996.

Vapor washout ratio m 3 [air]/m 3 [rain] 1,200 1.6E+06 0 USEPA 1997 b
, based on Petersen et al. 1995.

a All parameters in this table are TRIM.FaTE chemical properties. b On this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 20 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit_Add A 1
- 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[octanol] 7.24E+03 6.31E+05 8.32E+03 1.00E+04 6.17E+05 9.33E+05 6.03E+05 4.27E+06

Parameter Name Units Value BkF Chr DahA Fluoranthene Fluorene IcdP CAS number unitless 207

- 08

- 9 218

- 01

- 9 53

- 70

- 3 206

- 44

- 0 86

- 73

- 7 193

- 39

- 5 Diffusion coefficient in pure air m 2 /day 0.009 0.009 0.009 0.009 0.009 0.009

Diffusion coefficient in pure water m 2 /day 8.64E

- 05 8.64E

- 05 8.64E

- 05 8.64E

- 05 8.64E

- 05 8.64E

- 05
Henry's Law constant Pa

- m 3 /mol 0.04 0.53 0.01 1.96 9.81 0.03
Melting point degrees K 490 531 539 383.15 383.15 437 Molecular weight g/mol 252.32 228.29 278.33 202.26 166.20 276.34 Octanol

- water partition coefficient (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 Methodology for RTR

Attachment A, Addendum 1 1

- 21 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit_Add A 1
- 1 4. PAH Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Reference

CAS number unitless

- Diffusion coefficient in pure air m 2 /day USEPA 2005. Exceptions include USEPA 1997 a (7,12

- Dimethylbenz(a)anthracene), and USEPA 2007 (2

- Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene) Diffusion coefficient in pure water m 2 /day USEPA 2005. Exceptions include USEPA 1997 a (7,12

- Dimethylbenz(a)anthracene), and USEPA 2007 (2

- Methylnaphthalene, Acenaphthylene, and Benzo(g,h,i)perylene) Henry's Law constant Pa

- m 3 /mol USEPA 2005. Exceptions include USEPA 2003 (2

- Methylnaphthalene) HSDB 2001a (7,12

- Dimethylbenz(a)anthracene), HSDB 2001b (Acenaphthylene), and HSDB 2001c (Benzo(g,h,i)perylene) Melting point degrees K Budavari 1996. Exceptions include USEPA 2003 (2

- Methylnaphthalene), HSDB 2001a (7,12

- Dimethylbenz (a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c (Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene) Molecular weight g/mol Budavari 1996. Exceptions include USEPA 2003 (2

- Methylnaphthalene), HSDB 2001a (7,12

- Dimethylb enz(a)anthracene), HSDB 2001b (Acenaphthylene), HSDB 2001c (Benzo(g,h,i)perylene), and USEPA 2005 (Acenaphthene, Fluoranthene, and Fluorene) Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] Hansch et al. 1995. Exceptions include Passivirta et al. 1999 (Acenaphthylene, Benzo(k)fluoranthene, and Indeno(1,2,3

- cd)pyrene), and Sangster 1993 (Benzo(b)fluoranthene)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 22 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit_Add A 1
- 15

. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE Scree ning Scenario

Parameter Name Units Value 1,2,3,4,6,7,8,9 - OCDD

1,2,3,4,6,7,8,9 - OCDF

1,2,3,4,6,7,8 - HpCDD

1,2,3,4,6,7,8 - HpCDF

1,2,3,4,7,8,9 - HpCDF

1,2,3,4,7,8 - HxCDD

1,2,3,4,7,8 - HxCDF

CAS number unitless 3268

- 87

- 9 39001

- 02

- 0 35822

- 46

- 9 67562

- 39

- 4 55673

- 89

- 7 39227

- 28

- 6 70648

- 26

- 9 Diffusion coefficient in pure air m 2 /day 0.751 0.168 0.782 0.176 0.176 0.816 0.183

Diffusion coefficient in pure water m 2 /day 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05
Henry's Law constant Pa

- m 3 /mol 0.684 0.19 1.22 1.43 1.42 1.08 1.45 Melting p oint degrees K 603.0 259.0 538.0 236.5 222.0 546.0 499.0 Molecular weight g/mol 460 443.76 425.2 409.31 409.31 391 374.87 Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] 1.58E+08 1.00E+08 1.00E+08 2.51E+07 7.94E+06 6.31E+07 1.00E+07

Parameter Name Units Value 1,2,3,6,7,8 - HxCDD

1,2,3,6,7,8 - HxCDF

1,2,3,7,8,9 - HxCDD

1,2,3,7,8,9 - HxCDF

1,2,3,7,8 - PeCDD

1,2,3,7,8 - PeCDF

2,3,4,6,7,8 - HxCDF

CAS number unitless 57653

- 85

- 7 57117

- 44

- 9 19408

- 74

- 3 72918

- 21

- 9 40321

- 76

- 4 57117

- 41

- 6 60851

- 34

- 5 Diffusion coefficient in pure air m 2 /day 0.816 0.183 0.816 0.183 0.854 0.192 0.183

Diffusion coefficient in pure water m 2 /day 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05 6.91E

- 05
Henry's Law constant Pa

- m 3 /mol 1.11 0.741 1.11 1.11 0.263 0.507 1.11 Melting point degrees K 558.0 506.0 517.0 509.0 513.0 499.0 512.5 Molecular weight g/mol 390.84 374.87 390.84 374.87 356.4 340.42 374.87 Octanol

- water partition coefficient (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 Methodology for RTR

Attachment A, Addendum 1 1

- 23 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit _Add A 1
- 1 5. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE Screening Scenario

Parameter Name Units Value

Reference 2,3,4,7,8 - PeCDF

2,3,7,8 - TCDD

2,3,7,8 - TCDF

CAS number unitless 57117

- 31

- 4 1746

- 01

- 6 51207

- 31

- 9

- Diffusion coefficient in pure air m 2 /day 0.192 0.899 0.203 US EPA 2005

Diffusion coefficient in pure water m 2 /day 6.91E

- 05 4.84E

- 05 5.19E

- 05 US EPA 2005

Henry's Law constant Pa

- m 3 /mol 0.505 3.33 1.46 US EPA 2005 Melting point degrees K 469.3 578.0 500.0 Mackay et al. 2000. Exceptions include USEPA 2000a (1,2,3,6,7,8

- HxCDD, 1,2,3,7,8,9

- HxCDF, and 1,2,3,7,8

- PeCDD), ATSDR 1998 (1,2,3,6,7,8

- HxCDF, 1,2,3,7,8

- PeCDF, and 2,3,4,6,7,8

- HxCDF), and NLM 2002 (1,2,3,7,8,9

- HxCDD) Molecular weight g/mol 340.42 322 306 Mackay et al. 2000. Exceptions include ATSDR 1998 (1,2,3,6,7,8

- HxCDF, 1,2,3,7,8,9

- HxCDF, 1,2,3,7,8

- PeCDD, 1,2,3,7,8

- PeCDF, and 2,3,4,6,7,8

- HxCDF) and NLM 2002 (1,2,3,6,7,8

- HxCDD and 1, 2,3,7,8,9

- HxCDD) Octanol

- water partition coefficient (K ow ) L[water]/L[octanol] 3.16E+06 6.31E+06 1.26E+06 Mackay et al. 1992 as cited in USEPA 2000b. Exceptions include Mackay et al. 2000 (1,2,3,4,7,8,9

- HpCDF), USEPA 2000a (1,2,3,6,7,8

- HxCDD, 1,2,3,6,7,8

- HxCDF, 1,2,3,7,8,9

- HxCDD, 1,2,3,7,8,9

- HxCDF, and 2,3,4,6,7,8

- HxCDF), and Sijm et al. 1989 as cited in USEPA 2000b (1,2,3,7,8

- PeCDD)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 24 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 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 Professional judgment.

Root Zone Soil Compartment Type Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 Professional judgment.
Vadose Zone Soil Compartment Type Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 Professional judgment.

Surface Water Compartment Type Ratio of concentration in water to concentration in algae to concentration dissolved in water L[water]/g[algae wet wt] 1.87 McGeer et al. 2003.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 25 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 17

. Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg Air Compartment Type Particle dry deposition velocity m/day 500 500 500 CalTOX value cited in McKone et al. 2001. Demethylation rate 1/day N/A N/A 0 Professional judgment. Methylation rate 1/day 0 0 0 Professional judgment. Oxidation 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 Professional judgment. Washout r atio m 3 [air]/m 3 [rain] 200,000 200,000 200,000 Professional judgment. Surface Soil Compartment Type Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 0 0 Professional judgment.

Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 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 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 1.25E

- 05 0 Value used for untilled surface soil (2cm), 10% moisture content, in USEPA 1997 b ; general range is (0.0013/day)*moisture content to (0.0001/day)*moisture content for forested region (Lindberg 1996; Carpi and Lindberg 1997).TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 26 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 1 7 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg Root Zone Soil Compartment Type Use input characteristic depth ( Boolean ) 0 = no, Else = yes 0 0 0 Professional judgment. Soil

- water partition coefficient L[water]/kg[soil wet wt] 1,000 58,000 7,000 USEPA 1997 b.

Demethylation rate 1/day N/A N/A 0.06 Range reported in Porvari and Verta 1995 is 3E

- 2 to 6E

- 2 /day; value is average maximum potential demethylation rate constant under anaerobic conditions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation rate constant under anaerobic conditions. 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 Professional judgment. 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 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 Methodology for RTR

Attachment A, Addendum 1 1

- 27 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 1 7 . 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 conditions. Methylation rate 1/day 0 0.001 0 Range reported in Porvari and Verta 1995 is 2E

- 4 to 1E

- 3 /day; value is average maximum potential methylation rate constant under anaerobic conditions. Oxidation rate 1/day 1.00E

- 08 0 0 Small default nonzero value (0 assumed in USEPA 1997 b ). Reduction rate 1/day 0 3.25E

- 06 0 Value used for tilled surface soil (20cm), 10% moisture content, in USEPA 1997 b (Lindberg 1996; Carpi and Lindberg 1997). Surface Water Compartment Type Algal surface area

- specific uptake rate constant nmol/[μm2

- day

- nmol] 0 2.04E

- 10 3.60E

- 10 Assumes radius = 2.5mm, Mason et al. 1995b, Mason et al. 1996; Hg(0) assumed same as Hg(2). D ow ("overall K ow ") L[water]/kg[octanol] 0

- a
- b Mason et al. 1996. Solids

- water partition coefficient L[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 rate 1/day 0 0.001 0 Value used in EPA 1997; range is 1E

- 4 to 3E

4/day (Gilmour and Henry 1991). Oxidation rate 1/day 0 0 0 Professional judgment. 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 greater than 17m, up to 3.5/day (Xiao et al. 1995; Vandal et al. 1995; Mason et al. 1995a; Amyot et al. 1997).TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 28 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 1 7 . Mercury Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg Sediment Compartment Type Solids

- water partition coefficient L[water]/kg[solids wet wt] 3,000 50,000 3,000 USEPA 1997 b

. Demethylation rate 1/day N/A N/A 0.0501 Average range of 2E

- 4 to 1E

- 1/day from Gilmour and Henry 1991. Methylation 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 Professional judgment. 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 Methodology for RTR

Attachment A, Addendum 1 1

- 29 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit_Add A 1
- 18

. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenari o

Parameter Name Units Value 2Methyl 712DMB Acenaph

thene Acenaph

thylene BaA BaP BbF BghiP BkF Air Compartment Type Particle dry deposition velocity m/day 500 500 500 500 500 500 500 500 500 Half

- life day 0.154 0.092 0.3 0.208 0.125 0.046 0.596 0.215 0.458 Washout r atio 200000 200000 200000 200000 200000 200000 200000 200000 200000 Surface Soil Compartment Type User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 18 24 56 66.5 680 530 610 415 2140 Root Zone Soil Compartment Type User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 18 24 56 66.5 680 530 610 415 2140 Vadose Zone Soil Compartment Type User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0 0 0 0

Half

- life day 36 48 112 133 1360 1060 1220 830 4280 Groundwater Compartment Type Half

- life day 36 48 112 133 1360 1060 1220 830 4280 Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/kg[algae]) / (g[chem]/L[water]) 2.6 333.4 3 3.7 325 510 317 1539 473

Half

- life day 78 216 25 184 0.375 0.138 90 1670 62.4 Sediment Compartment Type Half

- life day 2290 2290 2290 2290 2290 2290 2290 2290 2290TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 30 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit _Add A 1
- 1 8. PAH Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Chr DahA Fluoran

thene Fluor

ene IcdP Air Compartment Type Particle dry deposition velocity m/day 500 500 500 500 500 McKone et al. 2001

. Half

- life day 0.334 0.178 0.46 0.46 0.262 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and Spero et al. 2000 (Fluorene)

. Washout r atio 200000 200000 200000 200000 200000 Mackay et al. 1986

. Surface Soil Compartment Type User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Professional judgment

.
Half

- life day 1000 940 275 33 730 MacKay et al. 2000 / average of range. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and HSDB 2001e (Fluorene)

. Root Zone Soil Compartment Type User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Professional judgment

.
Half

- life day 1000 940 275 33 730 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / average of range, HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and HSDB 2001e (Fluorene)

. Vadose Zone Soil Compartment Type User input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 Professional judgment

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 31 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit _Add A 1
- 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 Compartment Type , continued Half

- life day 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / twice average of range, HSDB 2001d (Acenaphthene) / multiplied by 2, HSDB 2001b (Acenaphthylene) / multiplied by 2, and HSDB 2001e (Fluorene) / multiplied by 2

. Groundwater Compartment Type Half

- life day 2000 1880 550 66 1460 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include ATSDR 2005 (2

- Methylnaphthalene), USEPA 1998 (7,12

- Dimethylbenz(a)anthracene, Benzo(g,h,i)perylene, and Fluoranthene) / twice average of range, HSDB 2001d (Acenaphthene) / multiplied by 2, HSDB 2001b (Acenap hthylene) / multiplied by 2, and HSDB 2001e (Fluorene) / multiplied by 2

. Surface Water Compartment Type RatioOfConcInAlgaeToConcD issolvedInWater (g[chem]/kg[algae])/ (g[chem]/L[water]) 280 1388 67.4 5.8 1653 K ow from Del Vento and Dachs 2002

.
Half

- life day 1.626 97.8 160 8.5 750 Howard et al. 1991 / upper bound measured or estimated value. Exceptions include HSDB 2005 (2

- Methylnaphthalene), HSDB 2001a (7

- 12 Dimethylbenz(a)anthracene), HSDB 2001d (Acenaphthene), HSDB 2001b (Acenaphthylene), and HSDB 2001c (Benzo(g,h,i)perylene), Montgomery 2000 (Fluoranthene), and Boyle 1985 (Fluorene)

. Sediment Compartment Type Half

- life day 2290 2290 2290 2290 2290 Mackay et al. 1992 / PAH values are the mean half

- life of the log class that Mackay et al. assigned for sediment, except for BbF and IcdP, which were not i n Table 2.3 of Mackay et. al.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 32 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit_Add A 1
- 19

. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value 1,2,3,4,6,7,8,9

- OCDD 1,2,3,4,6,7,8,9

- OCDF 1,2,3,4,6,7,8 - HpCDD 1,2,3,4,6,7,8 - HpCDF 1,2,3,4,7,8,9 - HpCDF 1,2,3,4,7,8 - HxCDD

Air Compartment Type Deposition Velocity m/day 500 500 500 500 500 500

Half

- life day 162 321 64 137 122 42 Washout Ratio m 3 [air]/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 day 1008 1008 1008 1008 1008 1008 Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 5.31 4.54 4.54 2.83 1.9 3.88

Half

- life day 0.67 0.58 47 0.58 0.58 6.3 Sediment Compartment Type Half

- life day 1095 1095 1095 1095 1095 1095TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 33 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit _Add A 1
- 1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value 1,2,3,4,7,8 - HxCDF 1,2,3,6,7,8 - HxCDD 1,2,3,6,7,8 - HxCDF 1,2,3,7,8,9 - HxCDD 1,2,3,7,8,9 - HxCDF 1,2,3,7,8 - PeCDD

Air Compartment Type Deposition 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 characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 Root Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 3650 3650 3650 3650 3650 3650 Vadose Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 1008 Groundwater Compartment Type Half

- life day 1008 1008 1008 1008 1008 1008 Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 2.06 5.36 4.25 5.36 3.26 1.55

Half

- life day 0.58 6.3 0.58 6.3 0.58 2.7 Sediment Compartment Type Half

- life day 1095 1095 1095 1095 1095 1095TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 34 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit _Add A 1
- 1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value 1,2,3,7,8 - PeCDF 2,3,4,6,7,8 - HxCDF 2,3,4,7,8 - PeCDF 2,3,7,8 - TCDD

2,3,7,8 - TCDF

Air Compartment Type Deposition 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 characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth 0 = No, Else = Yes 0 0 0 0 0 Half

- life day 3650 3650 3650 3650 3650 Vadose Zone Soil Compartment Type Input characteristic depth m 0.08 0.08 0.08 0.08 0.08 Use input characteristic depth ( Boolean ) 0 = No, Else = Yes 0 0 0 0 0

Half

- life day 1008 1008 1008 1008 1008 Groundwater Compartment Type Half

- life day 1008 1008 1008 1008 1008 Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water (g[chem]/g[algae])/ (g[chem]/L[water]) 1.75 4.26 1.39 1.76 0.71

Half

- life day 0.19 0.58 0.19 2.7 0.18 Sediment Compartment Type Half

- life day 1095 1095 1095 1095 1095TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 35 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit _Add A 1
- 1 9. Dioxin Chemical

- Specific Parameters for Abiotic Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Reference
Air Compartment Type Deposition v elocity McKone et al. 2001

. Half

- life Atkinson 1996 as cited in USEPA 2000 b ; vapor phase reaction with hydroxyl radical

. Washout r atio Vulykh et al. 2001

. Surface Soil Compartment Type Input characteristic depth Not used (model set to calculate value)

. Use input characteristic depth ( Boolean ) Professional judgment

. Half

- life Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8

- TCDD

. Root Zone Soil Compartment Type Input characteristic depth N ot used (model set to calculate value)

. Use input characteristic depth P rofessional judgment

. Half

- life Mackay et al. 2000; the degradation rate was cited by multiple authors, value is for 2,3,7,8

- TCDD

. Vadose Zone Soil Compartment Type Input characteristic depth Not used (model set to calculate value). Use input characteristic depth ( Boolean ) Professional judgment. Half

- life Average value of the range presented in Mackay et al. 2000; based on estimated unacclimated aerobic biodegradation half

- life, value is for 2,3,7,8

- TCDD. Groundwater Compartment Type Half

- life Average value of the range presented in Mackay et al. 2000; based on estimated unacclimated aerobic biodegradation half

- life, value is for 2,3,7,8

- TCDD. Surface Water Compartment Type Ratio Of Conc In Algae To Conc Dissolved In Water Estimated from K ow value using model from DelVento and Dachs 2002 Half

- life Kim and O’Keefe 1998, as cited in USEPA 2000 b

. Sediment Compartment Type Half

- life Estimation based on Adriaens and Grbic

- Galic 1992,1993 and Adriaens et al. 1995 as cited in USEPA 2000 b

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 36 6

- 29

- 2012 TRIM.FaTE Inputs

Exhibit_Add A 1
- 20

. Cadmium C hemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference
Leaf Compartment Type Transfer factor to leaf particle 1/day 0.002 Professional judgment. Particle on Leaf Compartment Type Transfer factor to leaf 1/day 0.200 Professional judgment. 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 various agricultural plant species. Root to Root Soil Partition

Time to Reach Alpha day 28 Henning et al. 2001.
Stem Compartment Type

- Grasses and Herbs a Transpiration stream concentration factor (TSCF) m 3 [soil pore water]/m 3 [xylem fluid] 0.45 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 Methodology for RTR

Attachment A, Addendum 1 1

- 37 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 21

. Mercury Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg Leaf Compartment Type Transfer factor to leaf particle 1/day 0.002 0.002 0.002 Professional judgment (assumed 1% of transfer factor from leaf particle to leaf). Demethylation rate 1/day N/A N/A 0.03 Calculated from Bache et al. 1973. Methylation rate 1/day 0 0 0 Assumed from Gay 1975, Bache et al. 1973. Oxidation rate 1/day 1.0E+06 0 0 Professional judgment; assumed close to instantaneous Reduction rate 1/day 0 0 0 Professional judgment. Particle on Leaf Compartment Type Transfer factor to leaf 1/day 0.2 0.2 0.2 Professional judgment. Demethylation rate 1/day N/A N/A 0 Professional judgment. Methylation rate 1/day 0 0 0 Professional judgment. Oxidation rate 1/day 0 0 0 Professional judgment. Reduction rate 1/day 0 0 0 Professional judgment. Root Compartment Type

- Grasses and Herbs a Alpha for root

- root zone bulk soil unitless 0.95 0.95 0.95 Selected value. Root/root

- zone
- soil

- water partition coefficient m 3 [bulk root soil]/ m 3 [root] 0 0.18 1.2 Hg 2

- geometric mean Leonard et al. 1998, John 1972, Hogg et al. 1978; MHg

assumed, based on Hogg et al. 1978. t

- alpha for root

- root zone bulk soil day 21 21 21 Professional judgment. Demethylation rate 1/day N/A N/A 0 Professional judgment. Methylation rate 1/day 0 0 0 Professional judgment. Oxidation rate 1/day 0 0 0 Professional judgment. Reduction rate 1/day 0 0 0 Professional judgment. Stem Compartment Type

- Grasses and 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 Professional judgment. Oxidation rate 1/day 0 0 0 Professional judgment. Reduction rate 1/day 0 0 0 Professional judgment.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 38 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 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 Methodology for RTR

Attachment A, Addendum 1 1

- 39 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 23

. PAH Chemical

- Specific Parameters for Plant Compartments in the TRIM.FaTE Screening Scenario

Parameter Name Units Value R eference 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 Professional judgment.

Half

- life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 ( calculated from metabolic rate constant )

. Particle on Leaf Compartment Type Transfer factor to leaf 1/day 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 1.00E

- 04 Professional judgment.

Half

- life day 4.12 17.80 2.31 2.31 17.80 Calculated as 2 times the measured photolysis half

- life from Mackay et al. 1992. Exceptions include values that have been set equal to Benzo(a)pyrene (2

- Methylnaphthalene; 7,12

- Dimethylbenz(a)anthracene; Acenaphthene; Acenaphthylene; Benzo(ghi)perylene; Fluoranthene; and Fluorene)

. Root Compartment Type

- Grasses and 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 Professional judgment.
Stem Compartment Type

- Grasses and Herbs a Half

- life day 3.50 3.50 3.50 3.50 3.50 Edwards 1988 ( calculated from metabolic rate constant )

.

a Roots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 40 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit _Add A 1
- 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 as 1 percent of transfer factor to leaf; highly uncertain. Half

- life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; soybean root cell culture metabolism test data for DDE. Particle on Leaf Compartment Type Transfer factor to leaf 1/day 0.3 Professional judgment based on USEPA 2000 b (an estimate for mercury) and Trapp 1995; highly uncertain. Half

- life day 4.4 McCrady and Maggard 1993; photodegradation sorbed to grass foliage in sunlight; assumed 10 % sunlight per day. Root Compartment Type

- Grasses and Herbs a Half

- life day 70 Arjmand and Sandermann 1985, as cited in Komoba et al. 1995; soybean root cell culture metabolism test data for DDE. Root s oil

- w ater i nteraction

- a lpha unitless 0.95 Professional judgment. 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 Methodology for RTR

Attachment A, Addendum 1 1

- 41 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 24

. Cadmium Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference
Zooplankton Compartment Type Absorption rate constant L[water]/kg[fish wet wt]

- day 1500 Goulet 2007

. Assimilation efficiency from algae unitless 0.5 Goulet 2007

. Elimination rate constant 1/day 0.03 Goulet 2007

. Benthic Invertebrate Compartment Type Sediment p artitioning

- a lpha of
e quilibrium unitless 0.95 Professional judgment

.
Sediment partitioning

- p artition
c oefficient kg[bulk sed/kg[invertebrate wet wt] 0.27 Professional judgment

.
Sediment p artitioning

- t ime to r each
a lpha of e quilibrium day 21 Hare et al. 2001

. Benthic Omnivore Compartment Type Assimilation efficiency from food unitless 0.1 Professional judgment based 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 Professional judgment

. Benthic Carnivore Compartment Type Assimilation efficiency from food unitless 0.1 Professional judgment based 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. Elimination r ate c onstant unitless 1.68E

- 03 Professional judgment

. Water

- column Herbivore Compartment Type Assimilation efficiency from food unitless 0.1 Assumed value based on Yan and Wang 2002. Assimilation efficiency from plants unitless 0.1 Assumed value based on Yan and Wang 2002. Absorption rate constant unitless 2.46 C alculated based on body weight from regression in Hendriks and Heikens 200 1. Elimination rate constant unitless 1.73E

- 02 Professional judgment

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 42 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit _Add A 1
- 2 4 . Cadmium Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Water

- column Omnivore Compartment Type Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 2002. Assimilation efficiency from plants unitless 0.1 Professional judgment based on Yan and Wang 2002. Absorption rate constant unitless 1.23 Calculated based on body weight from regression in Hendriks and Heikens 200 1. Elimination rate constant unitless 1.73E

- 02 Professional judgment

. Water

- column Carnivore Compartment Type Assimilation efficiency from food unitless 0.1 Professional judgment based on Yan and Wang 2002. Absorption rate constant unitless 0.66 Calculated based on body weight from regression in Hendriks and Heikens 200 1. Elimination rate constant unitless 1.73E

- 02 Professional judgment

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 43 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 25

. Mercury Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value Reference Hg(0) Hg(2) MHg Zooplankton Compartment Type Assimilation Efficiency From Algae unitless 0.2 0.015 0.5 Environment Canada 2002. Half

- life day 1.0E+09 1.0E+09 1.0E+09 Professional judgment. How Much Faster Hg Elimination Is Than For MHg unitless 3 3 1 Professional judgment.

Methylation rate 1/day 0 0 0 Professional judgment. Oxidation rate 1/day 0 1.0E+06 0 Professional judgment. Reduction rate 1/day 0 0 0 Professional judgment. Benthic Invertebrate Compartment Type Alpha of equilibrium for sediment partitioning unitless 0.95 0.95 0.95 Selected valu e (i.e., proportion of equilibrium achieved by time “t”). Benthic invertebrate

- bulk sediment partition coefficient kg[bulk sediment]/kg[invertebrate

wet wt] 0.0824 0.0824 5.04 Hg(0)

- assumed based on Hg(2) value; Hg(2) and MHg

- Saouter et al. 1991. t

- alpha for equilibrium for sediment partitioning day 14 14 14 Experiment duration from Saouter et al. 1991. All Fish Compartments Types a Elimination adjustment factor unitless 3 3 1 Trudel and Rasmussen 1997. Assimilation efficiency from food unitless 0.06 0.06 0.5 Williams et al. 2010. Demethylation rate 1/day N/A N/A 0 Professional judgment. Methylation rate 1/day 0 0 0 Professional judgment. Oxidation rate 1/day 1.0E+06 0 0 Professional judgment. Reduction rate 1/day 0 0 0 Professional judgment. Water

- column Herbivore Compartment Type Assimilation efficiency from plankton unitless 0.06 0.06 0.5 Williams et al. 2010.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omnivore, and Water

- column Carnivore.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 44 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 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 Compartment Type Absorption rate constant L[water]/kg[fish

wet wt]

- day 790 42650.94 42231 42302.18 42650.81 42652.78 42650.68 42655.77 42652.5

Assimilation efficiency from algae unitless 0.5 0.25 0.5 0.5 0.46 0.25 0.25 0.25 0.25

Elimination rate constant 1/day 169.68 2.03 148.07 123.44 2.073 1.3864 2.12 0.33 1.48

Half

- life day 0.007788 17 0.00239 0.00239 1.284 16.5 17 17 17 Benthic Invertebrate Compartment Type Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6 100.6

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 Water

- column Carnivore.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 45 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit _Add A 1
- 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 constant L[water]/kg[fish wet wt]

- day 42649.95 42655.48 142000 15000 42655.93 K ow from Arnot et al. 2004. Exception is Berrojalbiz et al. 2009 (2

- Methylnaphthalene, Fluoranthene, and Fluorene)

. Assimilation efficiency from algae unitless 0.46 0.25 0.49 0.5 0.25 K ow from Arnot et al. 2004. Exception is maximum value from Wang and Wang 2006 (7,12

- Dimethylbenz(a)anthracene, Benzo(a)pyrene, Benzo(b)fluoranthene, Benzo(g,h,i)perylene, Benzo(k)fluoranthene, Dibenz(a,h)anthracene, and Indeno(1,2,3

- cd)pyrene)

. Elimination rate constant 1/day 2.3746 0.4331 8.678 81.87 0.269 K ow from Arnot et al. 2004

.
Half

- life day 0.495 17 0.00239 0.0002476 17 McElroy 1990. Exceptions include Berrojalbiz et al. 2009 (2

- Methylnaphthalene, Fluoranthene, and Fluorene) and Moermond et al. 2007 (Benz(a)anthracene and Benzo(a)pyrene)

. Benthic Invertebrate Compartment Type Clearance constant unitless 100.6 100.6 100.6 100.6 100.6 Stehly et al. 1990

. V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 7235 7235 7235 7235 7235 Stehly et al. 1990

.

Half

- life day 0.495 17 0.722 0.722 17 Moermond et al. 2007

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 46 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit _Add A 1
- 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 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 Omnivore, and Water

- column Carnivore.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 47 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit_Add A 1
- 27 . Dioxin Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scena rio

Parameter Name Units Value 1,2,3,4,6,7,8,9 - OCDD 1,2,3,4,6,7,8,9 - OCDF 1,2,3,4,6,7,8 - HpCDD 1,2,3,4,6,7,8 - HpCDF 1,2,3,4,7,8,9 - HpCDF 1,2,3,4,7,8 - HxCDD 1,2,3,4,7,8 - HxCDF 1,2,3,6,7,8 - HxCDD 1,2,3,6,7,8 - HxCDF

Zooplankton Compartment Absorption r ate c onstant L[water]/kg[fish wet wt]

- day 8640 8640 8640 8640 8640 8640 8640 8640 8640

Assimilation e fficiency from a lgae unitless 0.08 0.05 0.21 0.09 0.2 0.31 0.31 0.31 0.31 Elimination r ate c onstant 1/day 0.0102 0.016 0.016 0.0616 0.1829 0.0252 0.1474 0.0099 0.0194 Half

- life day 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 7E+06 Benthic Invertebrate Compartment Clearance c onstant unitless 0 0 0 0 0 0 0 0 0 Sediment p artitioning p artition
c oefficient kg/kg 0.0013 0.0017 0.0055 0.0012 0.042 0.033 0.0081 0.013 0.02

Sediment p artitioning a lpha of
e quilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Sediment p artitioning t ime to
r each a lpha of e quilibrium days 120 42 120 42 42 120 42 120 42

V d (ratio of concentration in benthic invertebrates to concentration in water) ml/g 0 0 0 0 0 0 0 0 0

Half

- life day 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 5776.2 All Fish Compartments a Assimilation e fficiency from f ood unitless 0.08 0.05 0.21 0.09 0.2 0.37 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 Methodology for RTR

Attachment A, Addendum 1 1

- 48 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit _Add A 1
- 2 7. Dioxin Chemical

- Specific Parameters for Aquatic Species in the TRIM.FaTE Screening Scenario

Parameter Name Units Value 1,2,3,7,8,9 - HxCDD 1,2,3,7,8,9 - HxCDF 1,2,3,7,8 - PeCDD 1,2,3,7,8 - PeCDF 2,3,4,6,7,8 - HxCDF 2,3,4,7,8 - PeCDF

2,3,7,8 - TCDD

2,3,7,8 - TCDF

Zooplankton Compartment Absorption Rate Constant L[water]/kg[fish wet wt]

- day 8640 8640 8640 8640 8640 8640 8640 8640
Assimilation Efficiency from Algae unitless 0.31 0.31 0.42 0.42 0.31 0.42 0.41 0.51 Elimination Rate Constant 1/day 0.0099 0.0413 0.0819 0.2316 0.0192 0.4331 0.2268 1.0375 Half

- life day 7E+06 7E+06 7E+06 7E+06 7E+08 7E+08 7E+06 7E+08 Benthic Invertebrate Compartment Clearance Constant unitless 0 0 0 0 0 0 0 0 Sediment Partitioning Partition Coefficient kg/kg 0.015 0.067 0.098 0.024 0.072 0.17 0.205 0.056
Sediment Partitioning Alpha of Equilibrium unitless 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95
Sediment Partitioning Time to Reach Alpha of Equilibrium days 120 42 120 42 42 42 120 42

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 fficiency 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 Methodology for RTR

Attachment A, Addendum 1 1

- 49 6

- 29

- 2012 TRIM.FaTE Inputs Exhibit _Add A 1
- 2 7. Dioxin Chemical

- Specific Parameters for Aquatic 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 interpo lation between values for 1,2,3,4,7,8

- HxCDD and 1,2,3,4,6,7,8

- HpCDD / 1,2,3,4,6,7,8

- HpCDF (i.e., 0.3 to 0.1

– 0.2). Elimination 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 Invertebrate Compartment Clearance c onstant unitless Professional judgment

. 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 Professional judgment

.
Half

- life day Rubinstein et al. 1990; used TCDD data for sandworm

. All Fish Compartmentsa Assimilation Efficiency from Food unitless Morrison et al. 1999. Exceptions include Niimi and Oliver 1996 (1,2,3,4,6,7,8,9

- OCDD, 1,2,3,4,6,7,8,9

- OCDF), Van den Berg et al. 1994 (1,2,3,4,6,7,8

- HpCDD), Berntssen et al. 2007 ( 1,2,3,4,6,7,8

- HpCDF), and 1,2,3,4,7,8,9

- HpCDF set conservatively as approximate linear interpolation between values for 1,2,3,4,7,8

- HxCDD and 1,2,3,4,6,7,8

- HpCDD / 1,2,3,4,6,7,8

- HpCDF (i.e., 0.3 to 0.1

– 0.2). Chemical Uptake Rate Via Gill L[water]/kg[fish wet wt]

- day Muir et al. 1985. Exception is Opperhuizen et al. 1986 (1,2,3,7,8,9

- HxCDF, 1,2,3,7,8

-

PeCDD, 1,2,3,7,8

- PeCDF, 2,3,4,7,8

- PeCDF, 2,3,7,8

- TCDD, 2,3,7,8

- TCDF)

. Half

- life day Berntssen et al. 2007

.

a Screening scenario includes: Benthic Omnivore, Benthic Carnivore, Water

- column Herbivore, Water

- column Omnivore, and Water

- column Carnivore.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 50 6

- 29

- 2012 TRIM.FaTE Inputs 

## References

Ad ria e n s, P

. , Q. Fu
, a n d D

. Gr b i c
- Galic. 1 9 9 5 . B i o a v ail a b ility a n d tra n s f o r m at i o n o f h i gh ly c h l o ri n a t e d d i b e nz o
- p
- dioxins a n d d i b e nz o f u ra n s in a n a e r o b ic s o i l s a n d s e d i me n ts. E n v i r o nm e n tal S ci

e n c e a n d Technology 2 9 ( 9 ): 2 25 2

- 22 60

. Ad ria e n s, P

. , a n d D

. G r b ic

- Galic. 1 9 9 3 . R e du c ti v e d e c h l o ri n ati o n o f P C D D / F b y a n a e r o b ic c u lt u r e s a n d
s e d i me n ts. Or

g a n o h al o g e n C o m

p o und s 1 2 : 10 7

- 1 10

. Ad ria e n s, P

. , a n d D

. G r b ic

- Galic. 1 9 9 2 . Eff e ct o f c o c o n ta m i n a n ts a n d c o n c e n tr a ti o n o n t h e a n a e r o b ic b i o tra n s f o r m a ti o n o f P C D D / F in m et h a n o g e n ic r i v er s e d i me n ts. Or g a n o h a l o g e n C om

p o und s 8 : 2 0 9

- 2 12.
ATSDR (Ag e n cy f o r T o x i c Sub sta n c e s a n d D i s ease Re g ist r y). 2 0 05

. T o xi c o l o g ical p r o file f o r n a ph t h al e n e , 1

- m e t h y l n a ph t h al e n e , a n d 2

- m e t h y l n a ph t h al e n e

. A v a i la b le at: h tt p : / / w ww

. a ts d r

. c d c .go v

/ T o x P r o file s / T P

. as p ? i d = 2 40 & ti d = 4 3

. A T S D R. 1 9 99

. T o xi c o l o g ical p r o file f o r c a d m i u m

. A v aila b le at h tt p:/ / w ww

. a t s d r

. c d c .g o v

/ t o x p r o fil e s / t p

. as p ? i d = 4 8 & ti d = 1 5

. A T S D R. 1 9 98

. T o xi c o l o g ical p r o file f o r c h l o r o d i b e nzo
- p
- d i o xi n s ( C DD s ). A v aila b l e at: h tt p: / / w w w

. ats d r

. c d c

. g o v

/ t o x p r o fil e s / t p. as p ? i d =9 3 8 & ti d = 1 9 4

. A m b r o s e , R. A
., J r., T. A

. W o o l, a n d J

. L

. M ar t i n

. 1 9 95

. T h e wa t e r qu ali t y a n al y s i s s i m u lat i o n p r o g r a m , W AS P 5 , P art A : Mo d e l d o c u me n tat i o n

. A t h e n s, G A : U .S . E P A N at i o n al Ex p o s u re R e s e a r ch L a b o ra t o r y , Ec o s y s t e m s D i v is i o n

. A P H A ( American Public Health Association ). 19 95

. S t a nd ard m et h o d s f o r t h e e x a m i n at i o n o f wa t e r a n d waste w a t e r. Was h i ng t o n , D C.

A m y o t, M

. , D

. Le a n , a n d G. M ierl e

. 1 9 9 7

. P h o t o c h e m i c al f o r m at i o n o f v o lati l e me r c u ry in h i g h arct i c lak e s. E n v ir o n me n t al T o xi c o l o g y a n d C h e m ist

r y 1 6 ( 10 ) : 2 0 5 4

- 2 0 6 3

. A rj m a nd
, M

. , a n d H

. S a nd e r m a nn

. 1 9 8 5

. Me ta b o li s m o f DD T a n d r e l a t e d c o m pound s in c e ll s u s p e n s i o n c u lt u r e s o f s oy b e an ( G l y ci n e m ax L

. ) a n d w h e at ( Trit u c u m a e s ti v u m L. ) Pe st

i ci d e B i o c h e m ist r y a n d P h y s i o l o g y 2 3 : 3 89

. A r n o t, J ., a n d F.A

. G o b as. 2 0 04 . A f o o d w e b b i o a cc u m u l ati o n m o d e l f o r o r g a n ic c h e m ica l s in a qu atic
ec o s y s te m s. E

n v i r o n m e n tal T

o xic o l o g y a n d C h e m ist

r y 23 ( 1 0 ) : 2 3 43

- 2 3 55.

A tki n s o n , R. 1 9 9 6

. A t m o s p h e ric c h e m ist r y o f P CBs, P C DD s a n d P C D F s. Iss u e s in E n v ir o n me n tal S cie n ce a n d T

e c hn o l o g y 6 : 53

- 72.

Bac h e , C .A.
, W .J . G u t e n m a nn , L

. E. S t. J o hn
, J r., R. D

. S w e e t, H

. H

. H atfi e l d
, a n d D

. J

. L isk. 1 9 73

. Me r c u ry a n d me t h y l me rc u r y c o n t e n t o f a g ric u lt u ral c r o p s g r o w n o n s o ils t r e a t e d with v ar i o u s me rc u r y c o m p o und s. J o u r n al o f Ag r

i c u lt u ral a n d Fo o d C h e m ist r y 2 1 :6 07

- 6 1 3.

Ba e s, C. F
., III, R. D

. Sh ar p , A

. L . Sj o r ee n , a n d R

. W. Sh o r. 1 9 84 . A r e v iew a n d a n al y s i s o f p ar a m e t e rs f o r ass e ssi n g tra n s p o rt o f e n v i r o nm e n tally r e le a s e d ra d i o nu cli d e s t h r o ug h a g ric u l t u r e . OR N L

- 5 7 8 6

. O ak Ri dg e N ati o n al L a b o ra t o r y , Oak Ri dg e , T N

. Bar b e r, M .C. 20 08

. D ie t ary up take m o d els u s ed f o r m o d e li n g t h e b i o a c c u m u lat i o n o f o r g a n ic
c o n t a m i n a n ts in f i s h

. E n v i r o nm e n t al T o x i c o l o g y a n d C h em ist r y 2 7 ( 4 ) : 7 55

- 7 7 7.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 51 6

- 29

- 2012 TRIM.FaTE Inputs B e r n tsse n , M

. H .G

. , T .A . G i ske g jer de
, G. R o s e n l und
, B

. E. T o r ste n s en, a n d A

. K

. L u nd e b ye

. 20 07

. P r e d icti n g w o rld h e alth o r g a n i z ati o n t o xic e qu i v a l e n cy fac t o r d i o xin a n d d i o xi n
- li k e p o l y c h l o ri n a t e d b i ph e n y l le v e ls in fa r m e d A tla n tic sa l m o n ( S a l m o sala r ) b as e d o n k n o wn l e v e ls in f ee d

. E n v i r o n me n tal T

o xi c o l o g y a n d C h e m istr

y 26 ( 1 ) : 1 3

- 2 3

. d o i : 1 0

. 1 8 97 / 06

- 1 2 2 r

. 1

. B e rr o jal b i z , N.
, S

. L a c o r t e , A . Cal b e t, E. S ai z , C. Bar a ta, a n d J

. D ac h s. 2 0 09

. A cc u mu lati o n a n d c y cli n g o f p o l y c y clic a r o m atic h y d r o c ar b o n s in zo o p la n k t o n

. E n v ir o nm e n tal S c i

e n ce a n d T

e c hn o l o g y 4 3 ( 7 ) : 2 29 5

- 2 3 01

. d o i: 1 0

. 1 0 2 1 / e s 8 0 1 8 2 2 6

. Bidleman, T.F. 1 9 8 8. A t mo s pheric pro c ess e s. Environmental Science and Technology 2 2 : 3 6 1

- 3 67. Bishop, K

. H

. ; L ee, Y

. H

. ; Munthe, J

. ; Dambrine, E. 1 9 9 8. X yl e m sap as a pathway for t otal mercury and methylmercury transport from s oils t o tree canopy in t he bor e al forest. Biogeochemistry 4 0: 1 01

- 1 13.

B oy l e, T. 1 9 8 5

. V ali d at i o n a n d p r e d icta b ility o f la b o ra t o ry m e t h o d s f o r ass e ssi n g t h e fate a n d e ff e cts o f
c o n t a m i n a n ts in a qu a tic ec o s y s t e ms . Ba l ti m o r e , M D : AS TM I n t e r n ati o n al.

B ud a v ari, S. [ e d

. ]

. 1 9 96

. T h e M e rck I nd e x - A n e n c y cl op e d ia o f c h e m icals, d r ug s, a n d b i o l o g icals. W h ite h o u s e S t a ti o n , N J : M e rck a n d C o

. , I n c

. , p

. 1 78

. C A RB ( California Air Resources Board ). 1 9 94

. D e ve l o p me n t o f i n t e r me d ia tra n sf e r fac t o rs f o r p o ll u ta n ts, V o l um e II: M e tals a n d n o n
- vo l a tile o r g a n ic c o m p o un d s. P B 95

- 2 6 0 6 91

. Calif o r n i a : A ir R e s o u rc e s B o ar d

. M arc h

. Cal E P A ( California Environmental Protection Agency ). 1 9 93

. CalT O X
, A m u lt i me d i a t o tal

- e x p o s u re m o d e l f o r h a z ar d o u s
- was t e si t e s, P art I I : T h e d y n a m ic m u lt i me d ia tra n s p o r t a n d tra n s f o r m ati o n

. M o d e l P r e p ar e d f o r : T h e Offi c e o f S cie n tific A ffairs. De p a r t m e n t o f T o xic Sub sta n c e s C o n tr o l. S acr a m e n t o , Calif o r n ia. D e c e m b e r. D ra f t F i n al.
Car p i, A ., a n d S. E. L i ndb e r g

. 1 9 97

. Sun li gh t

- m e d iat e d e m iss i o n o f e l e m e n tal me r c u ry fr o m s o il
a m e nd e d with m un ici p al s e wa g e sl u dg e

. E n v i r o nm e n tal S ci

e n c e a n d T

e c hn o l o g y 3 1 ( 7 ) : 2 0 85

- 2 0 91.
C oe , J. M

. , a n d S. E. L i ndb e r g

. 1 9 87

. T h e m o r ph o l o g y a n d si z e d istri bu ti o n o f a t m o s ph e ric p a r ticl e s d e p o si t e d o n f o lia g e a n d i n e rt s u rfa c e s. J o u r n al o f t h e A ir P o ll u ti

o n C o n t r o l A s s o c i ati o n 3 7 : 2 37

- 2 4 3.
Cra n k, J.
, N. R. M c F arla n e , J

. C. N e w b y , G

. D

. P a t e r s o n , a n d J. B. Pe d l e y

. 1 9 8 1

. D iff u s i o n p r o c e s s e s in
e n v ir o n me n tal s y st e m s. I n : P a t e r s o n e t al. 1 9 9 1

. Lo n d o n : M a c m illan P r e s s , L t d

. D el V e n t o , S.
, a n d J

. D ac h s. 2 0 02

. P r e d i c ti o n o f up t a k e d y n a m ics o f p e rsis t e n t o r g a n ic p o ll u ta n ts b y b act e ria a n d phy t o p la n k t on

. E n v ir o n me n tal T

o xi c o l o g y a n d C h e m ist

r y 2 1 ( 1 0 ) : 2 0 99

- 2 1 07.

E d war d s, N. T. 1 9 8 8

. A ss i m ilati o n a n d m e ta b o li s m o f p o l y c y clic ar o m at i c h y d r o ca r b o n s b y ve g e tat i o n

– an a pp r o ach t o t h is c o n t r o ve rsial i ss u e a n d s ugg e s ti on s f o r f u t u r e r e s e arc h

. I n : ( M

. C o o ke a n d A.J. De nn is, e d s

. ) P oly

nuc le a r A r

om a tic H y d r o ca r bon s: A D e cad

e of P r o g r e s s

. Bat t e lle P r e s s, C o l u m bu s, O H ; pp

. 211

- 2 2 9.
Environment Canada . 2002

. Ecosystem Health Science

- Based Solutions: Canadian Tissue Residue Guidelines for the protection of Wildlife Consumers of Aquatic Biota: Methylmercury. Report No. 1

- 4.
Ga y , D

. D

. 1 9 75

. Bi o tra n sf o r m at i o n a n d c h e m ical f o r m o f me rc u r y in p la n ts. I n t e r n ati o n al C o n f e r e n ce o n H ea v y M etals in t h e E n v i r on m e n t, pp

. 87

- 95

. V o l. II, P art 1

. Oc t o b e r.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 52 6

- 29

- 2012 TRIM.FaTE Inputs Gil m o u r, C

. C., a n d E .A

. H e n r y

. 1 9 9 1

. Me rc u r y me t h y lat i o n in a qu atic s y s t e m s af f e c t e d b y a cid d e p o sit i o n

. E

n v ir o n me n t al P o ll u t i o n 7 1 : 1 3 1

- 1 69. Goulet, R.R., S. Krack, P.J. Doyle, L. Hare, B. Vigneault, and J.C. McGeer. 2007. Dynamic multipathway modeling of Cd bioaccumulation in Daphnia magna using waterbourne and dietborne exposures. Aquatic Toxicology

. 81: 117

- 125.

H a n sc h
, C

. , A

. L e o , a n d D

. H oe k m a n

. 1 9 95

. Ex p l o ri n g Q SA R

- H y d r o p h o b ic, e lec t r on ic, a n d ster i c c o n sta n t s. Was h i ng t o n , D C : Am e rican C h em i cal So cie t y 15 4

. H ar e , L ; T e ssie r , A ; and Warr e n , L

. 2 0 01

. Ca d m i u m a cc umu lati o n b y i n ve r t e b ra t e s l i v i n g at t h e s e d i me n t

– wat e r i n t e rfa c e

. Environmental Toxicology and Chemistry 2 0 : 8 8 0

- 8 89.

H ar n e r, T., a n d Bi d l e m a n, T.F

. 1 9 98 . Octa n o l

- air p ar t it i o n c oe f f icie n t f o r d e s cri b i n g p ar t ic l e/ g as p a r titi o n i n g o f ar o m atic c o m p o und s in u r b an air. E n v i r o nm e n tal S ci

e n ce a n d T

e c hn o l o g y 3 2 : 14 94

- 1 5 02.

H S D B ( Hazardous Substances Data Bank ). 2 0 0 5

. B et h es d a, M D : N at i o n al L i b ra r y o f Me d ici n e , U. S

. [ L a s t R ev is i o n D a t e 1 0 / 2 7/ 2 0 05 ]

. 2

- M e t h y l n a ph t h a l e n e ; H a z ar d o u s Sub sta n c e s D ata b a n k Nu m b e r : 5 2 7 4

. A v aila b le a t : h t t p: // t o x n e t

. n l m .n i h.g o v

/ c g i

b i n / si s / s e a rc h / a ? db s+ h s d b : @ t e r m + @ D OC N O +5 2 7 4

. H S D B. 2 0 0 1 a. Bet h es d a, M D : N at i o n al L i b ra r y o f Me d ici n e , U. S

. [ L a s t R ev is i o n D ate 0 8 / 0 9/ 2 0 01 ]

. 7 , 1 2

- D i m e t h y l b e nz ( a )a n t h rac e n e ; H a z ar d o u s Sub sta n c e s D ata b a n k N u m b e r : 2 9 38

. A v aila b le a t: h tt p: / / t o x n e t .n l m .n i h.g o v

/ c g i

- b i n/ sis / s e a rc h / a ? db s+ h s db : @ t e r m + @ D O C N O + 2 9 38

. H S D B. 2 0 0 1 b

. B e t h e s d a, M D : N ati o n al L i b ra r y o f M e d ici n e , U. S

. [ L ast R ev is i o n D ate 0 8 / 0 9/ 2 0 01 ]

. A c e n a ph t h y l e n e ; H a z ar d ou s Sub sta n c e s D ata b a n k Nu m b e r: 2 6 6 1

. Av aila b le at: h t t p : / / t o x n e t .n l m .n i h.g o v

/ c g i

- b i n / si s / s e arc h / a ? db s+ h s db : @ t e r m + @ D O C N O + 2 6 6 1

. H S D B. 2 0 0 1 c. Bet h es d a, M D : N at i o n al L i b ra r y o f Me d ici n e , U. S

. [ L a s t R ev is i o n D ate 0 8 / 0 9/ 2 0 01 ]

. B e nz o ( gh i) p e r y l e n e ; H a z a r d o u s Sub sta n c e s D a ta b a n k Nu m b e r: 6 1 7 7

. A v aila b le a t : h t t p: // t o x n e t

. n l m .n i h.g o v / c g i

- b i n / si s / s e a rc h / a ? db s+ h s d b : @ t e r m + @ D OC N O +6 1 7 7

. H S D B. 2 0 0 1 d

. B e t h e s d a, M D : N ati o n al L i b ra r y o f M e d ici n e , U. S

. [ L ast R ev is i o n D ate 0 8 / 0 9/ 2 0 01 ]

. A c e n a ph t h e n e ; H a z ar d o u s Sub sta n c e s D ata b a n k Numb e r: 2 6 59

. A v aila b le at: h t t p : / / t o x n e t .n l m .n i h.g o v

/ c g i

- b i n / si s / s e arc h / a ? db s+ h s db : @ t e r m + @ D O C N O + 2 6 59

. H e nd riks, AJ ; and H eike n s, A

. 2 0 0 1 . T h e po wer o f si z e. 2

. Rate c o n s t a n ts a n d e qu ili b r i u m ra t i o s f o r acc u m u lat i o n o f i no r g a n ic s ub sta n c e s r e lat e d to s p e c i e s w e i gh t. Environmental Toxicology and Chemistry 2 0 : 1 4 2 1

- 1 4 3 7

. H e nn i ng , B. J
., H .G. Sn ym an, a n d T. A

. S

. A ve li ng

. 2 0 01

. P la n t

- s o il i n t e ract i o n s o f sl u dg e
- b o r n e h e a v y m etals a n d t h e e f f e ct o n m ai z e (Z e a m a y s L

. ) s e e d li n g g r o wt h

. Wat e r S A 27 ( 1 ) : 7 1

- 7 8

. H o gg , T .J.
, J. R. B e tta n y , a n d J. W

. B. S te w art. 1 9 78

. T h e up take o f 2 0 3 H g
- la b e l e d m e rc u ry c o m p o und s b y b r o m e g rass f r o m irri g a t e d und ist u r b e d s o il c o l u m n s. J o u r n al o f E n v i r o n me n tal Q u alit

y 7 : 4 4 5

- 4 50.

H o l z w o r t h
, G

. C. 1 9 72

. M ix i n g h ei gh ts, wi n d s p e e d s, a n d p o t e n tial f o r u r b an air p o ll u ti o n t h r o ugh o u t t h e c o n ti gu o u s U n it e d S tat e s. P r e p ar e d f o r E P A Offi c e o f A ir P r o g ra m s. R e s e arch Tr i a ng le P ar k
, N C.

H o war d
, P .H.
, R .S

. B o e t h li ng , W .F

. J ar v is, W

. M

. M ey l a n , a n d E

. M

. M ichal e n k o

. 1 9 91

. H a ndbo o k o f
e n v ir o n me n tal d e g ra d a ti o n rat e s. C h e ls e a, M i c h i g a n : L e wis P ub lis h e rsTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 53 6

- 29

- 2012 TRIM.FaTE Inputs Hud s o n , R

. , S.A . G h e ri n i, C .J . Watras, a n d D

. P o rc e lla. 1 9 94

. M o d e li n g t h e b i o g e o c h em i c al c y cle o f
me rc u r y in l a k e s : T h e M e r c u ry C y cli n g M o d e l ( M C M ) a n d its a pp licat i o n t o t h e M TL S t ud y L a k e s. I n : C .J. Watras a n d J. W. Hu cka b e e , e d s. M e rc u ry p o ll u t i o n i n t e g rati o n a n d s y n t h e sis. Le wis P ub lis h e r s. pp

. 4 7 3

- 5 2 3

. IC F(ICF International ). 2 00 5

. M e m o ra nd u m : TRI M .F a TE sc r ee n i n g s c e n ari o : Aq u atic f oo d w e b a n a l y sis;
s ub m it t e d t o D e ir d re M u r p h y a n d T e rri H o lli ng s w o r t h
, U. S . E P A
, fr o m M ar g ar e t M c V e y a n d R e b e cca K a u ff m a n , ICF C o n s u lti ng

. Oct o b e r 18

. J acks o n , R

. B

. , J . Ca n a d e ll, J. R. E h leri ng e r, H.A

. M o o n e y , O

. E. S ala, a n d E

. D

. S c hu l z e

. 1 9 96 . A g l o b al
a n al y sis o f r o o t d istri bu t i o n s f o r t e r r e strial b i o me s. O e c o l o g ia 1 0 8 : 3 89

- 4 11.

J o hn
, M

. K

. 1 9 7 2

. M e rc u ry up ta k e f r o m s o il b y v ar i o u s p la n t s p e c i e s. B u lletin o f E n v ir o n me n tal C o n t a m i n at i o n a n d T o xi c o l o g y 8 : 77

- 80.

K a u s h al, S

. S
., P

. M . G r o f f m a n , G.E. L i k e n s, K .T. B e l t, W

. P

. S ta c k
, V.R. Ke ll y , L .E. Ba n d
, a n d G.T. F is h e r, 2 0 05 . I n c r e as e d S ali n i z at i o n o f Fr e sh Wa t er in t h e No r t h eastern U n ited S tates. P r o c e e d i ng s o f t h e N ati o n al A ca d

e m y o f S cie n c e s 1 0 2 : 1 3 5 1 7

- 1 3 5 20.

K i m , M

. , a n d P

. O ’ K e e f e

. 1 9 98

. T h e r o le o f n a t u ral o r g a n ic c o m p o und s in p h o t o s e n siti z e d d e g ra d at i o n 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 a n d d i b e nz o f u ra n s. Or g a n o h al o g e n C o m

p o und s 3 6 : 3 7 7

- 38 0

. K o mo b a , D

. , C. L a n g e b ar te l s, a n d H

. S a nd e r m a nn

. 1 99 5

. M e ta b o lic p r o c e s s e s f o r o r g a n ic c h e m ica l s in p la n ts. I n : P la n t c o n t a m i n a ti o n mo d e li n g a n d si m u l a t i o n o f o r g a n ic c h e m ical p r o c e ss e s. Tra pp , S.
, a n d M c F arla n e , J. C

. , e d s

. , CRC P r e ss, B o ca R a t o n , F L

. P a g e s 6 9

- 1 0 3

. Le it h
, H

. 1 9 75

. P r i m a r y p r odu cti v i t y in t h e b i o s ph e r e

. I n : H

. Le i th a n d R

. W. W h it a k e r. E c o l o g ical S t ud i e s, V o l um e 1 4

. Sp ri ng e r
- V e rl a g

. L e m air, P

. , A

. M at h ie u , S

. C arri e r e , J.F

. N ar b o nn e , M

. L afa u ri e , a n d J . Gi ud ic e lli. 1 9 92

. H e p a tic b i o tra n s f o r m a ti o n e nzy m e s in a qu ac u lt u re E u r o p e an s e a b ass ( D i c e n trarc hu s la b rax

): ki n e tic p ara m e t e rs a n d i ndu cti o n with b e nz o ( a ) p y r e n e

. C o m p ara t i v e Bi o c h em ist r y a n d P h y s i o l o g y 1 03 ( B ): 8 4 7

- 85 3

. L e o n ar d
, T. L
., G.E. T a y l o r, J r

. , M .S . G u sti n , a n d G

. C .J

. F e r n a nd e z

. 1 9 98

. M e rc u ry a n d p la n ts in
c o n t a m i n ated s o il s : 1 . U p t a ke, p art i ti o n i ng , a n d e m i s s i o n t o t h e a t m o s ph ere. E n v i r o nm e n tal T o x i c o l o g y a n d C h em i

s tr y 1 7 : 2 0 63

- 2 0 71.

L i ndb e r g , S .E

. , T. P

. M e ye r s, G.E. Ta y l o r, R.R. T u r n e r, a n d W. H

. S c h r oe d e r 1 9 92

. A t m o s ph e r e
- Su rface Exc h a ng e o f Me rc u r y t o a F o r e s t: R e s u lts o f M o d e lli n g a n d Gra d ie n t App r o a c h e s. Journal of Geo ph ysical Research 97 ( D 2 ) : 2 5 19

- 2 5 28.

L i ndb e r g , S. E. 1 9 9 6

. F o r e s t s a n d t h e g l o b al b i o g e o c h e m ical c y c l e o f me rc u r y : T h e i m po r t ance o f und e rsta nd i n g air

/ v e g e t a t i o n e x c h a ng e p r o c e s s e s. I n : W. Ba e y a n s e t al

. , e d s. G l ob al a n d r e g i o n al
me rc u r y c y cl e s: S o u rc e s , f l u x e s, a n d m ass b ala n c e s. p p

. 3 5 9

- 3 80.

M ack a y , D

. , W

. Y. Sh i u , a n d K

. C. M a. 2 0 00

. P h y sica l

- c h e m ical p r o p e rt i e s a n d e n v i r o nm e n tal f a te h a ndb ook

. B o ca Ra t o n , F L : CRC P r e ss LL C.

M ac k a y , D
., W.Y. Sh i u , a n d K .C. M a. 1 9 92

. Ill u stra t e d h a ndb oo k o f p h y sical

- c h e m i cal p r o p e r ti e s a n d
e n v ir o n me n tal fa t e f o r o r g a n ic c h e m icals: p o l y nu clear ar o m atic h y d r o car b o n s , p o l y c h l o ri n at e d d i o xi n s, a n d d i b e nz o f u ra n s. C h e ls e a, M I: L e wis P ub lis h e rs.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 54 6

- 29

- 2012 TRIM.FaTE Inputs M ack a y , D

. , S

. P at t e r s o n , a n d W. H . Sc h r oe d e r. 1 9 8 6

. Mo d e l d e scri b i n g t h e ra t e s o f tra n s f e r p r o c e s s e s o f o r g a n ic c h e m ica l s b e t w e e n at m o s ph e r e a n d w at e r. E n v ir o n me n tal S ci

e n c e a n d T

e c hn o l o g y 2 0 ( 8 ) : 8 1 0

- 8 1 6.

M a s o n , R

. P

. , J. R. R e i n f e l d e r , a n d F. M

. M

. M o r e l. 1 9 96

. U p tak e , t o xici t y , a n d t r o ph i c tra n sf e r o f m e rc u ry in a c o a stal d ia t o m

. E

n v ir on me n tal S cie n ce a n d T e c hn o l o g y 30 ( 6 ) : 1 8 3 5

- 1 8 4 5

. M a s o n , R

. P

. , F. M

. M

. M o r e l, a n d H.F

. H e m o nd

. 1 9 9 5 a. T h e r o le o f m ic r oo r g a n i s m s in e l e m e n tal m erc u r y f o r m ati o n in n at u ral wat e r s. Wat e r, A ir, a n d S o il Po ll u ti

o n 8 0 : 7 75

- 7 8 7.

M a s o n , R

. P

. , J. R. R e i n f e l d e r , a n d F. M

. M

. M o r e l. 1 9 95 b

. Bi o a c c u m u lat i o n o f me rc u ry a n d
me t h y l m e rc u r y

. Wat e r A ir a n d S o il P o ll u ti o n 8 0 ( 1

- 4 ) : 9 1 5

- 9 21.

M cCra dy
, J. K

. , a n d S

. P

. M a gg ar d

. 19 9 3 . U pd a t e a n d p h o t o d e g ra d ati o n o f 2 , 3 , 7 , 8

- t e trac h l o r o
- p
- d i o xin
s o r b e d t o g r a ss f o lia g e

. E n v ir o nm e n tal S c i

e n ce a n d T

e c hn o l o g y 2 7 : 3 43

- 3 5 0

. M cEl r oy , A . E. 1 9 90

. P o l y c y clic ar o m a tic h y d r o car b o n me t a b o li s m in t h e p o l y c h a e te N e r e is v ir e n s

. Aqu atic T

o xi c o l o g y 18 ( 1 ) : 3 5

- 5 0

. M cG e e r, J. C

. , K .V . Brix, J

. M

. S k e aff, D

. K

. D e F o re s t, S. I. Bri gh a m , W .J

. Ad a m s, a n d A. Gree n

. 2 0 03

. I n v er s e r e lati o n s h ip b e t w ee n b i o c on c e n tra t i o n fac t o r a n d e x p o s u re c o n c e n tra t i o n f o r m e tals: i m p licat i o n s f o r h a z ard ass e s s me n t o f m e t a ls in t h e a qu at i c e n v ir o n m e n t. E n v i r o nm e n tal T

o x i c o l og y a n d C h e m ist

r y ( 22 ) 5 : 1 0 1 7

– 1 0 37.

M c K o n e , T

. E

. , A

. B o dn ar, a n d E. H e rtw i c h

. 2 0 0 1

. D e v e l o pm e n t a n d e v al u at i o n o f stat e
- s p e cific la nd sca p e d ata s e t s f o r m u lti m e d ia s ou r ce
- t o
- d o se m o d e ls. U n i v e rsi t y o f Calif o r n ia at B e r k e le y

. Supp o r t e d b y t h e U. S . E n v ir o n me n t al P r o t e c t i o n Ag e n cy ( Su stai n a b le T e c hn o l o g y D i v is i o n , N at i o n al Risk M a n a g e me n t R e s e a r ch L a b o ra t o r y ) a n d E n v ir o n me n tal D e f e n se Fund

. Ju l y

. L B N L

- 4 3 7 2 2.

M illard, E.S., D

. D

. My l e s, O.E. J o hannss o n, and K

. M

. R a lph. 1 9 96

. P h y t o plank t o n ph o t o s y nth e s i s at t w o ind e x stat i o ns in L a k e Ont a rio 19 87

- 1 9 9 2 : as s e s s me nt o f the l o ng
- t e rm r e s p o nse to p h o sph o rus c o nt r o l. Canadian Journal of Fisheries and Aquatic Sciences 5 3 : 1 0 92

- 1 11 1

. M o e r m o nd
, C. T. A
., T

. P . T r aas, I. R oe ssi n k
, K. V el t m a m , A.J

. H e nd riks, a n d A

. A

. K oe l m a n s. 2 0 07

. Mo d e li n g d ecrea s ed f oo d c h ain acc u m u lat i o n o f P AH s du e to st r o n g s o r p ti o n to c ar b o n ac e o u s 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

. E n v i r o n me n tal S ci

e n ce a n d T

e c hn o l o g y 4 1 ( 1 7 ) : 61 85

- 6 1 91

. d o i: 10

. 10 2 1 /e s 0 7 0 2 3 6 4

. Mo n t g o m e r y , J

. 20 00 . G r o u nd wat e r c h em i cals d e s k r e f ere n c e

. B o c a Ra t o n , F L : CR C P r e ss L L C, p

. 1 7 0 1

. Mo rri son , H.A
., D.M

. W h itt le
, C .D

. Me tcalf e , a n d A

. J

. N ii m i. 1999

. App licat io n o f a f oo d w e b b i o acc umu lati o n mode l fo r t h e p r ed i c ti o n o f po l y c h l or i n at e d b i pheny l, dio xi n , a n d f u ran c ongene r 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

- 1400

. Muh l b aier, J ., a n d G

. 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

. Water, Air and Soil Pollution (15):45

- 49

. M u ir, D

. C

. , W

. K

. M ars h all, a n d G

. R. We b s ter. 1 9 85 . B i o c o n c e n tr a t i on o f P C DD s b y fis h : E ffe c ts o f
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

. C h e m o s p h ere 14 (6 /7 ) : 8 29

- 8 3 3

. NLM ( National Library of Medicine ). 2002

. H a z ar dou s Sub sta n ce Da ta Ba n k ( HSD B). Av aila b le at: h tt p://to x ne t .n l m.n i h.gov/ c g i

- b i n/ sis /htm l gen?HSD B.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 55 6

- 29

- 2012 TRIM.FaTE Inputs N ii m i, A ., a n d B. Oli v e r. 1 9 8 6 . Bi o l o g ical h alf

- l i ve s o f c h l o ri n at e d d i b e n z o
- p
- d i o xi n s a n d d i b e nz o f u ra n s in rai nb o w t r o u t ( S a l

mo ga i r d 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

- 53.

N ii m i, A

. J, . a n d V. P a l a zzo

. 1 9 86 . B i o l o g ical h alf

- li v e s o f e i gh t p o l y c y clic ar o m a tic hyd r o car b o n s ( P AH s ) in rai nb o w t r o u t ( S al

m o g air d n e ri ). Wa t e r R e s e a r

c h 2 0 (4 ) : 5 03

- 5 0 7.

N ria gu , J. O. 1 9 80

. C a d m i u m in t h e e n v ir o n me n t. P a r t I: E c o l o g ical c y cli ng

. N e w Y o rk: J o h n Wil e y & S o n s. C h a p t e r 1 5 : U p ta k e a n d e f f e cts o f c a d m i u m in h i gh e r p la n ts. pp

. 6 0 8

- 6 09.
O pp e r hu i z e n , A.
, W .J . Wa g e n aar, F .W. M

. v an d er W i e l en, M

. v an d e n B e r g , K . Ol i e , a n d F

. A

. P .C. G o b as. 1 9 86 . U p take a n d e l i m i n at i o n o f P C D D / P C D F c o ng e n e rs b y fish a f t e r a qu eo u s e x p o s u re t o a fl y - ash e xtra c t f r o m a mu n ici p al i n ci ne r a t o r. C h e m o s ph e r e 1 5 ( 9

– 1 2 ) : 2 04 9

- 2 0 5 3

. d o i : 10

. 10 16 / 0 0 4 5

- 6 5 35 ( 8 6 ) 9 0 5 11

- 4

. Passivirta, J., Sinkkonen, S., Mikkelson, P., Rantio, T., Wania, F. (1999) Estimation of vapor pressures, solubilities and Henry’s law constants of selected persistent organic pollutants as function s of temperature. Chemosphere 39, 811

– 832.

P at e r s o n , S.
, D

. M a c ka y , a n d A . Gla d m a n

. 1 9 91 . A f ug acity m o d e l o f c h em i cal up t ake b y p la n ts f r o m s o il a n d air. C h e m o s

ph e r e 2 3: 5 3 9

- 5 65.

Pe t e rs e n , G

. , A

. I ve rf e l d t , a n d J

. M un t h e

. 1 9 9 5

. A t m o s ph e ric m e rc u ry s p e c i e s o v e r C e n tral a n d N o rt h e rn E u r o p e

. Mo d e l calc u l a ti o n s a n d c o m p ari s o n w i th o b se r v at i o n s f r o m t h e N o r d ic A i r a n d P r e ci p itat i o n N e t w o rk f o r 1 9 8 7 a n d 1 9 8 8

. A t mo s p h e ric E n v ir o n m e n t 2 9 : 47

- 68.

Po r v ari, P ., a n d M . Ve r ta. 1 9 9 5

. M et h y l m e rc u ry p r o d u cti o n in f l o o d e d s o ils: A la b o ra t o ry st ud y

. Wa t e r, A ir, a n d S o il Po ll u t

i o n 8 0 : 7 65

- 7 7 3.

Rie d e r e r, M

. 1 99 5

. P a r tit i on i n g a n d tra n s p o rt o f o r g a n ic c h e m icals b e t w ee n t h e at m o s ph e ric
e n v ir o n me n t a n d l e a ve s. I n : Tra pp , S . a n d J . C. M c F arl a n e , e d s. P la n t c o n t a m i n at i on : 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 e m ical p r o c e ss e s. B o ca R a t o n , F L : Le w i s P ub lis h e r s. pp

. 15 3

- 1 90.

R ub i n st e i n , N. I

. , R .J

. P r u e ll, B

. K . Ta p li n , J.A

. L i V o lsi, a n d C

. B. N o r w o o d

. 1 9 9 0 . B i o a v aila b ility o f 2 , 3 , 7 , 8

- TC D D
, 2 , 3 , 7 , 8

- T C D F a n d P C Bs t o m ari n e b e n t h o s f r o m P assaic ri v e r s e d i me n t s. C h e m o s ph e r e 2 0 : 1 09 7

- 1 1 02

. Sangster, J. (1993) LOGKOW, A Databank of Evaluated Octanol

- Water Partition Coefficients. 1st Edition, Montreal, Quebec, Canada.
S a o u t e r, E

. , F . Ri b e y r e , A

. B o ud o u , a n d R. M a u r y b rac h e t. 1 9 9 1

. H e xa g e n ia ri

g i 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 - Ex p e ri m e n t al st ud ies o n me rc u r y tra n s f e rs f r o m 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.
S ij m , D .T. H

. 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

. C h e m o s ph e r e 1 9 ( 1

- 6 ) : 2 6 3

- 26 6

. S i mo n ic h
, 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

- 51. Sp e r o , J.
, B. De V i t o , a n d L

. T h e o d o r e

. [ e d s. ]

. 2 00 0 . R e gu lat o ry c h e m icals h a ndbo o k. N e w Y o r k
, N Y: CRC P r e ss, p

. 1 0 7 2

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 56 6

- 29

- 2012 TRIM.FaTE Inputs S t e h l y , G

. R

. , P .F

. L a nd r u m , M

. G. H e n r y , a n d C. K l e mm

. 1 9 90

. T o xi c o kin e t i c s o f P A H s in H e xa g e n ia E n v ir o n me n t al T o xi c o l o g y a n d C h em i

s tr y 9 : 1 67

- 1 7 4

. T h i b o d e a u x, L .J

. 1 9 9 6 . E n v i r o nm e n tal c h e m o d y n a m ic s : M o v e me n t o f c h e m icals i n air, wa t e r, a n d s o il. N e w Y o r k
, 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 ai 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

- 1 5 1.

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 xic
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

- 1 8 4.
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 sses 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 sti 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 re 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 Bc. 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 facil 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 m
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 (TRI) 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 rc 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.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 1 1

- 57 6

- 29

- 2012 TRIM.FaTE Inputs 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) bioaccumulation from water and food by the benthic amphipod Leptocherius plumulosus

. Environmental Toxicology and Chemistry 29(8):1755

- 1761.

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 sis 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 Tiere d Screening Methodology for RTR

Attachment A, Addendum 2 2

- 1 6

- 29

- 2012 Description of MIRC 

## Addendum 2. Description of Multimedia Ingestion Risk Calculator (MIRC) Used for RTR Exposure and Risk Estimates[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 3 6

- 29

- 2012 Description of MIRC 

## CONTENTS, ADDENDUM 2

1. Introduction

................................ ................................ ................................ ................... 2

- 11
1.1. Purpose and Overview

................................ ................................ ......................... 2

- 11
1.2. Scope of MIRC

................................ ................................ ................................ ..... 2

- 11 1.3. Use in EPA’s Air Toxics Program

................................ ................................ .......... 2

- 12
1.4. MIRC Highlights

................................ ................................ ................................ .... 2

- 12 1.5. Organization of This Addendum

................................ ................................ ............ 2

- 13

2. MIRC Overview

................................ ................................ ................................ ............. 2

- 13
2.1. Software

................................ ................................ ................................ ............... 2

- 14
2.2. Exposure Pathways

................................ ................................ .............................. 2

- 16 2.3. Receptor Groups

................................ ................................ ................................ .. 2

- 17

3. Exposur e Algorithms

................................ ................................ ................................ ..... 2

- 19 3.1. Farm Food Chain Algorithms

................................ ................................ ................ 2

- 19 3.1.1. Estimating Chemical Concentrations in Produce

................................ ... 2

- 20 3.1.2. Estimating Chemical Concentrations in Animal Products

....................... 2

- 26 3.2. Chemical Intake Calculations for Adults and Non

- Infant Children

......................... 2

- 29 3.2.1. Chemical Intake from Soil Ingestion

................................ ....................... 2

- 31 3.2.2. Chemical Intake from Fish Ingestion

................................ ...................... 2

- 31 3.2.3. Chemica l Intake from Fruit Ingestion

................................ ..................... 2

- 32 3.2.4. Chemical Intake from Vegetable Ingestion

................................ ............. 2

- 33 3.2.5. Chemical Intake from Animal Product Ingestion

................................ .... 2

- 34 3.2.6. Chemical Intake from Drinking Water Ingestion

................................ ..... 2

- 37 3.3. Total Chemical Intake

................................ ................................ ........................... 2

- 37 3.4. Chemical Intake Calculations for Nursing Infants

................................ .................. 2

- 38 3.4.1. Infant Average Daily Absorbed Dose

................................ ..................... 2

- 38 3.4.2. Chemical Concentration in Breast Milk Fat

................................ ............ 2

- 39 3.4.3. Chemical Concentration in Aqueous Phase of Breast Milk

.................... 2

- 43 3.4.4. Alternative Model for Infant Intake of Methyl Mercury

............................ 2

- 45

4. Dose

- Response Values Used for Assessment

................................ .............................. 2

- 46
4.1. Cadmium

................................ ................................ ................................ .............. 2

- 49
4.2. Dioxins (2,3,7,8

- TCDD)

................................ ................................ ........................ 2

- 49 4.3. Mercury

................................ ................................ ................................ ................ 2

- 50
4.4. Polycyclic Organic Matter

................................ ................................ ..................... 2

- 50

5. Risk Estimation

................................ ................................ ................................ ............. 2

- 52 5.1. Cancer Risks

................................ ................................ ................................ ........ 2

- 52 5.2. Non

- cancer Hazard Quotients

................................ ................................ .............. 2

- 54 5.2.1. Hazard Quotients for Chemicals with a Chronic RfD

.............................. 2

- 55TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 4 6

- 29

- 2012 Description of MIRC

5.2.2. Hazard Quotients for Chemicals with RfD Based on

Developmental Effects

................................ ................................ .......... 2

- 55 5.2.3. Hazard Index for Chemicals with RfDs

................................ .................. 2

- 55

6. Model Input Options

................................ ................................ ................................ ...... 2

- 56 6.1. Environmental Concentrations

................................ ................................ .............. 2

- 57 6.2. Farm

- Food

- Chain Parameter Values

................................ ................................ .... 2

- 57 6.2.1. List of Farm

- Food

- Chain (FFC) Parameters

................................ .......... 2

- 57 6.2.2. Produce Parameter Values

................................ ................................ .... 2

- 59 6.2.3. Animal Product Parameter Values

................................ ......................... 2

- 72 6.3. Adult and Non

- Infant Exposure Parameter Values

................................ ................ 2

- 74 6.3.1. Body Weights

................................ ................................ ........................ 2

- 75 6.3.2. Water Ingestion Rates

................................ ................................ ........... 2

- 76 6. 3.3. Local Food Ingestion Rates

................................ ................................ ... 2

- 77 6.3.4. Local Fish Ingestion Rates

................................ ................................ .... 2

- 81 6.3.5. Soil Ingestion Rates

................................ ................................ ............... 2

- 86 6.3.6. Total Food Ingestion Rates

................................ ................................ .... 2

- 87 6.4. Other Exposure Factor Values

................................ ................................ .............. 2

- 87 6.4.1. Exposure Frequency

................................ ................................ ............. 2

- 88 6.4.2. Fraction Contaminated

................................ ................................ .......... 2

- 88 6. 4.3. Preparation and Cooking Losses

................................ ........................... 2

- 88 6.5. Breast

- Milk Infant Exposure Pathway Parameter Values

................................ ...... 2

- 90 6.5.1. Receptor

- specific Parameters

................................ ................................ 2

- 90 6.5.2. Chemical

- Specific Parameter Values

................................ ..................... 2

- 94

7. Summary of MIRC Default Exposure Parameter Settings

................................ ............. 2

- 97
7.1. Default Ingestion Rates

................................ ................................ ........................ 2

- 98 7.1.1. Fish Ingestion Rates

................................ ................................ .............. 2

- 98 7.1.2. Farm Food Chain Ingestion

................................ ................................ ... 2

- 98
7.2. Default Screening

- Level Population

- Specific Parameter Values

......................... 2

- 100 7.3. Default Chemical

- Specific Parameter Values for Screening Analysis

................. 2

- 101
7.4. Screening

- Level Parameter Values for Nursing Infant Exposure

......................... 2

- 103 7.4.1. Dioxins 2

- 103 7.4.2. Methyl Mercury

................................ ................................ .................... 2

- 103

8. References

................................ ................................ ................................ ................. 2

- 104

1. Hazard Identi fication and Dose Response Assessment

................................ .................. 3

- 5

2. Dermal Exposure Estimation

................................ ................................ ........................... 3

- 6
2.1. Equations for Estimating Dermal Exposure

................................ ............................. 3

- 6
2.2. Exposure Factors and Assumptions

................................ ................................ ....... 3

- 7 2.3. Receptor

- Specific Parameters

................................ ................................ ................ 3

- 8
2.4. Scenario

- Specific Parameters

................................ ................................ ................ 3

- 8 2.5. Chemical

- Specific Parameters

................................ ................................ ................ 3

- 9TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 5 6

- 29

- 2012 Description of MIRC 3. Screening

- Level Cancer Risks and Non

- Cancer Hazard Quotients

............................... 3

- 10 3.1. Dermal Cancer Risk

................................ ................................ .............................. 3

- 10 3.2. Dermal Hazard Quotient

................................ ................................ ....................... 3

- 11

4. Dermal Screening Results

................................ ................................ ............................ 3

- 11

5. References

................................ ................................ ................................ ................... 3

- 14

6. Introduction

................................ ................................ ................................ ..................... 2

- 4 6.1. Angler Behavior

................................ ................................ ................................ ...... 2

- 4 6.2. Fish Biology

................................ ................................ ................................ ............ 2

- 5 6.2.1. Lake Productivity

................................ ................................ ..................... 2

- 5 6.2.2. Proportion of Fish Biomass by Trophic Level

................................ ........... 2

- 7 6.2.3. Minimum Viable Population Size

................................ ............................. 2

- 8 6.3. Summary of Assumptions for the Lake Size Analysis

................................ ............. 2

- 9 6.4. Equations Used to Determine Lake Fish Populations

................................ ............. 2

- 9 6.5. References

................................ ................................ ................................ ........... 2

- 11TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 6 6

- 29

- 2012 Description of MIRC Exhibits, Addendum 2
Exhibit_Add A1

- 1. TRIM.FaTE Simulation Parameters for the TRIM.FaTE

Screening Scenario

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

- 5 Exhibit_Add A1

- 2. Meteorological Inputs for the TRIM.FaTE Screening Scenario

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

- 6 Exhibit_Add A1

- 3. Air Parameters for the TRIM.FaTE Screening Scenario

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

- 7 Exhibit_Add A1

- 4. Soil and Groundwater Parameters for the TRIM.FaTE

Screening Scenario

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

- 8 Exhibit_Add A1

- 5. Runoff Assumptions for the TRIM.FaTE Screening Scenario

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

- 10 Exhibit_Add A1

- 6. USLE Erosion Parameters for the TRIM.FaTE Screening

Scenario

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

- 11 Exhibit_Add A1

- 7. Terrestrial Plant Placement for the TRIM.FaTE Screening

Scenario

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

- 12 Exhibit_Add A1

- 8. Terrestrial Plant Parameters for the TRIM.FaTE Screening

Scenario

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

- 13 Exhibit_Add A1

- 9. Surface Water Par ameters for the TRIM.FaTE Screening

Scenario

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

- 15 Exhibit_Add A1

- 10. Sediment Parameters for the TRIM.FaTE Screening

Scenario

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

- 16 Exhibit_Add A1

- 11. Aquatic Animals Food Chain, Density, and Mass for the

TRIM.FaTE Screening Scenario

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

- 17 Exhibit_Add A1

- 12. Cadmium Chemical

- Specific Parameters for the TRIM.FaTE

Screening Scenario

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

- 18 Exhibit_Add A1

- 13. Mercury Chemical

- Specific Parameters for the TRIM.FaTE

Screening Scenario

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

- 19 Exhibit_Add A1

- 14. PAH Chemical

- Specific Parameters for the TRIM.FaTE

Screening Scenario

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

- 20 Exhibit_Add A1

- 15. Dioxin Chemical

- Specific Parameters for the TRIM.FaTE

Screening Scenario

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

- 22 Exhibit_Add A1

- 16. Cadmium Chemical

- Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

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

- 24 Exhibit_Add A1

- 17. Mercury Chemical

- Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

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

- 25 Exhibit_Add A1

- 18. PAH Chemical

- Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

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

- 29 Exhibit_Add A1

- 19. Dioxin Chemical

- Specific Parameters for Abiotic

Compartments in the TRIM.FaTE Screening Scenario

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

- 32 Exhibit_Add A1

- 20. Cadmium Chemical

- Specific Parameters for Plant

Compartments in the TRIM.FaTE Screening Scenario

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

- 36 Exhibit_Add A1

- 21. Mercury Chemical

- Specific Parameters for Plant

Compartments in the TRIM.FaTE Screening Scenario

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

- 37TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 7 6

- 29

- 2012 Description of MIRC Exhibit_Add A1

- 22. PAH Chemical

- Specific Parameters for Plant Compartments

in the TRIM.FaTE Screening Scenario

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

- 38 Exhibit_Add A1

- 23. PAH Chemical

- Specific Parameters for Plant Compartments

in the TRIM.FaTE Screening Scenario

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

- 39 Exhibit_Add A1

- 24. Cadmium Chemical

- Specific Parameters for Aquatic

Species in the TRIM.FaTE Screening Scenario

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

- 41 Exhibit_Add A1

- 25. Mercury Chemical

- Specific Parameters for Aquatic Species

in the TRIM.FaTE Screening Scenario

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

- 43 Exhibit_Add A1

- 26. PAH Chemical

- Specific Parameters for Aquatic Species in

the TRIM.FaTE Screening Scenario

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

- 44 Exhibit_Add A1

- 27. Dioxin Chemical

- Specific Parameters for Aquatic Species in

the TRIM.FaTE Screening Scenario

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

- 47 Exhibit_Add A2

- 1. Overview of MIRC Software Application for Performing Farm

Food

- Chain Ingestion Exposure and Risk Calculations

................................ ... 2

- 15 Exhibit_Add A2

- 2. Transfer Pathways for Modeled Farm Food Chain (FFC)

Media

................................ ................................ ................................ .............. 2

- 17 Exhibit_Add A2

- 3. Chemical Transfer Pathways for Produce

................................ ............... 2

- 20 Exhibit_Add A2

- 4. Es timating Chemical Concentration in Aboveground Produce

................. 2

- 20 Exhibit_Add A2

- 5. Chemical Transfer Pathways for Animal Products

................................ ... 2

- 26 Exhibit_Add A2

- 6. Oral Dose

- response Values Used to Calculate RTR Screening

Threshold Emission Rates for PB

- HAP Chemicals a

................................ ........ 2

- 47 Exhibit_Add A2

- 7. WHO 2005 Toxic Equivalency Factors (TEFs) for Dioxin

Congeners

................................ ................................ ................................ ...... 2

- 50 Exhibit_Add A2

- 8. Oral Dose

- response Values for Polycyclic Organic Matter

(POM) Groups a

................................ ................................ ............................... 2

- 51 Exhibit_Add A2

- 10. Chemical

- Specific Inputs for Produce Parameters for

Chemicals Included in MIRC

................................ ................................ ........... 2

- 59 Exhibit_Add A2

- 11. Chemical

- Specific Inputs by Plant Type for Chemicals in

MIRC

................................ ................................ ................................ .............. 2

- 61 Exhibit_Add A2

- 12. Non

- Chemical

- Specific Produce Inputs

................................ ................. 2

- 71 Exhibit_Add A2

- 13. Animal Product Chemical

- spec ific Inputs for Chemicals

Included in MIRC

................................ ................................ ............................ 2

- 72 Exhibit_Add A2

- 14. Soil and Plant Ingestion Rates for Animals

................................ ........... 2

- 74 Exhibit_Add A2

- 15. Mean and Percentile Body Weight Estimates for Adults and

Children

................................ ................................ ................................ .......... 2

- 76 Exhibit_ Add A2

- 16. Estimated Daily Per Capita Mean and Percentile Water

Ingestion Rates for Children and Adults a

................................ ......................... 2

- 77 Exhibit_Add A2

- 17. Summary of Age

- Group Specific Food Ingestion Rates for

Farm Food Items

................................ ................................ ............................ 2

- 78 Exhibit_Add A2

- 18. Daily Mean and Percentile Consumer

- Only Fish Ingestion Rates for Children and Adults ( IR CO,y ) a

................................ ............................ 2

- 84TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 8 6

- 29

- 2012 Description of MIRC Exhibit_Add A2

- 19. Fraction of Population Consuming Fresh water/Estuarine

Fish on a Single Day ( F PC,y )

................................ ................................ ............ 2

- 84 Exhibit_Add A2

- 20. Calculated Long

- term Mean and Percentile per capita Fish

Ingestion Rates for Children and Adults ( IR PC,y )

................................ .............. 2

- 85 Exhibit_Add A2

- 21. Calculated Mean and 90 th Percentile Per capita Fish

Ingestion Rates for Populations of Recreational Fishers (IR PC,y )

.................... 2

- 85 Exhibit_Add A2

- 22. Daily Mean and Percentile Soil Ingestion Rates for Children

and Adults

................................ ................................ ................................ ....... 2

- 86 Exhibit_Add A2

- 23. Daily Mean and Percentile Per Capita Total Food Intake for

Children and Adults

................................ ................................ ......................... 2

- 87 Exhibit_Add A2

- 24. Fraction Weight Losses from Preparation of Various Foods

.................. 2

- 89 Exhibit_Add A2

- 25. Scenario

- and Receptor

- Specific Input Parameter Values

Used to Estimate Infant Exposures via Breast Milk

................................ ......... 2

- 91 Exhibit_Add A2

- 26. Average Body Weight for Infants

................................ ........................... 2

- 91 Exhibit_Add A2

- 27. Time

- weighted Average Body Weight for Mothers

................................ 2

- 92 Exhibit_Add A2

- 28. Infant Breast Milk Intake Rates

................................ ............................. 2

- 94 Exhibit_Add A2

- 29. Chemical

- specific Input Parameter Values for Breast Milk

Exposure Pathway

................................ ................................ .......................... 2

- 95 Exhibit_Add A2

- 30. Farm Food Category Ingestion Rates for Health Protective

Screening Scenario for Farming Households

................................ .................. 2

- 99 Exhibit_Add A2

- 31. Mean Body Weight Estimates for Adults and Children a

....................... 2

- 100 Exhibit_Add A2

- 32. Chemical

- Specific Parameter Values for Input to MIRC a

..................... 2

- 101 Exhibit_Add A2

- 33. Chemical and Animal

- Type Specific Biotransfer Factor (Ba)

Values for Input to MIRC

................................ ................................ ............... 2

- 102 Exhibit_Add A3

- 1. Cancer Slope Factors and Reference Doses Based on

Absorbed Dose

................................ ................................ ................................ . 3

- 6 Exhibit_Add A3

- 2. Receptor

- Specific Body Surface Area Assumed to be

Exposed to Chemicals

................................ ................................ ...................... 3

- 8 Exhibit_ Add A3

- 3. Scenario

- Specific Exposure Values for Water and Soil

Contact

................................ ................................ ................................ ............. 3

- 9 Exhibit_Add A3

- 4. Chemical

- Specific Dermal Exposure Val ues for Water and

Soil Contact

................................ ................................ ................................ .... 3

- 10 Exhibit_Add A3

- 5. Summary of Dermal Non

- Cancer Hazards

................................ .............. 3

- 12 Exhibit_Add A3

- 6. Summary of Dermal Cancer Risks

................................ .......................... 3

- 13TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 9 6

- 29

- 2012 Description of MIRC Equations, Addendum 2 Equation 2

- 1. Chemical Concentration in Aboveground Produce

................................ .......... 2

- 21 Equation 2

- 2. Chemical Concentration in Aboveground Produce Due to Root

Uptake

................................ ................................ ................................ ............ 2

- 21 Equation 2

- 3. Chemical Concentration in Aboveground Produce Due to

Deposition of Particle

- phase Chemical

................................ ............................ 2

- 22 Equation 2

- 4. Chemical Concentration in Aboveground Produce Due to

............................... 2

- 22 Equation 2

- 5. Conversion of Aboveground Produce Ch emical Concentration from

Dry

- to Wet

- Weight Basis

................................ ................................ ................ 2

- 23 Equation 2

- 6. Chemical Concentration in Belowground Produce: Nonionic

Organ ic Chemicals

................................ ................................ .......................... 2

- 24 Equation 2

- 7. Chemical Concentration in Belowground Produce: Inorganic

Chemicals

................................ ................................ ................................ ....... 2

- 25 Equation 2

- 8. Conversion of Belowground Produce Chemical Concentration from

Dry

- to Wet

- Weight Basis

................................ ................................ ................ 2

- 25 Equation 2

- 9. Chemical Concentration in Beef, Pork, or Total Dairy

................................ ...... 2

- 26 Equation 2

- 10. Chemical Concentration in Poultry or Eggs

................................ ................... 2

- 27 Equation 2

- 11. Incidental Ingestion of Chemical in Soil by Livestock

................................ ..... 2

- 27 Equation 2

- 12. Ingestion of Chemical in Feed by Livestock

................................ ................... 2

- 28 Equation 2

- 13. Chemical Concentration in Livestock Feed (All Aboveground)

...................... 2

- 28 Equation 2

- 14. Chemical Concentration in Livestock Feed Due to Root Uptake

................... 2

- 29 Equati on 2

- 15. Average Daily Dose for Specified Age Group and Food Type

...................... 2

- 29 Equation 2

- 16. Chemical Intake from Soil Ingestion

................................ ............................. 2

- 31 Equation 2

- 17. Chemical Intake from Fish Ingestion

................................ ............................ 2

- 31 Equation 2

- 18. Consumption

- weighted Chemical Concentration in Fish

.............................. 2

- 32 Equation 2

- 19. Chemical Intake from Consumption of Exposed Fruits

................................ . 2

- 33 Equation 2

- 20. Chemical Intake from Consumption of Protected Fruits

................................ 2

- 33 Equation 2

- 21. Chemical Intake from Exposed Vegetables

................................ .................. 2

- 34 Equation 2

- 22. Chemical Intake from Protected Vegetables

................................ ................. 2

- 34 Equation 2

- 23. Chemical Intake from Root Vegetables

................................ ........................ 2

- 34 Equation 2

- 24. Chemical Intake from Ingestion of Beef

................................ ........................ 2

- 35 Equation 2

- 25. Chemical Intake from Dairy Ingestion

................................ ........................... 2

- 35 Equation 2

- 26. Chemical Intake from Pork Ingestion

................................ ............................ 2

- 35 Equation 2

- 27. Chemical Intake from Poultry Ingestion

................................ ........................ 2

- 36 Equati on 2

- 28. Chemical Intake from Egg Ingestion

................................ ............................. 2

- 36 Equation 2

- 29. Chemical Intake from Drinking Water Ingestion

................................ ............ 2

- 37TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 10 6

- 29

- 2012 Description of MIRC Equations 2

- 30 to 2

- 35. Total Average Daily Dose of a Chemical for Different

Age Groups

................................ ................................ ................................ .... 2

- 37 Equation 2

- 31.

................................ ................................ ................................ ....................... 2

- 37 Equation 2

- 32.

................................ ................................ ................................ ....................... 2

- 37 Equation 2

- 33.

................................ ................................ ................................ ....................... 2

- 37 Equation 2

- 34.

................................ ................................ ................................ ....................... 2

- 37 Equation 2

- 35.

................................ ................................ ................................ ....................... 2

- 37 Equation 2

- 36. Lifetime Average Daily Dose (LADD)

................................ ........................... 2

- 38 Equation 2

- 37. Average Daily Dose of Chemical to the Nursing Infant

................................ . 2

- 39 Equation 2

- 38. Chemical Concentration in Breast Milk Fat

................................ ................... 2

- 40 Equation 2

- 39. Daily Maternal Absorbed Intake

................................ ................................ ... 2

- 41 Equation 2

- 40. Biological Elimination Rate Constant for Chemicals for Non

lactating Women

................................ ................................ ............................. 2

- 42 Equation 2

- 41. Biological Elimination Constant for Lipophilic Chemicals for

Lactating Women

................................ ................................ ............................ 2

- 42 Equation 2

- 42. Chemical Concentration in Aqueous Phase of Breast Milk

........................... 2

- 43 Equation 2

- 43. Fraction of Total Chemical in Body in the Blood Plasma

Compartment

................................ ................................ ................................ .. 2

- 44 Equation 2

- 44. Biological Elimination Rate Constant for Hydrophilic Chemicals

................... 2

- 45 Equation 2

- 45. Calculation of Infant Average Daily Absorbed Dose of Methyl

Mercury

................................ ................................ ................................ ........... 2

- 46 Equation 2

- 46. Calculation of Excess Lifetime Cancer Risk

................................ ................. 2

- 53 Equations 2

- 47 to 2

- 53. Lifetime Cancer Risk: Chemicals with a Mutagenic MOA

for Cancer

................................ ................................ ................................ ....... 2

- 54 Equation 2

- 48.

................................ ................................ ................................ ....................... 2

- 54 Equation 2

- 49.

................................ ................................ ................................ ....................... 2

- 54 Equation 2

- 50.

................................ ................................ ................................ ....................... 2

- 54 Equation 2

- 51.

................................ ................................ ................................ ....................... 2

- 54 Equation 2

- 52.

................................ ................................ ................................ ....................... 2

- 54
Equation 2

- 53. ELCR

................................ ................................ ................. 2

- 54 Equati on 2

- 54. Hazard Quotient for Chemicals with a Chronic RfD

................................ ...... 2

- 55 Equation 2

- 55. Hazard Index Calculation

................................ ................................ ............. 2

- 56 Equation 2

- 56. Calculation of Age

- Group

- Specific and Food

- Specific Ingestion

Rates

................................ ................................ ................................ .............. 2

- 80 Equation 2

- 57. Calculation of Alternative Age

- Group

- Specific Fish Ingestion

Rates

................................ ................................ ................................ .............. 2

- 83TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 11 6

- 29

- 2012 Description of MIRC 

## 1. Introduction
1.1. Purpose and Overview This document provides a detailed description of the Multimedia Ingestion Risk Calculator (MIRC), a modeling tool and database designed to assist in estimating risks via multiple ingestion pathways, particularly for food products grown or raised at home or on a farm

. 19 MIRC was designed to estimate risks to humans from ingestion of produce or animal products, fish, and water in the vicinity of a source of chemical emissions to air. The user can evaluate either generalized (e.g., health protective default) or more site

- specific scenarios using the same tool. MIRC includes a database of exposure parameter values, offering the user the option of selecting mean, median, and upper percentile values for many parameters, data permitting. Generally health protective default values were assigned to each parameter in the tool and the default configuration is used for initial risk screening efforts by EPA’s Office of Air Quality Planning and Standards’ (OAQPS) for Risk and Technology Review (RTR) multimedia risk assessments. MIRC also allows the user to define the farm food chain (FFC) parameter values and receptor characteristics to better represent a site

- specific scenario.

With user

- input concentrations for one or more chemicals in air and soil and air

- to

- surface deposition rates, MIRC calculates the chemical’s conce ntrations in home

- or farm

- grown produce and animal food products using FFC algorithms adapted from EPA’s Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (hereafter referred to as HHRAP; EPA 2005a). MIRC uses these calculat ed concentrations, along with user

- input chemical concentrations for fish and drinking wate r, to estimate chemical intake rates, as average daily doses (ADDs), for adults, children, and nursing infants. Users can obtain chemical input concentrations and d eposition 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 chemic al. The most appropriate HQ for a chemical depends on its toxic mode of action and the duration of exposure required to produce an effect. MIRC also estimates average lifetime ADDs and compares those to cancer slope factors (CSFs) to estimate cancer risk s. A breast milk ingestion pathway is available to estimate exposure and risks to nursing infants. MIRC was developed to be a flexible, transparent application. The tool includes chemical transfer and ingestion exposure algorithms and a database of param eter values, many with several options, used by these equations

. The MIRC database includes values for the relevant physiochemical properties and toxicity reference values for more than 500 chemicals, including approximately 60 inorganics taken primarily from a database developed for HHRAP (EPA 2005a). Although designed for OAQPS ’ RTR assessments for sources of hazardous air pollutants (HAPs), the tool is flexible in its design and can be used to assess risks in many other contexts where soil and air conc entrations are predicted or measured.


### 1.2. Scope of MIRC For persistent and bioaccumulative (PB) chemicals, risks from direct inhalation of the chemical can be much less than risks from ingestion of the chemical in water , fish, and food products

19 Fully functional versions of MIRC have been developed in both Access

- based and Excel

- based formats; however, MIRC currently is not publicly available.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 12 6

- 29

- 2012 Description of MIRC grown in an a rea 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 e xposed 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 populat ions. Households that consume high quantities of self

- caught fish or locally grown produce and animal products may be particularly susceptible to ingestion of chemicals transferred from air in the vicinity of an air emissions source. For PB chemicals in particular, therefore, EPA developed methods of estimating risk from indirect exposure pathways associated with the deposition of airborne chemicals to gardens and farms, as described in HHRAP (EPA 2005 a).


### 1.3. Use in EPA’s Air Toxics Program
For PB

- HAPs, indirect exposure pathways, such as ingestion, might contribute more to total risk than the inhalation pathway. EPA therefore developed several computer software tools to assist in evaluating exposure and risk from non

- inhalation pathways. EPA developed the Total Risk Integrated Methodology (TRIM) Environmental Fate, Transport, and Ecological Exposure (TRIM.FaTE) computer program to simulate the release, transport, and fate of HAPs from a specific source through out the area in which local (non

- source) che mical 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 eco systems by wet and dry deposition. Movement of the chemical through a watershed via erosion and runoff, uptake by plants, and other abiotic and biotic transfer processes also are simulated. For the chemical that reaches surface waters, TRIM.FaTE models u ptake and bioaccumulation to trophic level (TL) 3 and 4 fish (i.e., pan fish and game fish, respectively). MIRC was developed to process TRIM.FaTE results, in particular, air deposition rates and the concentrations of a chemical, after a specified durati on 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 fo ods, including various types of fruits and vegetables, poultry, swine, and dairy (and beef) cattle. MIRC also calculates the associated risks for individuals who consume those foods. MIRC was designed to use specific TRIM.FaTE results to estimate FFC
con centrations, ingestion exposures, and human health risks for OAQPS’ RTR assessments. It uses the same approach that OAQPS intends to implement directly in its TRIM system via three modules beyond TRIM.FaTE: TRIM Farm Food Chain, TRIM.Expo Ingestion , and TR IM.Risk

. 20



### 1.4. MIRC Highlights Although designed to assist EPA OAQPS in its RTR assessments, MIRC is a stand

- alone software application that can be used in other contexts. A user can supply either measured or estimated chemical concentrations for soil, air, wa ter, and fish , and also can provide air deposition rates likely for the location(s) of interest based on local meteorology. The user can accept the default values for many exposure parameters and screen for small possibilities of risk, or the user can sel ect other options or overwrite parameter values to tailor the estimates to a specific scenario or location.

20 General information about the TRIM system is available at http://www.epa.gov/ttn/fera/trim_gen.html

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 13 6

- 29

- 2012 Description of MIRC MIRC complies with EPA’s latest guidelines for exposure and risk assessment, including HHRAP ; the Agency’s 2005 Guidelines for Carcinogen Risk Ass essment (Cancer Guidelines)
, Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens (Supplemental Guidance), and Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants ( EPA 2005b, c,d); and its 2008 Child

- Specific Exposure Factors Handbook ( EPA 2008a)

. In particular, MIRC provides several important capabilities:

 When provided air and soil concentrations, the MIRC software package allows rapid calculation of screening

- level exposures and risks associated with household consumption of locally grown/raised foods.

 MIRC can calculate exposures and risks associated with incidental ingestion of surface soils, fish consumption, and drinking water.

 The tool calculates ADDs (i.e., chemical intake rates) for six “built

- in” age groups to allow use of age

- group
- specific body weights, ingestion rates, food preferences, and susceptibility to toxic effects.

 Its database of chemical information covers plant

- and animal

- speci fic transfer factors and other inputs that determine concentrations in farm food stuffs.

 Value options for receptor characteristics in the database include the mean and 50th, 90th, 95th, and 99th percentile values where data permit.

 For carcinogens with a mutagenic mode of action, MIRC estimates a lifetime ADD using the three lifestages and potency adjustment factors recommended in EPA’s 2005 Cancer Guidelines and Supplemental Guidance.

 The data for exposure parameters in the tool have been updated to inclu de the latest recommended values for children issued September 30, 2008, in the Agency’s Child

Specific Exposure Factors Handbook (CSEFH) (EPA 2008a).



### 1.5. O rganization of This Addendum Section s 2 through 5 of this addendum describe the exposure and risk models implemented in MIRC. Section 2 provides an overview of the FFC exposure scenario and indicates options available to a user to tailor the scenario to specific appl ications. Section 3 describes the exposure algorithms used in MIRC, including how ADDs are calculated. Section 4 presents the toxicity reference values included in MIRC to calculate risks. Section 5 describes the risk characterization algorithms in MIRC. Section 6 of this document describes data input options for the model. Section 7 describes the default parameterization of MIRC for application to health protective risk screening assessments, and Section 8 provides the references. Note that the default parameterization described in Section 7 was used to estimate Tier 1 screening thres hold emission rates of PB

- HAPs from RTR facilities. These emissions levels are assumed to pose negligible risk to subsistence communities in the vicinity of a facility emitting the PB

- HAPs to air. Users of MIRC can modify the default values for many of th e 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 re creational 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 Methodology for RTR

Attachment A, Addendum 2 2

- 14 6

- 29

- 2012 Description of MIRC consumption, incidental soil ingestion, fish ingestion, and in gestion of ten types of farm food chain (FFC) products : exposed fruits, protected fruits, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs. The tool also includes a breast milk ingestion and risk module for nursing infants

. For fruits and vegetables, the terms “exposed” and “protected” refer to whether the edible portion of the plant is exposed to the atmosphere

. The remainder of this overview consists of three sections. The first (Section 2.1 of this addendum ) provides an overview of the MIRC software package. The second and third sections summarize the ingestion exposure pathways included in the tool and th e “built

- in” receptor age categories, respectively ( Section s 2.2 and 2.3 of this addendum ).


### 2.1. Software
The MIRC application includes the following components:

 A graphical user interface through which the user locates and accesses various input and output tables.

 Input tables in which the user can enter environmental concen trations of a chemical estimated for air, soil, drinking water, and fish tissue.

 Internal chemical transfer and exposure algorithms and database of options for FFC algorithm parameter value, chemical

- specific inputs, and exposure factors.

 Tabulated outputs of calculated chemical concentrations in the various farm food products (e.g., fruits, vegetables, beef, eggs) and ADDs for those foods and for water and fish ingestion for each receptor category.

 Output tables with estimated cancer risks and non

- c ancer hazard estimates associated with total ingestion exposure to each chemical for each receptor category.
Exhibit_Add A 2

- 1 provides a flowchart displaying the types of required and optional inputs and the general flow of calculations carried out by the tool.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 15 6

- 29

- 2012 Description of MIRC

Exhibit_Add A 2
- 1

. Overview of MIRC Software Application for Performing Farm

- Food

- Ch ain Ingestion Exposure and Risk CalculationsTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 16 6

- 29

- 2012 Description of MIRC A form within the graphical user interface 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 ercent iles). This feature facilitate s the analysi s of various exposure scenarios. To begin an analysis, the user must supply values for the following chemical

- specific pa rameters for the scenario being evaluated:

 Air concentration of total chemical,

 Fraction c hemical in air in vapor

- phase,

 Wet and dry deposition rates for particle

- phase chemical,

 Drinking water concentration,

 Chemical concentration in surface soils (two locations; can be tilled and untilled),

 Chemical concentration in root

- zone soils (two locat ions; can be tilled and untilled), and

 Chemical concentrations in pan fish and in game fish.
Users can input measured values or values estimated by TRIM.FaTE or other models for these parameters. The MIRC application uses the input data and a variety of e mpirical transfer factor values (included in its database) to estimate chemical concentrations in nine categories of FFC food types ( see Section 2.2 of this addendu m ). The FFC algorithms and transfer factor values included in MIRC are based on those presented in Chapter 5 of EPA’s HHRAP (EPA 2005 a )

. For outputs, MIRC is designed to calculate individual cancer risk and non
- cancer hazard quotients for one chemical at a time

. It is up to the risk assessor to determine if cancer risks or hazard quotients may be additive across two or more chemicals (i.e., if they cause toxic effects in the same target organ by the same mode of action, such as multiple polycyclic arom atic hydrocarbons ( PAHs ) that are carcinogenic by a mutagenic mode of action).

The tool assumes that an individual is exposed via all of the pathways specified (e.g, fruits and vegetables, animal products, soil, etc.) . The tool therefore is useful in esti mating 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 (EPA 2005a). FFC foods are evaluated in ten categories: exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vege tables, beef, total dairy, pork, poultry, and eggs

. Exhibit_Add A 2

- 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 Methodology for RTR

Attachment A, Addendum 2 2

- 17 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 2

. Transfer Pathways for Modeled Farm Food Chain (FFC) Media

Farm Food Media Chemica l 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 grain are estimated via intermediate calculations analogous to those used for aboveground produce. Produce types included in the FFC can accumulate a chemical directly from air and/or soil. For exposed produce, chemical mass is assumed to be transferred to pl ants from the air in two ways. First, particle

- bound chemical can deposit directly on the plant surface. Second, the uptake of vapor

- phase chemicals by plants through their foliage can occur. For both exposed and protected produce, the concentration in the plant derived from exposure to the chemical in soil is estimated using an empirical bioconcentration factor (BCF) that relates the concentration in the plant to the concentration present in the soil. For belowground root vegetables, a root concentrati on factor is applied. The algorithms used to estimate produce concentrations are presented in Section 3.1.1 of this addendum

. Chemical concentrations in animal products are estimated based on the amount of chemical consumed through the diet, including incidental ingestion of soil while grazing. The diet options for farm animals in MIRC include forage (plants grown on

- site for an imal grazing, such as grass), silage (wet forage grasses, fresh

- cut hay, or other fresh plant material that has been stored and fermented), and feed grain products grown on the farm (e.g., corn, soybeans). All three animal feed products are assumed to acc umulate chemical via root uptake from the soil. Forage and silage also can accumulate chemical via direct deposition of particle

- bound chemical and vapor transfer.

T he algorithms in MIRC are based on the assumptions that beef and dairy cattle consume al l 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 alu es . For secondary animal products (dairy products and eggs), chemical concentrations are estimated by applying a biotransfer factor to the estimated concentration in the “source” animal (cows and chickens, respectively). The algorithms used to estimate a nimal product concentrations are described in Section 3.1.2 of this addendum

. 

### 2.3. Receptor Groups As noted in EPA risk assessment guidelines ( EPA 2005b,c,d, 2008 a ), ex posures of children are expected to differ from exposures of adults due to differences in body weights, ingestion rates, dietary preferences, and other factors

. It is important, therefore, to evaluate the contribution of exposures during childhood to tota l lifetime risk using appropriate exposure factor values

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 18 6

- 29

- 2012 Description of MIRC EPA’s HHRAP ( Chapter 4, EPA 2005a) recommends assessing exposures for children and adults separately, but considers all non

- infant children in one category. Specifically, HHRAP recommends eight c ategories of receptor: farmer, child farmer, resident, child resident, fisher, child fisher, acute receptor, and nursing infant . Over time, different EPA programs have used different child age groupings to evaluate body weights, ingestion rates, and other parameter values needed to estimate chemical exposures and risks to children. To improve the match between age groups used to estimate values across exposure parameters, i n 2005, EPA recommend ed a standard set of child age categories for exposure and ri sk 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 ra tes have bee n calculated. In Chapter 13 of both its Exposure Factors Handbook (EFH; EPA 2011) and its Child

- Specific Exposure Factors Handbook ( CSEFH ; EPA 2008a) , EPA summarized home

- grown /raised food ingestion rates for four children’s age groups: 1 to < 3 years; 3 to < 6 years; 6 to < 12 years; and 12 to < 20 years

. Intake rates were not calculated for children younger than 1 year because infants are unlikely to consume those foods . They are more likely to be nursing or to be fed formula and other com mercial baby

- food products. Although the age groupings used to estimate FFC ingestion rates do not match precisely the groupings that EPA recommended in 2005 for Agency exposure assessments ( EPA 2005b), they are the only age

- groupings for which such data are available. The U.S

. Department of Agriculture’s (USDA’s) 1987

- 1988 Nationwide Food Consumption Survey (USDA 1992, 1993, 1994a) remains the most recent survey of ingestion rates for home

- grown foods , and EPA’s analysis of those data, published in its 2011 EFH, remains the most 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 paramet er 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 ye ars (i.e., 1 to < 3 years);

 children ages 3 through 5 years (i.e., 3 to < 6 years);

 children ages 6 through 11 years (i.e., 6 to < 12 years) ; and

 children ages 12 through 19 years (i.e., 12 to < 20 years).

See Section s 5.1 and 5 .2 for descriptions of the risk characterization algorithms used to calculate cancer and non

- cancer effects, respectively, for the above age groupings

. Exposure and risks to i nfants under 1 year of age are estimated only for the breast

- milk

- ingestion pathway

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 19 6

- 29

- 2012 Description of MIRC For assessment of cancer risks from early

- life exposure, EPA recognizes that infants and children may be more sensitive to a carcinogenic chemical than adults, with cancers appearing earli er in life or with lower doses experienced during childhood ( EPA 2005c, d)

. Thus, the “potency” of a carcinogen might be higher for infants and children than for adults

. To date, however, data by which to evaluate the relative sensitivity of children and adults to the same daily dose of a carcinogen remain limited

. Based on analyses of radioactive and other carcinogenic chemicals, EPA recommends evaluating two lifestages for children separately from adults for chemicals that cause cancer by a mutagenic m ode of action (MOA) : from birth to < 2 years and from 2 to < 16 years ( EPA 2005c,d)

. EPA also suggests that, as data become available regarding carcinogens with a mutagenic MOA , further refinements of these age groupings may be considered

. For assessing risks from exposures to carcinogenic chemicals that act via a mutagenic MOA
, the two early lifestages recommended by EPA ( EPA 2005c,d) also are included in MIRC:

 children under the age of 2 years (i.e., 0 to < 2 years); and

 children from 2 through 15 year s (i.e., 2 to < 16 years). Different age groupings are needed for the assessment of risks from carcinogenic chemicals with a mutagenic MOA and other carcinogens with other or unknown MOAs. Currently in MIRC, t he only PB

- HAPs with a mutagenic mode of car cinogenesis are the carcinogenic PAHs

. See Section 5.1 for a description of the age
- dependent adjustment factors (ADAFs) that are used to calculate cancer risks for chemicals with a mutagenic MOA

. 

## 3. Exposure Algorithms The exposure algorithms in MIRC are described below in four sections. Section 3.1 of this addendum presents the algorithms used to estimate chemical 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

. As noted previously, the exposure algorithms used in MIRC are based on those presented in HHRAP (EPA 2005b). Any differences between MIRC and HHRAP are explained in this section.


### 3.1. Farm Food Chain Algorithms
T he algorithms and parameters used to es timate chemical concentrations in produce and animal products are described in S ections 3.1.1 and 3.1.2 of this addendum , respectively. D iscussion s of the parameter value options and the values selected as default s in MIRC for RTR risk assessment are provided in Section 6.2 . The use of TRIM.FaTE to model chemical fate and transport in the environment prior to FFC calculations drives the most significant difference between the FFC algorithms included in HHRAP and the equations used for RTR. The approach in HHRAP uses estimated ambient air concentrations and deposition rates from dispersion model simulations that use unit emission rates. Chemical

- specific emission rates (adjusted for vapor and particle

- b ound fractions) are then incorporated into some of the HHRAP FFC algorithms to calculate concentrations in FFC media. Soil concentrations are calculated using a similar approach in HHRAP. For assessment of multipathway exposures for RTR, TRIM.FaTE is use d to estimate air concentrations, air

- to

- surface deposition rates, and soil concentrations, and these outputs are used in the FFC algorithms.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 20 6

- 29

- 2012 Description of MIRC 

### 3.1.1. Estimating Chemical Concentrations in Produce Produce (vegetables and fruits) can become 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_Add A 2

- 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 pat hways and algorithms for aboveground and belowground produce, respectively.

Exhibit_Add A 2
- 3

. Chemical Transfer Pathways for Produce
Farm Food Media Chemical Transfer Pathways Aboveground Produce Exp osed fruits and

vegetables Direct deposition from air of particle

- bound chemical Air

- to

- plant transfer of vapor phase chemical Root uptake from soil Protected fruits and vegetables Root uptake from soil

Belowground Produce Root vegetables Root uptake from soil



### 3.1.1.1. Aboveground Produce For aboveground exposed produce, chemical mass is assumed to be transferred to plants from the air in three ways, as illustrated in Exhibit_Add A 2

- 4

. First, particle

- bound chemical can depo sit directly on the plant surface via deposition ( Pd )

. The amount of chemical accumulated is estimated based on the areal fraction of chemical deposition intercepted by the plant surface, minus a loss factor that is intended to account for removal of depo sited chemical by wind and rain and changes in concentration due to growth dilution

. Second, for chemical present in air in the vapor phase, the concentration of chemical accumulated by the plant’s foliage is estimated using an empirical air

- to

- plant biot ransfer 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_Add A 2
- 4

. Estimating Chemical Concentration in Aboveground ProduceTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 21 6

- 29

- 2012 Description of MIRC concentration in the plant to the average chemical concentration in the soil at the root

- zone depth in the produce

- growing area ( Cs root

- zone_produce )

. The edible portions of aboveground protected produce are not subject to contamination via particle deposition ( Pd

) or vapor transfer (

Pv ). Therefore, root uptake of chemicals is the primary mechanism through which aboveground protected produce becomes contaminated

. As shown below, t he chemical concentration in the aboveground plant due to root uptake from soil ( Pr AG

- produce

- DW ) i s 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 2
- 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, on a dry

- weight (DW) basis (mg/kg produce DW)

Pd (i) = Chemical concentration in edible portion of aboveground produce type i due to deposition of particles (mg/kg produce DW); for protected aboveground produce, Pd equ als zero

Pr AG

- produce

- DW(i) = Chemical concentration in edible portion of aboveground produce type i,

exposed or protected, due to root uptake from soil at the root

- zone depth of the produce growing area (mg/kg produce DW)

Pv (i) = Chemical concentration in edible portion of aboveground produce type i due to air

- to

- plant transfer (μg/g [or mg/kg] produce DW); for protected aboveground produce, Pv equals zero Equation 2
- 2

. Chemical Concentration in Abovegro und 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 produce type i
, exposed or protected (g soil DW / g produce DW)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 22 6

- 29

- 2012 Description of MIRC Equation 2
- 3

. Chemical Concentration in Aboveground Produce Due to Deposition of Particle

- phase Chemical

where:

Pd (i) = Chemical concentration in aboveground produce type i on a dry

- weight (DW) basis due to particle deposition (mg/kg produce DW); set equal to zero for protected aboveground pro duce Drdp = Average annual dry deposition of particle

- phase chemical (g/m 2

- yr)

Fw = Fraction of wet deposition that adheres to plant surfaces; 0.2 for anions, 0.6

for cations and most organics (unitless) Drwp = Average annual wet deposition of particle

- phase chemical (g/m 2

- yr) Rp (i) = Interception fraction of the edible portion of plant type i (unitless) kp (i) = Plant surface loss coefficient for plant type i (yr - 1 )
Tp (i) = Length of exposure to deposition in the field per harvest of the edible portion of

plant type i (yr)
Yp (i) = Yield or standing crop biomass of the edible portion of plant type i (kg produce

DW/m 2 )

Note that Equation 2

- 3 differs from Equation 5

- 14 in HHRAP, from which it is derived

. In HHRAP, Equation 5

- 14 includes the term Q x ( 1

– Fv ) to indicate the emissions rate, in g/sec, of chemical from the source an d the proportion of the chemical that remains in, or partitions to, the particle

- phase in the air

. Also in HHRAP, the dry and wet particle phase deposition rates, Dydp and Dywp, respectively, are normalized to the emission rate and are expressed in units of sec/m 2

- yr

. With MIRC, the user inputs both the dry and wet particle

- phase deposition rates, Drdp and Drwp , respectively, in units of g/m 2

- yr for a specific location relative to an emissions source

. Those deposition rates might be values measured near that location or estimated using a fate and transport model, such as TRIM.FaTE, in conjunction with local meteorological information and emissions rate data

. The chemical emissions term used in HHRAP
, Q , therefore, is not used in MIRC ’s Equation 2

- 3

. In addition, in MIRC, Drdp and Drwp , the average annual dry

and wet

- particle

- phase deposition rates , respectively, are in units of g/m 2

- yr . Users of TRIM.FaTE should note that the dry

- and wet

- particle

- deposition rates output from TRIM.FaTE are in units of g/m 2

- d ay; therefore, users must adjust the TRIM.FaTE output values to units of g/m 2

- yr (i.e., multiply by 365 days/yr) before inputting values for Drdp and Drwp into MIRC.

Equation 2
- 4

. Chemical Concentration in Aboveground Produce Due to

Air

- to
- Plant Transfer of Vapor

- phase Chemical

w here:TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 23 6

- 29

- 2012 Description of MIRC

Pv (i) = Concentration of chemical in edible p ortion of aboveground produce type i from air

- to

- plant transfer of vapor

- phase chemical on a dry

- weight (DW) basis (μg/g produce DW); set equal to zero for protected aboveground produce Ca = Average annual total chemical concentration in air (g/m 3 )
Fv = Fraction of airborne chemical in vapor phase (unitless)

Bv AG(i) = Air

- to

- plant biotransfer factor for aboveground produce type i for vapor

- phase

chemical in air ([mg/g produce DW] / [mg/g air], i.e., g air/ g produce DW)

VG AG(i) = Empirical correction f actor for aboveground exposed produce type i to address

possible overestimate of the diffusive transfer of chemical from the outside to the inside of bulky produce, such as fruit (unitless)

ρ a = Density of air (g/m 3 )

Note that Equation 2

- 4 differs from Equation 5

- 18 in HHRAP, from which it is derived

. In HHRAP, Equation 5

- 18 includes the term Q x Fv to indicate the emissions rate, in g/sec, of chemical from the source and the fraction of the chemical in vapor phase in the air

. HHRAP also includes the parameter Cyv , or the unitized yearly average air concentration of vapor

phase chemical in units of μg

- sec/g

- m 3

. For MIRC, the user inputs the average annual total air concentration of the chemical, Ca , for a specific location relative to the source in units of g /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 n ear 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 T RIM.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

- w eight (DW) basis. The family FFC food ingestion rates, on the other hand, are on a fresh

- or wet

- weight (WW) basis. MIRC therefore calculates the concentration in aboveground produce on a wet

- weight basis, C AG

- produce

- WW , using Equation 2

- 5 and the moisture content ( MAF

) of the FFC food category. Equation 2
- 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)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 24 6

- 29

- 2012 Description of MIRC 

### 3.1.1.2. Belowground Produce The equations by which chemical concentrations are estimated in belowground produce are different for nonionic organic chemicals than for inorganic chemicals and ionic organic chemicals.


### 3.1.1.2.1. Nonionic Organic Chemicals For belowground produce , the nonionic organic chemical concentration in the tuber or root vegetable is derived from exposure to the chemical in soil and is estimated using an empirical root concentration factor ( RCF ) and the average chemical concentration in the soil at the root

zone depth in the produce

- growing area ( Cs root

- zone_produce ), as shown in Equation 2

- 6

. The RCF relates the che mical concentration in the plant on a wet

- weight basis to the average chemical concentration in the root

- zone soil ( Cs root

- zone_produce ) on a dry

- weight basis

. Belowground produce (i.e., tubers or root vegetables) are protected from the deposition and va p or transfer by being covered by soil

. Therefore, root uptake of chemicals is the primary mechanism through which belowground produce becomes contaminated

. Equation 2
- 6

. Chemical Concentration in Belowgro und Produce : Nonionic Organic Chemicals

where:

C BG

- produce

- WW = Concentration of chemical in belowground (BG) produce (i.e., tuber or root

vegetable) on a wet

- weight (WW) basis (mg chemical/kg produce WW)*

Cs root

- zone_produce = Average chemical concentration in soil at root

- zone depth in produce

- growing

area, on a dry

- weight (DW) basis (mg chemical/kg soil DW)

RCF = Chemical

- specific root concentration factor for tubers and root produce (L soil

pore water/kg root WW) *

VG rootveg = Empirical cor rection factor for belowground produce (i.e., tuber or root

vegetable) to account for possible overestimate of the diffusive transfer of chemicals from the outside to the inside of bulky tubers or roots (based on carrots and potatoes) (unitless) *
Kds = C hemical

- specific soil/water partition coefficient (L soil pore water/kg soil DW)
UCF = Units conversion factor of 1 kg/L

* Note that there is only one type of BG produce; hence there are no plant

- type

- specific subscripts

. The RCF, as developed by Briggs et al . (1982), is the ratio of the chemical concentration in the edible root on a wet

- weight basis to its concentration in the soil pore water. RCFs are based on experiments with growth solutions ( hydroponic) instead of soi ls; 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 empiricallyTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 25 6

- 29

- 2012 Description of MIRC derived regression models. Those models are shown in HHRAP A ppendix A

- 2, Equations A

- 2

- 14 and A

- 2

- 15 ( RCF ) and in Equations A

- 29 and A

- 2

- 10 ( Kds ). The RCF and Kds values so calculated for many of the chemicals in HHRAP are included in the MIRC databas e (including the values for PAHs and dioxins)

.



### 3.1.1.2.2. Inorganic and Ion ic Organic Chemicals For inorganic chemicals and ionized organic chemicals, it is not possible to predict RCF or Kds values from K ow . For inorganic chemicals, chemical specific empirical values for the root/soil bioconcentration factor must be used . The root/soil bioconcentration factor, now specified as Br BG

- produce

- DW , must be obtained from the literature for each inorganic chemical on a DW basis. For inorganic chemicals, therefore, Equation 2

- 7 is used instead of Equation 2

- 6

. Equation 2
- 7

. Chemical Concentration in Belo wground Produce: Inorganic Chemicals

w here :

C

BG

- produce

- DW = Concentration of chemical in edible portion of aboveground produce, due to

root uptake from soil at root

- zone depth in the produce

- growing area, on a dry

weight (DW) basis (mg/kg produce DW)

Cs root

- zone_produce = Average chemical concentration in soil at root

- zone depth in produce

- growing

area (mg/kg soil DW)

Br BG

- produce

- DW = Chemical

- specific root/soil chemical bioconcentration factor for edible portion

of belowground produce (g soil DW / g produce DW)

VG rootveg = Empirical correction factor for belowground produce (as in Equation 2

- 6 )

( unitless )

As for the aboveground produce , the DW estimate of concentration of chemical in the root vegetables must be transformed to a WW estimate, as shown in Equation 2

- 8

. Equation 2
- 8 . Conversion of Belowground Produce Chemical Concentration from Dry

- to Wet

- Weight Basis

where:

C

BG

- produce

- WW = Chemical concentration in edible portion of belowground produce on a weight

weight (WW) basis (mg/kg produce WW)

C BG

- produce

- DW = Concentration of chemical in edible portion of aboveground produce, due to root

uptake from soil at root

- zone depth in the produce

- growing area, on a dry

weight (DW) basis (mg/kg produce DW)

MAF (BG) = Moisture adjustment factor (as in Equation 2

- 5 , but single value for below ground produce) (percent water)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 26 6

- 29

- 2012 Description of MIRC 

### 3.1.2. Estimating Chemical Concentrations in Animal Products Chemical concentrations in animal products are estimated based on the amount of chemical consumed by each animal group m through each plant feed type i ( Plant Ch

- Intake (

i,m ) ) and incidental ingestion of soil for ground

- foraging animals ( Soil Ch

- Intake (m) )

. Exhibit_Add A 2

- 5 summarizes the pathways by which chemicals are transferred to these home

- or farm

- raised animal foo d 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 for age (plants grown on

- site for animal grazing, such as grass), silage (wet forage grasses, fresh

- cut hay, or other fresh plant material that has been stored and fermented), and grain products grown on the farm

. As seen in Exhibit_Add A 2

- 5 , the algorithms in MIRC 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_Add A 2
- 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 grain 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 intermediate calculations (see Equation 2

- 13 , Equation 2

- 14 , Equation 2

- 3 , and Equation 2

- 4 ). Forage and silage are exposed to the air and can accumulate 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 th e air by a husk or pod (e.g., corn, soybeans)

. All three animal feed products are assumed to accumulate chemical via root uptake

. Chemical concentrations are estimated for animal feeds using algorithms analogous to those for aboveground farm produce des cribed above . MIRC uses Equation 2

- 9 to calculat e the concentration of chemical in beef, por k, or total dairy and Equation 2

- 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 2

- 9 )

. MF is set to 1.0 for chemicals that are not metabolized and for chemicals for which the metabolic degradation rate is unknown

. Although other vertebrates, including birds, a re likely to have similar metabolic pathways for most chemicals, the health protective assumption is that birds do not metabolize any chemicals; therefore, the MF is omitted from Equation 2

- 10 for poultry and eggs.

Equation 2
- 9

. Chemical Concentration in Beef, Pork, or Total Dairy

where:TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 27 6

- 29

- 2012 Description of MIRC

C mammal(m) = Concentration of chemical in mammalian animal product m , where m = beef,

pork, or total dairy (mg chemical/kg animal product WW)

Ba (m) = Chemical

- specific biotransfer factor for chemical in diet to chemical in animal

food product m , where m = beef, pork, or total dairy ([mg chemical/kg animal product WW] / [mg chemical intake/day] or day/kg WW)

MF = Chemical

- specific mammalian metabolism factor that accounts for endogenous

degradation of the chemical (unitless)

Soil Ch

- Intake(m) = Incidenta l ingestion of chemical in surface soils by livestock type m during

grazing or consumption of foods placed on the ground (mg/day); see Equation 2

- 11 below

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 2

- 12 below (If m = beef or total dairy, then n = 3 and i = forage, silage, and grain; m = pork, then n = 2 and i = silage and grain; m = poultry, then n = 1 and I = grain.) Equation 2
- 10

. Chemical Concentration in Poultry or Eggs

where:

C

poultry(m) = Concentration of chemical in food product m , where m = poultry or eggs (mg

chemical/kg animal product WW)

Ba (m) = Chemical

- specific biotransfer factor for food product m , where m = poultry or

eggs (day/kg animal product WW)

Soil Ch

- Intake(m) = Incidental ingestion of chemical in surface soils by consumption of food on the

ground (mg chemical/day) where m = poultry; see Equation 2

- 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 2

- 12
In MIRC, t he incidental ingestion of the chemical in soils by livestock during grazing or consumption of feed placed on the ground ( Soil Ch

- Intake (m) ) is estimated using empirical soil ingestion rate s ( Qs ) and a soil bioavailability factor for livestock ( Bs ), as shown in Equation 2

- 11 . At this time, the default value for Bs in MIRC for all chemicals is 1.0 (i.e., 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 chemical is deposited. MIRC allows the user t o 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 incidentally ingest soils (see Section 6.1 of this addendum )

.

Equation 2
- 11

. Incidental Ingestion of Chemical in Soil by Livestock

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)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 28 6

- 29

- 2012 Description of MIRC

Qs (m) = Quantity of soil eaten by animal type m each day (kg soil DW/day)

Cs s
- livestock = Chemical concentration in surface soil in contaminated area where livestock

feed (mg chemical/kg soil DW)

Bs = Soil bioavailability factor for livestock (unitless) (assumed to be the same for

birds and mammals) Animal ingestion of the chemical in feed is calculated for each type of livestock based on their assumed diets. For m = beef and dairy cattle, chemical intake is estimated for all three feed types: i = forage, silage, and grain

. For pork, chemical intake is estimated only for silage and grain

. The chemical intake for poultry is based on grain consumption only

. The intake of chemical with each f eed type, i
, Plant

Ch

- Intake(

i,m ) , is calculated separately according to Equation 2

- 12 . Note that the animal feed ingestion rates are on a dry

- weight (DW) basis; hence, no DW to wet weight (WW) conversion is needed.

Equation 2
- 12

. Ingestion of Chemical in Feed by Livestock

where:

Plant Ch

- Intake(i,m) = Ingestion of chemical in plant feed type i (mg chemical/day), where i = forage,

silage, or grain, for livestock type m

F (i,m) = Fraction of plant feed type i obtained from contaminated area used to grow animal feed, where I = forage, silage, or grain (unitless) for livestock type m Qp (i,m) = Quantity of plant feed type i consumed per animal per day (kg plant feed

DW/day), where i = forage, silage, or grain, fo r 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 2

- 13

. The equation is the same as that for aboveground produ ce in Equation 2

- 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 2
- 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 plant feed DW), where i = forage, silage, or grain

Pr feed(i) = Concentration of chemical in plant feed type i due to root uptake from soil

(mg/kg DW), where i = forage, silage, or grain; see Equation 2

- 14 below

Pd (i) = Concentration of chemical in plant feed type i due to wet and dry deposition of particle

- phase chemical (mg/kg DW), where i = forage, silage, or grain; when i = grain, the Pd term equals zeroTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 29 6

- 29

- 2012 Description of MIRC

Pv (i) = Concentration of chemical in plant feed type i due to air

- to

- plant transfer of vapor
- phase chemical (μg/g [or mg/kg] DW) where i = forage, silage, or grain; when i = grain, the Pd term equals zero MIRC calculates the chemical concentration in animal feed due to root uptake from the soil using Equation 2

- 14

. The equation is the same as Equation 2

- 2 , except that a Br value appropriate to grasses is used and MIRC allows for different soil concentrations in the area used to grow animal feed than in the area used to grow produce for human consumption (see Section 6.1 of this addendum , user inputs) . Note that for feed type i = grains , the Pd and Pv terms do not a pply (are set to zero), because the feed products (i.e., corn kernels, soy beans) are protected from the air (i.e., by husks, pods). Equation 2
- 14 . Chem ical Concentration in Livestock Feed Due to Root Upta ke

where:

Pr

feed(i) = Concentration of chemical in plant feed type i due to root uptake from soil on a dry

- weight (DW) basis (mg chemical/kg plant feed DW), where i = forage, silage, or grain

Cs root

- zone_feed(i) = Average chemical concentration in soil at root

- zone depth in area used to grow

plant feed type i (mg chemical/kg soil DW), where i = forage, silage, or grain

Br feed(i) = Chemical

- specific plant

- soil bioconcentration factor for plant feed type i (kg soil

DW/kg plant feed DW), where i = forage, silage, or grain The algorithms used to calculate Pd (i) and Pv

(i) when plant feed type i = forage and silage are identical to those used to calculate Pd (i) and Pv

(i) for aboveground exposed produce (i.e., Equation 2

- 3 and Equation 2

- 4 , respectively ).

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 C hildren MIRC calculates human chemical intake rates from the ingestion of home

- grown foods as average daily doses (ADDs) normalized to body weight for each age grou p, chemical, and food type separately

. ADDs, calculated using Equation 2

- 15 , are expressed in milligrams of chemical per kilogram of receptor body weight per day (mg/kg

- day). Equation 2
- 15

. Average Daily Dose for Specified Age Group and Food Type

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)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 30 6

- 29

- 2012 Description of MIRC

C (i) = Concentration of chemical in food type i harvested from the contaminated area (mg chemical/kg food or mg food/L water)
IR (y,i) = Ingestion rate for age group y of food type i (kg/day or L/day) FC (i) = Fraction of food type i that was harvested from contaminated area (unitless)
ED (y) = Exposure duration for age group y (years)
BW (y) = Body weight for age group y (kg)

AT (y) = Averaging time for calculation of daily dose (years) for age group y , set equal to ED in MIRC
EF (y) = Annual exposure frequency for age group y (days)
Equation 2

- 15 takes into account the chemical concentration in each food type i (or in water), the quantity of food brought into the home for consumption , the loss of some of the mass of the foods due to preparation and cooking, how much of the food is consumed per year, the amount of the food obtained from contaminated areas, and the consumer’s body weight (EPA 2011, 2003a). In MIRC, ADDs are calculated separately for each chemical, home

- grown food type, and consumer age group. ADD values, expressed as intakes, not absorbed doses, are appropriate for comparison with RfDs and for use with cancer slope factors ( C SFs) to estimate risk, as discussed in Section 5 of this addendum

. An exception is for the breast

- milk exposure pathway, where calculating the dose available to and absorbed by the nursing infant is rel ated to the dose absorbed by the mother as discussed in Section 3.4 of this addendum

. MIRC evaluates only one exposure scenario at a time. For screening

- level assessments, all
co mponents of this equation are assumed to remain constant for consumers in a given age group over time (e.g., seasonal and annual variations in diet are not explicitly taken into account)

. To calculate an ADD (y ,i ) from the contaminated area for food group i over an entire lifetime of exposure, age

- group
- specific ingestion rates and body weights are used for the age groups described in Section 2.3 of this addendum

. In MIRC, the averaging time used to calculate the daily dose for an age group ( AT y ) is equal to the exposure duration for that group ( ED y ); therefore these variables drop out of Equation 2

- 15

. For each chemical included in a screening scenario, total average daily exposure for age group y ( ADD (y) ) is estimated as the sum of chemical intake from all ingestion pathways combined: Note that the last exposure pathway is li mited to infants

.

 Incidental soil ingestion;

 Ingestion of fish;

 Ingestion of homegrown fruits (exposed and protected);

 Ingestion of homegrown vegetables (exposed, protected, and root);

 Ingestion of animal products from home

- raised animals:

– Milk and other dairy products from cows,

– Beef products,

– Pork products, andTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 31 6

- 29

- 2012 Description of MIRC – Poultry and eggs;

 Ingestion of drinking water from specified source; and

 Ingestion of breast milk by infants. The algorithms for the f irst six exposure pathways listed above are described in Sections 3.2.1 through 3.2.6 of this addendum

. The algorithms for the breast

- milk ingestion pathway are described in Section 3.4

. 

### 3.2.1. Chemical Intake from Soil Ingestion Equation 2

- 16 shows the equation used to estimate chemical intake through incidental ingestion of soil

. Equation 2
- 16

. Chemical Inta ke 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 contaminated area on a dry

- weight (DW) basis (μg/g soil DW)
IR Soil(y) = Soil ingestion rate for age group y (g DW/day) FC Soil = Fraction of soil ingested that is from contaminated area (unitless)
BW (y) = Body weight for age group y (kg)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario (  365 days)

Note: MIRC saves soil ingestion rates in units of mg/day (not g/day); therefore, there is an additional 0.001 g/mg conversion unit in the actual MIRC algorithm than shown her e

. 

### 3.2.2. Chemical Intake from Fish Ingestion Ingestion of locally caught fish is included as a possible exposure pathway in MIRC ( Equation 2

- 17 )

. Two types of fish are included in the exposure algorithm: trophic level 3 ( TL3 ) fish, equivalent to small “pan” fish such as bluegill, and trophic level 4 ( TL4 ) fish, equivalent to game fi sh such as trout and walleye

. The chemical concentration in fish in Equation 2

- 17 is estimated as the consumption

- weighted chemical concentration using Equation 2

- 18

.

Equation 2
- 17

. Chemical Intake from Fish IngestionTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 32 6

- 29

- 2012 Description of MIRC

Equation 2
- 18

. Consumption

- weighted Chemical Concentration in Fish

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 ( TL3 ) fish on a wet

- weight

(WW) basis (mg/kg WW)

C Fish TL4 = Chemical concentration in whole fi sh for trophic level 4 ( TL4 ) fish on a wet

- weight

(WW) basis (mg/kg WW)
F TL3 = Fraction of fish intake that is from TL3 (unitless)
F TL4 = Fraction of fish intake that is from TL4 (unitless)

C Fish = Consumption

- weighted mean chemical concentration in total fish (i.e., as specified by Equation 2

- 18 ) (mg/kg WW) FC Fish = Fraction of local fish consumed derived from contaminated area (unitless)
BW (y) = Body weight for age y (kg)
IR Fish(y) * = Local fish ingestion rate for age y (g WW/day)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario (  365 days)

* Parameter values must be internally consistent. In contrast to the tables included in MIRC for ingestion rate options for homegrown food products, which are based on the products as brought into the home from the field (see Section 6.3.3 of this addendum ), the tables of fish ingestion rate options included in MIRC are from CSFII data (see Section 6.3.4 of this addendum ) and, therefore, are on an “as consumed” basis (i.e., after preparation and cooking losses), and L1 and L2 therefore are set equal to zero. If the user wishes to enter local fish ingestion rates on an “as harv ested” basis, the user also should enter L1 and L2 values as specified in Section 6.4.3 of this addendum

. When whole fish are prepared for cooking, it is usual for the viscera, head, and fins to be removed, particularly for larger fish

. Many persons also remove (or do not eat) the skin, bones, and belly fat

. EPA has, therefore, estimated the proportion of the weight of whole fish that tends to be lost during pr eparation and cooking across a variety of fish species ( EFH
, EPA 2011 ) and included those losses in its HHRAP algorithms for chemical intake from fish (L1 Fish and L2 Fish in Equation 2

- 17 )

. 

### 3.2.3. Chemical Intake from Fruit Ingestion Average daily doses of a chemical from homegrown exposed fruits are calculated separately for exposed and protected fruits ( Equation 2

- 19 and Equation 2

- 20 , respectively).TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 33 6

- 29

- 2012 Description of MIRC

Equation 2
- 19 . C hemical Intake from Consumption of Exposed Fruits

Equation 2
- 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 subscript) (mg chemical/kg body weight

- day)

L1 ExpFruit = Mean reduction in fruit weight resulting from removal of skin or peel, core or pit,

stems or caps, seeds and defects, and from draining liquids from canned or frozen forms (unitless)

L1 ProFruit = Mean reduction in fruit weight that results from paring or other preparation

techniques for protected fruits (unitless)

L2 ExpFruit = Mean reduction in fruit weight that results from draining liquids fro m 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 scenario (  365 days) FC ExpFruit FC ProFruit = Fraction of exposed fruits or protected fruits (depending on subscript) obtained

from contaminated area (unitless)
IR ExpFruit(y) IR ProFruit(y) = Ingestion rate of home

- grown exposed fruits or protected fruits (depending on

subscript) for age y (g WW/kg body weight

- day) Fruit ingestion rates in the survey were based on weights of unprepared fruits (e.g., one apple; one pear) or the weight of a can of fruit (e.g., 8 oz can)

. The weight of the fruit ingested is less than the initial weight owing to common preparation actions ( L1 ExpFruit and L1 ProFruit ; e.g., coring apples and pears; pe e ling apples; pitting cherries)

. Cooking of exposed fruit (e.g., berries, apples, peaches) often results in further weight loss that results from liquids lost during cooking and drained from the cooking vessel ( L2 ExpFruit ). EPA has assumed that cooking of protected fruit results in no loss of weight for the fruit.


### 3.2.4. Che mical Intake from Vegetable Ingestion MIRC includes three separate algorithms for homegrown vegetables adapted from EPA’s HHRAP Modeling System (EPA 2005a ): one for exposed vegetables such as asparagus, broccoli, lettuce, and tomatoes ( although they are ac tually a fruit); one for protected vegetables such as corn, cabbage, soybeans, and peas; and one for root vegetables such as carrots, beets, and potatoes (see Equation 2

- 21 , Equation 2

- 22 , and Equation 2

- 23 , respectively ).TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 34 6

- 29

- 2012 Description of MIRC

Equation 2
- 21

. Chemical Intake from Exposed Vegetables

Equation 2
- 22 . Chemical Intake from Protected Vegetables

Equation 2
- 23 . Chemical Intake from Root Vegetables

where:

ADD ExpVeg(y) ADD ProVeg(y) ADD RootVeg(y) = Average chemical intake from ingestion of exposed vegetables, protected

vegetables, or root vegetables (depending on subscript) for age group y (mg chemical/kg body weight

- day)

L1 ExpVeg = Mean net preparation and cooking weight loss for exposed vegetables (unitless);

includes removing stalks, paring skins, discarding damaged leaves

L1 ProVeg = Mean net cooking weight loss for protected vegetables (unitless); includes

removing husks, discarding pods of beans and peas, removal of outer leaves

L1 RootVeg = Mean net cooking weight loss for root vegetables (unitless); includes losses from

removal of tops and paring skins

L2 RootVeg = Mean net post cooking weight loss for root vegetables from draining cook ing

liquids and removal of skin after cooking (unitless) C ExpVeg C ProVeg C RootVeg = Chemical concentration in exposed vegetables, protected vegetables, or root

vegetables (depending on subscript) on a wet

- weight (WW) basis (mg chemical/kg vegetable WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario (  365 days) FC ExpVeg FC ProVeg FC RootVeg = Fraction of exposed vegetables, protected vegetables, or root vegetables

(depending on subscript) obtained fro m 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)



### 3.2.5. Chemical Intake from Animal Product Ingestion Calculations of chemical intake from the consumption of farm animals and related food products are provided below in Eq uation 2

- 24 through Equation 2

- 28 for homegrown beef, dairy (milk), pork, poultry, and eggs, respectively.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 35 6

- 29

- 2012 Description of MIRC Eq uation 2
- 24

. Chemical Intake from Ingestion 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 contaminant in beef (mg/kg WW))

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario (  365 days)

IR Beef(y) = Ingestion rate of contaminated beef for age group y (g WW/kg

- day)

FCBeef = Fraction of beef consumed raised on contaminated area or fed contaminated

silage and grains (unitless) Equation 2
- 25

. Chemical Intake from Dairy Ingestion

where :

ADD Dairy(y) = Average daily chemical intake from ingestion of total dairy for age group y

(mg/kg

- day) C Dairy = Average concentration of contaminant in total dairy (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as specified for scenario (  365 days)
IR Dairy(y) = Ingestion rate of contaminated total dairy for age group y (g WW/kg

- day)
FC Dairy = Fraction of total dairy products from contaminated area (unitless)

Equation 2
- 26

. Chemical Intake from Pork Ingestion

where:

ADD Pork(y) = Average daily chemical intake from ingestion of pork for age group y (mg/kg

day)

L1 Pork = Mean net cooking loss for pork (unitless); includes dripping and volatile losses

during cooking; averaged over various cuts and preparation methodsTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 36 6

- 29

- 2012 Description of MIRC

L2 Pork = Mean n et 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 = Concentration of contaminant in pork (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as

specified for scenario (  365 days)
IR Pork(y) = Ingestion rate of contaminated pork for age y (g WW/kg

- day)
FC Pork = Fraction of pork obtained from contaminated area (unitless) Equation 2
- 27

. Chemical Intake from Poultry Ingestion

where:
ADDPoultry(y) = Average daily dose (chemical intake) from ingestion of poultry (mg/kg

- day)

L1Poultry = Mean net cooking loss for poultry (unitless) L2Poultry = Mean net post cooking loss for poultry (unitless) CPoultry = Concentration of chemical in poultry (mg/kg WW)

EF = Exposure frequency; number of days per year of exposure for family(ies) as

specified for scenario (  365 days) IRPoultry(y) = Ingestion rate of poultry for age group y (g WW/kg

- day)

FC Poultry = Fraction of poultry from contaminated area or fed contaminated grains

(unitless) The reduction in the weight of beef, pork , and poultry during and after cooking may correlate with an increase or decrease in the concentration of the chemical in the food as consumed depending on the chemical and depending on the cooking method.

Equation 2
- 28 . Chemical Intake from Egg Ingestion

where:

ADD Egg(y) = Average daily chemical intake from ingestion of eggs for age group y (mg/kg

day) 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)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 37 6

- 29

- 2012 Description of MIRC

FC Egg = Fraction of eggs obtained from contaminated area (unitless)


### 3.2.6. Chemical Intake from Drinking Water Ingestion If the user chooses to evaluate chemical ingestion via drinking water , the user specifies a chemical concentration in g/L (equivalent to mg/mL) based on their particular scenario. The chemical concentration could represent water from groundwater wells, community water, nearby surface waters, or other source

. For this exposure pathway, ingestion rates are in units of milliliters of water per day (mL/day ) (see Equation 2

- 29 )

.

Equation 2
- 29 . Chemical Intake from Drinking Water Ingestion

where:

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 individual in each age group is expected to contact (e.g., soil, local fish, five types of home

grown produce, and five types of home

- raised animals or animal p roducts), the media

- specific chemical intakes are summed for each age group

. Total average daily exposure for a particular age group y ( ADD (y) ) is estimated as the sum of chemical intake from all ingestion pathways combined, as illustrated in Equations 2

- 30 through Equation 2

- 35 below. Equations 2
- 30 to 2
- 35. Total Average Daily Dose of a Chemical for Different Age Groups

Equation 2
- 30

. Equation 2
- 31

. Equation 2
- 32

. Equation 2
- 33

. Equation 2
- 34

. Equation 2
- 35

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 38 6

- 29

- 2012 Description of MIRC where i represents the i th food type or ingestion medium and n equals the total number of food types or ingestion media, and ADD parameters are de fined below:

ADD (<1) = Total average daily dose of chemical for infants less than one year from

ingestion of breast milk (mg/kg

- day)

ADD (1

– 2) = Total average daily dose of chemical from all ingestion sources for children

ages 1 through 2 years (mg/kg

- day)
ADD (3

– 5) = Total average daily dose for children ages 3 through 5 years (mg/kg

- day)
ADD (6

– 11) = Total average daily dose for children ages 6 through 11 years (mg/kg

- day)
ADD (12

– 19) = Total average daily dose for children ages 12 through 19 years (mg/kg

- day)
ADD (adult) = Total average daily dose for adult age 20 up to 70 years (mg/kg

- day) The lifetime average daily dose ( LADD ) is calculated as the time

- weight average of the ADD values for each age group ( Equation 2

- 36 ).

Equation 2
- 36

. Lifetime Average Daily Dose (LADD)

The time

- weighting factors simply equal the duration of exposure for the specified age category in years divided by the total lifespan, assumed to be 70 years

. 

### 3.4. Chemical Intake Calculations for Nursing Infants The scientific literature indicates that infants can be exposed to some chemical s via their mothers’ breast milk . The magnitude of the exposure can be estimated from information on the mother’s exposure, data on the partitioning of the chemical into various compartments of the mother’s body and into breast milk, and information on the infant ’ s consumption of milk a nd absorption of the chemica l

. To add this exposure pathway to the MIRC application
, we adapted exposure algorithms and default assumptions from EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissi ons ( EPA 1998)
, hereafter referred to as MPE, as explained below

. Note that this pathway generally is of most concern for lipophilic bioaccumulative chemicals (e.g., dioxins) that can cause developmental effects

. The period of concern for the more hydro philic chemicals that cause developmental effects generally is earlier, that is
, from conception to birth . Hydrophilic chemicals generally exchange well between the maternal and fetal blood supplies at the placenta

. 

### 3.4.1. Infant Average Daily Absorbed Dose
The average daily dose of chemical absorbed by the infant ( DAI inf ) is estimated in MIRC with Equation 2

- 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 t he oral route of exposure ( AE inf ), the bodyweight of the infant ( BW inf ), and the duration of breast feeding ( ED ). Equation 2

- 37 is EPA’s (EPA 1998) modification of a n average daily dose for the infant model first published by Smith (1987) and includes variables for both the concentration of the chemical in the breast milk fat ( C milkfat ) and the concentration of the chemical in the aqueous phase of breast milk ( C aqueou s ). The remainder of the DAI inf - associatedTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 39 6

- 29

- 2012 Description of MIRC equations assume that most chemicals of concern will partition either to the lipid phase or to the aqueous phase of breast milk, although some chemicals may partition significantly to both phases of milk. Thus, the remaining equations in MIRC assume that either C milkfat or C

aqueous is equal to zero and hence drops out of the equation. For the parameters in Equation 2

- 37 (and the equations that follow) that are not calculated from another equation, an EPA default value and options for other values available in MIRC for the infant breast

- milk

- exposure pathway are described in Section 6.4 of this addendum . The user also can overwrite those parameter values with a different value from the literature as appropriate.

Equation 2
- 37

. Aver age 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 = Concentration of chemical in lipid phase of maternal milk (mg chemical/kg milk

lipid; calculated using Equation 2

- 38 )
f mbm = Fraction of fat in breast milk (unitless )

C aqueous = Concentration of chemical in aqueous phase of maternal milk (mg chemical/kg

aqueous phase milk; calculated using Equation 2

- 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 time associated with exposure of interest; equal to ED (days)
As mentioned above, Equation 2

- 37 includes terms for the chemical in both the lipid

- and non

lipid phases of milk. The remaining equations, however, assume that a chemical of concern will partition to the lipid or aqueous phase of breast milk , but not to both . Different models are used to estimate C milkfat (described in Section 3.4.2 of this addendum ) and C aqueous (described in Section 3.4.3 of this addendum ).


### 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) (U.S. EPA 1998), EPA reviewed three first

- order kinetics models for estimating ch emical concentration in breast milk fat. The model selected for use in MIRC is the model selected in MPE. The other two models were not considered in MPE because one used a biotransfer factor (BTF) approach considered more of a screening model than a predictive tool ( Travis et al. 1988 ) and the other assumed that the contaminant concentration in the maternal fat compartment is at steady state and that the concentration in breast milk fat is the same as in mat ernal body fat ( Smith 1987 ). The model inTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 40 6

- 29

- 2012 Description of MIRC MIRC is a changing

- concentration model that EPA adapted from a model by Sullivan et al

. (1991). The model, shown in Equation 2

- 38 , estimates the average chemical concentration in the breast milk over the entire period of breast feeding by reference to a maximum theoretical steady

- state concentration. Studies of lipophilic chemicals such as dioxins suggest that concentrations in the maternal milk are highest during the first few weeks of breast feeding and then decrease over ti me (ATSDR 1998). Equation 2

- 38 accounts for the changing
conce ntration in breast milk fat, but estimates one average value to represent the concentration over the entire duration of breast feeding. The model is dependent on the maternal body burden of the chemical and assumes that the chemical concentration in breas t milk fat is the same as the concentration in general maternal body fat. According to reviewers of the model, this assumption warrants further investigation because milk fat appears to be synthesized in the mammary glands and may have lower chemical conc entrations than general body fat stores (EPA 2001a). Equation 2
- 38

. C hemical 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 2

- 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 addendum )

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 2

- 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 calculated using Equation 2

- 41 )
t bf = Duration of breast feeding (days)

t pn = Duration of mother’s exposure prior to parturition and initiation of breast feeding (days) Equation 2

- 38 relies on the daily maternal absorbed intake ( DAI mat ) to determine the concentration of the chemical in the breast milk fat. DAI mat is multiplied by the fraction of the chemical that is stor ed in maternal fat ( f f ) to determine the amount (i.e., mass) of chemical in the fat. This product, divided by the chemical

- specific elimination rate constant ( k elim ) for 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 concentrati on 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 durationTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 41 6

- 29

- 2012 Description of MIRC of the mothe r’s exposure. However, for chemicals with half

- lives that are longer than the exposure duration, which are the chemicals of concern in the applications of MIRC to date, an additional term is needed to determine the average concentration in the milk fat ov er the duration of her exposure. To account for breast feeding losses and longer chemical half

- lives in the mother than the exposure duration, an additional term is included in Equation 2

- 38 . This term includes a fraction dependent on two rate constants, k elim and the elimination constant for a lipophilic chemical in lactating women via the lipid phase of breast milk ( k fat

_ elac ), the duration of the mother’s chemical exposure prior to nursing ( t pn ), and the duration of breast feeding ( t bf ). The whole body concentration ( DAI mat f f / k elim f fm ), the maximum theoretical steady

- state concentration, is multiplied by the rate of elimination averaged over the durati on of the mother’s exposure, including her exposure prior to and during lactation. To review the derivation of Equation 2

- 38 , see Appendix B of MPE ( EPA 1998).

To est imate 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 addendum
, Equation 2

- 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 2

- 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 o f 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 t o 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 2

- 39

. Equation 2
- 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 addendum
, Equation 2

- 35 )

AE mat = Absorption efficiency of the chemical by the oral route of exposure (i.e., chemical

- specific fraction of ingested chemical that is absorbed) by the mother (unitless) (value from literature or EPA default

– see Section 6.4 of this addendum ) Equation 2

- 35 , used to calculate ADD (adult), is based on many medium

- specific ingestion rates that are normalized to body weight . The adult body weights to which the homegrown food ingestion rates are normalized are the body weights of the consumers in the original USDA survey (see Section 6.3.3 of this addendum ), which included both males and females

. An assumption in the breast

- milk exposure pathway is that those ingestion rates also are applicable to nursing mothers. The origina l data for ingestion rates for soil, drinking water, and fish are on a per person basis for males and females combined. MIRC divides those chemical intakes by an adult body weight for males and females combined as specified by the user (e.g., 71.4 kg
mean value) to estimate the ADD normalized to body weight from those sources. If the userTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 42 6

- 29

- 2012 Description of MIRC finds that those exposure media contribute the majority of the chemical intake for the risk scenario under consideration, the user may use alternative ingestion rates fo r those media and alternative body weights for nursing women, as described in Section 6.5 of this addendum

. Elimination rates for chemicals often are reported as t he 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

- li fe of the slower phase of elimination, presumably from
non
- blood compartments of the body, is the more important of the two. Assuming first

- order kinetics, Equation 2

- 40 is used to convert a measured half

- life for elimination of a chemical for adults or non

- lactating women to an elimination rate constant (EPA 1998). The equation can be used to estimate any kind of chemical loss rate constant from a measured chemical half

- life.

Equation 2
- 40

. Biological Elimination Rate Constant for Chemicals

for Non

- lactating Women

where:

k

elim = Chemical

- specific elimination rate constant for elimination of the chemical for
non
- lactating women (per day; e.g., via urine, bile to feces, exhalation)
ln2 = Natural log of 2 (unitless constant)
h = Chemical

- specific biological half

- life of chemical for non

- lactating women (days) For chemicals transferred from the body of lactating women to breast milk, the rate of chemical elimination is augmented by the rate of chemical loss via the milk through breast feeding . The total elimination rate for lactating women sometimes is measured directly and reported in the literature. Where direct measurements are not available, and for chemicals that partition predominantly to the lipid

- phase of milk, EPA has used Equation 2

- 41 to estimate the total chemical elimination rate for lactating women, k fat

_ elac (EPA 1998 )

. Equation 2
- 41

. Biological Elimination Constant for Lipophilic Chemicals for Lactating Wo men

where:

k

fat_elac = Rate constant for total elimination of chemical during nursing (per day); accounts

for both elimination by adults in general and the additional chemical elimination via the lipid phase of milk in nursing women

k elim = Elimination rate constant for 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 2

- 40 )
IR milk = Infant milk ingestion rate over the duration of nursing (kg/d)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 43 6

- 29

- 2012 Description of MIRC

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 2

- 41 is based on a model from Smith (1987) and accounts for the additional elimination pathway for lip ophilic 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 total elimination rate constant for non

- lactating w omen. 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 ), the fraction of the mother’s breast milk that con sists 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 2

- 41 , the value for the mother’s body weight should be specific to women of child

- bearing age, as opposed to a body weight value for both males and females that is used to estimate an adult averag e daily dose and the mother’s absorbed daily intake in Equation 2

- 39 . Body weight values for the mother are described in Section 6.5 of this addendum . Smith’s (1987) model assumes that the chemical partitions to the lipid

- phase of breast milk to the same degree that it partitions into the mother’s body fat . For highly lipoph ilic 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 2

- 42 (EPA 1998). Chemicals that would partition to the aqueous phase of human milk include water

- soluble chemicals, such as salts of metals, and other hydrophilic chemicals that may be in equilibrium with bound forms of the chemical in diffe rent tissues

. The C aqueous equation assumes that the chemical concentration in the aqueous phase of milk is directly proportional to the chemical concentration in the mother’s blood plasma

. The portion of chemical sequestered in red blood cells (e.g., bound to RBC proteins) is assumed to be unavailable for direct transfer to breast milk

. Equation 2
- 42

. Chemical Concentration in Aqueous Phase of Breast Milk

where:

C aqueous = Concentration of chemical in aqueous phase of maternal milk (mg/kg)
DAI mat = Daily absorbed maternal chemical dose (mg/kg

- day; calculated by Equation 2

- 39 )

f pl = Fraction of chemical in the body (based on absorbed intake) that is in the blood plasma compartment (unitless; value from literature or calculated by Equation 2

- 43 )TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 44 6

- 29

- 2012 Description of MIRC 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 = Chemical

- specific rate constant for total elimination of chemical in the aqueous phase of milk during nursing (per day; value from literature or calculated in Equation 2

- 44 )
f pm = Fraction of maternal weight that is blood plasma (unitless)
Equation 2

- 42 is a steady

- state concentration model that, like the Equation 2

- 38 for C milkfat , is dependent on the m aternal absorbed daily intake ( DAI mat ). In Equation 2

- 42 , DAI mat is multiplied by the fraction of the absorbed chemical that is circulating in the blood plasma compar tment ( f pl ) and a partitioning coefficient for the chemical between plasma and the aqueous phase of breast milk ( Pc

bm ). For highly water

- soluble chemicals that are not transported via special carrier molecules, the chemical is assumed to diffuse passively from the mother’s blood serum to the aqueous phase of her milk, in which case Pc bm would equal 1.0. The denominator includes the biological elimination constant for the chemical in the aqueous phase of breast milk in lactating women ( k

aq_elac ) and the fr action 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 blood cells and bone

. While these latter chemicals or forms of these chemicals are water

- soluble when free, they have relatively long half

- lives because they are in equilibrium with the chemical bound to macromolecules in some tissue compartments

. Lead is of particular concern because it can be released from the bone into the blood duri ng lactation, and thus into the breast milk (EPA 2001a). Due to this limitation, the model may over

- or underestimate exposure to the infant.

Because Equation 2

- 42 is based on the relationship between the chemical concentrations in the aqueous phase of breast milk and the blood plasma, a value for the fraction of the chemical in the mother’s blood plasma ( f pl ) is required. Ideally, an empirical value for f pl should be used. If empirical values are not available, f pl can be estimated from Equation 2

- 43 , provided that an empirical value can be found for the fraction of the chemic al in the body that is in the mother’s whole blood compartment ( f bl ; EPA 1998). Equation 2
- 43

. Fraction of Total Chemical in Body in the Blood Plasma Compartment

w here:

f

pl = Fraction of chemical in body (based on absorbed intake) that is in the blood plasma compartment (unitless); chemical

- specific

f bl = Fraction of chemical in body (based on absorbed intake) in the whole blood compartment (unitless); chemical

- specific
f bp = Fraction of whole blood that is plasma (unitless)

PcRBC = Partition coefficient for chemical between red blood cells and plasma (unitless);

chemical

- specificTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 45 6

- 29

- 2012 Description of MIRC If the fraction of the total chemical in the body that is in the whole blood compartment ( f bl ) is known for a given chemical, then the fraction of that chemical that is in blood plasma depends only on the partition coefficient for the chemical between the red blood cells and the plasma ( Pc RBC ) and the fraction of whole blood that is plasma ( f bp ). Another parameter for w hich a value is needed to solve Equation 2

- 42 is the total chemical elimination rate for lactating women for hydrophilic chemicals, k aq_elac . As for k fat_elac for lip ophilic 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 basic physiological mechanisms, we assume that chemical loss rates via urine are likely to be significantly higher than loss rates from nursing, however. This is because th e counter

- current anatomy of kidney tubules allows substantial concentration of chemicals in the tubules for elimination in urine compared with the concentration in circulating blood and because of active secretion of some chemicals into urine. Therefore, the best estimation of elimination of hydrophilic chemicals by lactating women is simply k

elim , the elimination of the chemical from a non

- lactating woman, as shown in Equation 2

- 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 in k aq_elac . Equation 2
- 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

- life using Equation 2

- 40 )


### 3.4.4. Alternative Model for Infant Intake of Methyl Mercury In this version of MIRC, we were unable to fully parameterize the aqueous model for mercury. In particular, no empirical value could be found for the ste ady

- state fraction of total hydrophilic chemical body burden in the mother that is in the blood plasma (f pl , see Exhibit_Add A 2

- 28 ). This parameter could be estimated using Equation 2

- 43 if a suitable chemical

- specific fraction of
c hemical 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 id entify 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 fetus, including a PBTK dynamic compartmental model for gestational transfer of MeHg in humans developed by Gearhart et al . (1995, 1996), and reparameterized by Clewell et al

. (1999).
We did find, however, that Byczkowski and Lipscomb (2001) had added a lactational transfer module to the Clewell et al . (1999) model. Byczkowski and Lipscomb compared their model’s predictions to epidemiological data from mother

- nursing

- infant pairs obtained following anTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 46 6

- 29

- 2012 Description of MIRC accidental high

- dose poisoning in Iraq (Amin

- Zaki et al . 1976) and from 34 mother

- 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 be tween 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 o f 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, althou gh use of hair/blood partition coefficients based on the results of the 1977 study precluded use of this comparison as model validation. Both the model predictions and the mean values from the 1977 data indicated that the concentration of MeHg in the bloo d of nursing infants was close to the MeHg concentration in their mothers’ blood (approximately 0.025 to 0.027 mg/L, Figure 4 of report). At those blood concentrations, the PBTK model estimated the average maternal intake of MeHg to be 0.68 ± 0.33 (SD) μg /kg

- day and the average infant intake of MeHg to be 0.80 ± 0.38 μg/kg

- day. Therefore, for purposes of MIRC, the DAI inf of MeHg is estimated to be the same as the maternal intake per unit body weight ( Equation 2

- 42 ). Equation 2
- 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 carcinogenic 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_Add A 2

- 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 hier archy of sources: EPA’s Integrated Risk Information System (IRIS); the Centers for Disease Control’s Agency for Toxic Substances and Disease Registry (ATSDR); and the California Environmental Protection Agency’s (CalEPA’s) Office of Environmental Health Hazard Assessment ( OEHHA ) Toxicity Criteria Database . For PB

- HAP chemicals that are currently evaluated in MIRC but do not currently have dose

response values identified by OAQPS for RTR, alternative methods for deriving values were used (see Sections 4.2 and 4.4 of this addendum).TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 47 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP C h emicals 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

- 04 IRIS Methyl mercury (MeHg) 22967926 not available 1.0E

- 04 IRIS Dioxins 1,2,3,4,6,7,8

- Heptachlorodibenzo

- p
- dioxin 35822469 1.50E+03 Derived from WHO 2005 TEFs c not available
1,2,3,4,6,7,8

- Heptachlorodibenzofuran 67562394 1.50E+03 Derived from WHO 2005 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

- Hexachlorodibenzofuran 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 available
1,2,3,7,8,9

- Hexachlorodibenzo

- p
- dioxin 19408743 6.20E+03 IRIS not available
1,2,3,7,8,9

- Hexachlorodibenzofuran 72918219 1.50E+04 Derived from WHO 2005 TEFs c not available
2,3,4,6,7,8

- Hexachlorodibenzofuran 60851345 1.50E+04 Derived from WHO 2005 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

- Pentachlorodibenzo

- 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 available
2,3,7,8

- Tetrachlorodibenzo

- p
- dioxin 1746016 1.50E+05 EPA ORD 1.0E

- 09 ATSDR
2,3,7,8

- Tetrachlorodibenzofuran 51207319 1.50E+04 Derived from WHO 2005 TEFs c not availableTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 48 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP C h emicals 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 Group 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 not available

2

- Methylnaphthalene 91576 5.0E

- 01 EPA 1999 , POM Group 72002 d 4 .0E

- 02 ATSDR

3

- Methylcholanthrene 56495 2

. 2 E+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 available

Anthracene 120127 0 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 Dibenzo(a,h)anthracene 53703 4.1E+00 CalEPA not available Dibenzo(a,i)pyrene 189559 1 .2E+03 CalEPA not available Dibenzo(a,j)acridine 224420 1.2 E+00 CalEPA not available
Fluoranthene 206440 5.0E

- 01 EPA 1999 , POM Group 72002 d 4.0E

- 02 IRIS

Fluorene 86737 5.0E

- 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 85018 0 IRIS 0 IRIS Polycyclic Organic Matter 246 5.0E

- 01 EPA 1999, POM Group 71002 d not availableTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 49 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 6 . Oral Dose

- response Values Used to Calculate RTR Screening Threshold Emission Rates for PB

- HAP C h emicals 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 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 Registry, IRIS = Integrated Risk Information System
, EPA OAQPS = EPA’s Office of Air Quality Planning and Standards
, CalEPA = California EPA
, EPA ORD = EPA’s Office of Research and Development
, HEAST = EPA Health Effects Assessment Tables, TEF = toxic equivalency factor

a Values as of June 2012; these values may be updated as newer ones become available. b There are two chronic RfDs for cadmium

— one for food and one for water

. c Dose

- response values for these dioxin congeners are not available from EPA sources. CSFs for these congeners were derived as discussed in Section 4.2 of this addendum

. d The meth od to assign oral cancer slope factors to polycyclic organic matter (POM) without CSFs available from other EPA sources is the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf and is summarized in Section 4.4 of this addendum

. 

### 4.1. Cadmium EPA has developed two chronic RfDs for cadmium (Cd) , one for food and one for water, based on data in IRIS indicating a lower absorption efficiency of cadmium from food than from water

. The default RfD set in MIRC is the higher RfD for Cd compounds in food ( as described in Section A.2.3, the drinking water exposure pathway is not modeled in the screening scenario because the likelihood that humans would use a lake as a drinking water source is assumed to be low ). Users of MIRC who assess exposures via drinking water would need to use the RfD for Cd compounds in water (i.e., 5.0E

- 04 mg/kg

- day)

. 

### 4.2. Dioxins (2,3,7,8

- TCD D) For chemicals for which the critical health effect is developmental, either in utero and/or during the first months or years of life, the exposure duration and timing of exposure for comparison with the RfD (or comparable values) require special conside ration. The most sensitive health endpoints for both mercury and 2,3,7,8

- TCDD are neurological effects during development that have long

- lasting effects on learning and social behaviors. To ensure a protective risk characterization for these chemicals, i t is important to use the shortest exposure duration appropriate, at the appropriate life stage, for comparison with the toxicity reference values. This approach avoids “dilution” of an estimated average ADD that would result from averaging the lower dail y 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 ass igned a TEF of 1, with the other dioxin congener TEQ concentrations scaled relative to 2,3,7,8

- TCDD concentrations on the basis of toxicity. For risk assessment of dioxins for RTR (with one exception) , the World Health Organization (WHO) 2005 TEFs present ed in Exhibit_Add A 2

- 7 were used to derive the CSFs (shown in Exhibit_Add A 2

- 6 ) for dioxin congeners without available EPA dose response values. The one exception is 1,2,3,7,8,9

- Hexachlorodibenzo

- p
- dioxin , whose TEF was based on data from IRIS.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 50 6

- 29

- 2012 Description of MIRC

Exhibit_Add A 2
- 7 . WHO 2005 Toxi c 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 0.1 1,2,3,7,8,9

- Hexachlorodibenzofuran 72918219 0.1 2,3,4,6,7,8

- Hexachlorodibenzofuran 60851345 0.1 1,2,3,4,6,7,8,9

- Octachlorodibenzo

- p
- dioxin 3268879 3E

- 04 1,2,3,4,6,7,8,9

- Octachlorodibenzofuran 39001020 3E

- 04 1,2,3,7,8

- Pentachlorodibenzo

- p
- dioxin 40321764 1 1,2,3,7,8

- Pentachlorodibenzofuran 57117416 0.03 2,3,4,7,8

- Pentachlorodibenzofuran 57117314 0.3 2,3,7,8

- Tetrachlorodibenzo

- p
- dioxin 1746016 1 2,3,7,8

- Tetrachlorodibenzofuran 51207319 0.1

a Source: van den Berg et al. 2006 , with the one exception in the next footnote. b For 1,2,3,7,8,9

- HexCDD, OAQPS identified an oral cancer slope factor from IRIS. For the purposes of these multipathway analyses, EPA uses the TEF derived from this IRIS oral CSF (6200 1/mg/kd/d, equaling a TEF of 0.041) rather than the WHO 2005 TEF of 0.1.



### 4.3. Mercury The RfD applies to the pregnant mother as well as young children. EPA has not specified the minimum exposure duration at the RfD level of exposure that is appropriate to use in characterizing risk ; we assume 10 years for women of childbearing age and 1 year for infants. We note that human exposures to MeHg are primarily through the consumption of fish and shellfish (EPA 2001b). EPA found that, on average, approximately 76 percent of the exposure to MeHg for women of childbearing age could be attributed to ingestion of mercury in freshwater and estuarine fish and shellfish, with the remaining 24 percent derived from marine fish and shellfish. Other sources accounted for less than 0.06 percent of total exposures (EPA 2001b).


### 4.4. Polycyclic Organic Matter
D ose
- response values for some of the POM species that are included in the screening analysis were not identified by OAQPS ; for these POM species, an alternativ e methodology for identifying CSFs was needed. Previously, for risk assessment of inhalation exposures to polycyclic organic matter (POM) for EPA’s National

- Scale Air Toxics Assessments (NATA) and for RTR, OAQPS developed an approach for characterizing ris ks associated with the individual POM species and POM groups reported in the National Emissions Inventory (NEI). I ndividual POM s were assigned to one of eight POM groups according to cancer potencies derived by EPA for IRIS and by CalEPA, and based on ass umptions 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 Methodology for RTR

Attachment A, Addendum 2 2

- 51 6

- 29

- 2012 Description of MIRC POM species reported in NEI , not just the species currently evaluated in this analysis ) and the corresponding CSFs using this methodology are presented in Exhibit_Add A 2

- 8

. These group CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to the individual chemical.

Exhibit_Add A 2
- 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)pyrene 192972 Benzo(c)phenanthrene 195197 Perylene 198550 Benzo(g,h,i) f luoranthene 203123 Benzo(a)fluoranthene 203338 Fluoranthene 206440 Acenaphthylene 208968 1

- Methylpyrene 2381217 12

- Methylbenz(a)anthracene 2422794 Methylbenzopyrenes 247 Methylchrysene 248 Methylanthracene 26914181 Benzofluoranthenes 56832736 9

- Methylbenz(a)anthracene 779022 1

- Methylphenanthrene 832699 Acenaphthene 83329 Phenanthrene 85018 Fluorene 86737 2

- Methylnaphthalene 91576 2

- Chloronaphthalene 91587 POM Group 73002 7,12

- Dimethylbenz(a)anthracene 57976 1000 POM Group 74002 Dibenzo(a,i)pyrene 189559 100 Dibenzo(a,h)pyrene 189640TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 52 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 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 ) pyrene 50328 Dibenzo(a,h)anthracene 53703 POM Group 76002 Benzo(b+k)fluoranthene 102

1 Indeno(1,2,3

- c,d)pyrene 193395 Benzo(j)fluoranthene 205823 Benzo(b)fluoranthene 205992 Benzo(k)fluoranthene 207089 Dibenzo(a,j)acridine 224420 Benz(a)anthracene 56553 POM Group 77002 Chrysene 218019 0.1 POM Group 78002 7

- PAH 75 0.5

a These group CSFs are used only when OAQPS has not, for the purposes of RTR, identified a CSF specific to the individual chemical. b The method to assign oral cancer slope factors to POM groups was the same as that used in the 1999 National Air Toxics Assessment (EPA 1999). A complete description of the methodology is available at: http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf

. 

## 5. Risk Estimation For persistent and bioaccumulative hazardous air pollutants ( PB

- HAPs), risks from inhalation of a chemical directly from air generally will be negligible compared with risks from ingestion of the chemical from foodstuffs grown in an area subject to air deposition of the chemical R isk characterization for carcinogens with a linear mode of action at low doses is described in Section 5.1 of this addendum . Risk characterization for chemicals likely to exhibit a threshold for resp onse (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 so urce 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 with a low

- dose linear mode of action , the estimated ELCR is calculated as the product of the lifetime average daily dose (LADD) and the
cancer slope factor (CSF) :TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 53 6

- 29

- 2012 Description of MIRC

Equation 2
- 46

. Calculation of Excess Lifetime Cancer Risk

ELCR = LADD  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 addendum , t he LADD (in mg/kg

- day) for a chemical is calculated to reflect age

- related differences in exposure rates that are experienced by a hypothetical individual throughout his or her lifetime of exposure

. 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 considers the possibility that children might be more sensitive than adults to toxic chemicals, in cluding 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

- trichlo roethane ; 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 whic h EPA has adequate data to develop a reasonable quantitative default approach is mutagenesis ( EPA 2005 b, c) . For carcinogens with a mutagenic MOA for cancer, EPA concluded that the carcinogenic potency of a chemical may be approximately tenfold greater for the first 2 years of life (i.e., birth up to second birthday) and threefold greater for the next 14 years of life (i.e., ages 2 through 15) than for adults (EPA 2005c). These conclusions are represented by age

dependent adjustment factors (ADAFs) of 10, 3, and 1 for the first two lifestages and for adults, respectively

. These three lifestages do not match the age categories for the home

- grown food ingestion rates, the age categories in MIRC. As a consequence, ADAFs for the age groups in MIRC are adapte d as time

- weighted average values as follows:

To estimate total lifetime risk from a lifetime of exposure to such a chemical, EPA recommends estimating the cancer risk for each of the three lifestages separately and then adding the risks for i = 1 to 6 age groups.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 54 6

- 29

- 2012 Description of MIRC

Equations 2
- 47 to 2
- 53 . Lifetime Cancer Ri sk: Chemicals with a

Mutagenic MOA for Cancer Equation 2
- 47 Risk (<1) = ADD (0

– <1) × 10 × CSF × (1 yr/70 yr) Equation 2
- 48

. Risk (1

– 2) = ADD (1

– 2) × 6.5 × CSF × (2 yr/70 yr) Equation 2
- 49

. Risk (3

– 5) = ADD (3

– 5) × 3 × CSF × (3 yr/70 yr) Equation 2
- 50

. Risk (6

– 11) = ADD (6

– 11) × 3 × CSF × (6 yr/70 yr) Equation 2
- 51

. Risk (12

– 19) = ADD (12

– 19) × 2 × CSF × (8 yr/70 yr) Equation 2
- 52

. Risk (adult) = ADD (a dult) × 1 × CSF × (50 yr/70 yr)

Equation 2
- 53

. ELCR
In other words, Equation 2

- 53 indicates that the total excess lifetime cancer risk ( ELCR) equals the sum of the age

- group
- specific risks estimated by Equations 2

- 47 through Equation 2

- 52 , where:

Risk

(<1) = R isk from chemical ingestion in first year of life
Risk (1

- 2) = Risk from chemical ingestion from first birthday through age 2 years
Risk (3

- 5) = 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) = Average daily dose for adults age 20 to 70 years of age (mg/kg

- day) CSF = Oral carcinogenic potency slope factor for chemical (per mg/kg

- day)
Risk (i) = 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

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 55 6

- 29

- 2012 Description of MIRC 

### 5.2.1. H azard Quotients for Chemicals with a Chronic RfD For chemicals with a chronic RfD, MIRC calculates an HQ for each age group separately using Equation 2

- 54 to indicate the potential for adverse health effects associated with chronic exposure via ingestion pathways. 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 thres hold effects and are not additive across age groups.

Equation 2
- 54

. Hazard Quotient for Chemicals with a Chronic RfD

where:

HQ = Hazard quotient for chemical (unitless)

ADD = Average daily ingested dose of chemical (mg/kg

- day) from all food types and

ingested media for the age group
RfD = Chronic oral reference dose for chemical (mg/kg

- day)


### 5.2.2. Hazard Quotients for Chemicals with RfD Based on Developmental Effects
For chemicals for which the toxicity reference value is an RfD based on developmental effects
, a shorter exposure duration ( ED ) and averaging time ( AT ) may be required

. For this type of chemical (e.g., methylmercury, 2,3,7,8

- TCDD), the appropriate ED/AT and sensitive lifestage for exposure may need to be estimated from the information provided in the critical developmental study(ies) from which the RfD was derived (e.g., in consultation with the RfD documentation in EPA’s IRIS or in a toxicological profile developed fo r 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 W hen conducting screening

- level assessments for multiple chemicals, it can be informative to calculate a hazard index (HI) for toxicologically similar chemicals (EPA 2000 a ). The HI is the sum of HQs across chemicals (not age groups) as shown in Equation 2

- 55

. As with the HQ, if the HI value is less than 1, adverse health effects are not expected for that suite of chemicals

. If the screening level HI exceeds 1, howeve r, the risk assessor may in some instances, evaluate the assumptions of the screening

- level assessment to determine if more realistic local values are available for parameters that drive risk

. In addition, the risk assessor may need to examine the mode of action (MOA) and target organ(s) for the chemicals with the highest HQs to develop an appropriate approach to assessing their potential joint action.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 56 6

- 29

- 2012 Description of MIRC Equation 2
- 55

. Hazard Index Calculation

HI = HQ 1 + HQ 2 ... HQ n where:

HI = Hazard index (unitless) HQ 1 = Hazard Quotient for chemical 1 (unitless) HQ 2 = Hazard Quotient for chemical 2 (unitless)
HQ n = Hazard Quotient for chemical n (unitless) The HI approach can be appropriate for chemicals with the same MOA and same target organ ; however, MOA often is difficult to determine. An HI usually is “developed for each exposure route of interest, and for a single toxic effect or for toxicity to a single target organ” ( EPA 2000 a ; p 79 ). If a receptor is exp osed to multiple chemicals that affect different target organs or that operate by different MOAs, and if more than one HQ is close to 1, the risk assessor in some circumstances, may consider whether chemical interactions play a role in chemical toxicity ( EPA 2000 a )

. Exposures to more than one chemical can result in a greater or lesser toxic response than might be predicted on the basis of one or the other chemical acting alone (toxicologically independent) or acting in concert (toxicologically similar che micals)

. Users are referred to EPA’s Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures for approaches to assessing the potential for adverse health effects from exposure to multiple chemicals ( EPA 2000 a )

. Note that users of MIRC are responsible for determining how to interpret HQs for multiple chemicals

. 

## 6. Model Input Options This section describes the input options currently included in MIRC . Required user inputs for environmental media concentrations and air deposition rates, such as those predicted by (output of) TRIM.FaTE, are described in Section 6.1 of this addendum . Values for farm

- food

chain (FFC) parameters for specific t ypes 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 freq uency and loss of chemical during food preparation and cooking, are provided in Section 6.4

. Where values for chemical

- specific parameters are presented, values are presented only for PB

- HAP chemicals currently evaluated using the TRIM

- based RTR screening scenario. The database included with MIRC contains chemical

- specific parameter values for a large number of chemicals (more than 500) , because all of the chemica l

- specific input data compiled by EPA for use in HHRAP were uploaded into MIRC. However, only chemicals that are PB

- HAPs evaluated for RTR are discussed in this document (see Sections 2.1 and A.2.2 ) , and the HHRAP inputs provided for other chemicals have not been reviewed or verified

. The data presented in this chapter were reviewed and used to develop the set of modeling defaults used to calculate screening threshold emission rates for RTR. Note that the default values used to estimate RTR screening thresholds, and the justification for selecting a specific value from the d ata sets described in this chapter, are discussed in Chapter 7.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 57 6

- 29

- 2012 Description of MIRC 

### 6.1. Environmental Concentrations As noted in Section 2 of this addendum , MIRC is intended to estimate exposures and risks to self

- sufficient farming and fishing families from ingestion of FFC media in an area of airborne chemical deposition

. The tool analyzes one exposure scenario at a time (e.g., adult farmer exposed to dioxin from ingestion of beef) ; therefore, it is best used to evaluate a maximally exposed individual (MEI) or family when MIRC is used to screen for possible risks.

The followin g values specific to the air pollutant of concern are required inputs to MIRC:

 a single air concentration (in g/m3);

 the fraction of chemical in the air that is in the vapor phase;

 air

- to

- surface deposition rates for both vapor

- and particle

- phase chemical in the air (in g/m2

- yr);

 two fish tissue concentrations, one each for forage and game fish (i.e., fish in TL 3 and TL 4) (in mg/kg wet weight);

 concentrations in drinking water (in g/L); and

 four chemical concentrations in soil (in μg/g dry weight), one each for:

– surface soil in produce growing area,

– surface soil where livestock feed,

– root

- zone soil in produce growing area, and

– root

- zone soil in livestock feed growing area. The MIRC software is configured to estimate ingestion exposures via drinking wate r for a specified chemical concentration in the drinking water source (e.g., groundwater well)

. However
, no exposure via drinking water is assumed to occur when calculating the Tier 1 screening thresholds . As discussed in Section A.2.3
, t he drinking water exposure pathway is not modeled for the scenario developed for the Tier 1 analysis because the likelihood that humans would use a lake as a drinking water source is assumed to be low. The user must provide the inputs listed above; no default values are included for these parameters in MIRC

. Media concentrations output by TRIM.FaTE can be entered into the tool manually from model output fil es or can be imported directly from the TRIM.FaTE output files

. For RTR evaluations, a tool to facilitate this process was developed using a Microsoft Excel routine written in Visual Basic

. 

### 6.2. Farm

- Food

- Chain Parameter Values Using the chemical information specified in Section 6.1 above as inputs, MIRC calculate s chemical concentrations in foods that are commonly grown or raised on family farms: exposed and protecte d 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 addendum . P arameter values required for these HHRAP algorithms, including chemical

- specific med ia transfer f actors (e.g., soil

- to

-TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 58 6

- 29

- 2012 Description of MIRC plant transfer coefficients) and plant

- and animal

- specific properties (e.g., plant interception fraction, quantity of forage consumed by cattle) , are included in tables in MIRC. As described in Section 7 of this addendum , the HHRAP

- recommended parameter values are the default values in MIRC; however, 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_Add A 2
- 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) Moisture adjustment factor for aboveground produce type i to

convert the chemical concentration estimated for dry

- weight produce to the corresponding chemical concentration for ful l

weight fresh produce Percent water

RCF Chemical

- specific r oot concentration factor for tubers and root

produce on a wet

- weight (WW) basis L soil pore water/ kg root WW Rp (i) Plant

- specific i nterception fraction for the edible portion of

aboveground exp osed produce or animal forage and silage Unitless

Tp (i) Length of plant exposure to deposition per harvest of the edible

portion of aboveground exposed produce or animal forage and silage Year

VG AG(i) Empirical correction factor for aboveground exposed produce

type i to address possible overestimate of the diffusive transfer of chemical from the outside to the inside of bulky produce, such as fruit Unitless

VG rootveg Empirical correction factor for be lowground produce (i.e., tuber or

root vegetable) to account for possible overestimate of the diffusive transfer of chemicals from the outside to the inside of bulky tubers or roots (based on carrots and potatoes) Unitless

Yp (i) Plant

- specific 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 Methodology for RTR

Attachment A, Addendum 2 2

- 59 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 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; equivalent to WW) basis mg chemical/kg FW tissue/mg chemical/day or day/kg FW tissue Ba (d airy ) Biotrans fer 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 Ba (e ggs ) Biotransfer factor in eggs day/kg FW tissue Qs (m) Quantity of soil eaten by animal type m each day kg/day Qp (i,m) Quantity of plant feed type i consumed per animal type m each

day kg/day

Source: EPA Source: EPA 2005 a DW = dry weight; FW = fresh weight; WW = wet weight


### 6.2.2. Produce Parameter Values Exhibit_Add A 2

- 10 and Exhibit_Add A 2

- 11 provide the chemical

- specific input values that are the current defaults for pr oduce FFC food types in MIRC. Exhibit_Add A 2

- 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

. Options for other parameter values are not included in MIRC at this time; however, the user can overwrite values if appropriate

. Refer to HHRAP ( EPA 2005 a , Chapter 5 and associated appendices) for detailed descriptions of these parameters and documentation of input values

.

Exhibit_Add A 2
- 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
Mercuric chloride 0.6 NA 5.8E+04 1.8E+03 Methyl mercury 0.6 NA 7.0E+03 0 f
PAHs 2

- Methylnaphthalene 0.6 2.2E+02 5.0E+01 1.4E+00 7,12

- Dimethylbenz(a)anthrace
ne 0.6 6.8E+03 4.0E+03 4.2E+04

Acenaphthene 0.6 2.4E+02 3.9E+01 4.6E+00 Acenaphthylene 0.6 2.8E+02 6.8E+01 8.1E+00 Benz(a)anthracene 0.6 6.7E+03 2.9E+03 6.8E+03 Benzo(a)pyrene 0.6 9.2E+03 7.8E+03 1.7E+05TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 60 6

- 29

- 2012 Description of MIRC

Exhibit_Add A 2
- 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 Methodology for RTR

Attachment A, Addendum 2 2

- 61 6

- 29

- 2012 Description of MIRC

Exhibit_Add A 2
- 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 radionuclides were deposited onto pasture grass via simulated rain. Note that the values developed experimentally f or pasture grass may not accurately represent all aboveground produce

- specific values. Also note that values based on the behavior of insoluble particles tagged with radionuclides may not accurately represent the behavior of organic compounds unde r site

- specific conditions. b For nonionic organic chemicals, as described in HHRAP (EPA 2005a), Appendix A (available at http://www.epa.gov/osw/hazard/tsd/td/combust/finalmact/ssra/05hhrapapa.pdf ), RCF is used to calculate the below

- ground transfer of contaminants from soil to a r oot vegetable on a wet

- weight basis as shown in Equation 2

- 6 . EPA estimated chemical

specific values for RCF from empirical regression equations developed by Briggs et al. (1982) based on their experiments measuring uptake of compounds into barley roots from growth solution. Briggs’ regression equations allow calculation of RCF values from log Kow. For metals and mercuric compounds, empirical values for soil to root vegetable transfer on a dry

- weight basis are available in the literature, thus the RCF was not needed.

c As discussed in HHRAP (EPA 2005a), Appendix A, Kds describes the partitioning of a compound between soil pore

- water and soil particles and strongly inf luences the release and movement of a compound into the subsurface soils and underlying aquifer. Kds values for mercuric compounds were obtained from EPA (1997b). Kds for cadmium compounds were obtained from U.S. EPA 1996. For all PAHs and dioxins, Kds was calculated by multiplying Koc times the screening scenario’s fraction organic carbon content (0.008). Empirical information for Koc was available for acenaphthene, benz(a)anthracene, benzo(a)pyrene, dibenz(a,h)anthracene, fluoranthene, and fluorene in USEAP 1996. For all other organic compounds, the Koc was calculated using the correlation equations presented in USEAP 2005a. d As discussed in HHRAP (EPA 2005a), Appendix A, the value for mercuric chloride was obtained from EPA 1997b. BvAG(i) values for PAHs were calculated using the correlation equation derived for azalea leaves as cited in Bacci et al. (1992), then reducing this value by a factor of 100, as suggested by Lorber (1995), who concluded that the Bacci factor reduced by a facto r of 100 was s imilar to his own observations in various studies. The values for dioxins were obtained from Lorber and Pinsky (2000).

e It is assumed that metals, with the exception of vapor

- phase elemental mercury, do not transfer significantly from air into leaves. 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_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c Inorganics

Cadmium compounds Exp. Fruit 1.3E

- 01

- 1.0E+00 Exp. Veg. 1.3E

- 01

- 1.0E+00 Forage 3.6E

- 01

- 1.0E+00 Grain 6.2E

- 02

- - Prot. Fruit 1.3E

- 01

- - Prot. Veg. 1.3E

- 01

- - Root 6.4E

- 02 1.0E+00

- Silage 3.6E

- 01

- 5.0E

- 01TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 62 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c

Mercury (elemental) Exp. Fruit

- - 1.0E+00 Exp. Veg.

- - 1.0E+00 Forage

- - 1.0E+00 Grain

- - - Prot. Fruit

- - - Prot. Veg.

- - - Root

- 1.0E+00

- Silage

- - 5.0E

- 01

Mercuric chloride Exp. Fruit 1.5E

- 02

- 1.0E+00 Exp. Veg. 1.5E

- 02

- 1.0E+00 Forage 0.0E+00

- 1.0E+00 Grain 9.3E

- 03

- - Prot. Fruit 1.5E

- 02

- - Prot. Veg. 1.5E

- 02

- - Root 3.6E

- 02 1.0E+00

- Silage 0.0E+00

- 5.0E

- 01

Methyl mercury Exp. Fruit 2.9E

- 02

- 1.0E

- 02 Exp. Veg. 2.9E

- 02

- 1.0E

- 02 Forage 0.0E+00

- 1.0E+00 Grain 1.9E

- 02

- - Prot. Fruit 2.9E

- 02

- - Prot. Veg. 2.9E

- 02

- - Root 9.9E

- 02 1.0E

- 02

- Silage 0.0E+00

- 5.0E

- 01 PAHs

2

- Methylnaphthalene Exp. Fruit 2.3E

- 01

- 1.0E+00 Exp. Veg. 2.3E

- 01

- 1.0E+00 Forage 2.3E

- 01

- 1.0E+00 Grain 2.3E

- 01

- - Prot. Fruit 2.3E

- 01

- - Prot. Veg. 2.3E

- 01

- - Root 4.4E+00 1.0E+00

- Silage 2.3E

- 01

- 5.0E

- 01

7,12

- Dimethylbenz(a)anthra
cene Exp. Fruit 1.7E

- 02

- 1.0E

- 02 Exp. Veg. 1.7E

- 02

- 1.0E

- 02 Forage 1.7E

- 02

- 1.0E+00 Grain 1.7E

- 02

- - Prot. Fruit 1.7E

- 02

- - Prot. Veg. 1.7E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.7E

- 02

- 5.0E

- 01TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 63 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c

Acenaphthene Exp. Fruit 2.1E

- 01

- 1.0E+00 Exp. Veg. 2.1E

- 01

- 1.0E+00 Forage 2.1E

- 01

- 1.0E+00 Grain 2.1E

- 01

- - Prot. Fruit 2.1E

- 01

- - Prot. Veg. 2.1E

- 01

- - Root 6.2E+00 1.0E+00

- Silage 2.1E

- 01

- 5.0E

- 01

Acenaphthylene Exp. Fruit 1.9E

- 01

- 1.0E

- 02 Exp. Veg. 1.9E

- 01

- 1.0E

- 02 Forage 1.9E

- 01

- 1.0E+00 Grain 1.9E

- 01

- - Prot. Fruit 1.9E

- 01

- - Prot. Veg. 1.9E

- 01

- - Root 4.1E+00 1.0E

- 02

- Silage 1.9E

- 01

- 5.0E

- 01

Benz(a)anthracene Exp. Fruit 1.7E

- 02

- 1.0E

- 02 Exp. Veg. 1.7E

- 02

- 1.0E

- 02 Forage 1.7E

- 02

- 1.0E+00 Grain 1.7E

- 02

- - Prot. Fruit 1.7E

- 02

- - Prot. Veg. 1.7E

- 02

- - Root 2.3E+00 1.0E

- 02

- Silage 1.7E

- 02

- 5.0E

- 01

Benzo(a)pyrene Exp. Fruit 1.4E

- 02

- 1.0E

- 02 Exp. Veg. 1.4E

- 02

- 1.0E

- 02 Forage 1.4E

- 02

- 1.0E+00 Grain 1.4E

- 02

- - 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 Methodology for RTR

Attachment A, Addendum 2 2

- 64 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c

Benzo(b)fluoranthene Exp. Fruit 1.8E

- 02

- 1.0E

- 02 Exp. Veg. 1.8E

- 02

- 1.0E

- 02 Forage 1.8E

- 02

- 1.0E+00 Grain 1.8E

- 02

- - Prot. Fruit 1.8E

- 02

- - Prot. Veg. 1.8E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.8E

- 02

- 5.0E

- 01

Benzo(g,h,i)perylene Exp. Fruit 5.7E

- 03

- 1.0E

- 02 Exp. Veg. 5.7E

- 03

- 1.0E

- 02 Forage 5.7E

- 03

- 1.0E+00 Grain 5.7E

- 03

- - Prot. Fruit 5.7E

- 03

- - Prot. Veg. 5.7E

- 03

- - Root 1.1E+00 1.0E

- 02

- Silage 5.7E

- 03

- 5.0E

- 01

Benzo(k)fluoranthene Exp. Fruit 1.4E

- 02

- 1.0E

- 02 Exp. Veg. 1.4E

- 02

- 1.0E

- 02 Forage 1.4E

- 02

- 1.0E+00 Grain 1.4E

- 02

- - Prot. Fruit 1.4E

- 02

- - Prot. Veg. 1.4E

- 02

- - Root 1.6E+00 1.0E

- 02

- Silage 1.4E

- 02

- 5.0E

- 01

Chrysene Exp. Fruit 1.9E

- 02

- 1.0E

- 02 Exp. Veg. 1.9E

- 02

- 1.0E

- 02 Forage 1.9E

- 02

- 1.0E+00 Grain 1.9E

- 02

- - Prot. Fruit 1.9E

- 02

- - Prot. Veg. 1.9E

- 02

- - Root 1.7E+00 1.0E

- 02

- Silage 1.9E

- 02

- 5.0E

- 01TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 65 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c

Dibenz(a,h)anthracene Exp. Fruit 6.8E

- 03

- 1.0E

- 02 Exp. Veg. 6.8E

- 03

- 1.0E

- 02 Forage 6.8E

- 03

- 1.0E+00 Grain 6.8E

- 03

- - Prot. Fruit 6.8E

- 03

- - Prot. Veg. 6.8E

- 03

- - Root 1.6E+00 1.0E

- 02

- Silage 6.8E

- 03

- 5.0E

- 01

Fluoranthene Exp. Fruit 4.0E

- 02

- 1.0E

- 02 Exp. Veg. 4.0E

- 02

- 1.0E

- 02 Forage 4.0E

- 02

- 1.0E+00 Grain 4.0E

- 02

- - Prot. Fruit 4.0E

- 02

- - Prot. Veg. 4.0E

- 02

- - Root 5.6E+00 1.0E

- 02

- Silage 4.0E

- 02

- 5.0E

- 01

Fluorene Exp. Fruit 1.5E

- 01

- 1.0E

- 02 Exp. Veg. 1.5E

- 01

- 1.0E

- 02 Forage 1.5E

- 01

- 1.0E+00 Grain 1.5E

- 01

- - Prot. Fruit 1.5E

- 01

- - Prot. Veg. 1.5E

- 01

- - Root 6.2E+00 1.0E

- 02

- Silage 1.5E

- 01

- 5.0E

- 01

Indeno(1,2,3

- cd)pyrene Exp. Fruit 5.1E

- 03

- 1.0E

- 02 Exp. Veg. 5.1E

- 03

- 1.0E

- 02 Forage 5.1E

- 03

- 1.0E+00 Grain 5.1E

- 03

- - Prot. Fruit 5.1E

- 03

- - Prot. Veg. 5.1E

- 03

- - Root 1.1E+00 1.0E

- 02

- Silage 5.1E

- 03

- 5.0E

- 01TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 66 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c Dioxins

OctaCDD, 1,2,3,4,6,7,8,9

- Exp. Fruit 7.1E

- 04

- 1.0E

- 02 Exp. Veg. 7.1E

- 04

- 1.0E

- 02 Forage 7.1E

- 04

- 1.0E+00 Grain 7.1E

- 04

- - Prot. Fruit 7.1E

- 04

- - Prot. Veg. 7.1E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.1E

- 04

- 5.0E

- 01

OctaCDF, 1,2,3,4,6,7,8,9

- Exp. Fruit 9.2E

- 04

- 1.0E

- 02 Exp. Veg. 9.2E

- 04

- 1.0E

- 02 Forage 9.2E

- 04

- 1.0E+00 Grain 9.2E

- 04

- - Prot. 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 Methodology for RTR

Attachment A, Addendum 2 2

- 67 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c

HeptaCDF, 1,2,3,4,7,8,9

- Exp. Fruit 4.0E

- 03

- 1.0E

- 02 Exp. Veg. 4.0E

- 03

- 1.0E

- 02 Forage 4.0E

- 03

- 1.0E+00 Grain 4.0E

- 03

- - Prot. Fruit 4.0E

- 03

- - Prot. Veg. 4.0E

- 03

- - Root 1.2E+00 1.0E

- 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 Methodology for RTR

Attachment A, Addendum 2 2

- 68 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c

HexaCDF, 1,2,3,6,7,8

- Exp. Fruit 1.0E

- 03

- 1.0E

- 02 Exp. Veg. 1.0E

- 03

- 1.0E

- 02 Forage 1.0E

- 03

- 1.0E+00 Grain 1.0E

- 03

- - Prot. Fruit 1.0E

- 03

- - Prot. Veg. 1.0E

- 03

- - Root 7.1E

- 01 1.0E

- 02

- Silage 1.0E

- 03

- 5.0E

- 01

HexaCDD, 1,2,3,7,8,9

- Exp. Fruit 7.0E

- 04

- 1.0E

- 02 Exp. Veg. 7.0E

- 04

- 1.0E

- 02 Forage 7.0E

- 04

- 1.0E+00 Grain 7.0E

- 04

- - Prot. Fruit 7.0E

- 04

- - Prot. Veg. 7.0E

- 04

- - Root 6.1E

- 01 1.0E

- 02

- Silage 7.0E

- 04

- 5.0E

- 01

HexaCDF, 1,2,3,7,8,9

- Exp. Fruit 1.6E

- 03

- 1.0E

- 02 Exp. Veg. 1.6E

- 03

- 1.0E

- 02 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 Methodology for RTR

Attachment A, Addendum 2 2

- 69 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c

PentaCDD, 1,2,3,7,8

- Exp. Fruit 2.4E

- 03

- 1.0E

- 02 Exp. Veg. 2.4E

- 03

- 1.0E

- 02 Forage 2.4E

- 03

- 1.0E+00 Grain 2.4E

- 03

- - Prot. 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 Methodology for RTR

Attachment A, Addendum 2 2

- 70 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 11 . Chemical

- Specific Inputs by Plant Type for Chemicals in MIRC

Compound Name Plant Part Plant

- Soil Bio

Concentration Factor ( Br AG

- produce

- DW(i) ) (unitless) a Empirical Correction

Factor

- Belowground Produce ( VG rootveg ) (unitless) b Empirical

Correction Factor

Aboveground Produce ( VG AG(i) ) (unitless) c

TetraCDF, 2,3,7,8

- Exp. Fruit 1.2E

- 02

- 1.0E

- 02 Exp. Veg. 1.2E

- 02

- 1.0E

- 02 Forage 1.2E

- 02

- 1.0E+00 Grain 1.2E

- 02

- - Prot. Fruit 1.2E

- 02

- - Prot. Veg. 1.2E

- 02

- - Root 1.9E+00 1.0E

- 02

- Silage 1.2E

- 02

- 5.0E

- 01

a As discussed in HHRAP ( EPA 2005a), the Br AG

- produce

- DW(i) for aboveground produce and forage accounts for the uptake from soil and the subsequent transport of contaminants through the roots to the aboveground plant parts

. For organics, correlation equations to calculate values for Br on a dry weight basis were obtained from Travis and Arms (1988)

. For cadmium, Br values were derived from uptake slope factors provided in EPA 1992

. Uptake slope is the ratio of contaminant concentration in dry weight plant tissue to the mass of cont aminant applied per hectare soil

. Br aboveground values for mercuric chloride and methyl mercury were calculated using methodology and data from Baes, et al. (1984)

. Br forage values for mercuric chloride and methyl mercury (on a dry weight basis) were obtained from EPA 1997b

. The HHRAP methodology assumes that elemental mercury doesn’t deposit onto soils

. Therefore, it’s assumed that there is no plant uptake through the soil

. b As discussed in HHRAP ( EPA 2005 a ), Appendix B, VG rootveg 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 techniques, such as washing, peeling, and cooking, further reduce the concentration of the chemical in the vegetable as consumed by removing the high concentra tion of chemical on and in the outer skin, leaving the flesh with a lower concentration than would be the case if the entire vegetabl e were pureed without washing

. For belowground produce, HHRAP ( EPA 2005 a ) recommends using a VG r ootveg value of 0.01 for P B

- 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 (potent ially overestimating the concentration of PB

- HAP in belowground produce due to root uptake).

c As discussed in HHRAP ( EPA 2005 a ), Appendix B, VG ag represents an empirical correction factor that reduces ab oveground produce concentration and was developed to estimate the transfer of PB

- HAP into leafy vegetation versus bulkier aboveground produce (e.g., apples)

. Because of the protective outer skin, size, and shape of bulky produce, transfer of lipophilic PB

- HAP (log K ow greater than 4) to the center of the pr oduce is not likely

. In addition, typical preparation techniques, such as washing, peeling, and cooking, further reduces residues

. For aboveground produce, HHRAP ( EPA 2005 a ) recommends using a VG ag value of 0.01 for PB

- HAP with a log K ow greater than 4 and a value of 1.0 for PB

- HAP with a log K ow less than 4 based on information provided in EPA 1994 b. In developing these values, EPA (1994 b ) assumed the following: (1) translocation of compounds deposited on the surface of aboveground vege tation to inner parts of aboveground produce would be insignificant (potentially underestimating the concentration of PB

- HAP in aboveground produce due to air

- to

- plant transfer); (2) the density of the skin and the whole vegetable are equal (potentially ov erestimating 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 tran sfer unknown)

. For forage, HHRAP recommends a VG ag value of 1.0, also based on information provided in EPA 1994 b. A VG ag value for silage is not provided in EPA 1994 b ; the VGag value for silage of 0.5 was obtained from NC DEHNR (1997); however, NC DEHNR does not present a specific rationale for this recommendation

. Depending on the composition of the site

specific silage, this value may under

- or overestimate the actual value

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 71 6

- 29

- 2012 Description of MIRC

Exhibit_Add A 2
- 12

. Non

- Chemical

- Specific Produce Inputs

Plant Part Interception

Fraction ( Rp (i) ) (unitless) a Plant

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) C hamberlain’s (1970) empirical relationship developed for pasture grass may not accurately represent aboveground produce

. (2) The empirical constants developed by Baes et al

. (1984) for use in the empirical relationship developed by Chamberlain (1970) may not accurately represent the site

- specific mixes of aboveground produce consumed by humans or the site

- specific mixes of forage or silage consumed by livestock. b The term kp is a measure of the amount of chemical that is lost to natural physical processes (e.g., wind, water) over time

. The HHRAP

- recommended value of 18 yr - 1 (also recommended by EPA 1994a and 1998) represents the midpoint of a range of values reported by Mill er and Hoffman (1983)

. There are two key uncertainties associated with using these values for kp : (1) The recommended equation for calculating kp includes a health protective bias in that it does not consider chemical degradation processes

. ( 2) Given the reported range of kp values from 7.44 to 90.36 yr - 1 , plant concentrations could range from about 1.8 times higher to about 5 times lower than the plant concentrations estimated in FFC media using the midpoint kp value of 18.

c HHRAP ( EPA 2005a) recommends using a Tp value of 0.16 years for aboveground produce and cattle silage

. This is consistent with earlier reports by EPA (1994a, 1998) and NC DEHNR (1997), which recommended treating Tp as a constant based on the average period between successive hay harv ests

. Belcher and Travis (1989) estimated this period at 60 days

. Tp is calculated as 60 days ÷ 365 days/year = 0.16 years

. For forage, the average of the average period between successive hay harvests (60 days) and the average period between successive grazing (30 days) is used (that is, 45 days), and Tp is calculated as (60 days + 30 days)/ 2 ÷ 365 days/yr = 0.12 yr

. Two key uncertainties are associated with use of these values for Tp : (1) The average period between successive hay harvests (60 days) m ay not reflect the length of the growing season or the length between successive harvests for site

- specific aboveground produce crops

. The concentration of chemical in aboveground produce due to direct (wet and dry) deposition ( Pd ) will be underestimated if the site

- specific value of Tp is less than 60 days, or overestimated if the site

specific value of Tp is more than 60 days. d Yp values for aboveground produce and forage were calculated using an equation presented in Baes et al

. (1984) and Shor et al

. (1982): Yp = Y hi / A hi , where Y

hi = Harvest yield of i th crop (kg DW) and A hi = Area planted to i th crop (m2), and using values for Y h and A h from USDA (1994b and 1994c)

. A production

- weighted U.S

. average Yp of 0.8 kg DW/m2 for silage was obtained from Shor et al

. 1982.

e MAF represents the plant tissue

- specific moisture adjustment factor to convert dry

- weight concentrations into wet

- weight concentrations (which are lower owing to the dilution by water compare d 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 gr ain used as animal feed is based on corn and soybeans, not seed grains such as barley, oats, or wheat.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 72 6

- 29

- 2012 Description of MIRC 

### 6.2.3. Animal Product Parameter Values MIRC also requires chemical

- specific inputs for many of the animal product algorithms. The relevant values are shown in Exhibit_Add A 2

- 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 anim al and animal product type. The soil and plant ingestion rates recommended in HHRAP for beef cattle, dairy cattle, swine, and chicken are provided in Exhibit_Add A 2

- 14

.

Exhibit_Add A 2
- 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
Mammal Non

- mammal Beef ( Ba beef ) Dairy ( Ba dairy ) Pork ( Ba pork ) MF Eggs

( Ba eggs ) Poultry

( Ba poultry ) MF Inorgani cs Cadmium compounds 1 1.2E

- 04 6.5E

- 06 1.9E

- 04 1 2.5E

- 03 1.1E

- 01 NA Mercury (elemental) 1 0 0 0 1 0 0 NA Mercuric chloride 1 1.1E

- 04 1.4E

- 06 3.4E

- 05 1 2.4E

- 02 2.4E

- 02 NA Methyl mercury 1 1.2E

- 03 1.7E

- 05 5.1E

- 06 1 3.6E

- 03 3.6E

- 03 NA PAHs 2

- Methylnaphthalene 1 2.4E

- 02 5.0E

- 03 2.9E

- 02 0.01 1.0E

- 02 1.7E

- 02 NA 7,12

- Dimethylbenz(a)anthra
cene 1 3.9E

- 02 8.3E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA

Acenaphthene 1 2.5E

- 02 5.2E

- 03 3.0E

- 02 0.01 1.0E

- 02 1.8E

- 02 NA Acenaphthylene 1 2.6E

- 02 5.5E

- 03 3.1E

- 02 0.01 1.1E

- 02 1.9E

- 02 NA Benz(a)anthracene 1 3.9E

- 02 8.3E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA Benzo(a)pyrene 1 3.8E

- 02 8.0E

- 03 4.6E

- 02 0.01 1.6E

- 02 2.8E

- 02 NA Benzo(b)fluoranthene 1 3.9E

- 02 8.3E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA Benzo(g,h,i)perylene 1 2.9E

- 02 6.1E

- 03 3.5E

- 02 0.01 1.2E

- 02 2.1E

- 02 NA Benzo(k)fluoranthene 1 3.8E

- 02 8.0E

- 03 4.6E

- 02 0.01 1.6E

- 02 2.8E

- 02 NA Chrysene 1 4.0E

- 02 8.4E

- 03 4.8E

- 02 0.01 1.7E

- 02 2.9E

- 02 NA Dibenz(a,h)anthracen
e 1 3.1E

- 02 6.5E

- 03 3.8E

- 02 0.01 1.3E

- 02 2.3E

- 02 NA
Fluoranthene 1 4.0E

- 02 8.5E

- 03 4.9E

- 02 0.01 1.7E

- 02 3.0E

- 02 NA Fluorene 1 2.9E

- 02 6.1E

- 03 3.5E

- 02 0.01 1.2E

- 02 2.1E

- 02 NA Indeno(1,2,3

- cd)pyrene 1 2.7E

- 02 5.8E

- 03 3.3E

- 02 0.01 1.2E

- 02 2.0E

- 02 NA
Dioxins OctaCDD, 1,2,3,4,6,7,8,9

- 1 6.9E

- 03 1.4E

- 03 8.3E

- 03 1 2.9E

- 03 5.1E

- 03 NA OctaCDF, 1,2,3,4,6,7,8,9

- 1 8.8E

- 03 1.8E

- 03 1.1E

- 02 1 3.7E

- 03 6.5E

- 03 NA HeptaCDD, 1,2,3,4,6,7,8

- 1 8.8E

- 03 1.8E

- 03 1.1E

- 02 1 3.7E

- 03 6.5E

- 03 NATRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 73 6

- 29

- 2012 Description of MIRC

Exhibit_Add A 2
- 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
Mammal Non

- mammal Beef ( Ba beef ) Dairy ( Ba dairy ) Pork ( Ba pork ) MF Eggs

( Ba eggs ) Poultry

( Ba poultry ) MF HeptaCDF, 1,2,3,4,6,7,8

- 1 1.6E

- 02 3.5E

- 03 2.0E

- 02 1 6.9E

- 03 1.2E

- 02 NA HeptaCDF, 1,2,3,4,7,8,9

- 1 2.4E

- 02 5.1E

- 03 3.0E

- 02 1 1.0E

- 02 1.8E

- 02 NA HexaCDD, 1,2,3,4,7,8

- 1 1.1E

- 02 2.3E

- 03 1.3E

- 02 1 4.6E

- 03 8.1E

- 03 NA HexaCDF, 1,2,3,4,7,8

- 1 2.3E

- 02 4.8E

- 03 2.8E

- 02 1 9.6E

- 03 1.7E

- 02 NA HexaCDD, 1,2,3,6,7,8

- 1 6.8E

- 03 1.4E

- 03 8.2E

- 03 1 2.9E

- 03 5.0E

- 03 NA HexaCDF, 1,2,3,6,7,8

- 1 9.7E

- 03 2.0E

- 03 1.2E

- 02 1 4.1E

- 03 7.1E

- 03 NA HexaCDD, 1,2,3,7,8,9

- 1 6.8E

- 03 1.4E

- 03 8.2E

- 03 1 2.9E

- 03 5.0E

- 03 NA HexaCDF, 1,2,3,7,8,9

- 1 1.4E

- 02 2.9E

- 03 1.7E

- 02 1 5.8E

- 03 1.0E

- 02 NA HexaCDF, 2,3,4,6,7,8

- 1 9.6E

- 03 2.0E

- 03 1.2E

- 02 1 4.1E

- 03 7.1E

- 03 NA PentaCDD, 1,2,3,7,8

- 1 1.8E

- 02 3.9E

- 03 2.2E

- 02 1 7.8E

- 03 1.4E

- 02 NA PentaCDF, 1,2,3,7,8

- 1 2.6E

- 02 5.5E

- 03 3.2E

- 02 1 1.1E

- 02 1.9E

- 02 NA PentaCDF, 2,3,4,7,8

- 1 3.1E

- 02 6.5E

- 03 3.8E

- 02 1 1.3E

- 02 2.3E

- 02 NA TetraCDD, 2,3,7,8

- 1 2.6E

- 02 5.5E

- 03 3.2E

- 02 1 1.1E

- 02 1.9E

- 02 NA TetraCDF, 2,3,7,8

- 1 3.6E

- 02 7.7E

- 03 4.4E

- 02 1 1.5E

- 02 2.7E

- 02 NA Source: EPA 2005a, unless otherwise indicated. NA = not applicable.

a As discussed in HHRAP (EPA 2005a), Appendix A, biotransfer factors for mercury compounds were obtained from EPA 1997b. Considering speciation, fate, and transport of mercury from emission sources, elemental mercury is assumed to be vapor

- phase and hence is assumed not to deposit to soil or transfer into aboveground plant parts. As a consequence, there is no transfer of elemental mercury into animal tissu es. Biotransfer factors for cadmium compounds were obtained from EPA 1995b. Biotransfer factors for dioxins and PAHs were calcul ated 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 2005 a), EPA (1995c) recommends using a metabolism factor (MF) to account for metabolism of PAHs by mammals to offset the amount of bioaccumulation suggested by biotransfer factors. EPA has recommended an MF of 0.01 for bis(2

- ethylhexyl)phthalate (BEHP) and 1. 0 for all other chemicals (EPA 1995d). For MIRC, an MF of 0.01 is also used to calculate concentrations of PAHs in food prod ucts from mammalian species based on the work of Hofelt et al. (2001). This factor takes into account the P450

- mediated metabolism of PAHs in mammals; applying this factor in our approach reduced the concentrations of chemicals in beef, pork, and dairy by two orders of magnitude.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 74 6

- 29

- 2012 Description of MIRC

Exhibit_Add A 2
- 14

. Soil and Plant Ingestion Rates for Animals

Animal Soil Ingestion Rate

– Qs (m) (kg/day) a Plant Part Consumed by Animal Plant Ingestion Rate

– Qp (I,m) (kg/day)

Beef cattle b 0.5 Silage 2.5 Forage 8.8 Grain 0.47

Dairy cattle c 0.4 Silage 4.1 Forage 13.2 Grain 3.0

Swine d 0.37 Silage 1.4 Grain 3.3 Chicken (eggs) e 0.022 Grain 0.2
Source: EPA 2005 a HHRAP (Chapter 5).

a Beef cattle : NC DEHNR (1997) and EPA (1994 b ) recommended a soil ingestion rate for subsistence beef cattle of 0.5 kg/day based on Fries (1994) and NAS (1987)

. As discussed in HHRAP, Fries (1994) reported soil ingestion to be 4 percent of the total dry matter intake

. NAS (1987) 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 weight of 630 kg and a daily dry matter intake rate (non

- lactating cows) of 3.2 pe rcent 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 rate s for dair y cattle and the assumption of uniform contamination o f soil ingested by cattle

. Swine : NC DEHNR (1997) recommended a soil ingestion rate for swine of 0.37, estimated by assuming a soil intake that is 8% of the plant ingestion rate of 4.3 kg DW/ day

. Uncertainties include the lack of current empirical data to support soil ingestion rates and the ass umption of uniform contamination of the soil ingested by swine. Chicken : HHRAP (EPA 2005a) assumes that chickens consume 10 percent of their total di et (which is approximately 0.2 kg/day grain) as soil, a percentage that is consistent with the study from Stephens et al

. (1995)

. Uncertainties include the lack of current empirical data to support soil ingestion rates for chicken and the assumption of un iform contamination of soil ingested by chicken. b The beef cattle ingestion rates of forage, silage, and grain are based on the total daily intake rate of about 12 kg DW/ day (based on NAS [1987] reporting a daily dry matter intake that is 2 percent of an a verage 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 animals and are assumed not to eat forage (EPA 1998). EPA (1994 b and 1998) and NC DEHNR (1997) recommended including only silage and grains in the diet of swine. EPA ( 1995c) recommended an ingestion rate of 4.7 kg DW/day for a swine, referencing N AS (1987)

. Ass uming a diet of 70 percent grain and 30 percent silage ( EPA 1990), HHRAP estimated ingestion rates of 3.3 kg DW/day (grain) and 1.4 kg DW/day (silage). Uncertainties associated with Qp i nclude variability of the proportion of grain and silage in the diet, which varies from location to location

. e Chickens consume grain provided by the farmer. The daily quantity of grain feed consumed by chicken is assumed to be 0.2 kg/day (Ensminger ( 1980 ) , Fries ( 1982 ) , and NAS ( 1987)

. Uncertainties associated with thi s variable include the variability of actual grain ingestion rates from site to site

. In addition, assuming uniform contamination of plant materials consumed by chicken introduces some uncertainty.


### 6.3. Adult and Non

- Infant Exposure Parameter Values The exposure parameters included in MIRC and 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 2011 ) and Child

- Specific Exposure Factors Handbook (CSEFH; EPA 2008a)

. Where values were reported for age groupings other than those used in MIRC (see Section 2.3 above for MIRC age groups), time

weighted average values were estimated for the MIRC age groups from the available data

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 75 6

- 29

- 2012 Description of MIRC In MIRC, ingestion rates for h ome
- p roduce d farm food i tems are included for exposed fruit, protected fruit, exposed vegetables, protected vegetables, root vegetables, beef, total dairy, pork, poultry, and eggs . Those ingestion rates are already normalized to body weight (i.e., g wet weight /kg

- day) (EPA 2011). The body weight parameter values presented in Exhibit_Add A 2

- 15 , therefore, are not applied in the chemical intake (ADD) equations for these food types.
In MIRC, i ngestion rates also are included for drinking water (mL/day), soil (mg/day), and fish (g/day). These ingestion rates, however, are on a per person basis (i.e., not normalized for body weight). The body weight parameter values presented in Exhibit_Add A 2

- 15 , therefore, are applied in the chemical intake (ADD) equations for these media.


### 6.3.1. Body Weights Body weight ( BW ) options included in MIRC include mean, 5 th , 10 th , 50 t h , 90 th , and 95 th percentiles for adults and the five children’s age groups : < 1 year; 1

– 2 years; 3

– 5 years; 6

– 11 years; and 12

– 19 years

. For its default screening assessment, EPA uses the mean BW for each age group. The BWs currently in the MIRC databas e are listed in Exhibit_Add A 2

- 15

. For adults, BW represents the weighted average of male and female mean body weights for all races, ages 18

- 74 years, from EPA’s 1997 EFH ( EPA 1997a; Tables 7

- 4 and 7

- 5)

. We considered upda ting the body weight for use in the RTR assessment to the revised body weights in the 2011 Exposure Factors Handbook (EPA 2011) . However, the 2011 EFH warns that assessors should consider if the dose estimate (for which the body weight is used to calculat e) will be used to estimate risk by combining it with a dose

- response that was derived assuming a body weight of 70 kg, and that if such an inconsistency exists, an adjustment may be necessary. Because a 70

- kg body weight commonly is used to develop IRIS d ose
- response values, using an 80 kg body weight could result in this inconsistency. Given these issues, we have retained the use of an adult body weight of 71.4 kg. In general, BW values for the five children’s age groups were calculated from the summary d ata 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_Add A 2

- 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 perc entile values for this age group using the two methods differed by approximately 10 percent or less.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 76 6

- 29

- 2012 Description of MIRC

Exhibit_Add A 2
- 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 71.4 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 sample

- size weighted average of male and female mean body weights (all races, 18

- 74 years) from EPA’s 1997 EFH (Tables 7

- 4 for males and 7

- 5 for females)

. Note that these weights include the weight of clothing, estimated t o range from 0.09 to 0.28 kg. Although the 18 to 74 year age category in EPA’s EFH does not match exactly the age 20 to 70 year categ orization of adults in MIRC, the magnitude of error in the mean and percentile body weights is likely to be very small (i.e., less than 1%). We considered updating the body weight for use in the RTR assessment to the revised body weights in the 2011 Exposu re Factors Handbook. However
, t he 2011 EFH warns that assessors should consider if the dose estimate (for which the body weight is used to calculate) will be used to estimate risk by combining it with a dose

- response that was derived assuming a body weigh t of 70 kg, and that if such an inconsistency exists, an adjustment may be necessary. Because a 70 kg body weight commonly is used to develop IRIS dose

- response values, using an 80 kg body weight could result in this inconsistency. Given these issues, we h ave retained the u se of an adult body weight of 71.4 kg

. b Each BW represents a time

- weighted average of body weights for age groups birth to <1 month, 1 to <3 months, 3 to <6 months, and 6 to <12 months from Table 8

- 3 of the 2008 CSEFH

. Original sample si zes for each of these age groups can also be found in Table 8

- 3.

c Each BW represents a time

- weighted average of body weights for age groups 1 to <2 years and 2 to <3 years from Table 8

- 3 of the 2008 CSEFH

. Original sample sizes for each of these age group s 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 CS EFH

. Original sample sizes for each of these age groups can also be found in Table 8

- 3

. f Mean BW estimated using Table 8

- 22 of the 2008 CSEFH, which is based on NHANES IV data as presented in Portier et al. (2007) . This estimate was c alculated as the ave rage 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 age groups 11 to <16 years and 16 to <21 years from Table 8

- 3 of the 2008 CSEFH

. Note that estimated values include 11

- year
- olds and individuals through age 20, which contributes to uncertainty in the estimates for 12 to 19 years . Those values are provided for comparison purposes only and are not included in MIRC.


### 6.3.2. Water Ingestion Rates MIRC also includes the option of calculating chemical ingestion via drinking water obtained from surface

- water sources or from wells (i.e., from groundwater) in the contaminated area

. Users have the option in MIRC to set drinking water ingestion rates to zero or to revise the drinking water ingestion rates in MIRC to better reflect site

- specific water uses

. The 2008 CSEFH recommends values for drinking water ingestion rates for childre n based on a study reported by Kahn and Stralka (2008)

. Table 3

- 4 of the CSEFH provides per capita estimates of community water ingestion rates by age categories

. Community water ingestion includes both direct and indirect ingestion of water from the tap

. Direct ingestion is defined as direct consumption of water as a beverage, while indirect ingestion includes water added during food or beverage preparation

. The source of these data is the 1994

- 1996 and 1998 U.S. Department of Agriculture’s ( USDA ’s) C ontinuing Survey of Food Intakes by Individuals ( CSFII ) ( USDA 2000 )

. Exhibit_Add A 2

- 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 19 98 CSFII (USDA 2000 )

. Adult ingestion rates,TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 77 6

- 29

- 2012 Description of MIRC presented in Exhibit_Add A 2

- 16 , represent community water ingestion, both direct and indirect as defined above, for male s and females combined, ages 20 years and older

. Exhibit_Add A 2
- 16

. Estimated Daily Per Capita Mean and Percentile Water Ingestio n Rates for Children and Adults a

Lifestage (years) Ingestion Rates, Community Water (mL/day) Mean 50 th 90 th 95 th 99 th Child <1 b 504 482 969 1113 1440 Child 1

- 2 c 332 255 687 903 1318 Child 3

- 5 d 382 316 778 999 1592 Child 6

- 11 e 532 417 1149 1499 2274 Child 12

- 19 f 698 473 1641 2163 3467 Adult g 1219 981 2534 3087 4567 Sources: EPA 2004b, 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 tim e
- 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 2008 CSEFH.

e Each IR represents the ingestion rate for age group 6 to <11 years from Table 3

- 4 of the 2008 CSEFH . This value 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 through age 20, which contributes to uncertainty in the estimates for 12 to 19 years.
g Adult drinking water ingestion rates were obtained from EPA (2004 b ), Appendix E, Part I, Table A1 for community water, both sexes (ages 20+), direct plus indirect water ingestion.


### 6.3.3. Local Food Ingestion Rates MIRC includes m ean, median, 90 th , 95 th , and 99 th percentile food

- specific ingestion rates (IRs) for consumers

- only of farm food chain (FFC) media for adults and children

. The mean and percentile values are from EPA’s analysis of data from the USDA’s 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993), as presented in Chap ter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for Various Home Produced Food Items) ( EPA 2011). Consumers

- only means that individuals who did not report eating a specified type of food during the three

- day period covered by the food ingestion part of the survey were not included in the analysis of ingestion rates for that food type. The questionnaire included the options for a household to self

- identify in one or more of five categories: as a household that gardens, raises animals, hunts, fishes, or farms. As of September 2008, that survey was the most recent NFCS available ( EPA 2008a, CSEFH), and we are not aware of any that might be more recent. 21
For the adult age group in MIRC, data were compiled on food

- specific IRs separately for two types of households as indicated in the “Response to Questionnaire” ( EPA 20 11 , Chapter 13):

21 Note that EPA’s 2008 CSEFH does not distinguish between exposed and protected fruits and vegetables when recommending food ingestion rates based on the same data set for the same age categories

. EPA’s 199 7 analysis for its EFH therefore remains the most appropriate data source for use in MIRC.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 78 6

- 29

- 2012 Description of MIRC (1) households that farm (F) and (2) households that garden or raise animals (HG for homegrown)

. This division reflects EPA’s data analysis

. EPA tabulate d IRs for fruits and vegetables separately for households that farm and households that garden

. Similarly, EPA tabulated IRs for animals and animal products for households that farm and for households that raise animals

. Thus, the first type of household, F, represents farmers who may both grow crops and raise animals and who are likely to consume more homegrown/raised foods than the second type of household

. The second type of household, HG, represents the non

- farming households that may consum e lower amounts of home

- grown or raised foods (i.e., HG encompasses both households that garden and households that raise animals )

. Th e food

- specific ingestion rates are based on the amount of each food type that households that farm (F) or households th at 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 i ngestion rates in MIRC for adults are those for farming households (F) in Exhibit_Add A 2

- 17 . The user can specify use of the non

- farming household (HG) ingestion rate s if they are more appropriate for the user’s exposure scenario

. Exhibit_Add A 2
- 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 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 Vegetable a N/A 2.52 1.28 1.32 0.94 1.03 Water (mL/day) c N/A 332 382 532 698 1218 Median ingestion rates (g/kg

- day) Beef a N/A 2.51 2.49 2.11 1.51 1.55 Dairy b N/A 125 66.0 34.4 15.5 2.58 Eggs a N/A 1.51 0.83 0.561 0.435 0.474 Exposed Fruit a N/A 1.82 1.11 0.61 0.62 0.593 Exposed Vegetable a N/A 1.89 1.16 0.64 0.66 0.812 Pork a N/A 1.80 1.49 1.04 0.89 0.802 Poultry a N/A 3.01 2.90 1.48 1.30 0.922 Protected Fruit a N/A 7.59 5.94 3.63 1.23 2.08 Protected Vegetable a N/A 1.94 1.04 0.79 0.58 0.564 Root Vegetable a N/A 0.46 0.52 0.57 0.56 0.59TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 79 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 17

. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70 yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19 Water (mL/day) c N/A 255 316 417 473 981 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 10.4 4.73 5.59 3.32 3.37 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 5.56TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 80 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 17

. Summary of Age

- Group Specific Food Ingestion Rates for Farm Food Items

Product Child (age in yr) Adult (20

– 70 yrs) <1 1

– 2 3

– 5 6

– 11 12

– 19 Root 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 ) . When data were unavailable for a particular age group, intake rate for all age groups was used multiplied by the age

- specific ratio of intake based on national population intake rates from CSFII. b Primary source for values was 1987

– 1988 NFCS survey, compiled results presented in Chapter 13 of 2011 Exposure Factors Handbook ( EPA, 2011 ) . When data were u navailable 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 Prima ry source for children less than 3 years of age was a Khan et al. ( 2008 ) analysis of CSFII data, and from EPA’s analysis of NHANES 2003

– 2006 data for children and adults greater than three. All data tables that were used and justifications for data sources are presented in Chapter 3 of the 2011 Exposure Factors’ Handbook. d Default ingestion rate percentile used in MIRC for Tier 1 assessments and chemi cal threshold calculations
For children, EPA estimated food

- specific IRs for four age categories ( EPA 2011 ): 1

– 2 years, 3

– 5 years, 6

– 11 years, and 12

– 19 years

. Sample sizes were insufficient to distinguish IRs for children in different types of households; hence, for children, a single IR value represents both F and HG households for a given food type and age category ( Exhibit_Add A 2

- 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 observat ions ). 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 o r percentile

- specific per capita intake of the farm food item for the total CSFII survey population (from Chapter 3 of EPA 2003c)

– IRPC_total.

The ratio of IR PC, age_ group

_x to IR PC_tot al from the CSFII data shows the consumption rate of a particular foo d type by a specific age group relative to the consumption rate for that food type for the population as a whole

. The ratio of IR CO, age

_ group _x to IR CO_total , that is the consumption rate of a particular food type by a specific age group (consumers only) relative to the consumption rate for that food type for the NFCS survey population as a whole (consumers only), should be approximately the same

. Given the assumption that the two ratios are equal, Equation 2

- 56 was used to calculate the “missing” age

- specific consumer

- only IRs : Equation 2
- 56

. Calculation of Age

- Group

- Specific and Food

- Specific Ingestion RatesTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 81 6

- 29

- 2012 Description of MIRC where:

IR CO, age_group_x = Mean or percentile

- specific consumer
- only intake of the food type from all sources, as consumed, for the specific child age group X

IR CO_total = Mean or percentile

- specific consumer
- only intake of the farm food item, as brought into the home, for the total NFCS survey population

IR PC, age_group_x = Mean or percentile

- specific per capita intake of the food type from all sources,

as consumed, for the specific child age group X from the CSFII

IR PC_total = Mean or percentile

- specific per capita intake of the farm food item for the total

CSFII survey p opulation
In this discussion, per capita (as opposed to consumer
- only ) indicates the intake rates are based on the entire population rather than the subset of the population that ingests the particular food category (i.e., consumers). Here, the use of per capita ingestion rates are
recomme nded by the HHRAP methodology because no consumer

- only percentile

- specific intakes are provided for the different age groups. The above calculation implicitly assumes that the distribution of the consumption rate for a food type for a specific age group ( consumers only) has the same shape as the distribution of the consumption rate for a food type for a specific age group in the general population ( per capita ). Otherwise, the separate calculation of each percentile might yield intake estimates that decrea se as the percentile increases. This calculation artifact could occur if the shapes of the two distributions differ in the upper percentiles (or “tails”) of the distributions. In the instances where the above calculations were used to fill data gaps in t he 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 percentil es, with deviations occurring due to outlier ingestion rates based on only a few respondents in the tails of the distributions. The MIRC defaults use the 90 th percentile ingestion estimates, which are likely more reliable than the 95 th or 99 th percentile estimates in this particular calculation.


### 6.3.4. Local Fish Ingestion Rates The USDA’s 1987 to 1988 Nationwide Food Consumption Survey (NFCS) (USDA 1993, 1994a), as presented in Chapter 13 of the Agency’s Exposure Factors Handbook (i.e., Intake Rates for Various Home Produced Food Items) ( EPA 2011 ), includes family

- caught fish ingestion rates by age category

. There are several disadvantages, however, to using that data source to estimate fish ingestion rates

. First, due to inadequate sample sizes, EPA did not re port fish IRs for children less than 6 years of age

. Second, the NFCS data were collected approximately two decades ago

. Third, the reported fish IRs are for ages 6 to 11 and 12 to 19 and are based on 29 and 21 individuals in each age category, respectiv ely ( EPA 2011 , Table 13

- 20 )

. Finally, the IRs from NFCS data are based on total weight of fish as brought into the home, and do not include losses from preparation of the fish (i.e., removal of inedible parts and, possibly, the skin)

. Estimates of prepar ation losses for fish intended to apply to the NFCS fish IR data are very uncertain and are based on a wide variety of freshwater, estuarine, and marine fish, and squid ( EPA 2011 , Table 13

- 69 ) . Therefore, a more recent survey was sought that included larg er sample sizes, data for children younger than six years, and IRs for the parts of fish actually consumed

. EPA’s (2002) analysis freshwater and estuarine fish consumption from the USDA’s Continuing Survey of Food Intake by Individuals (CSFII) for 1994

- 96 and 1998 was chosen to provide perTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 82 6

- 29

- 2012 Description of MIRC capita fish IR options by age category in MIRC

. Although the fish consum ption rates reported in the CSFII include all sources, commercial and self

- caught, for purposes of screening level risk assessments, it was assume d that all freshwater and estuarine fish consumed are self

- caught

. The inclusion of commercially obtained and estuarine fish will overestimate locally caught freshwater fish IRs for many rural populations in the United States; however, it also may underestimate locally caught fish IRs for some populations (e.g., Native Americans, Asian and Pacific Island communit ies, 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 rele vant data whenever available

. For children ages 3 to 17 years and for adults, MIRC includes values for the mean and the 90th, 95th, and 99th percentile fish ingestion rates (freshwater and estuarine fish only) based on EPA’s analysis of 1994

- 96 and 1998 CSFII data ( EPA 2002, 2008a)

. As shown in EPA’s 2008(a) CSEFH, Table 10

- 7, the 90th percentile per capita ingestion rates estimated from the two

- day CSFII recall period are zero for some child age groups

. Although not presented in CSEFH Table 10

- 7, medi an ingestion rates for all child age groups would be zero (considering the “consumer only” sample sizes [CSEFH Table 10

- 9] relative to the “ per capita ” sample sizes in Table 10

- 7)

. The high percentile fish IRs that are zero result from the short duration of the CSFII survey (two days) compared with the averaging time of interest (a year) and the relatively infrequent consumption of fish (e.g., on the order of once a week to once a month or less) compared with the near daily ingestion of other types of foo d products (e.g., dairy, produce, meat)

. Use of zero for fish IRs, however, is not useful in MIRC

. As a result, an alternative method was used to estimate fish ingestion rates for children and adults that could provide reasonable, non

- zero values for all age groups and percentiles

. The alternative, age

- group
- specific fish ingestion rates were derived using values for each age group, y :

 Mean or other appropriate percentile consumer

- only fish ingestion rates for age group y, IRCO,y, from EPA’s Estimated Per Capita Fish Consumption in the United States (EPA 2002), Section 5.2.1.1, Table 5, for freshwater/estuarine habitat. 22

 Fraction of the population consuming freshwater/estuarine fish, FPC,y, calculated as
consumer
- only sample size / U.S. population sa mple for age group y. The data to calculate these fractions are available in the 2008 CSEFH and EPA 2002.
Equation 2

- 57 was used to calculate the alternative, per ca pita fish ingestion rates by age group 

### ( IR PC,y 

### ):

22 Most of these data also are provided in Table 10

- 9 of the CSEFH; the median values, however, are not presented in the CSEFH, and values for the mean and all other p ercentiles are slightly different due to rounding.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 83 6

- 29

- 2012 Description of MIRC Equation 2
- 57

. Calculation of Alternative Age

- Group

- Specific Fish Ingestion Rates

where:

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 / t otal U.S

. population sample size for age group y (unitless) (2008 CSEFH, EPA 2002)
In the above, per capita (as opposed to consumer
- only ) indicates the intake rates are based on the entire population rather than the subset of the population that ingests the particular food category. Here, per capita ingestions are recommended by the HHRAP methodology because
no consumer
- only percentile

- spe cific intakes are provided for the different age groups. The mean and percentile consumer
- only fish ingestion rates for children and adults and the fraction of the population consuming freshwater/estuarine fish used in calculating long

- term per capita fis h ingestion rates by age group are presented in Exhibit_Add A 2

- 18 and Exhibit_Add A 2

- 19 . The mean and percentile per capita fish ingestion rates estimated using this methodology are summarized in Exhibit_Add A 2

- 20 and are available in MIRC. The fish ingesti on rates provided in Exhibit_Add A 2

- 20 and included in MIRC are intended to represent the harvest and consumption of fish in surface waters in a hypot hetical depositional area

. For site

- specific application of this tool, users should consider using more localized survey data to estimate more appropriate fish ingestion rates

. The fishing season varies substantially across the United States by latitude, and fish consumption patterns also vary by type of water body (e.g., ponds, lakes, rivers, streams, estuaries, coastal marine), cultural heritage, and general geographic area

. Therefore, use of more localized information is encouraged

. Note that as indicated in Exhibit_Att A

- 14 and Exhibit_Att A

- 16 , in developing the screening threshold emission rates, health protective fish ingestion rates for child and adult fish consumers that more closely represent exposures of a high

- end recreational angler were used.
As noted in Section 6.4.3 of this addendum , if the user overwrites the fish IRs shown in Exhibit_Add A 2

- 20 with fresh

- weight as caught values (e.g., values obtained from a local creel survey), the user is advised to set non

- zero values for the preparation and cooking loss factors L1 and L2 in Equation 2

- 15 . Suggested values are presented in Section 6.4.3

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 84 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 19

. Fraction of Population Consuming Freshwater/Estuarine Fish on a Single Day ( F PC,y ) Lifestage (years) Fraction Consuming Fish Child 3

- 5 0.0503 a Child 6

- 11 0.0440 b Child 12

- 19 0.0493 c Adult 0.08509 d
Sources: EPA 2002, 2008a

a This value was calculated using the ages 3 to 5 sample size for consumers only divided by the sample size for the U.S. population divided by 2 to represent the proportion consuming fish on a single day (the consumers

- only group includes individuals who consumed fish on at least one of two survey days) to match the one

- day ingestion rate. b As in footnote a, the value was calculated using 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 f or consumers only divided by the sample size for U.S. population from Section 5.1.1.1 Table 4. The result was divided by 2 to represent a one

- day sampling period in order to match the
one
- day ingestion rate. Exhibit_Add A 2
- 18

. Daily Mean and Percentile Consumer

- Only Fish Ingestion Rates for Children and Adults ( IR CO,y ) a

Lifestage (year s) Ingestion Rates, All Fish (g/day) Mean 50th 90 th 95 th 99 th Child <1 NA NA NA NA NA Child 1

- 2 b 27.31 15.61 64.46 87.60 138.76 * Child 3

- 5 c 40.31 23.04 95.16 129.31 204.84 * Child 6

- 11 d 61.49 28.46 156.86 * 247.69 * 385.64 * Child 12

- 19 e 79.07 43.18 181.40 * 211.15 * 423.38 * Adult f 81.08 47.39 199.62 * 278.91 505.65 * Sources: EPA 2002, 2008a N A = not applicable; it is assumed that children < 1 year of age do not consume fish.

* Indicates that the sample size does not meet minimum reporting requirements as described in EPA 2002

. Owing to the small sample sizes, these upper percentiles values are highly uncertain.

a Per capita fish ingestion (FI) rates for children by age group are available from Chapter 10 of the CSEFH ( EPA 2008a); however, all 50th and some 90th percentile ingestion rates are zero

. Per capita FI rates were therefore estimated as described in Equation 2

- 57 to provide reasonable, non

- zero values for all age groups and percentiles. b A fish IR for ages 1

- 2 years was not available . The v alue represents the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002) (Section 5.2.1.1 Table 5 [freshwater/estuarine habitat]), scaled down by the ratio of the mean Child 1

- 2 body weight to the mean Child 3

- 5 body weight

. c These values represent the consumer

- only fish ingestion rate for ages 3 to 5 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine habitat)

. Sample size = 442. d These values represent the consumer

- only fish ingestion rate for ages 6 to 10 from EPA (2002), Section 5.2.1.1 Table 5 (freshwater/estuarine habitat)

. Sample size = 147.

e These values represent the time

- weighted average per capita fish ingestion rate for ages 11 to 15 and 16 to 17 years from EPA (2002), Section 5.1.1.1 Table 5 (freshwater/estuarine habitat); the value may underestimate ingestion rate for ages 12 to 19 years

. Sam ple size = 135. f These values r epresent the consumer

- only fish ingestion rate for individuals 18 years and older from EPA (2002), Section 5.2.1.1 Table 4 (freshwater/estuarine habitat)

. Sample size = 1,633.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 85 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 20

. 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 f or Child 12

- 19).

e Values were c alculated as (consumer

- only IR for Adults) x (fraction of population consuming fish for Adults). MIRC also includes values for the mean and the 90th percentile fish ingestion rates for recreational anglers , black and female recreational anglers, and anglers of Hispanic, L aotian, and Vietnamese descent which are shown in Exhibit_Add A 2

- 21 . T hese latter three populations are culturally or economically disposed to higher rates of fish ingestion than the general population. Recreational angler values are from the EFH (EPA, 2011 )

. Black and female recreational anglers ingestion rates are presented in Burger et al . (2010)

. The fish ingestion rates for Hispanic, Laotian, and Vietnamese populations were derived from a study by Shil l ing et al

. (2010) of c ontaminated fish consumption in California’s Central Valley Delta. Shilling et al

. (2010) reported mean and 95 th percentile ingestion rates for each subpopulation. In part due to the low sample size in the Shilling study (n of 30 to 45), 95 th percentile values were believed to be u nrealistically high. The 90 th percentile ingestion rate estimates presented in Exhibit_Add A 2

- 21 for Hispanic, Laotian, and Vietnamese fishers were derived by EPA usi ng information from Shil l ing et al

. (2010 ; EPA, 2010 ).
Exhibit_Add A 2
- 21 . Calculated Mean and 90 th Percentile Per capita Fish Ingestion Rates

for Populations of Recreational Fishers (IR PC,y )

Subpopulation

Percentile Units Recrea

tional Fisher a Female

Recrea

tional Fisher b Black

Recrea

tional Fisher b Hispanic

Recrea

tional Fisher c Laotian

Recrea

tional Fisher c Vietnamese

Recrea

tional Fisher c Inge stion 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) b Burger et al., 2010, weights reported are post

- cooking for locally caught fish.

c Shilling, F., A. White, L. Lippert, and M. Lubell. 2010. Contaminated fish consumption in California’s Central Valley Delta. Environmental Research 110: 334

- 344., weights reported are post

- cooking for locally caught fish Burger et al., 2010. Applications to date of MIRC have used whole fish concentrations estimated by TRIM.FaTE

. The proportion of lipid in TL3 and TL4 fish in TRIM.FaTE is assumed to be 5.7 percent (byTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 86 6

- 29

- 2012 Description of MIRC weight) for the whole fish, based on information provided by Thomann (1989)

. The lipid content of the part(s) of the fish normally consumed is likely to be less than 5.7 percent. For example, EPA estimated a consumption

- weighted mean lipid value for fillets of fish from TL3 to be 2.6 percent and from TL4 to be 3.0 percent (Table 6

- 9 in EPA 2003b)

. If a user of MIRC wis hes to account for reduced chemical concentration in fillet compared with whole fish for lipophilic chemicals, the user can specify a “preparation” loss of chemical (see Section 6.4 of this addendum ). For lipophilic chemicals (e.g., log 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., bell y fat, viscera which includes fat in liver, etc

. , fat under skin)

. The degree to which the concentration of chemical in a fillet is less than the average total concentration in the whole fish is chemical specific

. Assuming the chemical concentration in t he fillet is the same as in the whole fish may result in a health protective bias for highly lipophilic chemicals

. For persons who prefer to consume fillets with the skin on and do not discard belly fat
, assuming the same concentration of chemical in the fish consumed as in the whole fish also is protective.


### 6.3.5. Soil Ingestion Rates Adult gardeners and farmers may incidentally ingest soils from gardening activities, and gardening and farming families might ingest soil particles that adhere to exposed fruits an d 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 children who may exhibit pica, or the recurrent ingestion of unusually high amounts of soi l (i.e., on the order of 1,000

- 5,000 mg/day or more) ( EPA 2008 a )

. Data on soil ingestion rates are sparse; the soil ingestion rates listed in Exhibit_Add A 2

- 22 and included in MIRC are based on very limited data, as is evident from the values listed. The studies evaluated by EPA for children generally focused on children between the ages of 1 and 3 to 6 years and were not specific to families that garden or far m. The default ingestion rates in MIRC are the 90 th percentile values, as for other ingestion rate parameters.

Exhibit_Add A 2
- 22 . Daily Mean and Percentile Soil Ingestion Rates for Children and Adul ts
Age Group (years) Soil Ingestion Rate (mg/day) Mean a 50 tha 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, EPA 2011

a For mean and 50 th percentile soil ingestion rates for children, value represents a “central tendency” estimate from EPA’s 2008 CSEFH, Table 5

- 1. For adults, value is the recommended mean value for adults from EPA’s 2011 EFH, Chapter 5, Table 5

- 1. b V alues are the recommend ed “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4

- 23. The 2008 CSEFH and 2011 ESH included a high

- end value associated with pica only, but this value has not been used.

c V alues are 90 th percentile adult ingestion rates calcu lated 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 represent older children and adults.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 87 6

- 29

- 2012 Description of MIRC 

### 6.3.6. Total Food Ingestion Rates Although not included in MIRC for deterministic screening

- level exposure and risk assessments, total food ingestion rates w ould be included in any probabilistic module develop ed for MIRC

. The total food ingestion rates presented in Exhibit_Add A 2

- 23 will be used to normalize or to truncate the sum of food

- specific ingestion rates to reasonable values

. This procedure is particularly important when chemical intake from multiple upper

- percentile food ingestion rates for different types of food are added together . Individuals representing the upper percentile ingestion rate for one food category might not be the same individuals who reported high percentile ingestion rate s for one or any of the other food categories.
Exhibit_Add A 2
- 23 . Daily Mean and Percentile Per Capita Total Food Intake for Children and Adults

Lifestage (years) Percent of Group

Consuming Food Mean 50th 90 th 95 th 99 th
Total Food Intake (g/day, as consumed) Child < 1 a 67.0%

- 99.7% h 322 270 599 779 1152 Child 1

- 2 b 100% 1,032 996 1537 1703 2143 Child 3

- 5 c 100% 1,066 1,020 1,548 1,746 2,168 Child 6

- 11 d 100% 1,118 1,052 1,642 1,825 2,218 Child 12

- 19 e 100% 1,197 1,093 1,872 2,231 2,975 Adult f 100% 1,100 1,034 1,738 2,002 2,736 Total Food Intake (g/kg

- day, as consumed) Child < 1 a 67.0%

- 99.7% h 39 34 72 95 147 Child 1

- 2 b 100% 82 79 125 144 177 Child 3

- 5 c 100% 61 57 91 102 132 Child 6

- 11 d 100% 40 38 61 70 88 Child 12

- 19 e 100% 21 19 34 40 51 Adult g 100% 14.8 13.9 23.7 27.6 35.5
Sources: EPA 2005e, 2008a

a These values represent a time

- weighted average for age groups birth to <1 month (N=88), 1 to <3 months (N=245), 3 to < 6 months (N=411), and 6 to <12 months (N=678) from Table 14

- 3 of the 2008 CSEFH

. 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 btai ned 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 ages 6 through 11 years since 11

- year olds are not included in this CSEFH age group.
e These values represent a time

- weighted average for age groups 11 to <16 years (N=975) and 16 to <21 (N=743) years from Table 14

- 3 of th e 2008 CSEFH

. Note that estimated values include 11

- year
- olds and individuals through age 20, which contributes to uncertainty in the estimates. f These values represent a time

- weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N= 4,818) from Table 5B of the 2005 EPA analysis of CSFII

. g These values represent a time

- weighted average for age groups 20 to 39 years (N=2,950) and 40 to 69 years (N=4,818) from Table 5A of the 2005 EPA analysis of CSFII

. h Percents consuming foods from Table 14

- 3 of the 2008 CSEFH include: 67.0% (birth to <1 month); 74.7% (1 to <3 months); 93.7% (3 to <6 months); and 99.7% (6 to <12 months)

. Infants under the age of 1 that consume breast milk are classified as “non

consumers” of food.


### 6.4. Other Exp osure Factor Values The other exposure parameters included in the MIRC algorithms are exposure frequency (Section 6.4.1 ), fraction of the food type obtained from t he contaminated area (Section 6.4.2 ),TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 88 6

- 29

- 2012 Description of MIRC and reduction in the weight of the food types during preparation and cooking (Section 6.4.3 ). For the breast milk ingestion pathway, additional exposure parameters are included in the FFC algorithms (Section 6.5 ).


### 6.4.1. Exposure Frequency
The exposure frequency (EF) represents the number of d ays per year that an individual
consumes home

- produced food items that are contaminated with the chemical being evaluated. In MIRC, the default value for EF is 365 days/year for all exposure sources and all potential receptors. This assumption is consistent with the food ingestion rates used i n MIRC (i.e., daily intake rates equivalent to annual totals divided by 365 days) and does not imply that residents necessarily consume home

- produced food products every day of the year

. If the user wishes to evaluate daily intake rates based on shorter averaging times, the user can overwrite both the food

- specific ingestion rates and the EF for each home

- grown food product. Users of MIRC might want to specify a lower EF values for various food types where residents obtain some of their diet from commerc ial 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 consumed (i.e., 100 percent) are from the location represented by the chemical concentrations inp ut into MIRC.

For non

- infant children and the adult age cohorts, MIRC includes the default FC of 1, assuming that 100 percent of the food product consumed is produced by households that farm, garden, or raise animals

. The user can vary this default FC v alue 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 w eight 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 p reparation while other parts might not be consumed even after cooking (e.g., bones). Thus, the actual food ingested is generally less than the amount brought into the home.

Three distinct types of preparation and cooking losses are included in the ingesti on 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 durin g 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 mean values for these three types of preparation and c ooking loss es for exposed fruit, protected f ruit, exposed vegetables, protected vegetables, root vegetables, beef, pork, poultry, and fish . Different types of losses apply to different types of foods. Therefore, the losses can be represented by only two parameters, L1 and L2 , the definitions of wh ich vary according to the food type as explained in the endnotes in Exhibit_Add A 2

- 24 . All preparationTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 89 6

- 29

- 2012 Description of MIRC and cooking loss parameter values were estimated as specified in the exhibit’s endnotes from data presented in Chapter 13 of the EPA’s 1997 and 2011 EFH (EPA 1997a and 2011)

. Exhibit_Add A 2
- 24

. Fraction Weight Losses from Preparation of Various Foods

Product Mean Cooking, Paring, or Preparation Loss (Cooking Loss Type 1 [L1])

(unitless) a Mean Net Post Cooking (Cooking Loss Type 2 [L2])

(unitles s) b
Exposed Fruit c 0.244 0.305 Exposed Vegetable 0.162 d NA Protected Fruit 0.29 e NA Protected Vegetable 0.088 f NA Root Vegetable g 0.075 0.22 Beef 0.27 0.24 Pork 0.28 0.36 Poultry 0.32 0.295 h
Fish i 0.0 0.0 or 1.5 j

Source: EPA 1997 a and 2011 NA = Not Available

a For fruits , includes losses from draining cooked forms

. For vegetables , includes losses due to paring, trimming, flowering the stalk, thawing, draining, scraping, shelling, slicing, husking, chopping, and dicing and gains from the addition of water, fat, or other ingredients

. For meats , includes dripping and volatile losses during cooking. b For fruits , includes losses from removal of skin or peel, core or pit, stems or caps, seeds and defects; may also in clude losses from removal of drained liquids from canned or frozen forms

. For vegetables , includes losses from draining or removal of skin

. For meats , includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices.

c These values represent 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 13

- 6). f This value r epresents an average of means for all protected vegetables with available data (Table 13

- 7)

. Protected vegetables include pump kin, corn, peas, and lima beans.
g These values r ep resent average s of means for all root vegetables with available data (Table 13

- 7)

. Root vegetables include beets, carrots, onions, and potatoes. h This value r epresents an average of means for chicken and t urkey (Table 13

- 5). i If the user changes fish ingestion rates to match a survey of the whole weight of fish brought into the home from the field ( divided by the consumers of the fish), an appropriate value for L1 would be 0.3 1 ( EPA 2011 , Table 13

- 5)

. For volatile or water soluble chemicals, a non

- zero value for L2 also may be appropriate

. Although EPA ( 2011 ) recommended 0.11 for L2, it varies substantially by chemical. j C ooking loss accounts for the difference between ingestion rates for cooked fish and c alculated Hg concentrations in whole fish. Cooking fish tends to re duce the overall weight of fish, and volatilization of mercury is unlikely to occur during cooking, increasing the concentration of mercury by a factor of roughly 1.5 (Morgan et al. 1997)

. However, the default value of 0 is assumed for all chemicals in the screening assessment. The value of 1.5 for methyl mercury might be more appropriate, but was not used due t o uncertainties associated with the value (also see text).

There are substant ial 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 wei ght loss during cooking by water evaporation, which might leave the chemical in the fruit (chemical not lost) , and pouring the cooking liquid down the drain (chemical lost) or using the liquid to create a sauce (chemical not lost) . In addition, the concen tration of chemical might be highest in t he skin, which often is discarded , and lowe r in the consumed portion of many bulky fruits and vegetable s. Finally, the data EPA used to evaluate L1 included negative losses (i.e., weight gains) due to hydration of dried vegetables (e.g., peas and lima beans), which increases the range of L1 values across different vegetables.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 90 6

- 29

- 2012 Description of MIRC Note that the default L1 and L2 values for fish are set to zero

. That is because the data source for the fish ingestion rates is not the USDA’s 1987 to 1988 NFCS (USDA 1993, 1994 a ) as reported in EPA’s EFH , which reported food as brought into the home, as is the case for the other food categories

. Instead, the fish IR data included in MIRC are from a more recent and larger survey, EPA’s (2002) analysis of freshwater and estuarine fish consumption from the USDA’s 1994

- 96 and 1998 CSFII

. That survey reports ingestion rates of fish parts actually consumed, and so no loss processes for preparation are needed

. The zero value for L2 assumes that no chemical is lost by volatilization and that pan juices are consumed

. The user may reset that value where chemical

- specific data are available

. For example, i n the case of methyl mercury, the chemical is not lost by volatilization during cooking, but liquid from the fish is lost. This lowers the mass of fish consumed, thereby increasing the methyl mercury concentration. As such, an L2 value of 1.5 based on the Morgan et al . (1997) study might be more appr opriate

. ( Note that due to uncertainties associated with the cooking conversion factor of 1.5 for methyl mercury, it was not used in the RTR multipathway screening assessment. However, this is an area that should be investigated further for potential use i n the future.)

If the user manually changes fish ingestion rates to match a local survey of the whole weight of fish brought into the home from the field (divided by number of persons consum ing the fish), the user should also set the L1 and L2 parameter to non
- zero values. A n appropriate value for L1 would be 0.3 ( EPA 2011 , Table 13

- 69 )

. For volatile or water soluble chemicals, a non

- zero value for L2 also may be appropriate

. Although EPA recommended 0.11 for L2 (EPA 2011)
, it
can var y substantially by c hemical

. 

### 6.5. Breast

- Milk Infant Exposure Pathway Parameter Values Values used for parameters in the breast

- milk exposure pathway algorithms ( see Section 3.4 of this a ddendum ) can be scenario

- specific, receptor

- specific, and/or chemical

- specific and might be empirically derived or estimated by an appropriate model. For parameters that are scenario

specific or for which empirical values are required, the default values provided in MIRC are listed. For parameters for which MIRC calculates values, the appropriate equation is listed. Scenario

- and receptor

- specific parameters are discussed in Section 6.5.1 and chemical

specific parameters are discussed in Section 6.5.2

. 

### 6.5.1. Receptor

- specific Parameters Receptor

- specific values are needed for parameters that describe the characteristics or activities of the exposed individual. In this context, there are two relevant receptors: the mother and the infant. Exhibit_Add A 2

- 25 lists the parameters and their default values. The text that follows describes the input value or value options for each exposure parameter required by MIRC to calculate the infant absorbed chemical intake rate, or DAI inf . For parameter values that
can be estimated when empirical values are not available, see the equation description in Section 3.4 of this addendum

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 91 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 25 . Scenario

- and Receptor

- Specific Input Parameter Values Used to Estimate Infant Exposures via Breast Mil k

Parameter Description Default Value

AT Averaging time for infant’s exposure via breast milk, i.e., duration of

nursing (days) =ED

BW inf Body weight of infant (kg) averaged over duration of nursing exposure 7.8

BW mat Body weight of mother (kg) averaged over duration of mother’s

exposure 66

DAI mat Daily absorbed intake of chemical by mother (mg/kg

- day) Equation 2

- 36 ED Exposure duration for infant, i.e., duration of breast feeding (days) =AT AT/ED Averaging time divided by exposure duration 1.0 f bp Fraction of mother’s whole blood that is plasma (unitless) 0.65 f fm Fraction of mother’s body weight that is fat (unitless) 0.30 f mbm Fraction of fat in mother’s breast milk (unitless) 0.04 f pm Fraction of mother’s body weight that is plasma (unitless) 0.046 IR milk Mean infant milk ingestion rate over duration of nursing (kg/day) 0.709 t bf Duration of breast feeding (days) 365 t pn Duration of maternal chemical exposure prior to nursing (days) 3285
Averaging time ( AT

) and exposure duration ( ED ). AT refers to the time over which the infant’s exposure to the chemical of concern is averaged. ED refers to the duration of the infant’s exposure. For the exposure scenario considered for this age group, both AT and ED equal the duration of the nursing period, and they therefore cancel each other out in the infant average daily dose equation

. Infant body weight ( BW 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

- weig hted 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_Add A 2

- 26 presents additional values for the infant body weight parameter that the user can select instead of the MIRC default.
Exhibit_Add A 2
- 26

. Average Body Weight for Infants

Statistic 0 to < 6 months

(kg) 0 to < 12 months

(kg) 0 to < 18 months (kg) 0 to < 24 months

(kg) Mean 6.5 7.8 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 10.5TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 92 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 26

. Average Body Weight for Infants

Statistic 0 to < 6 months

(kg) 0 to < 12 months

(kg) 0 to < 18 months (kg) 0 to < 24 months

(kg) Mean 6.5 7.8 a 9.0 9.6 85 th percentile 7.4 9.0 10.3 11.0 90 th percentile 7.7 9.2 10.6 11.3 95 th percentile 8.0 9.7 11.1 11.8
Source: EPA 2008a; each value is the time

- weighted average from the data summaries presented in the CSEFH, Table 8

- 3.

a MIRC default Maternal body weight ( BW mat )

. This parameter represents the body weight of the mother averaged over the entire duration of the mother’s exposure to the chemical of concern. The maternal body weight is needed to calculate the biological elimination constant for the lipophilic chemical in lactating women ( k fat_elac ). MIRC assumes that the mother will be pregnant for 9 months (i.e., 0.75 year) and will be lactating for 1 year. The MIRC default maternal body weight also assumes that the mother has been exposed for 10 years total. For 8. 25 years, she is not pregnant or lactating, for 0.75 year she is pregnant, and for 1 year she is lactating. The MIRC default BW

mat of 66 kg is based on CSFII data compiled by EPA for non

- lactating and non

pregnant women between the ages of 15 and 44 (i.e., women of child

- bearing age), lactating women, and pregnant women (EPA 2004b). Exhibit_Add A 2

- 27 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 absor bed 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 t o body weight but are based on both men and women. For those ingestion rates, MIRC uses an average body weight value for males and females to estimate the average daily dose (intake) of the chemical in mg/kg

- day. These values are subject to the assumptio n that the body

- weight normalized ingestion rates and resulting ADD values are applicable to nursing mothers.
Exhibit_Add A 2
- 27

. Time

- weighted Average

Body Weight for Mothers

Statistic Weight (kg) Mean 66.0 a 5 th 47.1 10 th 50.2 25 th 54.3 50 th 62.0 75 th 72.0 90 th 85.7 95 th 97.0
Source: EPA 2004b

a MIRC default value
Exposure duration ( ED )

. See discussion of AT and ED above. Fraction of mother’s whole blood that is plasma ( f bp )

. Steinbeck (1954) reported that plasma volume accounts for approximately 60 percent of the total blood volume in non

- lactating humanTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 93 6

- 29

- 2012 Description of MIRC females (EPA 1998). Harrison (1967) and Ueland (1976) reported plasma volumes between 63 to 70 percent in postpartum women (EPA 1998) . The default value in MIRC of 65 percent ( 0.65 ) is the value recommended by EPA in its Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 1998). Fraction of mother’s body weight that is f at ( 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 2

- 38 , used to estimate the concentration of a lipophilic chemical in breast milk fat, assumes that the mother’s body fat will remain constant over the entire duration of breast feeding (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 establish a health protective screening scenario, the MIRC default value for f fm is 0.30

. Fraction of fat in mother’s breast milk ( f mbm )

. The C

milkfat model ( Equation 2

- 38 ) assumes that a constant fraction of breast milk is fat, even though there is evidence that indicates variation in the fat content of breast milk throughout lactation (Sim and McNeil 1992). Different studies suggest a fat content of breast milk in humans of betwe en 1 and 5 percent (Jensen 1987, Schecter et al

. 1994, Hong et al

. 1994, McLachlan 1993, Bates et al

. 1994, NAS 1991, Butte et al . 1984, Maxwell and Burmaster 1993, EPA 2011, Smith 1987, Sullivan et al . 1991). The MIRC default value for f mbm of 0.04 (i.e. , 4 percent) is the value EPA recommended for MPE (EPA 1998). Fraction of maternal weight that is plasma ( f pm ) . Altmann and Dittmer (1964) estimated that plasma volume for adult women ranged from 37 to 60 mL/kg of body weight and averaged about 45 mL/kg . Ueland (1976) observed that the average plasma volume of women 6 weeks postpartum was 45 mL/kg of body weight. Using a value of 1.026 for the specific gravity of plasma from Conley (1974), EPA estimated a value of 0.046 for the fraction of maternal wei ght 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. Base d on its review of a several studies, EPA recommended time

- weighted average and upper percentile milk ingestion rates for infants that nurse for six and for twelve months (EPA 2011, Table 15

- 3). To estimate an “average” value, EPA first estimated study

- sa mple

- size weighted average values for 1 through 12 months of age and then developed time

- weighted average milk ingestion rates from those (EPA 2011). EPA estimated an upper percentile (upper bound) value as the mean plus two standard deviations. MIRC con verts the ingestion rates measured volumetrically (mL/day) to mass

- based estimates (kg/day) assuming the density of human milk to be 1.03 g/mL (reported by NAS 1991 and recommended by EPA 2011). The resulting values are shown in the first two rows of Exhibit_Add A 2

- 28 . The MIRC screening

- level default of 980 mL/day is an upper
- bound estimate based on a one

- year nursing period.
Exhibit_Add A 2

- 28 also includes the recommended values for four non

- overlapping age categories from the CSEFH (U.S. EPA 2008a, Table 15

- 1). The values demonstrate thatTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 94 6

- 29

- 2012 Description of MIRC although infants grow substantially from birth to one year of age, the “upper bound” estimates of their milk ingestion rates are very close to 1 liter per day at all stages of development in the first year.
Exhibit_Add A 2
- 28

. Infant Bre ast Milk Intake Rates

Age Category Average

(mL/d) Average

(kg/d) “Upper Bound”

(mL/d) “Upper Bound”

(kg/d) Reference

1 to 6 months 742 0.764 1,033 1.064 EPA 2011 †
0 to < 12 months 688 0.709 980 a 1.01 a EPA 2011 †

0 to < 1 month 510 0.525 950 0.979 EPA 2008 †† 1 to < 3 months 690 0.711 980 1.01 EPA 2008 † 3 to < 6 months 770 0.793 1,000 1.03 EPA 2008 †
6 to < 12 months 620 0.639 1,000 1.03 EPA 2008 †

a MIRC default ; † Based on review of multiple studies ; †† Based on a single study

Duration of breast feeding ( t bf )

. This parameter is equal to the infant’s exposure duration ( ED ) and the infant’s averaging time ( AT ). In its MPE Methodology, EPA asserts a health protective value for the duration of breast feeding of 1 year (i.e., 365 days) and a central tendency estimate of 6 months (180 days) (EPA 1998). Reviewers of MPE noted that 365 days may be overly health protective, given that only 20 percent of infants are breast fed for 6 months, at which point alternative foods are introduced, at least in addi tion to breast milk (EPA 2001a). Nonetheless, to establish a health protective screening scenario, the MIRC default for t bf is 365 days. Duration of the mother’s exposure to the chemical of concern prior to nursing ( t pn )

. The model shown as Equation 2

- 38 includes this parameter to reduce the over

- estimate of chemical concentration in milk fat that occurs if the model is applied to a chemical with a long biological ha lf

- 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 sce nario 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 2

- 38 without the t pn term results in an over

- estimate of the concentration of the chemical in breast milk by a factor of 28.1 compared with the prediction using Equation 2

- 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 expo sure, 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 me rit accepting a health protective bias for this parameter value.


### 6.5.2. Chemical

- Specific Parameter Values
The chemical

- specific parameters in the breast

- milk pathway in MIRC are listed in Exhibit_Add A 2

- 29 . Note that the parameters for which values are needed are different for the lipophilicTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 95 6

- 29

- 2012 Description of MIRC chemicals (i.e., dioxins), for which lactational transfer is assumed to occur via milk fat, and inorganic chemicals, for which the transfer is assumed to occur via the aqueous phase of breast milk (i.e., mercury). All dioxin congeners were assumed to manifest identical values as TCDD in regard to breast milk

- related parameters.

Exhibit_Add A 2
- 29

. Chemical

- specific Input Parameter Values for

Breast Milk Exposure Pathway
Parameter and Description 2,3,7,8
- TCDD MeHg

AE inf Infant absorption efficiency of the chemical by the

oral route of exposure (i.e., fraction of ingested chemical that is absorbed by the infant; unitless) 1.0 (default) 1.0 (default)

AE mat Maternal absorption efficiency of the chemical by

the oral route of exposure (i.e., fraction of ingested chemical that is absorbed by the mother; unitless) 1.0 (default) 1.0 (default)

f bl Fraction of steady

- state total body burden of

hydrophilic chemical in mother that is in the mother’s whole blood compartment (unitless) NA 0.059 (Kershaw et al . 1980) a

f f Fraction of steady

- state lipophilic chemical body

burden in mother that is stored in body fat (unitless) ≥0.90 (ATSDR 1992) NA

f pl Fraction of steady

- state total hydrophilic chemical

body burden in mother that is in the blood plasma compartment (unitless) NA Not yet identified b

h Biological half

- life for chemical in non

- lactating

women (days) 3650 (EPA 1994c) 50 (Sherlock et al

. 1984) k aq_elac Rate constant for total elimination of hydrophilic

chemicals by lactating women (per day) NA = k elim

k elim Rate constant for elimination of chemical for non

lactating women (per day; related to chemical half

life) 1.9E

- 04 b 1.4E

- 02 c

k fat_elac Rate constant for total elimination of lipophilic

chemicals by lactating women (per day) Est. using Equation 2

- 41 NA

Pc bm Partition coefficient for hydrophilic chemical

between maternal blood plasma and aqueous phase of breast milk (g milk/g plasma; model assumption) NA 1.0 (model assumption)

Pc RBC Partition coefficient for hydrophilic or protein

- bound

chemical between red blood cells (RBC) and plasma in maternal blood (mL whole blood/mL RBC) NA 40 (Hollins et al

. 1975)

NA = not applicable . ND = not yet determined from literature.

a This value is based on a single

- dose study and may not be appropriate for a chronic exposure model. b An empirical value for this variable is currently missing for application of model.

c This value was calculated from biological half

- life ( h ) using Equation 2

- 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 the AE inf from the aqueous phase of the milk. Reviewers of the model stated that this assumption may not be valid and that ideally, the equation DAI inf would include variables for the AE inf from the breast milk fat and the AE inf from the aqueous phase of breast milk (EPA 2001a). However, since the MIRC assumption is that chemicals will partition to either the lipid or aqueous phase of milk, it is not necessary at this time to have multiple AE inf values for a given chemical. If data on the AE from the mother or an adult but not for the infant are available, data for the adult may beTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 96 6

- 29

- 2012 Description of MIRC used for AE

inf . Reviewers also recommended that chemical

- specific values come from studies that account for absorption of the chemical from milk, because absorption from other matrices (e.g., solid foods) may not be relevant (EPA 2001a). If chemical

- specific data are not available for adults or infants, a health protective default value for AE inf for a screening level assessment is 1 .0, which assumes 100 percent absorption (EPA 1998).

The default value for AE inf in MIRC for both MeHg and dioxin is 1.0

. For ingested lipophilic chemicals, it is reasonable to assume that absorption will be high ( EPA 2004c). ATSDR (1998) reported that dioxins are well absorbed by the oral route of exposure, with one human experiment indicating more than 86 percent absorption. It is EPA policy to assume 100 percent absorption for chemicals with reported AEs of 50 percent or higher (EPA 2004c). Me Hg also is well absorbed, with measured values as high as 95 percent, and so a value of 100 percent is used in MIRC (EPA 2001b). Absorption efficiency of the chemical by the oral route of exposure for the mother ( AE mat )

. The default value for bo th dioxins and MeHg is 1.0, as described in the previous paragraph

. Fraction of total maternal chemical body burden that is in the whole blood ( f bl )

. The default value fo r MeHg in MIRC, 0.059, is fro m Kershaw et al

. (1980), which reported kinetics of bl ood 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 per cent 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) a nd Sullivan et al . (1991), EPA concluded that the “fraction of ingested contaminant stored in fat may be >90%” for lipophilic chemicals such as PCBs and dioxins (EPA 1998). This statement was interpreted to mean that 90 percent of the maternal body burden of chemical at “steady state” is located in body fat for dioxins at steady state. Fraction of total maternal chemical body burden that is in blood plasma ( f pl )

. For hydrophilic chemicals, this parameter represents the steady

- state fraction of the total chemical in the 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 c an be calculated using Equation 2

- 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

- lacta ting women ( h ) . In general, highly lipophilic chemicals tend to have relatively long biological half

- lives. EPA estimates that the half

- life for dioxins is between 7 and 10 years (EPA 1994a). ATSDR estimates that the half

- life for 2,3,7,8

- TCDD in partic ular may be as long as 12 years (ATSDR 1998)

. To establish a health protective screening scenario, the MIRC default half

- life for dioxins is set to 10 years or 3650 days

. The half

- life for methylmercury is on the order of weeks, not years

. Greenwood et al

. (1978) measured blood clearance rates for MeHg in lactating Iraqi women exposed accidentally to MeHg via bread prepared from wheat treated with a fungicide that contained MeHg

. The data indicated a mean half

- life for MeHg of approximately 42 days

. S herlock et al . (1984) reported
an average measured half

- l ife for MeHg of 50 days with a range of 42

- 70 days . The MIRC default for MeHg is set to the longer average half

- life of 50 days

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 97 6

- 29

- 2012 Description of MIRC Chemical elimination rate constant for lactating women

– aqueous ( 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 brea st feeding. In the absence of empirical values, a reasonable assumption for water soluble chemicals is that k aq_elac is equal to k elim as discussed for Equation 2

- 43 . The extent to which k elim is an underestimate of k aq_elac for a given chemical will determine the extent of health protective bias in k aq_elac . Chemical elimination rate constant for non

- lactating women ( k e lim ). Although values for this parameter often are reported directly in the literature, MIRC estimates k elim from chemical half

life assuming first

- order kinetics as shown in Equation 2

- 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 2

- 41 . When the parameters in that equation are set to the default values in MIRC for dioxins, MIRC estimates a value of 0.0015 per day for k fat_elac . Partition coefficient for chemical between maternal blood plasma and aqueous phase of breast milk ( Pc

bm ). The aqueous model, presented in Equation 2

- 42 , assumes that the concentrations in the plasma an d 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

- spec ific 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 o f 1.0 ( EPA 1998)

. F or MeHg, MIRC includes a va lue of 40 b ased on Hollins et al . (1975) study of cats exposed to MeHg, which reported a ratio of radio

- labeled mercury in red blood cells to plasma of 97.7 to 2.3 (i.e., ratio of 42.5).


## 7. Summary of MIRC Default Exposure Parameter Settings
T he default settings included in MIRC are intended to be characteristic of a health protective (but plausible) exposure scenario that results in a negligible or extremely low chance of underestimating risk. These defau lt parameter values were used to derive the screening threshold emission rates used for screening emissions of PB

- HAPs from sources included in RTR risk assessments. These values are the default, or initial setting, for parameter values in MIRC as describ ed in Section 6 of this addendum . This section summarizes the default parameter values used to calculate screening thresholds. This chapter is organized to presen t the chemical

- and scenario

- specific inputs to MIRC by data type. The screening

- level analysis uses 90 th percentile ingestion rates for soil, breast milk, and farm food items and 99 th percentile ingestion rates for fish ( presented in Section 7.1 ) and population

- specific characteristic assumptions ( presented in Section 7.2 )
, that are generally health protective in nature. Screening thresholds were derived for five RTR chemical species: benzo(a)pyrene, cadmium, mercuric chloride, methyl mercury, and 2,3,7,8

- TCDD; Section 7.3 presents chemical

- specific parameter inputs for these five chemicals. Finally, Section 7.4 presents default parameter values for the nursing infant exposure scenario, which applied only to dioxin and methyl mercury as discussed in Section 3.4

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 98 6

- 29

- 2012 Description of MIRC 

### 7.1. Default Ingestion Rates The screening

- level (or default ) values for ingestion rates for soil, breast milk, and for each farm food item are equal to the 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_Add A 2

- 30 ). Fish ingestion rates are also availabl e from these sources; however, as described in Section 6.3.4 , these data were not used.


### 7.1.1. Fish Ingestion Rates The fish ingestion rates for the Tier 1 screening cor respond to “as prepared” total freshwater/estuarine fish ingestion rates at the 99 th percentile of the distribution for the general population, inclusive of both consumers and non

- consumers of fish, as estimated in EPA’s Estimated Per Capita Fish Consumpti on in the United States (EPA 2002), Section 4

. The original data were collected as part of the 1994

– 96 and 1998 USDA Continuing Survey of Intakes by Individuals (CSFII ; USDA 2000 ). These data reflect per capita consumption (i.e., consumption rates for the general population) rather than consumption only for individuals who consume fish

. Values reflect “as prepared” ingestion rates for which cooking and preparation losses ( L1 and L2 ) did not need to be considered. “Total” fish is meant to represent
con sumption of finfish plus shellfish. Some adjustments to EPA (2002) fish ingestion rates were necessary because the age groups evaluated for RTR (which correspond to the age groups for which farm food ingestion rates are available) do not all directly corr espond to the age groups in the EPA (2002) report. As described in the footnotes to Exhibit_Add A 2

- 30 , these adjustments convert the available age

- specific data on f ish ingestion rates to the age

- specific values needed for MIRC using body weight to scale the ingestion rates

. For the screening

- level fish ingestion exposure scenario, the consumer evaluated is an individual who regularly consumes a large amount of fish t hat he or she has caught locally over the course of a 70

- year lifetime. Modeled exposures are intended to encompass those of a “high

- end” recreational angler whose diet comprises a substantial proportion of fish. This amount of fish is close to that cons umed by a “subsistence fisher,” with total exposures from fish consumption similar to those observed for specific populations that consume large amounts of fish, such as certain ethnic groups. The scenario is not, however, intended to represent the maximu m exposure an individual subsistence fisher might experience. As shown in Exhibit_Add A 2

- 30 , f or adults, the rate of fish ingestion assumed in the screening scenario is 105.1 g/day. This consumption rate is similar to the “default” value of 142.4 g/day that EPA’s Office of Water recommends for use in estimating exposures to subsistence fishers (EPA 2000 b )

. Although the fish ingestion rates presented here are representative of the 99 th percentile of the evaluated data set, the use of these inputs (compared with 90 th percentile values used for other food types) is not considered to be inconsistent. This is due to the idiosyncrasies of the survey data on fish con sumption, the fact that the data sets for homegrown foods and fish are not parallel, and the consideration of rates appropriate for subsistence fishers, as described above. The 99 th percentile values below are believed to represent a reasonable approximati on of a high

- end consumption rate along the lines of the 90 th percentile values used for homegrown foods and soil.


### 7.1.2. Farm Food Chain Ingestion
The default settings assume that a ll food types are obtained from the area of chemical deposition specified by the user (i.e., fraction of food from contaminated area = 1.0).TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 99 6

- 29

- 2012 Description of MIRC For estimates of screening threshold emission rates for PB

- HAPS, environmental concentrations and air deposition rates were estimated using TRIM.FaTE for the area of maximal deposition in the vi cinity of a hypothetical facility, and thus represent risks estimated for a maximally exposed individual/farm/family.
Exhibit_Add A 2

- 30 also includes a sum of the 90 th percentile ingestion rates for homegrown food categories and 99 th percentile fish ingestion (preceding rows) to show the implied total food ingestion rate associated with setting multiple food

- type

- specific ingestion rates at upper perce ntile s . Because the se upper percentile value s for each farm food category are likely to reflect different individuals, it is likely that addition of multiple upper percentile intake values will exceed the total food ingestion rates expected for the general population. This sum is shown on the third row from the bottom (Total Food Homegrown Only). The second row from the bottom presents the 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 ingestion rates from CSFII (which are based on consumption of prepared foods). The final row of Exhibit_Add A 2

- 30 shows the likely magnitude of the overestimates by age category by presenting the ratio of the two preceding rows. The values in this row demonstrate the potential for overestimating intake by using upper percentile va lues for all food groups. This bias may be considered when evaluating the results estimated by MIRC.
Exhibit_Add A 2
- 30 . Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening

- Level Consumer Ingestion Rate Units

Infants <1 yr Child

1

– 2 yrs Child

3

– 5 yrs Child

6

– 11 yrs Child

12

– 19 yrs Adult

20

– 70 yrs Farm Food Item Beef a NA 9.49 8.83 11.4 3.53 4.41 g/kg

- day Dairy b NA 185 92.5 57.4 30.9 6.16 g/kg

- day Eggs a NA 4.90 3.06 1.90 1.30 1.31 g/kg

- day Exposed Fruit a NA 12.7 5.41 6.98 3.41 2.37 g/kg

- day Exposed Vegetable a NA 10.7 3.47 3.22 2.35 3.09 g/kg

- day Pork a NA 4.90 4.83 3.72 3.69 2.23 g/kg

- day Poultry a NA 7.17 6.52 4.51 3.13 2.69 g/kg

- day Protected Fruit a NA 44.8 32.0 23.3 7.44 15.1 g/kg

- day Protected Vegetable a NA 3.88 2.51 2.14 1.85 1.81 g/kg

- day Root Vegetable a NA 7.25 4.26 3.83 2.26 2.49 g/kg

- day Other Breast milk c 1.01 NA NA NA NA NA kg/day Soil (dry) NA 200 d 200 d 201 e 201 e 201 e mg/day Fish (per individual) f NA 26.23 g 38.72 g 62.29 h 81.39 h 105.1 h g/day Fish (per kg BW) i NA 2.08 2.08 1.73 1.27 1.47 g/kg

- day Total Food Ingestion Rates for Comparison Only (not in MIRC; excludes soil and water) Total Food: Homegrown only j NA 259 142 99 51 32 g/kg

- dayTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 100 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 30 . Farm Food Category Ingestion Rates for Health Protective Screening Scenario for Farming Households

Product Screening

- Level Consumer Ingestion Rate Units

Infants <1 yr Child

1

– 2 yrs Child

3

– 5 yrs Child

6

– 11 yrs Child

12

– 19 yrs Adult

20

– 70 yrs Total Food: All Sources k NA 125 91 61 34 23.7 g/kg

- day Overestimate (ratio of Homegrown/Total) NA 2.1 1.6 1.6 1.5 1.3 (unitless)

Sources: EPA 2011, EPA 2008, unless otherwise noted. NA = not applicable

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 ) . 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 re sults are presented in Chapter 13 of the 2011 Exposure Factors Handbook ( EPA, 2011 ) . When data were unavailable for a particular age group, the intake rate f or all age groups was multiplied by the age
- specific ratio of intake based on national population intake rates from an NHANES 2003

– 2006 analysis in Chapter 11 of the Exposure Factors Handbook.

c Infants are assumed to consume only breast milk for one year. d These values are the recommended “upper percentile” value for children from EPA’s 2011 EFH, Chapter 4, Table 4

- 23. The 2008 CSEFH and 2011 EFH included a high

- end value associated with pica only, but this value has not been used.

e These values are 90th percentile adult ingestion rates calculated in Stanek et al. 1997, and they are used to represent older children and adults. f Values were d erived based on EPA’s Estimated Per Capita Fish Consum ption in the United States (2002)

— Section 4.1.1.1 Table 4 (for adults) and Section 4.1.1.1 Table 5 (for child age categories) and are representative of 99 th percentile "as prepared" values.
g The fish ingestion rate for children aged 3

– 5 years was obtaine d directly from Table 5 in the EPA (2002) report; for these children, the RTR age

- group range matches the EPA (2002) age category. Fish ingestion rates for children less than 3 years old, however, were not provided. Therefore, f or children aged 1

– 2 years, the fish ingestion rate was calculated using the ingestion rate for children aged 3

– 5 years scaled downward by the ratio of the mean body weight of children aged 1

– 2 years (12.6 kg) to the mean body weight of children aged 3

– 5

- years (18.6 kg). h T he EPA (2002) fish ingestion estimates and Exposure Factor Handbook (EPA 2011) body weight data were used to derive time

- and body weight

- weighted average ingestion rates that adjust for the differences between the age group ranges used for the RTR screening and those presented in the 2002 EPA report. i Fish ingestion rates, original data in g/day, have been normalized to body weight in this table to allow addition into total food estimate using the mean body weight for each age category. j Sum of post

- cooking food ingestion rates. This estimate is calculated by multiplying the food ingestion rates on previous rows (excluding soil and water) by (1

- L 1 )x(1

- L 2 ), where L 1 and L 2 are the loss rates from Exhibit_Add A 2

- 24

. The rows are then summed to get the total post

- cooking ingestion rate. k 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_Add A 2

- 31 . As stated in Section 6 of this addendum
, EPA recommends using the mean BW for each age group when using upper percentile values for medium ingestion rates. Use of the mean body weights introduces no bias toward ov er
- or underestimating risk.
Exhibit_Add A 2
- 31

. Mean Body Weight Estimates for Adults and Childre n a
Lifestage (years) Duration (years) Mean Body Weight (kg) Adult b (20

- 70) 50 71.4 Child < 1 c 1 7.83 Child 1

- 2 c 2 12.6 Child 3

- 5 d 3 18.6TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 101 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 31

. 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 BW represents the sample

- size weighted average of male and female mean body weights (all races, 18

- 74 years) from EPA’s 1997 EFH (Tables 7

- 4 for males and 7

- 5 for females)

. Note that these weights include the weight of clothing, estimated t o range from 0.09 to 0.28 kg. Although the 18 to 74 year age category in EPA’s EFH does not match exactly the age 20 to 70 year categorization of adults in MIR C, the magnitude of error in the mean and percentile body weights is likely to be very small (i.e., less than 1%). We considered updating the body weight for use in the RTR assessment to the revised body weights in the 2011 Exposure Factors Handbook. Howe ver , t he 2011 EFH warns that assessors should consider if the dose estimate (for which the body weight is used to calculate) will be used to estimate risk by combining it with a dose

- response that was derived assuming a body weight of 70 kg, and that if su ch an inconsistency exists, an adjustment may be necessary. Because a 70 kg body weight commonly is used to develop IRIS dose

- response values, using an 80 kg body weight could result in this inconsistency. Given these issues, we have retained the u se of an adult body weight of 71.4 kg

. c These values were calculated as time

- weighted average body weight (BW) from data presented in Table 8

- 3 of EPA’s (2008a) Child

- Specific Exposure Factors Handbook (CSEFH)

. d These values were obtained directly f rom Table 8

- 3 of the 2008 CSEFH.

e Each BW represents a time

- weighted average of BWs for age groups 6 to <11 years and 11 to <16 years from Table 8

- 3 of the 2008 CSEFH

. Original sample sizes for each of these age groups can also be found in Table 8

- 3. f T hese values were calculated as time

- weighted average BW for age groups 11 to <16 years and 16 to <21 years from Table 8

- 3 of the 2008 CSEFH . The direction of the possible bias is unknown. The values match the estimate based on Table 8

- 22 of the NHANES IV data as presented by Portier et al. (2007).


### 7.3. Default Chemical

- Specific Parameter Values for Screening Analysis Exhibit_Add A 2

- 32 presents chemical

- specific parameter values for input to MIRC for the screening

- level analysis. Values for bioavailability when ingested in soil ( Bs ), mammalian metabolism factors ( MF ), correction factors for belowground produce ( VG rootveg ), wet depositi on fractions ( Fw

), air to plant transfer factors ( Bv AG ), root concentration factors ( RCF ), and soil

water partition coefficient ( Kds ) are presented in Exhibit_Add A 2

- 32

. Exhibit_Add A 2
- 32 . Chemical

- Specific Parameter Values for Input to MIRC a

Parameter Description Benzo(a)

- pyrene Cadmium Mercuric chloride Methyl

mercur y 2,3,7,8
- TCDD Units

Bs Soil bioavailability

factor for livestock 1 1 1 1 1 unitless

MF Mammalian

metabolism factor 0.01 1 1 1 1 unitless
VGrootveg Empirical correction

factor for belowground produce, i.e., tuber or root vegetable, to account for possible overestimate of the transfer of chemicals from the outside to the inside of bulky tubers or roots (based on carrots and potatoes) 0.01 1 1 0.01 0.01 unitlessTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 102 6

- 29

- 2012 Description of MIRC Exhibit_Add A 2
- 32 . Chemical

- Specific Parameter Values for Input to MIRC a

Parameter Description Benzo(a)

- pyrene Cadmium Mercuric chloride Methyl

mercur y 2,3,7,8
- TCDD Units

Fw Fraction of wet

deposition that adheres to plant surfaces; 0.2 for anions, 0.6 for cations and most organics 0.6 0.6 0.6 0.6 0.6 unitless

BvAG Air

- to

- plant

biotransfer factor for aboveground produce for vapor

phase chemical in air 124 , 742 0 1,800 0 65,500 [mg/g produce DW]/[mg/g air]

RCF Chemical

- specific

root concentration factor for tubers and root produce 9 , 684 0 0 0 39,999 L soil pore water/kg root WW

Kds Chemical

- specific

soil/water partition coefficient 160 , 000 75 58,000 7,000 38,904. 51 L soil pore water/kg soil DW

a Values presented in this exhibit are also presented in previous exhibits; however exact values used in the analysis are presented here, rather than values restricted by significant figures. In addition, only values for those chemicals that are specifical ly used in the screening analysis are provided here. Only single estimates were developed for each of these parameters for HHRAP (EPA 2005a), and the potential direction and magnitude of bias toward over

- or underestimating risks were not investigated in this analysis. The inputs that are both chemical

- spec ific and plant

- type

- specific, as presented in Exhibit_Add A 2

- 11 , are not repeated here. Again, only single estimates were developed for these parameters and the poten tial direction and magnitude of bias toward over

or underestimating risks were not investigated. Finally, Exhibit_Add A 2

- 33 presents biotransfer factors for each of the chemicals and animal types for which screening threshold emissions were calculated.
Exhibit_Add A 2
- 33 . Chemical and Animal

- Type Specific Biotransfer Factor (Ba) Values for Input to MIRC ([mg chemical/kg WW tissue or dairy] / [mg chemical intake/day] = day/kg WW tissue or dairy) Chemical Beef Dairy Pork Eggs Poultry Benzo(a)pyrene 3.8E

- 02 8.0E

- 03 4.6E

- 02 1.6E

- 02 2.8E

- 02 Cadmium 1.2E

- 04 6.5E

- 06 1.9E

- 04 2.5E

- 03 1.1E

- 01 Mercuric 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

- 02TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 103 6

- 29

- 2012 Description of MIRC 

### 7.4. Screening

- Level Parameter Values for Nursing Infant Exposure MIRC is configured to evaluate risk to nursing infants exposed to dioxins and to methylmercury (MeHg) in their mother’s milk for a family farming and catching fish in the area of maximal air deposition of chemical. Input values were summarized in Section 6.5 of this addendum

. 

### 7.4.1. Dioxins For dioxins, chemical intake via breast milk by nursing infants was estimated using the model presented in EPA’s Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions (MPE, EPA 199 8) . The assumption that lactational transfer of dioxins to the infant occurs via the lipid

- phase of milk appears reasonable. The following screening

- level assumptions used in that model should bias the results toward 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 f ood 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 discuss ion for Exhibit_Add A 2

- 30 ); this assumption might overestimate total ingestion of homegrown foods by a factor of more than 2 (see Exhibit_Add A 2

- 30 )

.

 If the fraction of the maternal body burden of dioxin that is in the body fat compartment is greater than 90 percent, as suggested by ATSDR (1998), then actual exposures of the infant may be less than estimated.
There also are parameter values and model assumptions for the lipid

- phase breast

- milk pathway for which possible bias is unknown.

 The accuracy of the model is unknown; it has not been verified or validated with empirical data.

 Using a half

- life of 10 years for dioxins may over

- or under

- estimate risks.
Finally, there is one assumption that might possibly introduce some bias toward underestimating risks. The model results are sensitive to the biological half

- life of the chemical in the mother relative to the length of her exposure prior to the lactation period. Using an exposure duration for the mother equal to the assumed half

- life for dioxins, 10 years, may underestimate the duration of exposure of the mother.


### 7.4.2. Methyl Mercury
F or MeHg, empirical data from a single human study (Fujita and Takabatake 1977) was used in conjunction with a physiologically based pharmacokinetic (PBPK) model of lactational transfer of MeHg developed and partially validated by Byczkowski and Lipscomb (2 001) 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 PB PK model suggested in addition that the averageTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 104 6

- 29

- 2012 Description of MIRC daily dose of MeHg absorbed from milk by the nursing infant ( 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 var ious assumptions; however, there is no known directional bias in the estimates

. 

## 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 Reg istry) . 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 1998 )

. ATSDR

. 1998

. Toxicological profile for chlorinated dibenzo

- p
- dioxins

. Atlanta, GA: U.S

. Department of Health and Human Services, Public Health Service. Bacci E., M. Cerejeira, C. Gaggi, G. Chemello, D. Calamari, and M. Vighi. 1992. Chlorinated dioxins: Volatilization from soils and 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 women . Environmental Health Perspecti ves 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 chain 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 particulates. 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, Appendix A

- 2). Burger J. 2010. Daily consumption of wild fish and game: Exposures of high end recreationalists. International Journal of Environmental Health Research 12 ( 4 ): 343

- 354, July, 2010

. Butte, N.F., C. Garza, E.O. Smith, and B.L. Nichols. 1984. Human milk intake and growth in exclusively breast

- fed infants. The Journal of Pediatrics 104(2): 187

- 195.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 105 6

- 29

- 2012 Description of MIRC Byczkowski, J.Z., and J.C. Lipscomb. 2001. Physiologically based pharmacokinetic modeling of the lactational transfer of methylmercury. Risk Analysis 21(5): 869

- 882. Chamberlain, A.C. 1970. Interception and retention of r adio active aerosols by vegetation. Atmospheric Environment 4: 57

- 78. Clewell, H.J, J.M. Gearhart, R.P. Gentry, T.R Covington, C.B. VanLandingham, C.B. Crump, and A.M. Shipp. 1999. Evaluation of the uncertainty in an oral reference dose for methylmercury due to interindividual variability in pharmacokinetics. Risk Analysis 1 9: 547

- 558 (As cited in Byczkowski and Lipscomb 2001). Conley, C.L. 1974. The blood. Medical Physiology 13 ( 2 ). V.B. Moutcasle, Ed. 1997. The C.V. Mosby Company, St. Louis, MO. 1027

- 1046 (As cited in EPA 1998). Ensminger, M.E. 1980. Poultry Science

. Inters tate Printers and Publishers, Inc. Danville, Illinois.
Fries, G. F. 1982. Potential polychlorinated biphenyl residues in animal products from application of contaminated sewage sludge to land. J. Environ. Qual. 11:14.

Fri es, G.F. 1994. Personal c ommuni cati on 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 level s 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 c urves of performance tests in a large epidemiological study. Water, Air, Soil Pollut. 80: 49

- 58 (As cited in Byczkowski and Lipscomb 2001). Gearhart, J., T. Covington, and H. Clewell III. 1996. Application of a physiologically based pharmacokinetic model f or 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).
Greenwood, M. R., T.W. Clarkson, R.A. Doherty, A.H. Gates, L. Amin

- Zaki, S. Elhassani, and M.A. Majeed. 1978. Blood clearance half

- times in lactating and nonlactating members of a population exposed to mercury. Environ. Res. 16: 48

- 54.
Harrison, K.A. 1967. Blood volume changes in severe anemia in pregnancy. Lancet 1(7480):20

- 25. Hofelt, C.S., M. Honeycutt, J.T. McCoy, and L.C. Haws. 2001. Development of a metabolism factor for polycyclic aromatic hydrocarbons for use in multipathway risk assessments of hazardous waste co mbustion facilities. Reg. Toxicol. Pharmacol . 33:60

- 65.
Hoffman, F.O., K.M. Thiessen, M.L. F rank, and B.G. Blaylock. 1992. Quantification of the interception and initial retention of radioactive contaminants deposited on pasture grass by simulated rain. Atmospheric Environ. 26 a( 18 ) : 3313

- 3321

. Hollins, J.G., R.F. Willes, F.R. Bryce, S.M. Charbonneau, and I.C. Munro. 1975. The whole body retention and tissue distribution of Hg methylmercury in adult cats. Toxicol. App. Pharmacol. 33: 438

- 449.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 106 6

- 29

- 2012 Description of MIRC Hong, C.S., J. Xiao, A.C. Casey, B. Bush, E.F. Fitzgerald, and S.A. Hwang. 1994. Mono

- ortho and non

- ortho

- substituted polychlorinated biphenyls in human milk from Mohawk and control women: Effects of maternal factors and previous lactation. Arch. Environ. Contam. Toxi col. 27(3): 431

- 437. Jensen, A.A. 1987. Polychlorinated biphenyls (PCBs), polychlorodibenzo

- p
- dioxins (PCDDs) and polychlorodibenzofurans (PCDFs) in human milk, blood and adipose tissue. Sci. Total Environ. 64(3): 259

- 293.
Kahn, H.
, and K. Stralka. 2008. E stimated daily average per capita water ingestion by child and adult age categories based on USDA’s 1994

- 96 and 1998 continuing survey of food intakes individuals. J. Expo. Anal. Environ. Epidemiol. 1

- 9.

Kershaw
, T

. G

. , T

. W

. Clarkson
, and P

. H

. Dhahir

. 1980. The relationship between blood levels and dose of methylmercury in man. Arch. Environ. Health 35(1): 28

- 36.
Lorber, M. 1995 . Development of an air

- to

- plant vapor phase t ransfer for d ioxins and f urans. Presented at the 15th International Symposium on Chlorinate d Dioxins and Related Compounds

. August 21

- 25, 1995 in Edmonton, Canada. Abstract in Organohalogen Compounds 24:179

- 186

. Lorber, M., and P. Pinsky. 2000. An evaluation of three empirical air

- to

- leaf models for polychlorinated dibenzo

- p
- dioxins and dibenzofurans. Chemosphere 41(6):931

- 41.
Maxwell, N.I., and D.E. Burmaster. 1993. A simulation model to estimate a distribution of lipid intake from breast milk during the first year of life. Journal of Exposure Analysis and Environmental Epidemiology 3(4): 383

- 406. McLachlan, M.S. 1993. Digestive tract absorption of polychlorinated di benzo

- p
- dioxins, dibenzofurans and biphenyls in a nursing infant. Toxicology and Applied Pharmacology 123(1): 68

- 72.
Miller, C.W., and F.O. Hoffman. 1983. An examination of the environmental half

- time for radionuclides deposited on vegetation. Health Phys ics 45(3): 731

- 744. Morgan, J.N., M.R. Berry, and R.L. Graves. 1997. Effects of Commonly Used Cooking Practices on Total Mercury Concentration in Fish and Their Impact on Exposure Assessments. Journal of Exposure Analysis and Environmental Epidemiology 7(1 ):119

- 133.
NAS (National Academy of Sciences)

. 1987. Predicting Feed intake of Food

- Producing A nimals

. National Research Council, Committee on Animal Nutrition, Washington, D.C. NAS. 1991. Nutrition During L actation . National Academies Press. Washington, DC.
NC DEHNR ( North Carolina Department of Health, Envi ronment, and Natural Resources)

. 1997. North Carolina Protocol for Performing Indirect Exposure Risk Assessments for Hazardous Waste Combustion Units

. January

. Portier K., J. Tolson, and S. Roberts. 2007. Body weight distributions for risk assessment. Risk Anal. 27(1) : 11

- 26

. RTI. 2005. Methodology for predicting cattle biotransfer f actors. Prepared for U.S. Environmental Protection Agency (EPA) Office of Solid W aste. EPA Contract No. 68

- W

- 03

-TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 107 6

- 29

- 2012 Description of MIRC 042. September. Available at: http://www.epa.gov/waste/hazard/tsd/td/combust/ finalmact/ssra/btfreportfull05.pdf

. Schecter, A., P. Fürst, C. Fürst, O. Päpke, M. Ball, J. Ryan, H. Cau, L. Dai, H. Quynh, H.Q. Cuong, N. Phuong, P. Phiet, A. Beim, J. Constable, J. Startin, M. Samedy, and Y. Seng. 1994. Chlorinated dioxins and dibenzofurans in human tissue from general pop ulations: A selective review. Environmental Health Perspectives 102(Supplement 1): 159

- 171. Sherlock, J., D. Hislop, G. Newton, G. Topping, and K. Whittle. 1984. Elevation of mercury in human blood from controlled ingestion of methylmercury in fish. Human Toxicology 3: 117

- 131 (As cited in USFDA 2009). Shilling, F., White, A. Lippert, L, and M. Lubell. 2010. Contaminated fish consumption in California's Central Valley Delta. Environmental Research 110: 334

- 344. Shor, R.W., C.F. Baes, and R.D. Sharp. 1982. Agricultural p roduction in the United States by
c ount y: A compilation of information from the 1974 census of agriculture for use in terrestrial food

- chain transport and assessment m odels

. Oak Ridge National Laboratory Publication. ORNL

- 5786
Sim, M.R. and J .J. McNeil. 1992. Monitoring chemical exposure using breast milk: A methodological review. American Journal of Epidemiology 136(1): 1

- 11.
Smith, A.H. 1987. Infant exposure assessment for breast milk dioxins and furans derived from waste incineration emissi ons. Risk Analysis 7:347

- 353.
Stanek, E.J., E.J. Calabrese, R. Barnes, P. Pekow. 1997. Soil ingestion in adults

– results of a second pilot study. Toxicol. Environ. Safety 36:249

- 257.
Steinbeck, A.W. 1954. Plasma and blood volumes of normal Australian fema les. Australian Journal of Experimental Biology and Medical Science 32(1): 95

- 9. Stephens, R.D., M. Pe treas, and G.H. Hayward. 1995. Biotransfer and bioaccumulation of dioxins and furans from soil: Chickens as a model for foraging animals. Science Total En vironment 175: 253

- 273. July 20

. Sullivan, M.J., S.R. Custance, and C.F. Miller. 1991. Infant exposure to dioxin in mother’s milk resulting from maternal ingestion of contaminated fish. Chemosphere 23(8

- 10): 1387

- 1396

. Thomann, R.V. 1989. Bioaccumulation model of organic

- chemical distribution i n aquatic food

chains. Environ. Sci. Technol

. 23(6): 699

- 707.
Travis, C.C.
, and A.D. Arms. 1988. Bioconcentration of organics in beef, milk, and v egetation. Environ. Sci. Technol. 22:271

- 274.
Travis, C.C., H.A. Hattemer

- Frey and A.A. Arms. 1988. Relationship between dietary intake of organic chemicals and their concentrations in human adipose tissue and breast milk. Arch. Environ. Contam. Toxicol. 17:473

- 478.
Ueland, K. 1976. Maternal cardiovascular dynamics. VII . Maternal cardiovascular dynamics. Intrapartum blood volume changes. Am. J. Obstetrics Gynecol

. 126(6): 671

- 677.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 108 6

- 29

- 2012 Description of MIRC USDA (U.S. Department of Agriculture). 1992. Changes in Food Consumption and E xpe nditures in American H ouseholds during the 1980’s. USDA, Wash ington, D.C. Statistical Bulletin o. 849. (As cited in EPA 1997) USDA. 1993. Food and Nutrient Intakes by I ndividuals in the United States, 1 Day, 1987

- 88. Nationwide Food Consumption Survey 1987

- 88, NFCS Report No. 87

- I

- 1. (As cited in EPA 1997)
USDA. 199 4a. Food Consumption and Dietary Levels of H ouseholds in the United States, 1987

- 88. Agricultural Research Service, Report No. 87

- H

- 1. (As cited in EPA 1997)
USDA . 1994b. Vegetables 1993 Summary . National Agricultural Statistics Service, Agricultural Stati stics Board. Washington, D.C. Vg 1

- 2 (94). Jan.
USDA . 1994c. Noncitrus Fruits and Nuts 1993 Summary . National Agricultural Statistics Service, Agricultural Statistics Board, Washington, D.C. Fr Nt 1

- 3 (94).
USDA

. 2000. 1994

– 96, 1998 Continuing Survey of Fo od Intakes by Individuals (CSFII) . CD

ROM. Agricultural Research Service, Beltsville Human Nutrition Research Center, Beltsville, MD. Available from the National Technical Information Service, Springfield, VA, Accession Number PB

- 2000500027. (As cited in EPA 2008a, Chapter 14) USFDA (US Food and Drug Administration) . 2009. Draft Risk and Benefit Assessment Report and Draft Summary of Published Research. Center for Food Safety and Applied Nutrition. January 15. Available at: http://www.cfsan.fda.gov/seafood1.html EPA (U S. E nvironmental Protection Agency)

. 1990. Interim Final Methodology for Assessing Health Risks Associated with Indirect Exposure to Combustor Emissions

. Environmental Criteria and Assessmen t Office. ORD.EPA

- 600

- 90

- 003. January.

EPA

. 1992. Technical Support Document for the Land Application of Sewage Sludge : Volumes I and II. EPA 822/R

- 93

- 001a. Office of Water. Washington, D.C.

EPA

. 1994a. Revised Draft Guidance for Performing Screening Level Risk Analysis at Combustion Facilities Burning Hazardous Wastes. Attachment C, Draft Exposure Assessment Guidance for RCRA Hazardous Waste Combustion Facilities. Office of Emergency and Remedial Response. Office of Solid Waste. December 14.

EPA

. 1994b. Es timating Ex posure to Dioxin

- Like Compounds. Volume II: Properties, Sources, Occurrence, and Background Exposures. External Review Draft. Office of Research and Development. Washington, DC. EPA/600/6

- 88/005Cc. June.

EPA

. 1994c. Estimating Exposure to Dioxin

- Like Compounds. External Review Draft. Office of Research and Development, Washington, D.C. EPA/600/6

- 88/005Cb. June. Available at: http://oaspub.epa.gov/eims/eimscomm.ge tfile?p_download_id=438673

. EPA

. 1995a. Review Draft Development of Human Health

- Based and Ecologically

- Based Exit Criteria for the Hazardous Waste Identification Project

. Volumes I and II. Office of Solid Waste. March 3.

EPA

. 1995b. Memorandum Regarding Further Studies for Modeling the Indirect Exposure Impacts from Combustor Emissions

. From Mathew Lorber, Exposure Assessment Group,TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 109 6

- 29

- 2012 Description of MIRC and Glenn Rice, Indirect Exposure Team, Environmental Criteria and Assessment Office. Washington, D.C. January 20.

EPA

. 1995 c. Further Issues for Modeling the Indirect Exposure Impacts from Combustor Emissions

. O ffice of Research and Development

. Washington, D.C. January 20.

EPA

. 1995d. Waste Technologies Industries Screening Human Health Risk Assessment (SHHRA): Evaluation of Potential Risk from Exposure to Routine Operating Emissions

. Volume V. External Review Draft. U.S. EPA Region 5, Chicago, Illinois. EPA. 1996. Soil Screening Guidance: User’s Guide. Office of Solid Waste and Emergency Response, Washington D.C. EPA/540/R

- 9 6/018, April 1996.

EPA

. 1997a. Exp osure Factors Handbook. Volumes I, II, and III. Office of Research and Development, Washington, D.C. EPA

- 600

- P

- 95

- 002Fa,b,c. August. Available at: http://www.epa.gov/nceawww1/efh/

. EPA

. 1997b. M ercury Study Report to Congress. Volume III: Fate and Transport of Mercury in the Environment. O ffice of Air Quality Planning and Standards and Office of Research and Development. E PA

- 452/R

- 97

- 005. December.

EPA

. 1997c. Parameter Guidance Document. National Center for Environmental Assessment, NCEA

- 0238

. EPA

. 1998. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions. National Center for Environmental Ass essment, Cincinnati, OH. EPA

- 600

- R

- 98

- 137. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=55525

. EPA. 1999. 1999 National

- Scale Air Toxics Assessment Results; Approa ch for Modeling POM

. Available at: http://www.epa.gov/ttn/atw/nata1999/nsata99.html

. EPA

. 2000 a

. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures. Risk Assessment Forum, Washington D.C. EPA/630/R

- 00/002. August. Available at: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf

. EPA. 2000b. Methodology for Deriving Ambi ent Water Quality Criteria for the Protection of Human Health. EPA

- 822

- B

- 00

- 004 . October.

EPA

. 2001 a

. Peer Review of EPA’s Hazardous Waste Identification Rule Risk Assessment Model: Breast milk exposure model for the HWIR 3MRA Model . Prepared by Eastern Re search Group for EPA Office of Solid Wastes. 68

- W5

- 0057.

EPA

. 2001b. Water Quality Criterion for the Protection of Human Health: Methylmercury. Office of Water, Office of Science and Technology. Washington D.C. EPA

- 823

- R

- 01

- 001. January. Available at: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf

. EPA

. 2002. Estimated Per capita Fish Consumption in the United States. Office of Water, Of fice of Science and Technolog y, W ashington, D

. C. EPA

- 821

- C

- 02

- 003. August. Available at: http://www.epa.gov/waterscience/fish/files/consumption_report.pdf

. EPA

. 2003a. Chapter 10 In: Multimedia, Multipa thway, 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 ResearchTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 110 6

- 29

- 2012 Description of MIRC Triangle Park, NC, and Office of Solid Waste, Washington, D.C. July. Available at: http://www.epa.gov/osw/hazard/wastetypes/wasteid/hwirwste/risk03.htm

. EPA

. 2003b. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (20 00): Technical Support Document. Volume 2: Development of National Bioaccumulation Factors

. Office of Water, Office of Science and Technology, Washington, D.C. EPA

- 822

- R

- 03

- 030. December. Available at: http://www.epa.gov/waterscience/criteria/humanhealth/method/tsdvol2.pdf

. EPA

. 2003c. CSFII Analysis of Food Intake Distributions. Office of Research and Development, National Ce nter for Environmental Assessment, Washington, D.C. EPA

- 600

- R

- 03

- 29. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56610

. EPA

. 2004 a

. Superfund Chemical Data Matrix

. OERR. Washington, D.C. January.

EPA

. 2004b. Estimated Per capita Water Ingestion and Body Weight in the United States

– An Update. Office of Water, Office of Science and Technology, Washington, D.C. EPA

- 822

- R

00

- 001. October. Available at: http://www.epa.gov/waterscience/criteria/drinking/percapita/2004.pdf

EPA

. 2004c. Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation Manual (Part E, Supplemental Gui dance for Dermal Risk Assessment), Final . Office of Superfund Remediation and Technology Innovation, Washington, D.C. EPA/540/R/99/005; OSWER 9285.7

- 02EP; NTIS PB99

- 963312. July.

EPA

. 2005a. Human Health Risk Assessment Protocol for Hazardou s Waste Combustion Facilities. Office of Solid Waste and Emergency Response, Washington, DC. EPA

- 530

- R

05

- 006. September. Available at: http://www.epa.gov/osw/hazard/tsd/td/combust/risk.ht m

. EPA

. 2005b. Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants. Risk Assessment Forum. Washington, DC. November. EPA/630/P

- 03/003F. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=146583

. EPA

. 2005c. Guidelines for Carcinogen Risk Assessment. Risk Assessment Forum, Washington, DC. EPA/630/P

- 03/001F. March. Available fr om: http://www.epa.gov/IRIS/cancer032505

- final.pdf

. EPA

. 2005d. Supplemental Guidance for Assessing Susceptibility from Early

- Life Exposure to Carcinogens. Risk Assessment Forum: Washington, D.C. EPA

- 630/R

- 03

- 003F. March. Available at: http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf

. EPA

. 2005e. Analysis of Total Food Intake and Composition of Individual's Diet Based on the U.S. Department of Agriculture's 1994

- 96, 1998 Continuing Survey of Food Intakes By Individuals (CSFII) (Final). Office of Research and Development, National Center for Environmental Assessment, Washington, D.C. EPA/600/R

- 05/062F. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=132173

. EPA

. 2007a. Prioritized Chronic Dose

- Response Values for Screening Risk Assessments (Table 1). Office of Air Quality Planning and Standards; June 12, 2007. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 2 2

- 111 6

- 29

- 2012 Description of MIRC EPA

. 2007b. Toxicological Review of 1,1,1

- Trichloroethane (CAS No. 71

- 55

- 6) In Support of Summa ry Information on the Integrated Risk Information System (IRIS). Office of Research and Development, Washington, DC. EPA/635/R

- 03/006. August. Available at: http://www.epa.gov/iris

EPA

. 2008a. Child

- Specific Exposure F actors Handbook. Office of Research and Development, Washington, D

. C

. EPA/600/R

- 06/096F. September. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243

. EPA

. 200 8b. Draft Report on EPA OAQPS Risk and Technology Review Methodologies: For Review by the EPA Science Advisory Board; Case Studies

– MACT I Petroleum Refining Sources, Portland Cement Manufacturing. Office of Air Quality Planning and Standards, Office of A ir and Radiation, Research Triangle Park, NC. July 14, 2008.

EPA

. 2010. Personal communication from Zachary Pekar, Ambient Standards Group, Office of Air and Radiation, U.S. Environmental Protection Agency, to Heidi Hubbard, ICF International, [ December 15, 2010]

EPA

. 2011

. Exposure Factors Handbook: 2011 Edition

. Office of Research and Development, Washington, D.C. EPA/600/R

- 090/052F September 2011 Available at: http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252

. Van den Berg, M., L.S. Birnbaum , M. Denison, M. De vito, W. Farlans, M. Feeley, H. Fiedler, H. Hakansson, A. Hanberg, L.. Haws, M. Rose, S. Safe, D. Schrenk, C. Tohyama, A. Tritscher, J. tuomisto, M. Tysklind, N. Walker, and R.E. Peterson. 2006. The 2005 World Health Organiza tion reeval uation of human and m ammalian toxic equivalency factors for dioxins and dioxin

- like compounds. Toxicol Sci. 93(2): 223

- 41.[This page intentionally left blank

. ]TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 1 6

- 29

- 2012 Dermal Risk Screening 

## Addendum 3. Dermal Risk Screening[ This page intentionally left blank. ]TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 3 6

- 29

- 2012 Dermal Risk Screening 

## CONTENTS , ADDENDUM 3

1. Hazard Identification and Dose Response Assessment

................................ .................. 3

- 5

2. Dermal Exposure Estimation

................................ ................................ ........................... 3

- 6
2.1. Equations for Estimating Dermal Exposure

................................ ............................. 3

- 6
2.2. Exposure Factors and Assumptions

................................ ................................ ....... 3

- 7 2.3. Receptor

- Specific Parameters

................................ ................................ ................ 3

- 8
2.4. Scenario

- Specific Parameters

................................ ................................ ................ 3

- 8 2.5. Chemical

- Specific Parameters

................................ ................................ ................ 3

- 9

3. Screening

- Level Cancer Risks and Non

- Cancer Hazard Quotients

............................... 3

- 10 3.1. Dermal Cancer Risk

................................ ................................ .............................. 3

- 10 3.2. Dermal Hazard Quotient

................................ ................................ ....................... 3

- 11

4. Dermal Screening Results

................................ ................................ ............................ 3

- 11

5. References

................................ ................................ ................................ ................... 3

- 14TRIM

- Based T iered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 4 6

- 29

- 2012 Dermal Risk Screening Exhibi t s, Addendum 3
Exhibit_Add A3

- 1. Cancer Slope Factors and Reference Doses Based on

Absorbed Dose

................................ ................................ ................................ . 3

- 6 Exhibit_Add A3

- 2. Receptor

- Specific Body Surface Area Assumed to be

Exposed to Chemicals

................................ ................................ ...................... 3

- 8 Exhibit_Add A3

- 3. Scenario

- Specific Exposure Values for Water and Soil

Contact

................................ ................................ ................................ ............. 3

- 9 Exhibit_Add A3

- 4. Chemical

- Specific Dermal Exposure Values for Water and

Soil Contact

................................ ................................ ................................ .... 3

- 10 Exhibit_Add A3

- 5. Summary o f Dermal Non

- Cancer Hazards

................................ .............. 3

- 12 Exhibit_Add A3

- 6. Summary of Dermal Cancer Risks

................................ .......................... 3

- 13TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 5 6

- 29

- 2012 Dermal Risk Screening Non

- inhalation exposure to PB

- HAPs can occur by way of the dermal pathway through contact with PB

- HAP

- contaminated soil and water. H owever, dermal absorption of chemicals t hat are originally airborne is generally a relatively minor pathway of exposure compared to other exposure pathwa ys (U.S. EPA 2006, Cal/EP A 2000) . This section demonstrates that for the conservative tierd screening scenario developed for RTR multipathway evaluation, the dermal exposure route is not a significant risk pathway when compared to the ingestion pathway. In general, the RTR multipathway assessment followed the protocol for evaluating a reasonable maximum exposure as described in EPA’s Risk Assessment Guidance for Superfund (RAGS), Volume I: Human Health Evaluation Model, Part E, Supplemental Guidance for Dermal Risk Assessment ( U.S. EPA 2004 c )

. 

## 1. Hazard Identification and Dose Response Assessment To assess the potential contribution of derma l exposure to non

- inhalation exposure, w e evaluated the potential for cancer and chronic non

- cancer effects for the four PB

- HAPs currently assessed in the multipathway scre ening evaluation for RTR: cadmium, divalent mercury, 2,3,7,8

- TCDD, and benzo(a)pyren e

. EPA has not developed carcinogenic potency slope factors ( C SFs) and non

- cancer reference doses (RfDs) specifically for evaluating potential human health concerns associated with dermal exposure to PB

- HAPs. Instead, dermal toxicity values can be derive d from oral toxicity values via route

- to

- route extrapolation by adjusting for gastrointestinal (GI) absorption

. EPA recommends making this adjustment only when GI absorption of the chemical is significantly less than 100% (i.e., less than 50 percent)

. Ot herwise, a default value of complete (100 percent) oral absorption is assumed, and no adjustment is made (U.S. EPA 2004c). The absorbed cancer slope factor ( C SF ABS ) is based on the oral cancer slope factor ( C SF O ) and the fraction of the contaminant abso rbed 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 the fraction of the contaminant absorbed in the gastrointestinal tract (ABS GI ) , as shown below

.

where:

RfD ABS = Absorbed reference dose (mg/kg

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

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

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 6 6

- 29

- 2012 Dermal Risk Screening The GI absorptions for 2,3,7,8

- TCDD and all polycyclic aromatic hydrocarbons (PAHs) (which includes benzo[a]pyrene) were estimated to be greater than 50 percent. Therefore, as shown in Exhibit_Add A 3

- 1 , no adjustments to the available oral CSFs were required. For cadmium and divalent mercury, adjustments were made based on absorption data provided in RAGS Part E, Exhibit 4

- 1. The RfDs for dermal exposure to cadmium and divalent mercury are also shown in Exhibit_Add A 3

- 1

. Exhibit_Add A 3
- 1 . Cancer Slope Factors and Reference Doses Based on Absorbed Dose

PB

- HAP F raction of C ontaminant

A bsorbed in GI T ract ( ABS GI )

(unitless) A bsorbed C ancer Slope F actor ( CSF ABS ) a (mg/kg

- day) - 1 Absorbed R eference D ose ( RfD ABS ) a (mg/kg

- day) Cadmium Compounds 0.05 NA 2.5E

- 05

Divalent Mercury 0.07 NA 2.1E

- 05

2,3,7,8

- TCDD No adjustment required b 1.5E+05 1.0E

- 09
Benzo[a]pyrene No adjustment required b 1.0E+01 NA

NA = Not applicable

a Oral dose response values are presented in Appendix 2 . Only the resulting adjusted dose response values are presented in this table. b According to RAGS Part E, Exhibit 4

- 1, GI absorption is expected to be greater than 50%.


## 2. Dermal Exposure Estimation
Dermal exposures and 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 da ily basis. For the water evaluation, individuals were assumed to be exposed to contaminated surface water with the same PB

- HAP concentration as the TRIM.FaTE screening scenario lake over their entire bodies on a daily basis.


### 2.1. Equations for Estimating Derm al Exposure
The general equation used to estimate dermal absorbed dose (DAD) for water or soil is shown below, and is expressed in milligrams of PB

- HAP per kilogram of receptor body weight per day (mg/kg

- day). DAD s are calculated separately for the water and soil pathways and then added together for each age group

.

where:

DA event = Absorbed dose per event; chemical

- specific; equation for DA event also differs

depending on water or soil contact (mg/cm 2

- event)
EV = Event frequency (events/day)
ED = Exposure duration (years)TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 7 6

- 29

- 2012 Dermal Risk Screening

EF = Exposure frequency (days/year) SA = Skin surface area available for contact (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 through the skin at the end of exposure and the equation for calculation is different for organic and inorganic chemicals in water and for soil. The equations for calculating these chemi cal

- specific DA event values for water contact are provided in RAGS Part E, Chapter 3 (see Equations 3.2

– 3.4). For soil, the equation for calculating these chemical

- specific DA event values is provided in RAGS Part E, Chapter 3 (see Equation 3.12).

Water

– Organic Chemicals:

Water

– Inorganic Chemicals:

Soil

– All Chemicals:

where:

DA event = Absorbed dose per event (mg/cm 2

- event) C w C s = Chemical concentration in water (mg/cm 3 ) or soil (mg/kg)

K p = Chemical

- specific dermal permeability coefficient of compound in water (cm/hr)

FA = Chemical

- specific fraction absorbed; accounts for loss due to the regular shedding of skin cells of some chemical originally dissolved into skin (unitless)


## τ event = Chemical

- specific lag time per event (hr/event)
t event = Receptor

- specific event duration (hr/event)
AF = Receptor

- and activity

- specific adherence factor of soil to skin (mg/cm 2

- event) ABS = Chemical

- specific dermal absorption fraction (unitless)
CF = Conversion factor (10 - 6 kg/mg)



### 2.2. Exposure Factors and Assumptions The exposure parameters included in this assessment and their default and other value options are summarized in this subsection

. Default values were selected to result in a highly conservative estimate of exposure (i.e., exposures are likely overestimated). Parameter values were primarily obtained or estimated from RAGS Part E ( EPA 2004 c ) and the Child

- Specific Exposure Factors Handbook ( CSEFH
, EPA 2008)

. Receptor

- and scenario

- specific exposure assumptions are discussed first, and a discussion of chemical

- specific parameters values follows . Estimated water and soil exposure concentrations are presented at the end of this subsection.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 8 6

- 29

- 2012 Dermal Risk Screening 

### 2.3. Receptor

- Specific Parameters Dermal exposures and risks were estimated for the same age groups used in the ingestion exposure assessment: adults (ages 20 to 70 years) and five child age groups: <1 year; 1 to 2 years; 3 to 5 years; 6 to 11 years; and 12 to 19 years. The body weight values used in the ingestion exposure assessment were used in the dermal exposure assessment. Body surface areas (SAs) for water and soil exposures for adults were calculated using Appendix C, Exhibit C

- 1 , of RAGS Part E. For children, SAs for water and soil exposures for the five children’s age groups were estimated using 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 th eir body exposed. For SA (soil), it was assumed that individuals were exposed on a fraction of their total body, specifically their head, forearms, hands, lower legs, and feet. Based on information provided in RAGS Part E, the SA for forearms was calcula ted using the SA for arms and assuming a forearm

- to

- arm ratio of 0.45, and the SA for lower legs was estimated using the SA for legs and assuming a lower leg

- to

- leg ratio of 0.4. Values for body SA by age group are summarized in Exhibit_Add A 3

- 2

.

Exhibit_Add A 3
- 2 . Receptor

- Specific Body Surface Area Assumed to be Exposed to Chemicals Age Group a (years) Surface Ar ea 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 time

- weighted average for age groups 11 to <16 years and 16 to <21 years. Note that estimated values include 11

- year
- olds and individuals through age 2 0, which contributes to uncertainty in the estimates for 12 to 19 years.
g Represents the average total surface area of adults from Table C

- 1 of RAGS Part E. h Represents the average surface area of adults for head, forearms, hands, lower legs, and feet from Table C

- 1 of RAGS Part E.



### 2.4. Scenario

- Specific Parameters Exhibit_Add A 3

- 3 summarizes the exposure values related to frequency and duration of contact. In general, these are the recommended defaults for calculating a reasonable maximum exposure (RME) for a resident ial scenario as proposed by EPA in RAGS Part E, Chapter 3.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 9 6

- 29

- 2012 Dermal Risk Screening Exhibit_Add A 3
- 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 350
Exposure Duration (ED) (years) Child <1 1 Represents the number of years included in the age group; also used in ingestion exposure calculations. Child 1

- 2 2 Child 3

- 5 3 Child 6

- 11 6 Child 12

- 19 8 Adult 20

- 70 50 Averaging Time (AT) (days) For cancer assessment, an AT equal to a lifetime (70 years) × 365 days/year is used. Same value used in ingestion exposure calculations. For non

- cancer assessment, an AT equal to the exposure duration (ED)
× 365 days/year is used, so AT will vary by receptor group. Same value used in ingestion exposure calculation s.



### 2.5. Chemical

- Specific Parameters
The chemical

- specific parameters required to quantitatively evaluate dermal pathway exposures are listed in Exhibit_Add A 3

- 4 . For the water concentration in the dermal analysis, t he modeled TRIM.FaTE chemical concentration in the screening scenario pond at the screening threshold emission rate was used. For the s oil concentration, the modeled TRIM.FaTE chemical concentration in surface soil in parcel N1 ( untilled soil, closest to facility ) of the screening scenario at threshold emission rate was used. This same soil concentration was also used in ingestion exposu re 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 t he use of a dermal absorption fraction (ABS), which is a measure of how much of a chemical the skin absorbs through contact with soil.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 10 6

- 29

- 2012 Dermal Risk Screening

Exhibit_Add A 3
- 4 . Chemical

- Specific Dermal Exposure Values for Water and Soil Contact

PB

- HAP Cadmium Divalent Mercury 2,3,7,8
-

TCDD Benzo[a]

pyrene Source
Chemical concentration in Water (Cw) (mg/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 TRIM.FaTE modeled concentration in surface soil in parcel N1 in screening scenario Permeability coefficient in water (Kp) (cm/hour) 0.001 0.001 0.81 0.7 Values from RAGS Part E, Exhibits B

- 3 (organics) and B

4 (inorganics) Fraction absorbed water (FA) (unitless) NA NA 0.5 1.00 Values from RAGS Part E, Exhibits B

- 3; only used for organic chemicals Lag time per event ( event) (hr/event) NA NA 6.82 2.69 Values from RAGS Part E, Exhibits B

- 3; only used for organic chemicals Dermal absorption fraction (ABS) from soil (unitless) 0.001 0.045 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 lifeti me (7 0 years).TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 11 6

- 29

- 2012 Dermal Risk Screening 

### 3.2. Dermal Hazard Quotient
Dermal hazard quotient (HQ) was estimated as the ratio of age

- specific 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_Add A 3

- 5 presents a summary of estimated dermal non

- cancer hazards by age group. A summary of estimated lifetime dermal cancer risks is provided in Exhibit_Add A 3

- 6

. The highest HQ value w as 0.006 (representing divalent mercury exposure for children less than 1 year of age) or less. This is approximately 1 70 times less than the potential ingestion hazard quotient s 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 same screening threshold emission rate.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 12 6

- 29

- 2012 Dermal Risk Screening

Exhibit_Add A 3
- 5 . Summary of Dermal Non

- Cancer HazardsTRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 13 6

- 29

- 2012 Dermal Risk Screening

Exhibit_Add A 3
- 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 >300 Total 4.12E

- 09 >200

a Represents the magnitude of difference b etween the e stimated d ermal r isk and the p otential i ngestion r isk associated with the s creening s cenario

. Based on these results and taking into consideration the extremely conservative nature of the dermal exposure calculations, it was assumed that it is not necessary to incorporate dermal exposures in calculating multipathway screening threshold levels. Specifically, the daily exposure durations of 0.58 hour for adults and 1 hour for children used to calculate dermal exposure from water are highly conservative and assume that the individual is bathing in surface water taken directly from a conta minated lake or is swimming in the lake for 350 days of the year. The exposure frequency of 350 days and corresponding skin surface area available for contact with contaminated soils (i.e., head, hands, arms, legs, and feet) likely also grossly overestima tes dermal exposure to soil.TRIM

- Based Tiered Screening Methodology for RTR

Attachment A, Addendum 3 3

- 14 6

- 29

- 2012 Dermal Risk Screening 

## 5. References Cal/EPA ( California Environmental Protection Agency ) Office of Environmental Health Hazard Assessment (OEHHA)

. 2000

. Air Toxics Hot Spo ts Program Risk Assessment Guidelines; Part IV, Exposure Assessment and Stochastic Analysis Technical Support Document

. Section 6, Dermal Exposure Assessment

. September

. Available at: http://www.oehha.ca.gov/air/hot_spots/pdf/chap6.pdf

. Hrudey, S.E., W. Chen
, and C.G. Roussex, 1996. Bioavailability in environmental risk assessment. CRC Press, Inc, Lewis publishers.

U.S. EPA ( Environmental Protection Agency )

. 2004c. Risk Assessment Guida nce 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/riskassessm ent/ragse/index.htm
U.S

. EPA

. 2006

. Risk and Technology Review (RTR) Assessment Plan

. Office of Air and Radiation

. November 20 . Available at: http://www.epa.gov/sab/panels /consul_risk_and_tech_assessment_plan.htm

. U.S. EPA. 2008. Child

- Specific Exposure Factors Handbook. Office of Research and Development, Washington, D.C. EPA/600/R

- 06/096F. September. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243

.[This page intentionally left blank

. ][This page intentionally left blank.]TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 1 6

- 29

- 2012



## Attachment B. Tier 2 Screening Methodology[This page intentionally left blank.]TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 3 6

- 29

- 2012 

## CONTENTS , ATTACHMENT B

Attachment B. Tier 2 Screening Methodology

................................ ................................ . B

- 1

B.1 Overview of Approach

................................ ................................ ................................ ..... B

- 5

B.2 Selection of Site

- Specific Characteristics to Include in the Tier 2 Analysis

...................... B

- 7

B.3 Estimation of Adjustment Factors for Selected Site

- Specific Parameters

........................ B

- 8 B. 3.1 Selection Values for Variables of Interest

................................ .............................. B

- 9 B.3.2 Estimation of Adjustment Factors

................................ ................................ ........ B

- 12 B.3.2.1 General Approach

................................ ................................ ................. B

- 12 B.3.2. 2 Incorporation of the Risk Equivalency Approach

................................ .... B

- 14

B.4 Preparing National Databases of Lake and Meteorological Data

................................ ... B

- 16 B.4.1 Processing Lake Data for Tier 2 Analysis

................................ ............................ B

- 16 B.4.2 Processing Meteorological Data for Tier 2 Analysis

................................ ............. B

- 19 B.4.2.1 Sources of Meteorological Data

................................ ............................. B

- 19 B.4.2.2 Coverage of Meteorological Stations Compared with Facility

Locations

................................ ................................ ............................... B

- 20 B.4.2.3 Data Processing

................................ ................................ .................... B

- 20

B.5 Implementation of Tier 2 Analysis

................................ ................................ ................. B

- 23 B.5.1 Facility List for Tier 2 Screen (Step 3)

................................ ................................ .. B

- 26 B.5.2 Facility/Lake Distance Table (Step 4)

................................ ................................ ... B

- 26 B.5.3 Finding the Closest Meteorological Station (Step 5)

................................ ............. B

- 27 B.5.4 Assembling Threshold Adjustment Factors (Step 6)

................................ ............ B

- 29 B.5.5 Finalizing Threshold Adjustment Fact ors and Assembling Screening Results (Step 7)

................................ ................................ ................................ ... B

- 30 B.5.6 Assembling Results (Step 8)

................................ ................................ ................ B

- 30

B.6 References

................................ ................................ ................................ ................... B

- 33TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 4 6

- 29

- 2012 

## EXHIBITS, ATTACHMENT B

Exhibit_Att B

- 1. Basic Implementation Process for Implementing Tier 2

................................ . B

- 6 Exhibit_Att B

- 2. Lake Distance and Meteorological Parameter Values for which

Adjustment Factors were Developed in Tier 2 a,b

................................ ............. B

- 10 Exhibit_Att B

- 3. Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate

Distances Between the Facility and the Fishable Lake a

................................ . B

- 13 Exhibit_Att B

- 4. Maximum Fish Ingestion Rate (g/day) Associated with

Sustainable Fishing a

................................ ................................ ....................... B

- 18 Exhibit_Att B

- 6. An Early Analysis of the Distance Between the Point Source

Facilities and the Closest Meteorological Station a,b,c

................................ ....... B

- 22 Exhibit_Att B

- 7. Example of the Dashboard To Conduct the Tier 2 Analysis

........................ B

- 25 Exhibit_Att B

- 8. Example of Global Inputs Used in the Tier 2 Analysis

................................ . B

- 25 Exhibit_Att B

- 9. Example of the Facility Input Data Required To Conduct the

Tier 2 Analysis

................................ ................................ ................................ B

- 26 Exhibit_Att B

- 10. Example of the Lake Distance Data Required To Conduct the

Tier 2 Analysis

................................ ................................ ................................ B

- 27 Exhibit_Att B

- 11. Examp le Results of the Meteorological Station Matching

Required To Conduct the Tier 2 Analysis

................................ ........................ B

- 28 Exhibit_Att B

- 12. Example Results of the Octant Analysis Required To Conduct

the Tier 2 Analysis

................................ ................................ .......................... B

- 29 Exhibit_Att B

- 13. Example of the TRIM.FaTE Matrix Results Req uired To

Conduct the Tier 2 Analysis

................................ ................................ ............ B

- 30 Exhibit_Att B

- 15. Example of Detailed Output Table Created by the Tier 2 Tool a

................. B

- 32 Exhibit_Att B

- 16. Example of Individual Output Table Created by the Tier 2 Tool

................ B

- 32TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 5 6

- 29

- 2012 

## B.1 Overview of Approach The Tier 1 screening scenario is, by design, generic and health

- protective. It was constructed for quick application to a large number of facilities in a source category with the least chance of returning false negatives for risk. Based on scree ning analyses conducted for RTR to date, many facilities can “fail” the Tier 1 screen. Because the Tier 1 screen uses “worst case” assumptions, the analysis must be refined to determine whether the failing facility is actually expected to pose a risk.
One way to refine the risk estimates is to conduct a site

- specific assessment where the Tier 1 model values and layout are replaced with site

- specific values wherever possible. However, this approach would not be feasible given the resource intensive nature o f a site

- specific assessment and the number of facilities that tend to fail the Tier 1 screen in the different source categories. As an intermediate approach, we instead refine our Tier 1 screening estimates to Tier 2 screening estimates. This is done by replacing some of the worst

- case assumptions in a Tier 1 screen with more site

- appropriate values. Specifically, for Tier 2, the following variable values are varied from their Tier 1 values:

 Meteorological characteristics, including the fraction of time the wind blows in the direction of the farm and lake ( using wind direction), the wind speed, the precipitation rate, and the mixing height; and

 Location of the nearest fishable lake(s) relative to the facility. 23
In selecting the fate and transport variables listed above to include in Tier 2, a balance was struck between the: 1) degree of impact on the potential risk estimate; 2) ease of implementation in TRIM.FaTE; and 3) ease of obtaining site

- specific values on a facility

- by

facility basis. Because of the expected variability in exposure parameters (such as fish or food ingestion rates) amongst any population living around a given facility, only the fate and transport parameters were candidates to be varied in th e Tier 2 analysis, and the exposure parameters remain at their health

- protective Tier 1 values. The overall implementation of Tier 2 is shown in Exhibit_Att B

- 1 . Firs t (shown in green), the starting point is the ratio of the facility emission to the Tier 1 threshold. Next, two different sets of information are needed. The facility

- specific estimates of the Tier 2 meteorological and lake location parameters listed above must be gathered for each facility (shown in red). Then, the associated TRIM.FaTE and MIRC estimates of risk must be estimated (shown in orange). Because so many facilities need to be evaluated in Tier 2, the implementation focuses on estimating refined r isk using pre

- calculated databases rather than trying to gather the input parameters and running them through TRIM.FaTE and MIRC for each facility.

23 The lake size was also changed for each new facility lake distance. This change allowed the simulations to maintain a constant ratio between watershed and erosion area compared with the lake area.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 6 6

- 29

- 2012

Exhibit_Att B

- 1

. Basic Implementation Process for Impl ementing Tier 2

First, databases of the relevant U.S. meteorological and lake data were created that could be accessed readily during a Tier 2 evaluation (shown in red in the figure). These meteorological and lake data are discussed in more detail in S ection B.4. The meteorological database includes annual

- average summary statistics on wind direction, wind speed, and precipitation for more than 1,500 surface stations located throughout the United States. These surface data cover years 2006 through 2010 and were obtained from the National Climatic Data Center’s Quality Controlled Local Climatological Data . The mixing heights for each surface station were estimated using data from the upper

- air station closest to the surface station. These mixing height v alues cover years 1987 through 1990 (where available) for over 50 stations, and they are available from EPA’s Support Center for Regulatory Atmospheric Modeling. The database of lakes, available from ESRI and based on U.S. Geological Survey data, includes information on the location, size, use or type designation, and name (if available) of all lakes in the United States. To focus on lakes that can support angling of upper trophic level fish, lakes used for disposal, evaporation, or treatment were excluded , and only lakes greater than 25 acres in area are included. In parallel to the meterological and lake data collection, a series of TRIM.FaTE simulations was performed that systematically varied the values for four of the five selected fate and transport
v ariables (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 direction s can be evaluated outside TRIM.FaTE simulations. These simulations do not simulate specific facilities; instead, four or five alternative values for each of the four variables were selected using statistics on U.S. meteorological data or professional jud gment to capture the expected range in the facility data. TRIM.FaTE simulations were performed for every possible combination of these variable values to enable the estimation of appropriate site

- specific threshold adjustment factors for scenarios with th e corresponding characteristics. Based on the TRIM.FaTE results of these simulations (and the subsequent exposure and risk characterization, conducted using MIRC), a matrix of Tier 2 threshold adjustment factors was calculated, with each element of the ma trix corresponding to a unique combination of values from each of the selected variables.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 7 6

- 29

- 2012 These TRIM.FaTE and MIRC simulations are used to estimate part of the Tier 2 threshold adjustment factors. Wind direction is the other part of the Tier 2 threshold a djustment factors. The wind direction adjustment for a given facility is the ratio of the frequency that winds blow toward the Tier 1 farm and lake (43 percent of the time) and the frequency that winds blow toward the facility

- specific farm and lake in Tie r 2. These Tier 2 threshold adjustment factors are multiplied by each other and represent the ratio between the risk metric (i.e., cancer risk or HQ) obtained using the baseline Tier 1 screening scenario and the risk metric obtained from the Tier 2 TRIM.Fa TE runs. For a given facility, an adjusted Tier 2 ratio (emissions compared to the emission threshold) can be estimated by dividing the Tier 1 emission ratio (the output of the Tier 1 screen) by the adjustment factor that best corresponds to the meteorolo gical conditions present at the site and the presence and location of lakes at the site:

Matrices of threshold adjustment factors from the TRIM.FaTE and MIRC simulations were separately developed for th e four PB

- HAPs that currently have screening emission thresholds in the Tier 1 process: benzo(a)pyrene (BaP, representative of PAHs), cadmium, divalent mercury and 2,3,7,8

- Tetrachlorodibenzo

- p
- Dioxin (TCDD, representative of dioxins). The wind direction threshold adjustment factors were irrespective of chemical (specific only to the facility). In implementing the Tier 2 process, a risk equivalency approach was used to convert speciated emissions of PA Hs and dioxins into BaP and TCDD equivalents, respectively, similar to the Tier 1 screening approach. Finally, to implement the Tier 2 screen, a Microsoft® Excel tool was created that includes the database of meteorological data and lake data as well as th e Tier 2 adjustment factors for the combinations of variables simulated in the Tier 2 TRIM.FaTE simulations. In the tool, each facility is matched with the closest surface meteorological station, and the values for the four relevant parameters at that sta tion are recorded (wind speed, wind direction, precipitation rate, and mixing height). The distance from the facility to the nearest qualifying lake in each octant around the facility are estimated using GIS and are also imported. These five values become the set of facility

- specific parameters. The threshold adjustment factor corresponding to this set of site

- specific data is then estimated using the matrix of adjustment factors and the wind direction adjustment Wind direction values are used as

- is with no rounding or binning. If one of the four facility matrix variables (wind speed, precipitation rate, mixing height, or lake location) is between two of the computed levels available for that variable in the simulation matrix, the more health

- protective of the two levels is selected (i.e., the level that resulted in the smaller adjustment to the baseline Tier 1 exposure). The Tier 1 screening emission threshold for a PB

HAP is then multiplied by the appropriate adjustment factors to obtain an updated Tier 2 emission threshold for that PB

- HAP at that facility. A facility then passes the Tier 2 screen if the emission is below the Tier 2 threshold. Selection of Site

- Specific Characteristics to Include in the Tier 2 Analysis The screening scenario used to deri ve Tier 1 thresholds incorporates assumptions regarding meteorological conditions, the spatial configuration of the hypothetical exposure setting, physical parameters of the environment, and chemical

- specific parameters that result in generally health

- prot ective results. In Tier 2, selected assumptions used in the fate and transport modeling conducted using TRIM.FaTE are modified to reflect more site

- specific information for the facilities being evaluated. 24 To determine which scenario characteristics

24 Only T RIM.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). TheTRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 8 6

- 29

- 2012 shou ld be incorporated into the Tier 2 analysis, the following issues were considered for TRIM.FaTE model parameters:

 How sensitive are the modeled risks to a specific user
- input model parameter (e.g., wind direction, wind speed) ?

 Do the plausible values for a given parameter span a large range when comparing different RTR facilty locations ?

 Which site

- specific characteristics can be found easily and reliably for facilities with emissions exceeding Tier 1 thresholds?

 In general, is the uncertainty associated with the parameter high or low ?

 How complicated or time

- consuming is the incorporation of a given parameter (e.g., wind speed) into the Tier 1 screening scenario set

- up ?

Addendum 1 to this attachment provides an exhibit showing all the TRIM.FaTE variables considered for the Tier 2 analysis. These variables were evaluated qualitatively using the criteria above to determ ine whether the variable was of high, medium, or low priority. The following five “high priority” variables were selected for implementation in the current Tier 2 analysis:

 Wind direction (the percent of time the wind blows toward the lake and farm),

 Wind speed,

 Precipitation,

 Mixing height, and

 Location of the nearest fishable lake relative to the facility

. These parameters were judged to represent a balance between range of potential variability, ease of implementation within the modeling scheme used for RTR, and ease of obtaining site

specific values with a relatively high level of confidence.


## B.2 Estimation of Adjustment Factors for Selected Site

- Specific Parameters The purpose of including site

- specific detail for a facility evaluated in Tier 2 is to devel op a more realistic estimate of risk associated with facility emissions. This purpose is achieved within the analysis by generating revised emission thresholds of potential concern specific to a given PB

- HAP on a facility

- by

- facility basis. However, inst ead of performing full

- fledged, site

- specific model runs for each facility that does not “screen out” in Tier 1, a set of generally applicable threshold adjustment factors for each PB

- HAP was developed based on a set of model runs. For each PB

- HAP, these runs corresponded to unique combinations of values for wind speed, precipitation rate, mixing height, and lake location (wind direction is assessed separately). These adjustment factors were based on a set of runs in which the values for these parameters were varied systematically. The wind direction adjustment factor is the ratio of the frequency that winds blow toward the lake and farm in the Tier 1 screening scenario (43 percent) and the frequency that winds blow toward the facility’s farm and lake in Tier 2. The appropriate adjustment factors are then applied to the Tier 1 threshold for that PB

- HAP when evaluating a facility in Tier 2.

exposure characteristics used in MIRC are considered to be generally consistent across different locations and facilities.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 9 6

- 29

- 2012 The analyses conducted to select the parameters to derive the threshold adjustment factors are described in this sec tion. Section B.2.2 also describes the development of appropriate “bins” for the selected parameters. These bins correspond to the subset of parameter values for which adjustment factors were calculated, based on the anticipated range of plausible values for facilities evaluated in RTR.



### B.2.1 Selection Values for Variables of Interest For each site

- specific parameter identified in Section 0 other than wind direction, adjustment factors were estimated that correspond to a set of four to six particular values for the parameter. The adjustment factor for wind direction directly rela tes Tier 1 and site

- specific Tier 2 wind direction frequencies, with respect to the directions of the lake and farm. These individual adjustment factors can the be multiplied for a particular variable combination to get an overall adjustment factor. To fac ilitate this, bins were created for each parameter of interest and a relevant range of values, with the exception of wind direction (as described below, representative bins were not necessary for this parameter). The rationale for selecting the range for each bin for each parameter of interest is described below.

Wind Direction : Within the context of the hypothetical exposure scenario used in Tier 1 (and under actual conditions), when 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 estimate Tier 1 thresholds , the wind is assumed to blow toward the modeled domain 3 days a week, or 43 percent of the time. This assumption is intended to approximate an unusually consistent long

- term wind pattern and is representative of wind direction patterns in Yakima, Washington ( where the wind blows eastward approximately 40 percent of the time
, based on a review of wind direction data compiled by the Nati onal Weather Service)

. I n the model run s to develop the Tier 2 bins , this factor was changed to 1, 2
, and 4 days a week, corresponding to 14 percent , 29 percent , and 57 percent of the time. This range of values was chosen to obtain a good understanding of the impact of wind direction on risk f or the range of wind direction patterns likely to be present at real facilities. Review of these results indicated that, within this modeling scenario, estimated ingestion exposure varies directly with percentage of time the wind blows toward the farm and lake. Given the exactly linear nature of the relationship observed in model results obtained from these runs, the adjustment of the threshold for wind direction in Tier 2 is a direct, linear adjustment using the actual site value rather than an increment

al, bin

- based approach . In other words, the Tier 1 threshold is adjusted for wind direction in direct proportion to the difference between conditions for the actual facility location and the wind direction parameters included in the Tier 1 screening scena rio (i.e., blowing toward the lake/farm 43 percent of the time on average). Wind Speed : Although the impact of wind speed on non

- inhalation risks also is likely to depend on configurational parameters such as the location of farms and lakes, in general it is reasonable to assume that higher wind speeds lead to more rapid chemical transfer out of the modeled domain
, allowing less time for chemical deposition and
, therefore
, less total near

- field deposition and a lower exposure and risk

. The Tier 1 screening analysis assumed a wind speed of 2.8 m/s, corresponding to the 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 200TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 10 6

- 29

- 2012 stations nationwide). 25 This value is similar to the annual average wind speeds of the U.S. Deep S outh. 26 In the model runs to develop the Tier 2 threshold adjustment factors
, we calculat ed the change to exposure resulting from increasing this value to 3.5 m/s, 4 m/s, and 5 m/s ( values also shown in Exhibit_Att B

- 2 ; 5 m/s is the 88 th percentile in the NOAA data). Based on these values, the bins used to classify wind speed are: 2.8

– 3.5 m/s, 3.5

– 4 m/s, 4

– 5 m/s and above 5 m/s. In all modeled cases, increasing the wind speed while maintaining constant lake location, wind direction, mixing height, and pre cipitation led to Tier 2 estimations of high

- end risk or hazard that were smaller than that of Tier 1. To ensure that the approach is health

- protective, a facility was assigned the lower end value of the bin into which it was placed. Facilities with wind speeds less than 2.8 m/s were assumed to have a wind speed of 2.8 m/s. Exhibit_Att B

- 2

. Lake Distance and Meteorological Parameter Values for which

Adjustment Factors were Developed in Tier 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.

Precipitation : Higher levels of precipitation over the modeled domain are expected to increase
non
- inhalation risks by increasing particulat e and gaseous wet deposition near

- field to the
source. The screening scenario used in Tier 1 analysis assumed an annual precipitation rate of 1,500 mm/year. This level of annual precipitation was estimated to represent rainy conditions in the U.S., such a s parts of the U.S. Deep South and parts of the U.S. Northwest Coast 27 . Though the rate was an estimate, it does correspond to approximately the 95 th percentile (i.e., higher

25 http://ols.nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00095

- PUB

- A0001#TABLES

– this website is updated every year, so the data it currently shows is not the exact data used to develop the wind speeds for screening analyses.
26 National Clima tic Data Center CliMaps (NCDC

- CliMaps) (2007). http://cdo.ncdc.noaa.gov/cgi

bin/climaps/climaps.pl
27 National Climatic Data Center Historical Climate Series (NCDC

- HCS) (2007).TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 11 6

- 29

- 2012 rate) precipitation in t he N ational Climatic Data Center’s (NCDC) 30

- year ( 1981

– 20 10 ) data from U

. S

. stations

. 28 To estimate adjustment factors i n the Tier 2 analysis, model simulations were conducted with the parameter value set to three lower values (i.e., 1,187 mm/year, 924 mm/year and 512 mm/year; values also shown in Exhibit_Att B

- 2 ), corresponding to the 75 th

, 50 th and 25 th percentiles, respectively, of the NCDC data

. Locations with lower precipitation levels were assumed to have a minimum precipitation of 512 mm/year. Based on these levels, the bins used to classify precipitation were: 0

- 512 mm/year, 512

- 924 mm/year, 924

- 1,187 mm/year, 1,187

- 1,500 mm/year and above 1,500 mm/year. In nearly all modeled cases, decreasing the precipitation rate while maintaining constant lake location, wind speed, wind direction, and mixing height led to Tier 2 estimations of high

- end risk or hazard that were smaller than that of Tier 1 . To be health

- protective, a facility was assigned the upper end value of the bin in which it was placed. Facilities with precipitation levels above 1,500 mm/year were assumed to experience precipitation of 1,500 mm/year. Mixing Height: Greater mixing heights can dilute the concentration of pollutants in air, resulting in lower deposition and other transfer rates from air to surfaces and consequently also lower ingestion exposures. The Tier 1 screening analysis assumed a mixing height of 710 meters. Th is v alue is the 5 th percentile (i.e., lower) of annual average mixing heights for 463 U.S. locations, based on data obtained from EPA’s SCRAM Web site. 29 To estimate adjustment factors in the Tier 2 analysis, mdoel simulations were conducted with the param eter value set to three larger values (i.e., 865 m, 1,079 m, and 1,537 m; values also shown in Exhibit_Att B

- 2 ). These values correspond to North Little Rock, Arkans as, Boise, Idaho, and Tucson, Arizona and are intended to encompass the range of annual average mixing heights experienced in different parts of the United States. Based on these levels, the following bins were selected for categorization of mixing height : 710

– 865 m, 865

– 1,079 m, 1,079

– 1,537 m, and above 1,537. In all modeled cases, increasing the mixing heights while maintaining constant lake location, wind direction, wind speed, and precipitation rate led to Tier 2 estimations of high

- end risk or hazar d that were smaller than that of Tier 1. To be health

- protective, a facility was assigned the lower end value of the bin into which it was placed. Facilities with mixing heights above 1,537 m and those below 710 m were assumed to have mixing heights of 1, 537 m and 710 m, respectively.
Lake Location : Moving the lake included in the hypothetical Tier 1 scenario to a location farther from the actual source in the modeled domain will reduce modeled (TRIM.FaTE) deposition to the lake and its watershed and con sequently reduce exposures associated with the fish consumption pathway, which is an important pathway of exposure for several chemicals (for example, in the case of methyl mercury, it is by far the predominant exposure pathway). For the scenario modeled in Tier 1, the center of the lake was situated approximately 2 km from the source. To estimate lake location adjustment factors for use in Tier 2, we completed a series of model runs in which the lake was located 5 km, 10 km, 20 km, and 40 km from the
sou rce, as well as runs with no lake (values also shown in Exhibit_Att B

- 2 ). Accordingly, the bins used to classify lake location relative to the facility are: no lake, 2

– 5 km, 5

– 10 km, 10

– 20 km, 20

– 40 km, and 40

– 50 km (lakes farther than 50 km are not considered). To be health

protective, a facility was assigned the lower end value of the bin into which it was placed. A facility with a lake closer than 2 km was assumed to have a lake at 2 km. In resituating the lake at these alternative locations, we maintained ratios consistent with those included in the Tier 1 screening scenario for (1) lake area to total land area in the modeled domain, (2) runoff watershed area to lake area, and (3) erosion watershed area to lake area. Exhibit_Att B

- 3 provides a diagram of the TRIM.FaTE layout in each of the alternate lake

28 http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html
29 Support Center for Regulatory Atmospheric Modeling; http://www.epa.gov/scram001/tt24. htmTRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 12 6

- 29

- 2012 location simulations except no

- lake. We used a “thin” lake shape (i.e., downwind width much smaller than the cross

- wi nd length) that minimized the potential effect of declining deposition with distance from stack that might affect lakes that are long in the downwind direction. By controlling for these potentially confounding effects, we could isolate the effect of lake location on risk appropriately . Moving the lakes to increasing distances from the stack required expansion of the modeled domain. Maintaining the same overall ratio of land area to lake area in each domain resulted in scenarios with increasingly large la ke s, with surface area increasing with distance from the source. This approach also was taken for reasons of modeling convenience (i.e., taking into account resource requirements associated with reconfiguring the TRIM.FaTE spatial layout). The changes in lake size between these four runs are not expected to have a substantial independent effect on exposure and risk because the effect of increased lake size is offset by greater total deposition and runoff . Furthermore, the lake depth was not changed, whic h might be as important a parameter as lake surface area in determining the chemical concentrations in the water column and sediment

. As noted above
, we set up the configurations to ensure that the lakes in the different scenarios received runoff and eros ion from equivalent watersheds on a per surface area basis.



### B.2.2 Estimation of Adjustment Factors Adjustment factors were estimated for each variable described above and applied as multipliers to the Tier 1 emissions thresholds. The resulting Tier 2 emission s thresholds are used to assess whether facilities with corresponding configurations pose multipathway risks. Notably, facilities substantially exceeding a Tier 2 emission threshold carry some potential for significant multipathway health risk.



### B.2.2.1 General Ap proach The core principle in the estimation of adjustment factors is the assumption of direct proportionality of risk and emissions in the modeling approach used for RTR involving TRIM.FaTE and MIRC. Although not strictly present across all variations due to feedback mechanisms and other processes encompassed by the TRIM.FaTE model, a generally linear relationship between risk and emissions has been observed across model simulations conducted for RTR. This suggests that the ratio of total estimated exposures (and consequently risks) obtained for the screening scenario and any alternative configuration could be used as an adjustment factor to scale emissions for that spec ific alternative configuration. The risk in the alternative configuration following such a scaling of emissions would be equal to the risk in the screening scenario (which in Tier 1 was set at a risk level of 1E−6 incremental lifetime cancer risk or an incremental hazard quotient of 1, depending on the toxic effe ct of the chemical in question).TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 13 6

- 29

- 2012 Exhibit_Att B

- 3 . Layouts for Tier 2 TRIM.FaTE Simulations Using Alternate Distances

Between the Facility an d the Fishable Lake a

a The no

- lake scenario is not shown.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 14 6

- 29

- 2012 To account for potential interactions between the chosen Tier 2 variables, matrix adjustment factors were estimated by performing TRIM.FaTE runs for each unique combination of the specified variable values (that is, all permutations of the selected values for the wind speed, precipitation, mixing height, and lake location). Adjustment factors for each configuration were then estimated as the ratio of risks in the Tier 1 analysis to the estimated risk for the particular TRIM.FaTE configuration. This approa ch results in a large matrix of adjustment factors and has the advantage of accounting for all possible interaction effects between the variables. The adjustment factor for wind direction is handled separately. Based on TRIM.FaTE test runs, the fraction of the time the wind blows in the direction of the farm and lake was observed to have a direct, linear effect on the resulting risk estimates. For this reason, it was not necessary to include this variable in the TRIM runs conducted to create the matrix of adjustment factors, as described above. Instead, the adjustment factor is calculated with a linear factor that divides the Tier 1 value (0.43, or 43% of the time in the direction of the farm and lake) by the site

- specific facility value, as follows:

Ad justment 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 acco unts for all the five variables considered in Tier 2.



### B.2.2.2 Incorporation of the Risk Equivalency Approach The adoption of a risk

- equivalency approach to convert speciated emissions of PAHs and dioxins to BaP and TCDD equivalents, respectively, in Tier 1 require d 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 emiss ions rates in a given scenario. The REFs can be represented as the product of exposure equivalency factors (EEFs) and toxicity equivalency factors (TEFs). For the PAHs, this can be expressed as:

H H H The EEFs for PAHs represent the ratio of the exposure to a particular species to the exposure to BaP (and similarly for dioxins and TCDD) at equivalent emission rates. These ratios are thus specific to the TRIM.FaTE layout and input assumptions. The TEFs for PAHs and dioxins represent th e ratio of the oral cancer slope factor (CSF) for a particular species to the CSF for BaP and TCDD, respectively, and are the same in Tier 1 and in Tier 2. The EEFs depend on the TRIM.FaTE configuration, including layout and meteorlogical input values. For example, the exposure profile (i.e., how the different ingestion pathways contribute to total exposure and risk) is different for each PAH. For a PAH where fish is a dominant driver of risk, moving the lake will have a large effect on the overall risk; ho wever, if produce is the dominant driver of risk, moving the lake will have a much smaller effect on overall risk. Thus, for Tier 2, EEFs were recomputed for each of the representative scenarios modeled in Tier 2 separately for the PAH species currently ev aluated (some based on direct TRIM.FaTE modeling, others 15 based on K ow - based regression estimates) and the dioxin species currently evaluated (most based on direct TRIM.FaTE modeling, and a small number assumed to behave like TCDD).TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 15 6

- 29

- 2012 The following mathem atical formulas demonstrate how the Tier 2 adjustment factors are estimated for the PAH and dioxin species. The formulas presented below are for a representative PAH species, but they are also applied to dioxin species. For a given PAH species emitted at a rate E PAH at a facility , the risk

- equivalent BaP emission level can be expressed as:

Q H H H Then, the Tier 1 risk ratio is estimated by comparing the risk

- equivalent BaP emissions to the BaP emission t hreshold:

H Q

If the ratio is less than 1, the facility “screens out” of the Tier 1 analysis. Similarly, for Tier 2, the ratio of risk

- equivalent BaP emissions to the Tier 2 BaP threshold may be ex pressed as:

H Q

Using the definition of the risk

- equivalent BaP emissions, this can be re

- expressed for a given PAH species as:

H H H H

This expression may be further reconfigured, after some algebraic rearrangement, in terms of the Tier 1 ratio as:

H H H
H

H
H These equations simply state that to adjust the Tier 1 threshold to a Tier 2 th reshold 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 Tie r 1. This additional EEF factor is needed to account for the fact that the EEFs are different for each Tier 2 TRIM.FaTE configuration. Finally, the ratio of total BaP equivalents contributed by all PAH species at a given facility to the BaP Tier 2 thresho ld may be expressed, by summing the above expression, as:TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 16 6

- 29

- 2012

H H H
H H

If the ratio of total BaP equivalents contributed by all PAH species to the BaP Tier 2 threshold is greater than 1, the facility would be deemed to have failed the Tier 2 screen for the PAH group.


## B.3 Preparing National Databases of Lake and Meteorological Data To facilitate the effective application of the Tier 2 screening procedures, databases were prepared containing national

- scale information about lakes (locations and sizes) and meteorological data at available surface stations (including wind direction, wind speed, precipitation, and estimated mixing height). The development and content of these two databases are discussed in the following sections.


### B.3.1 Processing Lake Data for Tier 2 Analysis The lake database was built using a geospatial file (U.S. Water Bodies) provided by ESRI for their ArcGIS products. 30 Beca use this geospatial file excluded water bodies in Alaska, Puerto Rico, and the U.S. Virgin Islands, it was augmented with water body information (directly from the USGS National Hydrography Dataset) for those other locations. The data generally have an est imated horizontal accuracy of 50 m. For the Tier 2 analysis, we focused on the hundreds of thousands of water bodies classified as “Lake/Pond” or “Reservoir” but not designated for disposal, evaporation, or treatment. We refer to these water bodies simpl y as “lakes” in the remainder of this document. The approximately 100,000 other water bodies (classified as canal/ditch, ice mass, inundation area, playa, stream/river, swamp marsh, or unclassified) were not included. In a more subjective step during the processing of the lake database for a specific source category (when Tier 2 is run “operationally”), the lake names (when provided) are scrutinized manually, and lakes are removed from the analysis when their names suggest industrial or treatment use (e.g. , wastewater treatment ponds, sludge ponds, fly ash ponds, paper mill ponds, sewage pools, etc.). In this respect, the lake dataset is never truly final; lake names are scrutinized each time a new set of facilities is run through the Tier 2 methods, which can lead to the permanent removal of some lakes from the dataset. Early in the process of compiling this database, we encountered the question: “What size water body qualifies as a ‘lake’ for the purposes of this assessment?” The Tier 2 analysis must focus on lakes large enough to support relatively intensive angling p ressure to be compatible with the characteristics inherent to the assumed exposure scenario. To estimate the relationship between fish consumption at a high level, harvest rates
, and lake size, the following key assumptions were made. Information and cit ations to peer

- reviewed literature that support these assumptions are provided in Addendum 2 to this attachment. Note that in the TRIM.FaTE model screening scenari o, trophic level 3 fish (TL3) assumed to be consumed by anglers are represented by benthic carnivores (BC) and trophic level 4 fish (TL4) assumed to be consumed by anglers are represented by water column carnivores (WCC). Together, these two trophic level s are referred to as piscivorous fish.

1. Piscivorous fish , when present, comprise approximately 21 percent of the standing biomass of fish in an annual average

. TL3 fish represent 17 .5 percent of the standing fish biomass;

30 Specifically , the geospatial file in the ESRI Data & Maps 2009 Data Update for ArcGIS version 9.3.1. It was derived by the United States Geological Survey (USGS), EPA, and ESRI from the USGS National Hydrography Dataset (USGS 2012).TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 17 6

- 29

- 2012 TL4 fish account for 3 .5 percent o f this total fish biomass. Thus, TL4 fish, when present, represent the limiting compartment for angler fish harvesting and consumption.
2. Humans can harvest 10 percent of the TL4 biomass without threatening the population due to overharvesting. 3. The minimum viable effective population size for a single TL4 species is at least 50 adult fish for a local population to survive over the short term (more than a decade)

. 4. Only 33 percent of a TL4 fish is edible fillet muscle

. A 0.33 edible fraction is used to estima te total fish biomass associated with human consumption.
5. A productivity level 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 Addendum 2 to this attachment), the maximum fish ingestion rates as a function of standing biomass and lake size were estimated. Exhibit_Att B

- 4 presents these findings, where the shading in the exhibit provides an indication of where short

- and longer term sustainable fishing is expected to occur (see exhibit notes). The Tier 2 analy sis is intended to retain most of the health

- protective attributes of the screening scenario used in Tier 1 so that no facilities of potential concern erroneously “pass” the screen. For a given facility, the smaller the lake size threshold, the greater th e number of lakes and the greater the probability that a lake is closer to the facility. Lakes closer to a facility will result in higher chemical concentrations in fish compared with lakes farther from the facility. Thus, Exhibit_Att B

- 4 was used to determine the smallest lake size that would support a TL4 population. At the assumed upper

- limit standing fish biomass of 40 g ww/m 2 , this corresponds to 25 acres (the first “white” box when moving from smaller to larger lakes)

. Thus, a lake size threshold of 25 acres was used. The final database included over 100,000 lakes with surface areas of at least 25 acres but smaller than 100,000 acres. Larger lakes were not c onsidered since they cannot be readily modeled in TRIM.FaTE. For the purposes of proximity matching lakes to emitting facilities (as described in Section B.4.2 ), t he location of each lake is identified as the geographic centroid inside the lake. The ingestion rate supported by a 25

- acre lake is not as high as the adult ingestion rate used in the Tier 1 and Tier 2 analyses for subsistence anglers (i.e., 26 g ww fille t per day in a 25

- acre lake compared with 78.13 g ww fillet per day in the Tier 1 analysis). However, the decision was made to use the smallest lake size that supported an upper trophic level population without explicitly linking the lake size with the su bsistence angler ingestion rate in order to maintain an overall health

- protective approach

. To place an upper bound on the radial distance from the source up to which lake

- derived risks need to be assessed, we took into account the limitations of TRIM .FaTE. We limited the lake analysis to an outward radial bound of 50 km from the stack. For facilities with no lakes within 50 km, lake

- derived risk was 0

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 18 6

- 29

- 2012

Exhibit_Att B

- 4

. Maximum Fish I ngestion R ate (g/day) A ssociated with S ustainable F ishing 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 83 117 166 250 333 499 666 1332

a Dark g ray shading indicates insufficient population size for T L 4 (WCC) fish (<50 adults) to be sustainable for more than a decade ; y ellow

- shaded cells indicate the likel ihood to provide long

- term sustainable fish population s with at least 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 , a lake that is 25 acres i n size would sustain a maximum fish ingestion rate of 26 g/day for a single angler.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 19 6

- 29

- 2012 

### B.3.2 Processing Meteorological Data for Tier 2 Analysis In addition to the lake database, a database of U.S. surface stations with complete data was created, so that each source category facility can be paired with the closest meteorological station data. This process of pairing dozens or hundreds of facilities with meteorological data is not unprecedented

. In their report to the Science Advisory Board (SAB) on the 1996 NATA, EPA described pairing each facility with the closest meteorological station in an inventory of over 350 meteorological stations nationwide, creating an average fa cility

- to

- station distance of less than 50 km in the 1996 NATA (EPA 2001b) . In a separate 2009 report to the SAB on the RTR program, EPA described using 158 meteorological stations to choose from nationwide, with a standard practice of selecting the stati on nearest to each facility unless the facility provides onsite meteorological data (EPA 2009) . Using 156 petroleum refineries as a sample data set, the average facility

- to

- station distance was 72 km. In both instances, t he SAB accepted this matching as s tandard practice when modeling large numbers of sources, al though they recommended providing high

- level siting maps (e.g., meteorological stations overlaid with terrain gradients or regional climate regimes) to qualify some of the uncertainties related to meteorological data in air dispersion modeling (EPA 2001a; EPA 2010a)

. The current effort builds on this practice but increases the number of available meteorological stations as described below.


### B.3.2.1 Sources of Meteorological Data To construct a database of m eteorological statistics for all available U.S. surface stations, EPA obtained surface meteorological data from the Quality Controlled Local Climatological Data (QCLCD), a dataset available from the National Climatic Data Center (NCDC 2012)

. The processin g began with over 2,000 stations located across the 50 states plus Puerto Rico and the U.S. Virgin Islands with at least some data in the 5

- year period of 2006 through 2010. We narrowed the stations to consider over 1,500 stations with data at both the be ginning and end of the period. This number of stations is far greater than the 350 and 158 stations
, respectively
, used in the 1996 NATA report (EPA 2001b) and the 2009 RTR report (EPA 2009)

. In the QCLCD data, many stations had unreliable or missing prec ipitation data. The numeric or text flag used to indicate missing data was not consistent, making automated processing of the whole dataset with respect to this one variable difficult. To reduce the uncertainty in the precipitation estimates, we used ave rage annual precipitation data from the 1981

− 2010 National Climatic Data Center 30

- year normal dataset wherever possible. If 30

- year normal precipitation data were not available for a station, as was the case for a few hundred stations in the QCLCD datase t, we used the QCLCD precipitation data where available. If 30

- year normal precipitation data and QCLCD precipitation data were not available for a station, as was the case for over 50 stations in the QCLCD dataset, we flagged the station.

As described la ter in Section B.3.2.3 , over 50 additional stations were flagged due to missing wind data. Stations with precipitation or wind flags, totaling over 100, were not re moved from the Tier 2 meteorology database. Flagged stations were kept in the database for informational purposes (i.e., so the user knows that the station was in the original 5

- year dataset but its data cannot be used for Tier 2 screening methods).

The QCLCD s urface meteorological data cannot provide information on mixing heights, which must be estimated from upper air measurements

. EPA’s Support Center for Regulatory Atmospheric Modeling archived estimated morning an d afternoon mixing heights at more than 50 locations across the 50 states and Puerto Rico corresponding to years in the late 1980s and early 1990s (EPA 2010b). More recent years are not available in that archived dataset. We selected a 4

- year period ( 1987 through 1990 ; an arbitrary selecti on) of mixing height data for the Tier 2 analysis

. Each surface meteorological station was paired with the closest mixing heightTRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 20 6

- 29

- 2012 location. At this time, these pairings of surface meteorological stations with mixing height locations were not further scrut inized.


### B.3.2.2 Coverage of Meteorological Stations Compared with Facility Locations Early in the process of examining the meteorology data, we compared the locations of the U.S. point source facilities (from the 2005 NATA) to locations of surface and upper

- air st ations. The se locations are shown in Exhibit_Att B

- 5 so that the spatial coverage of the surface meteorological stations can be compared to the spatial coverage of mo st of the point source facilities that might undergo an RTR multipathway analysis in the future. 31 . It should be noted that there are a couple hundred meteorology stations that were included in the final dataset discussed in the previous section but not sho wn in the exhibit. This is because additional scrutiny and processing of the meteorology data took place after the exhibit was created, leading to the inclusion of a couple hundred additional stations

. These differences are not expected to significantly a ffect the analysis presented in the next paragraph. 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 Gr eat Lakes region, along the East and West Coasts, and in the Southern Plains, and tends to be lowest in the Rockies (except Colorado) and Northern Plains. Exhibit_ Att B

- 6 again shows each of the U.S. point source facilities from the 2005 NATA, and the location markers are shaded and sized according to how far the facility is from the closest of the surface meteorological station s. See again the cautionary note in the previous paragraph regarding the completeness of the meteorology dataset

. The average and median distances are 21 km and 15 km, respectively, between a facility and its closest surface meteorological station with complete data

. This average distance is far smaller than the average distance between the petroleum refining facilities and meteorology stations used in the 2009 RTR report (72 km ; EPA 2009 ) and is likely much smaller than the average distance between all facilities and meteorological stations u sed in the 1996 NATA (less than 50 km ; EPA 2001b ). Approximately 3 2 percent of facilities are less than 10 km from a surface meteorological station, while 29 percent are between 10 and 20 km, 7 percent are farther than 50 km, and less than 1 percent are f arther than 100 km. If this analysis were performed again using the final meteorology dataset used in Tier 2, these facility

- to

- station distances would likely decrease by small amounts due to a greater number of stations in the final dataset.



### B.3.2.3 Data Processi ng To facilitate application of the meteorological data to the Tier 2 analysis, EPA gathered wind information in directional octants that could be linked to the direction of the closest lake (see Introduction and Section B.4 ). EPA divided the periphery around a meteorological station into eight octants representing the direction toward which the wind was blowing:

N: >337.5 to 22.5 degrees NE: >22.5 to 67.5 d egrees E: >67.5 to 112.5 degrees SE: >112.5 to 157.5 degrees S: >157.5 to 202.5 degrees SW: >202.5 to 247.5 degrees W: >247.5 to 292.5 degrees NW: >292.5 to 337.5 degrees

31 The 2005 NATA w as the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous air pollutants, and is used here only for illustrative purposes. The 2005 NATA used a meteorological dataset different from the one used in this report.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 21 6

- 29

- 2012 Exhibit_Att B

- 5 . An Early Analysis of the Locations of Meteorological Stations and Point Source Facilities a ,b

a The 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous air poll utants, and it is used here only for illustrative purposes. The 2005 NATA used a meteorological dataset different from the one used in this report. b Note that this analysis was performed early in the Tier 2 development process. The final meteorology dataset used in Tier 2 c ontains a couple hundred more surface stations than are shown here.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 22 6

- 29

- 2012 Exhibit_ Att B

- 6 . An Early Analysis of the Distance Between the Point Source Facilities and the Closest Meteorological Station a,b ,c

a The high spatial density of facilities in some areas (e.g., the Great Lakes area) causes some facility dots to be plotted on top of other facility dots. In these areas, most or all stations might appear to have distances of a certain range (e.g., less than 50 km, denoted by blue shades), when in fact some other di stance values ( e.g., at least 50 km but less than 100 km, denoted by green shades) might also be present but are not visible. b The 2005 NATA was the most recent, comprehensive, finalized dataset of nationwide point source emitters of hazardous air poll utants, and it is u sed here only for illustrative purposes. The 2005 NATA used a meteorological dataset different from the one used in this report.

c Note that this analysis was performed early in the Tier 2 development process. The final meteorology dataset used in Tier 2 c ontains a couple hundred more surface stations than are shown here.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 23 6

- 29

- 2012 Using the data for each of the surface meteorological stations, EPA developed a software program to calculate the following statistics for each station

- month combination in the 5

- year period of surface meteorological data:

 Number of hourly observations,

 Number of hours with calm winds or missing winds,

 Numbe r of hours the wind was blowing into each directional octant,

 Average wind speed blowing into each octant, and

 Total precipitation (irrespective of wind octant and preferring 30

- year normal data if available).

This program then calculated the following sta tistics for each station’s period of record, using the above monthly totals and averages:

 Number of months with more than 10 percent of hours missing wind data,

 Percentage of time the wind blows into each octant (after excluding missing wind hours),

 Median wind speed when wind was blowing into each octant, and

 Average annual precipitation at the station

. The program flags a station if more than 10 percent of the months in the data period are missing wind data for more than 10 percent of the hours in those m onths. The program also flags a station if no precipitation data are available (as described in Section B.3.2.1 )

. These flagged stations, totaling over 100, remain ed in the Tier 2 meteorology database for informational purposes, but their data were not used. Mixing height averages were calculated outside of the software program used for surface meteorological data. The a verage annual mixing height was calculated at each mixing height location ( averaged between the morning and afternoon mixing height values provided in the EPA upper

- air dataset ). These mixing height locations, with their average mixing height values, were proximity matched to surface meteorological st ations and their wind and precipitation statistics.


## B.4 Implementation of Tier 2 Analysis The Tier 2 screening analysis is conducted using a Microsoft ® Excel tool. The tool has two main functions: 1) finding the closest surface meteorological station to a facility, and 2) calculating Tier 2 threshold values. The tool is created so that all fa cilities in multiple source categories can be screened at once, if desired. The tool is controlled by a dashboard control panel (see Exhibit_Att B

- 7 ), where each of 8 sequential steps are controlled by a button on the panel. From the dashboard, the user is prompted to enter data on four worksheets. Steps 1 and 2 are user input steps to prepare the tool for the analysis. In Step 1, the user verifies that the Tier 2 ma trix results developed using the TRIM.FaTE model are current. In Step 2, the user enters basic ‘global inputs’ that include the PB

- HAPs 32 and source categories included in the analysis ( Exhibit_Att B

- 8 ). Steps 1 and 2 on the dashboard should be performed prior to the start of any analysis to ensure that the user is using the most current information. Two additional input tables are created using the Tier 1 Microsoft A ccess screening tool and the ArcGIS ® lake database (see Section B.2.1 ), as described in Sections B.4.1 and B.4.2 below.

32 At this time, the user cannot “turn on” or “turn off” processing of individual species of a larger PB

- HAP “group”. For example, the user can select to process all PAH species, or none, but not some.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 24 6

- 29

- 2012 Once these tables are created, the user enters the associated data in S teps 3 and 4, respectively. After all the se input data have been supplied, S teps 5 and 6 conduct the Tier 2 analysis and S teps 7 and 8 produce output tables that summarize the results, as described in Sections B.4.3 and B.4.4

. To facilitate explanation of the Tier 2 analysis implementation, example source categories with hypothetical PB

- HAP emissions were run through the tool and screen shots including these results are provided to illustrate the overall 8

- step process. These screen shots an d accompanying descriptions represent the tool at one point in time (approximately three months into its development). While the tool is enhanced and improved periodically (e.g, to fix “bugs” and add/change features), these modifications generally do not s ignificantly alter the look, feel, and purpose of the tool.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 25 6

- 29

- 2012 Exhibit_Att B

- 7

. Example of the Dashboard To Conduct the Tier 2 Analysis

Exhibit_Att B

- 8

. Example of Global Inputs Used in the Tier 2 AnalysisTRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 26 6

- 29

- 2012 

### B.4.1 Facility List for Tier 2 Screen (Step 3) After clicking Button 3 on the dashboard (see Exhibit_Att B

- 7 )
, the user is brought to the facility input sheet to enter a list of all facilities in a source category ( or multiple source categories) with emissions for any PB

- HAP. Inc luded in this list a re the average latitude and longitude of the emission sources at the facility, the list of chemicals emitted (and their respective PB

- HAP groups) , and the ratio of the REF

- adjusted emissi on rate to the Tier 1 screening threshold for each evaluated chemical

. This table is generated using the Tier 1 Microsoft ® Access screening tool , and it is pasted into the “Facility Inputs” sheet of the Excel tool, as shown in Exhibit_Att B

- 9

. Exhibit_Att B

- 9

. Example o f the Facility Input Data Required T o Conduct the Tier 2 Analysis



### B.4.2 Facility/Lake Distance Table (Step 4) After clicking Button 4 on the dashboard (see Exhibit_Att B

- 7 )
, the user is brought to the lake distance sheet to enter details on the closest lake to each facility in each of the directional octants (N, NE, E, SE , S, SW, W, and NW). These lake data are assembled outside of this Excel tool using the following steps. First, the location of each facility is imported into ArcGIS ® along with the final database of lake centroids (Section B.2.1 ). Then, from within the ArcGIS ® software, all lakes with centroids within 50 km of each facility are identified. A table is created that shows the lake name, location relative to the f acility (octant and distance), and size (note: the lake size is informational only, it is not used as part of Tier 2 except to be sure it is not smaller than 25 acres or larger than 100,000 acres). If no lake is present within 50 km in a given octant, the table includes dashes to indicate no lake is present within the radius selected. The lake names (where available) are scrutinized to subjectively remove industrial andTRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 27 6

- 29

- 2012 treatment water bodies, as discussed in Section B.2.1 . This table is pasted in the “LakeDistance” sheet as shown in Exhibit_Att B

- 10

.

Exhibit_Att B

- 10

. Example of the Lake Distance Data Required

T o Conduct the Tier 2 Analysis



### B.4.3 Finding the Closest Meteorological Station (Step 5) Now that the user has entered all required input data, clicking Button 5 initiates an analysis that finds the closest surface meteorological station (having complete summary statistics) to each facility in the Facility List using straight

- line distance. I f the facility and the meteorological station are more than 50 km apart, the tool returns the closest station but flags the match with the text “Review” to alert the user that further scrutiny of the facility/station pair might be necessary. In addition, the tool also alerts the user if the closest station was flagged for incomplete data, in which case the next closest station was selected instead. The results of the meteorological analysis are shown in the “MetAnalysis” sheet (an example is shown in Exhibit_Att B

- 11 ). In the example in Exhibit_Att B

- 11 , the closest surface mete orological station to Facility 005 was approximately 34 km away, but the methodology rejected that station because it had too much missing data, choosing instead a different station approximately 37 km away with fewer missing data. For Facility 008, the c losest station had complete data but was located 57 km from the facility, causing the analysis to display a warning about being beyond 50 km away.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 28 6

- 29

- 2012 Exhibit_Att B

- 11 . Example Results of the Meteorological Station Matching

Required T o Conduct the Tier 2 AnalysisTRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 29 6

- 29

- 2012 

### B.4.4 Assembling Threshold Adjustment F actor s (Step 6) Next, the tool calculates the facility

- specific, octant

- specific threshold adjustment factor for each chemical. When Button 6 i s clicked (see Exhibit_Att B

- 7 ), the tool uses the meteorological parameters and lake locations to find the correct adjustment factor for each of the Tier 2 characteristics, as discussed in Section B.2 . See example screen shot in Exhibit_Att B

- 12 . The completed analysis is shown in the “OctantAnalysis” sheet. For each octant associated with a facility, t he tool pairs its lake and meteorology parameters (lake location, wind speed, mixing height, and precipitation) with the closest matches in the Tier 2 matrix of EEF and screening threshold adjustment factors ( Section B.2 describes this matrix, and a sample of the matrix is shown in Exhibit_Att B

- 13 ). The selection of adjustment factors is done in a health

- protective manner, taking note of the direction of the correlation of the variable with risk. For example, smaller mixing heights are associated with larger risks, so a facility’s mixing height is matched with the closest evalu ated Tier 2 value that is smaller than the facility’s value. Each octant also is associated with a wind direction adjustment factor, where the frequency of winds blowing into the Tier 1 screening scenario (43 percent) is ratioed to the observed frequency of winds blowing into the octant. The octant’s total adjustment factor is the product of the EEF, screening scearnio, and wind direction adjustment factors. The adjustment factors indicate the ratio of the Tier 2 threshold to the Tier 1 threshold. Thus,
v alues above 1 (which nearly all are) indicate a risk reduction in Tier 2 relative to Tier 1, and larger and larger values (above 1) indicate larger and larger risk reductions. The Tier 1 result (the ratio of emissions to the Tier 1 emission screening thre shold) is divided by the Tier 2 adjustment factor, providing the Tier 2 result (the ratio of emissions to the Tier 2 emission screening threshold). This evaluation occurs for each chemical in each octant of each facility, and for PAHs and dioxins the emiss ions are converted to BaP

- and TCDD

- equivalent using the REFs. For PAHs and dioxins, the species

- specific Tier 2 ratios (equivalent emissions/threshold) at a facility are summed to create the total PAH and total dioxin Tier 2 ratios. To maintain a health

- protective focus in the analysis, the octant analysis then identifies the octant with the largest Tier 2 ratio for each PB

- HAP at each facility.

Exhibit_Att B

- 12

. Example Results of the Octant Anal ysis Required

T o Conduct the Tier 2 AnalysisTRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 30 6

- 29

- 2012

Exhibit_Att B

- 13

. Example of the TRIM.FaTE Matrix Results Required

T o Conduct the Tier 2 Analysis



### B.4.5 Finalizing Threshold Adjustment F actor s and Assembling Screening Results (Step 7) This step was removed from the tool because its information was largely redundant relative to other sheets.


### B.4.6 Assembling Results (Step 8) By clicking Button 8, the Excel tool creates three separate results tables, shown in the sheets “SummaryOutput,” “DetailedOutput,” and “IndividualOutput.” The summary table shows the Tiers 1 and 2 human health multipathway screening results at the level o f source category and PB

- HAP group (cadmium, mercury, total PAH, and total dioxin; see example screen shot in Exhibit_Att B

- 14 ). Cells with red highlighting call att ention to cases where facilities exceeded the Tier 1 or Tier 2 thresholds. 33 Using Source Category A as an example

– there were 52 facilities in the category, 51 of which emitted cadmium, 46 emitted dioxins, 51 emitted mercury, and 20 emitted PAHs. No fac ilities in this category emitted cadmium or mercury at levels exceeding the Tier 1 threshold, so the summary table shows “Below Tier 1 Threshold” as the result for these two PBHAP groups. Because they did not exceed the Tier 1 threshold, they by definitio n did not exceed the Tier 2 threshold; the Tier 2 analysis was still conducted for these PB

- HAPs from these facilities, for simplicity and transparency in displaying Tier 1 results alongside Tier 2 results. Five of the facilities in the category exceeded the Tier 1 threshold for PAHs, with the maximum

- emitting facility exceeding the threshold by a factor of 3.8 (or 4; in practice, these numbers are often interpreted by rounding). The Tier 2 methodology brought 4 of these 5 facilities below the Tier 2 thr eshold; the

33 The tool shading schemes have been revised so that only ratios of at least 1.5 indicate an exceedance of the threshold. The 1.1 value in the fourth data row would not be shaded red, and its value for number of facilities exceeding would go from 1 to 0.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 31 6

- 29

- 2012 fifth facility was very close to the Tier 2 threshold (1.1; typically interpreted by rounding to 1). All 46 dioxin

- emitting facilities in the category exceed the Tier 1 dioxin threshold. Three of the 46 facilities did not exceed the Tier 2 th reshold, leaving 43 exceeding the Tier 2 threshold but by much smaller amounts compared to Tier 1 (the maximum

- emitting facility exceeds the Tier 2 threshold by a factor of 958, compared to a factor of 5,570 for for Tier 1).
Exhibit_Att B

- 14

. Example of Summary Output Table Created by the Tier 2 Tool a

a The tool shading schemes have been revised so that only ratios of at least 1.5 indicate an exceedance of the threshold. The 1 .1 value in the fourth data row would not be shaded red, it would instead say “Below Tier 2 Threshold”, and its value for number of facilities exceeding would go from 1 to 0.

The detailed summary table shows much of the same information as the summary table, but the screening resul ts for each facility are shown (see example screen shot in Exhibit_Att B

- 15 ). This detailed table is helpful because it shows which facilities exceeded the thresholds and by how much. As discussed in the previous paragraph, the summary table shows that 46 and 43 fac ilities, respectively, exceeded the Tiers 1 and 2 thresholds for dioxins, with the maximum

emitting facility exceeding Tiers 1 and 2 thresholds by respective factors of 5,570 and 958. This detailed summary table shows that Facility 001 is the facility emi tting the most dioxins after adjusting the emissions for toxicity and exposure. The screen shot in Exhibit_Att B

- 15 shows exceedance factors for dioxins and PAHs for a subset of 8 other facilities in Source Category A (other facilities and source categories are in the table but not shown in the screen shot). The individual summary table is similar to the detailed summary table, but it adds the details of the screenin g results for each chemical (not just PB

- HAP groups) and the Tier 2 adjustment factors (see example screen shot in Exhibit_Att B

- 16 ). The Tier 2 adjustment factors ar e specific to each chemical and each facility. The ratios to screening level by chemical are summed at the facility level to produce the ratios to screening level by PB

- HAP group.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 32 6

- 29

- 2012 Exhibit_Att B

- 15

. Ex ample of Detailed Output Table Created by the Tier 2 Tool a

a The tool shading schemes have been revised so that only ratios of at least 1.5 indicate an exceedance of the threshold. The 1 .1 value in the tenth data row would not be shaded red and it would i nstead say “Below Tier 2 Threshold”.

Exhibit_Att B

- 16

. Example of Individual Output Table Created by the Tier 2 Tool

a The tool shading schemes have been revised so that only ratios of at least 1.5 indicate an exceedance of the threshold. For example, the 1.3 value in the third data row would not be shaded red.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B

– Tier 2 B

- 33 6

- 29

- 2012 

## B.5 References National Climatic Data Center (NCDC) . (2012) Quality Controlled Local Climatological Data. Available online at http://cdo.ncdc.noaa.gov/qclcd/QCLCD?prior=N

EPA . (2001 a ) NATA

– Evaluating the National

- scale Air Toxics Assessment, 1996 DATA

– An SAB Advisory. U.S EPA Science Advisory Board. EPA

- SAB

- EC

- ADV

- 02

- 001. 12/2001.

EPA (U.S. Environmental Protection Agency). (2001 b ) National

- scale Air Toxics Assessment for 1996, Draft for EPA Science Advisory Board Review. EPA Office of Air Quality Planning and Standards. EPA

- 453/R

- 01

- 003. 01/18/2001.

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

– MACT I Petroleum Refining Sources and Portland Cement Manufacturing. EPA Office of Air Quality Planning and Standards. EPA

- 452/R

- 09

- 006. 06/2009.

EPA . (2010 a ) Review of EPA’s draft entitled, “Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies

– MACT I Petroleum Refining Sources and Portland Cement Manufacturing.” EPA Science Advisory Board. EPA

- SAB

- 10

- 007. 05/07/2010. EPA. (2010b) Support Center for Regulatory Atmospheric Modeling (SCRAM) Mixing Height Data. Available online at http://www.epa.gov/ttn/scram/mixingheightdata.htm . Accessed 01/15/2012. Last updated 05/13/2010. USGS (U.S. Geological Survey). (2012) National Hydrography Dataset. Available online at http://nhd.usgs.gov/

.TRIM

- Based Tiered Screening Methodology for RTR

Attachment B , Addendum 1 B 1

- 1 6

- 29

- 2012 TRIM.FaTE Parameters 

## Addendum 1. Summary of TRIM.FaTE Parameters Considered for

Inclusion in Tier 2 Analysis[This page intentionally left blank.]Attachment B , Addendum 1 B 1

- 3 6

- 29

- 2012

TRIM.FaTE Parameters Exhibits, Addendum 1

Exhibit Add B1

- 1. TRIM.FaTE Parameters Considered for Inclusion in Tier 2

Analysis

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

- 4Attachment B , Addendum 1 B 1

- 4 6

- 29

- 2012

TRIM.FaTE Parameters Exhibit Add B 1
- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential

Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority

for Inclusion Meteorological Parameters Wind direction (% of time wind blows toward the lake and farm) In previous runs, direct

deposition accounted for the bulk of chemical input onto farms and into lakes. Because wind direction is strongly correlated to direct deposition in a given location , media concentrations are potentially highly sensitive to this pa rameter. Also, because the percentage of time the prevailing wind blows in the direction of lakes and farms can vary considerably across locations, differences in this parameter might also result in significant changes in important environmental concentrat ions. Highly Significant : Previous sensitivity analyses have confirmed this to be a very sensitive parameter in the Tier 1 Screening modeling set

- up. Changing the fraction of time the wind blows toward the lake and farm by a factor of two corresponds to a change in the risk by a factor of two. Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening 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 to the closest surface meteorological station. High

Wind speed Wind speed can affect the

location of the “peak” concentration and deposition patterns in a given model configuration, as well as the risk

- distance profile. Highly Significant : Previous sensitivity analyses have confirmed this to be a very sensitive parameter. However, wind speed does not vary widely across U.S. loca tions 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 meteorolog ical station. Then, facilities can be linked to the closest surface meteorological station. HighAttachment B , Addendum 1 B 1

- 5 6

- 29

- 2012

TRIM.FaTE Parameters Exhibit Add B 1
- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential

Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority

for Inclusion Precipitation Chemicals for which wet vapor

or wet particle deposition processes are important are likely to be sensitive to the assumed level of precipitation

. Highly Significant : Previous sensitivity analyses have indicated a relatively high sensitivity of risk to precipitation for most PB

HAPs (PAHs, cadmium, and mercury)

. Moderate Effort to Implement: In implementing changes in precipitation i n 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 c an be linked to the closest surface meteorological station with available data. HighAttachment B , Addendum 1 B 1

- 6 6

- 29

- 2012

TRIM.FaTE Parameters Exhibit Add B 1
- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential

Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority

for Inclusion Mixing height Greater mixing heights

increase the dispersion of pollutants in the atmosphere and consequently reduce deposition to the ground in the areas around the stack. This is likely to be a highly sensitive parameter if there is a sizeable variation in mixing heights between facilities. Highly Significant : Previous sensitivity analyses have shown risk to be very sensitive to mixing height. Low Effort to Implement: This variable is relatively straightforward to vary in the Tier 2 screening scenario s

. Moderate to High: Mixing height estimates are available for upper air meteorological stations, and this set of stations is more limited than the set of surface meteorological stations. Each surface station can be linked to the closest upper air station to estimate the average mixing height. Then, facilities can be linked to the closest surface meteorological station. The relative uncertainty in mi xing height for a given facility is high, given diurnal variations in mixing height and the smaller number of upper air stations. HighAttachment B , Addendum 1 B 1

- 7 6

- 29

- 2012

TRIM.FaTE Parameters Exhibit Add B 1
- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential

Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority

for Inclusion Configurational Parameters Distance of lake from stack Deposition is known to

decrease with distance from stack, although this relationship also depends on meteorological parameters such as wind speed and wind direction. Significance Difficult to Determine: Limited results from previous TRIM model runs show an inconclusive relationship between risk and distance from stack, possibly as a result of limited statistical power. Some studies in the literature show a definite decreasing risk gradient with distance but others report too many confounding factors to is olate 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
ra dius of each facility can be found using ArcGIS (see section 4). High

Distance of farm from stack Deposition is known to

decrease with distance from stack, although this relationship also depends on meteorological parameters such as wind speed and wind direction. Significance Difficult to Determine: Limited results from previous TRIM model runs show an inconclusive relationship between risk and distance from stack, possibly as a result of limited statistical power. Some studies in the literature show a definite decreasing risk gradient with distance but others report too many confounding factors to is olate 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 th e farm will likely affect risk, it is difficult to determine the precise land parcels near each facility that are actually used for farming now or in the future. MediumAttachment B , Addendum 1 B 1

- 8 6

- 29

- 2012

TRIM.FaTE Parameters Exhibit Add B 1
- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential

Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority

for Inclusion

Watershed: lake area ratio A higher watershed:lake area

ratio potentially increases the chemical input of water

soluble or particle

- attached chemicals into the lake. But the associated higher flush rate will likely reduce this effect. Significance Difficult to Determine : Changes in the watershed to lake ratio affect risk, but the interaction depends on other variables involved in the water balance.

Moderate Effort to Implement: In implementing changes in the watershed:lake ratios in TRIM, care must be taken to also preserve the ove rall water balance in the model. High: The portion of land serving as a watershed to a particular lake is difficult to determine. Medium

Area and depth of lake A higher lake area would

capture more deposition but this effect might be counterbalanced by the ensuing larger volume of water, which reduces chemical concentration. Similarly, a deeper lake would also reduce concentrations, but this effect might be count erbalanced by the ensuing lower flush rates at a constant level of precipitation/runoff. Significance Difficult to Determine : The impact of these parameters is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: The lake area variable requires updates to the layout coordinates and requires more effort to vary in the Tier 2 screening 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 canno t. MediumAttachment B , Addendum 1 B 1

- 9 6

- 29

- 2012

TRIM.FaTE Parameters Exhibit Add B 1
- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential

Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority

for Inclusion Physical Parameters Flush rate A higher flush rate out of the

lake would result in a higher rate of chemical output from the lake, assuming constant inflow and volume. Significance Difficult to Determine : The impact of this parameter is inconclusive based on current studies using the TRIM model.

Moderate Effort to Implement: In implementing changes in the flush rate in TRIM, care must be taken to also preserve the overall water balance in the model. High: The flush rate of a lake cannot be determined easily for any lake found near a facility. In addition, erosion rates, watershed information, and lake depth needed to estimate the flushing rate are not readily available. Medium

Runoff rate and fraction A higher runoff rate (or

fraction) would likely result in greater chemical input into the lake for some chemicals but also potentially a higher flush rate out of the lake. Significance Difficult to Determine : The impact of this parameter is inconclusive based on current studies using the TR IM 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 n ear a facility cannot be readily determined. Medium

Erosion rate and fraction A higher erosion rate would

likely result in greater chemical input into the lake for particle

bound chemicals. It would also result in greater chemical transport onto farmlands, but this might be counterbalanced by equally greater erosion off farmland. Highly Significant : Previous analyses have shown risk to be sensitive to this parameter for some chemicals.

Moderate Effort to Implement: In implementing changes in the erosion rate and fraction in TRIM, care must be taken to also preserve the overall water balance in the model. High: As with the flush rate, the erosion rate and fraction for any lake near a facility cannot be readily determined. MediumAttachment B , Addendum 1 B 1

- 10 6

- 29

- 2012

TRIM.FaTE Parameters Exhibit Add B 1
- 1 . TRIM.FaTE Parameters Considered for Inclusion in Tier 2 Analysis

Parameter Mechanism of Potential

Influence in TRIM Assessment of Parameter Significance and Ease in Implementation Uncertainty in Site

- Specific Data for Facilities Priority

for Inclusion
Chemical Parameters Methylation/ demethylation rates (Hg) For Hg, methylation and

demethylation rates in lake sediment and surface water are potentially sensitive parameters affecting risk. A literature survey has indicated a relatively high range for rate consta nts 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 scenari o 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 lake The total phosphorus content

of a lake is used as part of the TRIM.FaTE parameterization process to estimate the biomass content of different trophic levels. These biomass levels affect the biomagnification of chemicals up the food chain and potentially risk to human consumers of fish. Not Significant : Previous analyses have s hown 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 readi ly determined. LowAttachment B , Addendum 2 B 2

- 1 6

- 29

- 2012 TRIM.FaTE Parameters



## Addendum 2. Analysis of Lake Size and Sustainable Fish Population[This page intentionally left blank.]Attachment B , Addendum 2 B 2

- 3 6

- 29

- 2012 Lake Size Analysis 

## CONTENTS , ADDENDUM 2

1. Introduction

................................ ................................ ................................ ..................... 2

- 4
1.1. Angler Behavior

................................ ................................ ................................ ...... 2

- 4
1.2. Fish Biology

................................ ................................ ................................ ............ 2

- 5 1.2.1. Lake Pr oductivity

................................ ................................ ..................... 2

- 5 1.2.2. Proportion of Fish Biomass by Trophic Level

................................ ........... 2

- 7 1.2.3. Minimum Viable Population Size

................................ ............................. 2

- 8 1.3. Summary of Assumptions for the Lake Size Analysis

................................ ............. 2

- 9
1.4. Equations Used to Determine Lake Fish Populations

................................ ............. 2

- 9 1.5. References

................................ ................................ ................................ ........... 2

- 11Attachment B , Addendum 2 B 2

- 4 6

- 29

- 2012 Lake Size Analysis 

## 1. Introduction Identifying the smallest size of a lake that might maintain self

- sustaining populations of fish from trophic levels (TL) 3 and 4 and is sufficient to support at least one angler at a specified fish ingestion rate requires consideration of many factors. Some factors depend on assumptions about the behavior of anglers who consume fish from a lake ( see Section 1.1 below ). Other factors depend upon the general biology of fish populations in North American ecoregions (see Section 1.2 ). Based on evaluation of these factors, a set of assumptions was developed to support the estimation of minimum lake sizes that are needed to sustain a particular total human ingestion rate in grams/day (see Section 1.3 ). Then, equations were developed (see Section 1.4 ) that were used to create Exhibit_Att B

- 4 , which was used to determine the threshold lake size of 25 acres.


### 1.1. Angler Behavior Several assumptions regarding angler behavior are important for estimating a minimum lake size that is fishable. The first assumption is that anglers (and their family members) consume about 50:50 TL3 and TL4 fish by biomass. TL3 fish are benthic carnivo res (BC) such as catfish and chub that consume benthic invertebrates and small benthic fish. TL4 fish are pelagic piscivores

— water column carnivores (WCC) such as largemouth bass and walleye. This assumption of 50:50 TL3 and TL4 consumption appears reaso nable without being overly conservative . Although anglers might prefer to catch and consume the pelagic (TL4) game fish species, their generally lower abundance compared with lower trophic

- level fish dictates that anglers will more often capture the benth ic (TL3) carnivores. Some TL2 (herbivorous) fish such as carp attain “catchable” size, but they generally are not popular fish for consumption and are not considered here. A second assumption is that anglers and their family members consume only the fil let portion of a fish. According to Ebert et al. (1993)
, the edible fraction of fish as a proportion of total fresh body weight is 0.4 for salmon, 0.78 for smelt, and 0.3 for all other species. EPA recommends use of 0.30 for the consumable fraction of fi sh (USEPA, 1989). For this analysis, a 0.33 edible fraction for TL4 fish was assumed

. That factor is roughly one
- third , which we considered preferable to 0.30 to account for some proportion of salmon likely in the diet. This factor is used in the lake s ize analysis to estimate total fish biomass associated with human consumption. A third assumption relates to ingestion rates of the angler or angler family. Fish ingestion rates used for the purpose of the Tier 2 analysis are the same as those in Tier 1 a nd are consistent with subsistence angler ingestion rates

. Another important consideration related to angler behavior is how much fish removal in a lake is possible without resulting in local fish populations declining to extirpation. The productivity o f any particular fishery (local population of a species of fish) and the proportion of adult fish that can be harvested for human consumption are difficult values to estimate, and various models to predict sustainable harvests of different fisheries are nu merous and complex. Species

- specific parameters of importance include fecundity with age and size; survivorship of eggs, fry, and juveniles to sexual maturity (recruitment); natural predation pressures; and temporal variation in food availability. For th e purpose of this analysis, simplifying assumptions are required. In the analysis by Håkanson and Boulion (2004), which included a survey of 122 lakes, the authors noted that a typical loss from fishing by birds, mammals, and humans would be approximately 10 percent of the fish biomass in the prey fish compartment (T L 3) and 10 percent of the biomass in the predator fi sh compartment (TL4). The authors also found that as overall lake productivity increased, the biomass of prey (TL3) fish increased more rapi dly than the biomassAttachment B , Addendum 2 B 2

- 5 6

- 29

- 2012 Lake Size Analysis of predator (TL4) fish. For this lake size analysis, angler harvesting of fish is assumed to be 10 percent of the biomass of pelagic TL4 fish.


### 1.2. Fish Biology Estimation of the minimum surface area of a pond or lake that is fishable al so must be based on assumptions concerning fish biology, and these assumptions have been drawn from several published studies across the United States. The productivity and trophic structure of fish communities in ponds and lakes across the United States are varied. Thus, any set of assumptions is unlikely to all hold true at any given location. Nonetheless, three factors are important to any estimate of a minimum lake surface area for Tier 2: the general productivity of a lake (expressed as grams of fis h 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 (i.e., TL3 and TL4 fish); and the minimum viable population (MVP) size required for the fish species to be self

- sustaining in the short term (for at least a few decades).


### 1.2.1. Lake Productivity The general productivity of a lake depends on many factors, including latitude, seasonal temperatures, and nutrients supporting the base of the food web. For lakes at approximately the same latitude in the same climate, nutrients play a key role in the total fish biomass that a lake might support. In a regression analysis of data on total phosphorus (TP) and fish biomass for 31 lakes across North America, Europe, an d Russia, Nürnberg (1996) summarized the “limits” among three TP

- defined lake trophic status categories with respect to total fish wet weight biomass per unit area for three categories of lakes:
Oligo

- ( 0 μg/ ) = 1.9 g ww/m 2
Meso

- eutro (TP = 30 μg/ ) = 3.7 g ww/m 2

Eutro

- yp ( 00 μg/ ) = 8.5 g ww/m 2
Nürnberg (1996) also summarized total fish biomass limits from Bachmann et al. (1996) for the lake trophic status categories based on a sample of 60 lakes in Florida:
Oligo

- meso (TP 0 μg/ ) = 7.4 g ww/m 2
Meso

- ( 30 μg/ ) = 10.6 g ww/m 2

Eutro

- yp ( 00 μg/ ) = 15.6 g ww/m 2
As might be expected, for the same TP concentrations, standing fish biomass per unit area in the Florida lakes is two to three times higher than standing fish biomass for more northerly lakes with shorter growing seasons. Hanson and Legget (1982) estimated the relationship betwe en TP and standing stock of fish using a regression model based on samples from 21 lakes ranging in sur face ar ea from 0.1

– 25,000 hectares (~0.25

– 62,000 acres) and located between 0° and 56° N latitude and 121° E to 122° W longitude. Their linear regression relating TP to total fish standing biomass (B) used the following equation and had a correlation coefficien t ( r 2 ) of 0.84 :

B = 0.792 + 0 .07

2 (TP)Attachment B , Addendum 2 B 2

- 6 6

- 29

- 2012 Lake Size Analysis 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 ww/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 concentrati ons. 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 south

- ce ntral 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 acr es)

— 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. Ideally, one would have data indicating TP levels in lakes in the vicinity of facilities for a Tier 2 analysis. Such data, however, are rarely readily available. The general ranges of fish productivity across lakes of various surface ar eas, therefore, are considered to be independent of lake size over a wide range of lake sizes. Thus, lake size alone cannot be used to estimate fish productivity or maximal fish harvesting rates. Scientists have also examined the relationship between TP a nd total fish biomass in reservoirs. Yurk and Ney (1989) examined the relationship between TP and standing stock of fish in 22 reservoirs in southern Appalachia sampled in 1973. Their logarithmic regression that related TP to total fish standing biomass (B) used the following equation and had an r 2 of 0.75:

Log 10 (B) = 1.07 + 1.14 × Log 10 (TP) Use of the equations from Hanson and Legget (1982) and Yurk and Ney (1989) yielded similar predications of total fish biomass at intermediate TP concentrations. At low TP (e.g., 10 μg/L), predictions of total fish biomass were 3.0 g ww/m 2 (Yurk 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.

Leid y and Jenkins (1977) reported analyses of several large data sets to support modeling of fish productivity and carrying capacity in reservoirs across the United States for the National Reservoir Research Program. The analyses included studies of fish stan ding biomass by species in 61 reservoirs across the midwestern and eastern United States sampled at different times between 1952 and 1975. Only reservoirs of at least 500 acres (202 hectares) in size were included, with some exceeding 65,000 acres (in the Missouri drainage basin). Considering all 61 reservoirs, the mean biomass density of fish was 41.3 (± 30.4 SD) g ww/m 2

. The minimum and maximum total fish biomass densities were 3.2 and 133.2 g ww/m 2 , respectively , and the median 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.Attachment B , Addendum 2 B 2

- 7 6

- 29

- 2012 Lake Size Analysis In summary, the fish productivity in lakes and reservoirs can vary by more than three orders of magnitude. The reservoi rs 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 somewhat higher than a mean value for representative sample s of natural lakes in the United States . For the purpose of the Tier 2 analysis, we therefore rounded that value down to a single significant digit of 40 g ww/m 2 as the uppe r limit for total fish biomass in a lake.


### 1.2.2. Proportion of Fish Biomass by Trophic Level As indicated in Section 1.1 of this addendum, for the Tier 2 analysis, the pr oportion of fish in an angler’s diet that consists of TL3 and TL4 fish is assumed to be 50:50 by biomass (not numbers) for lakes that support the four trophic levels. In smaller lakes, TL4 fish are likely to be missing or rare, with TL3 fish (e.g., sunfis h) 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 nex t trophic level (i.e., approximately 90 percent loss of energy) per trophic step. However, with different species having different energy assimilation efficiencies and with smaller species generally having higher turnover rates than larger species, the 10 percent energy rule does not necessarily translate into a standing biomass pyramid of similar proportions. In this section, the proportion of fish (based on biomass) that might be expected in TL3 and TL4 relative to total standing fish biomass are examin ed assuming TL4 fish are present. Examination of several studies of fish biomass by trophic habits indicated that top trophic level fish might comprise approximately 20 percent of the standing fish biomass in many locations. Ploskey and Jenkins (1982) est imated 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 biomas s in DeGray Lake, Arkansas (averaged across several years). Leidy and Jenkins (1977) estimated that 18 percent of the fish biomass across the 61 reservoirs they examined was piscivorous (minimum of 14 percent and maximum 24 percent). Demers et al. (2001) categorized 2 percent and 15 percent of the total fish biomass in two small lakes of 27 and 22
acres in size, respectively, as piscivorous/benthivorous fish ( e.g., largemouth bass
, creek chub) ; primary benthivores (e.g., catfish, suckers) dominated at > 70 percent in both lakes.
One of the more recent food web models for freshwater lakes is that of Håkanson and Boulion (2004). Their model was designed to predict the productivity and standing crop of prey and predatory fishes in lakes of northern Europe . The authors acknowledged that fish feeding patterns are complicated by the fact that fish change their feeding preferences as they age

— especially dur ing early development, but also as adults, when they may switch between zooplankton and zoobenthos and from small fish to larger fish as they grow. A “distribution coefficient” was used to indicate what proportion of the total fish biomass in a lake is prey versus predatory fish. Based on data from 122 lakes in Europe and North America, Håkanson and Bouli on (2004) concluded that 27 percent by biomass is a “normal” portion of predatory fish in a balanced system. They noted further, however, that for eutrophic lakes with TP levels >100 μg/L, the proportion of fish represented by piscivores declines to less than 20 percent

. Based on the information above, the top trophic level ( TL3 and TL4 ) fish are assumed to comprise 21 percent of the total fish biomass. With the bulk of productivity in lakes originating from detritus in the benthos, the total biomass of s trictly 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 theAttachment B , Addendum 2 B 2

- 8 6

- 29

- 2012 Lake Size Analysis total fish biomass in a benthic carnivore ( TL3 ) compartment and 3.5 percent of the total in a pelagic piscivore ( TL4 ) compartment. Thus, the TL4 fish, when present, represent the limiting compartment for angler fish harvesting and consumption.


### 1.2.3. Minimum Viable Population Size The final step in estimating the minimum lake size that can support sustainable fishing of its TL4 fish species is to invoke the concept of minimum viable population (MVP) size. MVP is a concept used frequently in conservation biology for animals and is d efined as the smallest population that will persist for a specified duration (e.g., 100 years) with a given probability (e.g., 95 percent). MVP for any given species and general location depends on many attributes of the species biology (e.g., body size, reproductive rate, home range size, corridors between populations, variability in environmental characteristics that impact fecundity and survival). At lower numbers, the likelihood of population extinction increases due to environmental and demographic s tochasticity (Menzie et al., 2008). As for fisheries biology, entire text books have been dedicated to applied population ecology with population simulations incorporating demographic and life

- history characteristics, spatial separation of habitat patches and metapopulations, the probability of local catastrophes, genetic variation (e.g., drift), and other factors with predictions of time

- to

- extinction or probability of extinction within specified time periods (e.g., Soulé, 1987; Akçakaya et al., 1999). C onsideration of such models in population

level ecological risk assessment has begun, but faces many challenges (Barnthouse et al., 2008). Much of the initial work on MVP investigated the genetic minima required for short

- term survival, continuing adaptati on to environmental change, and ultimately evolution. Inbreeding has been considered the primary threat to short

- term survival and genetic drift as the principal threat to losing the genetic variation required for adaptation (Shaffer, 1987). S everal anal yses (Senner
, 1980 ; Franklin
, 1980 ; Soulé
, 1980 ; Frankel and Soulé , 1981; Lande and Barrowclough
, 1987) have led to the conclusion that minimum effective population sizes on the order of 50 are required for short

- term survival (e.g., several generations, d ecades), 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 populat ion 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 ratios, the effective population size is essentially the same as the actual breeding adult population size (Ewens et al., 1987). One of the most extensive population viability analyses in the United States has been cond ucted 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 again st using a “rule” for MVP across circumstances (Ewens et al.
, 1987). Note that the MVP is appropriate for a single species of fish, not for generic categories of fish such as TL3 or TL4. For this Tier 2 analysis
, the MVP of 50 associated with short

- term p opulation survival was assumed for a TL4 fish species isolated in a lake. In reality, short

- term extirpations from a lake can be countered by purposeful introductions from other lakes or during flooding events. Thus, an MVP of 500 was not considered nece ssary for game fish in lakes.Attachment B , Addendum 2 B 2

- 9 6

- 29

- 2012 Lake Size Analysis 

### 1.3. Summary of Assumptions for the Lake Size Analysis The following assumptions were used in processing lake d ata for the Tier 2 a nalysis and in estimat ing the relationship between sustainable fish ingestion and harvest rates an d lake size (see Section B.3.1 of the main report)

. 1. Piscivorous fish ( TL3 plus TL4 ), when present, comprise approximately 21 percent of the standing biomass of fish (ignoring seasonal changes). The TL3 fish represent 17 .5 percent of the standing fish biomass; TL4 fish account for 3 .5 percent of this total fish biomass. Thus, TL4 fish, when present, represent the limiting compartment for angler fish harvesting and c onsumption.
2. H umans can harvest 10 percent of the TL4 biomass without threatening the population due to overharvesting. 3. The MVP size for a single TL4 species is at least 50 adult fish for a local population to survive over the short term (more than a decade )

. 4. O nly 33 percent of a TL4 fish is edible fillet muscle

. 

### 1.4. Equations Used to Determine Lake Fish Populations
The standing biomass of TL4 fish supported in Lake X ( TL4 SB ) can be calculated as the total standing biomass of fish (Total SB) multiplied by 0.035, based on the assumption that TL4 fish represent approximately 3.5 percent of the standing biomass in Lake X

.

TL4 SB = Total S B × Fraction TL4 ( Equation 1)

where:

TL4 SB = Standing biomass of TL4 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 TL4 = Fraction of TL4 fish in Lake X (0.035) Using TL4 SB and the size of Lake X (Lake Size) , the total number of TL4 fish supported in Lake X can be calculated using Equation 2 below

.

( Equation 2

)

where:

No. TL4 = Total number of TL4 fish in Lake X Lake Size = Size of Lake X ( acres )
TL4 SB = Standing biomass of TL4 fish ( g ww/m 2 ; from Equation 1 ) CF = 4047 m 2 / acre
BW a = Body weight of adult TL4 fish ( 2000 g; assumed based on professional judgment ) The likely a nnual p roductivity of TL4 fish (kg/year) in Lake X can be estimated using Equation 3

.Attachment B , Addendum 2 B 2

- 10 6

- 29

- 2012 Lake Size Analysis ( Equation 3

)

where:

Productivity TL4 = Likely annual productivity of TL4 fish in Lake X (kg/year) Lake Size = Size of the Lake X ( acres )
TL4 SB = Standing biomass of TL4 fish ( g ww/m 2 ; from Equation 1 ) CF1 = 4047 m 2 / acre
CF2 = 1000 g/kg The maximum fish ingestion rate (g/day) for TL3 plus TL4 fish associated with sustainable fishing can be predicted using Equation 4 . It assumes 50:50 consumption behaviors of TL3 and TL4 fish, represented by the factor of 2 in Equation 4

.

( Equation 4

)

where:

Max Sustain IR TL3 +

TL4 = Predicted maximum sustainable ingestion rate for TL3 plus TL4 fish (g/day) Productivity TL4 = Likely annual productivity of TL4 fish in Lake X (kg /year; from Equation 3 )

FF = Fillet fraction; represents the assumed edible portion of fish ( 0.33; unitless )
HF = Annual harvest fracti on (0.10; unitless ) CF1 = 1000 g/kg
CF2 = 365 days/yearAttachment B , Addendum 2 B 2

- 11 6

- 29

- 2012 Lake Size Analysis 

### 1.5. References Akçakaya, H.R., Burgman, M.A., and Ginzburg, L.R. 1999. Applied Population Ecology: Principles and Computer Exercises using RAMAS EcoLab. Sunderland, MA: Sinauer Associates, Inc. Publishers. Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population

- Level Ecological Risk Assessment. Boca Raton, FL: CRC Taylor & Francis Group; Pnesacola, FL: Society of Environmental Toxicology and Chemistry (SETAC). Boyce, M.S. 1993. Pop ulation viability analysis: adaptive management for threatened and endangered species. In: Wildlife Management Institute, Transactions of the 58th North American Wildlife and Natural Resources Conferences. Proceedings WWRC

- 93

- 19, pp. 520

- 527.
Brönmark, C. and S.E.B. Weisner. 1996. Decoupling of cascading trophic interactions in a freshwater benthic food chain. Oecologia 108: 534

- 541.
Demers, E., McQueen, D.J., Ramcharan, C.W., and A. Perez

- Fuentetaja. 2001. Did piscivores regulate changes in fish community structure? Advanc. Limnol. 56: 49

- 80. Ebert, E., N. Harrington, K. Boyle, J. Knight, and R. Keenan. 1993. Estimating consumption of freshwater fish among Maine anglers. N. Am. J. Fisheries Manage. 13: 737

- 745.
Ewens, W.J., Brockwell, P.J., Gani, J.M., and Resnick, S.I. 1987. Minimum viable population sizes in the presence of catastrophes. In: M.E. Soulé (ed.) Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 59

- 68.
Frankel, O.H., and Soulé, M.E. 1981. Conservati on 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: Sin auer 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 f ish biomass and yield. Can. J. Fish. Aquat. Sci. 39: 257

- 263. Hoyer, M.V., and D.E. Canfield, Jr. 1991. A phosphorus

- fish standing crop relationship for streams? Lake Reserv. Manage. 7: 25

- 32. Lande, R., and Barrowclough, G.F. 1987. Effective population s ize, genetic variation, and their use in population management. In: M.E. Soulé (ed.) Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 87

- 123. Leidy, G.R., and Jenkins, R.M. 1977. The development of fishery com partments and population rate coefficients for use in reservoir ecosystem modeling. Contract Report Y

- 77

- 1 prepared by the National Reservoir Research Program, U.S. Fish and Wildlife Service, for the U.S. Army Corps of Engineers, Waterways Experiment Stati on, Vicksburg, Mississippi, USA.Attachment B , Addendum 2 B 2

- 12 6

- 29

- 2012 Lake Size Analysis Menzie, C., Bettinger, N., Fritz, A., Kapustka, L., Regan, H., Moller, V., and Noel, H. 2008. Population protection goals. In: Barnthouse, L.W., Munns, W.R. Jr., and Sorensen, M.T. 2008. Population

- Level Ecological Risk Ass essment. 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 Applic ations of Ecological Theory to Environmental Problems. Washington, DC: National Academies of Science Press. Nürnberg, G.K. 1996. Trophic state of clear and colored, soft

- and hardwater lakes with special consideration of nutrients, anoxia, phytoplankton an d fish. Journal of Lake and Reservoir Management 12(4): 432

- 447. Ploskey, G.R., and R.M. Jenkins. 1982. Biomass model of reservoir fish and fish

- food interactions, with implications for management. North American Journal of Fisheries Management 2(2): 105

- 1 21. Rieman, B.E., and Allendorf, F.W. 2001. Effective population size and genetic conservation criteria for bull trout. N. A. J. Fisheries Manage. 21:756

- 764. Senner, J.W. 1980. Inbreeding depression and the survival of zoo populations. In: M.E. Soulé and B.A. Wilcox (eds.) Conservation Biology: An Evolutionary

- Ecological Perspective. Sunderland, MA: Sinauer Associates; pp. 209

- 244. As cited by Shaffer, 1987. Schaffer, M.L. 1981. Minimum population sizes for species conservation. Bioscience 31: 131

- 134.
Sha ffer, M. 1987. Minimum viable populations: coping with uncertainty. In: M.E. Soulé (ed.) Viable Populations for Conservation. United Kingdom, Cambridge: Cambridge University Press, pp. 69

- 86. Soulé, M.E. 1980. Thresholds for survival: maintaining fitness a nd 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 Conser vation. United Kingdom, Cambridge: Cambridge University Press. USEPA (US Environmental Protection Agency). 1989. Assessing human health risks from chemically contaminated fish and shellfish: a guidance manual. EPA

- 503/8

- 89

- 002. Washington, DC.
Yurk, J.J., and J.J. Ney. 1989. Phosphorus

- fish community biomass relationships in southern Appalachian reservoirs: can lakes be too clean for fish? Lake and Reservoir Management 5(2): 83

- 90.[This page intentionally left blank.]

### Appendix 5 Detailed Risk Modeling Results1 of 2 Facility NEI ID Facility Name Address City State

NEI13475 Ash Grove Cement Company 4343 Highway 108 Foreman AR NEI880 Clean Harbors El Dorado, LLC 309 American Circle El Dorado AR

NEIIL031069A Safety-Kleen Systems, Inc. - Dolton Recycle Center 633 E. 138th Street Dolton IL

NEIIL03153 Clean Harbors Recycling Services of Chicago LLC 1445 W 42nd St Chicago IL NEI16354 Essroc Cement Corp. 3084 W. CR 225 S. Logansport KS NEIIN423 RECLAIMED ENERGY COMPANY, INC. 1500 WESTERN AVENUE CONNERSVILLE IN NEIIN00345 Tradebe Treatment and Recycling, LLC 4343 Kennedy Avenue East Chicago IN

NEI32342 Evonik Degussa Corporation Tippecanoe Laboratories 1650 LILLY ROAD LAFAYETTE IN

NEI16356 Ash Grove Cement Company 1801 N. Santa Fe Chanute KS NEI16355 Systech Environmental Corporation 1420 South Cement Plant Road Fredonia KS

NEIKY103236 Safety-Kleen Systems, Inc. - Smithfield Recycle Center 3700 La Grange Road Smithfield KY

NEI32966 ROHM & HAAS COMPANY LOUISVILLE PLANT 4300 CAMPGROUND RD. LOUISVILLE KY NEILA0930002 GRAMERCY ALUMINA LLC 1111 AIRLINE HWY GRAMERCY LA NEIMILF450 WAYNE DISPOSAL INC 49350 N. I-94 Service Drive Belleville MI

NEIMIN0731 PETRO-CHEM & SOLVENT DISTILLERS GROUPS OF NORTRU. 421 LYCASTE DETROIT MI NEINE00009 Clean Harbors Environmental Services, Inc. 2247 S Highway 71 Kimball NE NEI16605 Thermalkem (Norlite) 628 S. SARATOGA ST. Cohoes NY NEI37022 EASTMAN KODAK CO KODAK PARK 1669 LAKE AVE ROCHESTER NY
NEIOHT$5915 Heritage-WTI, Inc. d/b/a Heritage Thermal Services 1250 St. George Street East Liverpool OH
NEIOHT$5677 Clean Harbors Recycling Services of Ohio L.L.C. 581 Milliken Drive S.E. Hebron OH NEI40462 ROSS INCINERATION SERVICES, INC. 36790 GILES ROAD GRAFTON OH NEI11681 Systech Environmental Corporation 11397 County Rd 176 Paulding OH

NEI12988 CONOCOPHILLIPS CO PONCA CITY REFI NERY 1000 S PINE ST PONCA CITY OK NEIOK4013107 Systech Environmental Corporation 2701 N. 145th E. Ave Tulsa OK NEI16665 Keystone Cement Company Route 329 Bath PA NEI16783 Giant Cement Company 654 Judge Street Harleyville SC NEI41416 ENGELHARD CORP 554 ENGELHARD DR SENECA SC NEI11295 DOW CHEMICAL CO FREEPORT FACILITY 2301 N BRAZOSPORT BLVD BUI Freeport TX NEI7868 Safety-Kleen Systems Denton Recycle Center 1722 Cooper Creek Road Denton TX Table 1 - Facility Identification Information2 of 2 Facility NEI ID Facility Name Address City State Table 1 - Facility Identification Information

NEI7162 SAFETY KLEEN DEER PARK INCORPORATED 2027 BATTLEGROUND ROAD DEER PARK TX

NEI11200 Valero Refining Co. - Port Arthur (prev. Premcor Refining Group Inc.) 1801 S. GULFWAY DRIVE PORT ARTHUR TX NEI6631 ONYX ENVIRONMENTAL SERVICES LLC HWY. 73, W. OF TAYLOR BAYOU Port Arthur TX NEI6481 DUPONT VICTORIA 2695 OLD BLOOMINGTON RD N VICTORIA TX NEIUT19459 Clean Harbors Aragonite, LLC 11600 North Aptus Road Aragonte UT NEI17512 Solite LLC RTE. 1 STATE RD. 652 Arvonia VA NEI51710SEWELL US Navy Norfolk Naval Base-Sewells Point 1510 Gilbert St Norfolk VA NEI42539 Hydrite Chemical Co. 114 N. Main Street Cottage Grove WI

NEI18116 WRR ENVIRONMENTAL SERVICES CO., INC 5200 STATE ROAD 93 EAU CLAIRE WI1 of 1 Cancer

MIR Cancer Incidence Noncancer Max HI Target Organ Cancer MIR Noncancer Max HI Target Organ NEI13475 3.69E-09 1.71E-07 2.75E-05 respiratory 1.42E-06 2.38E-02 respiratory 0% NEI880 2.62E-06 6.66E-05 2.72E-03 liver 3.33E-06 1.72E-02 respiratory 79% NEIIL031069A 2.72E-07 3.50E-05 1.95E-03 neurological 2.89E-07 6.83E-03 respiratory 94% NEIIL03153 1.52E-10 1.17E-07 9.36E-05 developmental 2.01E-08 3.43E-03 respiratory 1% NEI16354 1.60E-06 2.77E-05 1.24E-02 respiratory 2.05E-04 4.24E+00 respiratory 1% NEIIN423 5.12E-07 5.46E-06 3.23E-02 reproductive 2.57E-05 3.23E-02 reproductive 2% NEIIN00345 2.16E-08 1.59E-06 2.65E-04 neurological 2.16E-08 2.65E-04 neurological 100% NEI32342 0.00E+00 0.00E+00 1.41E-03 whole_body 2.29E-07 2.50E-02 respiratory 0% NEI16356 6.12E-08 6.93E-07 3.72E-04 respiratory 6.80E-08 7.16E-03 respiratory 90% NEI16355 2.91E-07 9.50E-07 2.86E-03 neurological 3.51E-07 9.74E-03 respiratory 83% NEIKY103236 3.97E-08 7.09E-06 2.24E-03 respiratory 3.97E-08 2.24E-03 respiratory 100% NEI32966 4.24E-13 1.85E-10 1.13E-05 respiratory 6.09E-07 8.52E-02 respiratory 0% NEILA0930002 0.00E+00 0.00E+00 9.34E-04 respiratory 0.00E+00 9.34E-04 respiratory 0% NEIMILF450 2.66E-08 4.91E-06 6.09E-06 respiratory 9.89E-08 2.10E-04 kidney 27% NEIMIN0731 1.19E-11 4.91E-09 1.26E-05 neurological 2.57E-09 5.06E-05 kidney 0% NEINE00009 8.82E-09 3.30E-08 9.55E-05 neurological 6.04E-07 1.78E-02 respiratory 1% NEI16605 7.94E-07 2.39E-05 6.12E-03 respiratory 2.27E-05 1.94E-01 respiratory 3% NEI37022 0.00E+00 0.00E+00 1.69E-03 respiratory 3.51E-06 1.36E-01 respiratory 0% NEIOHT$5915 6.28E-06 4.94E-04 2.96E-03 liver 6.83E-06 2.52E-02 respiratory 92% NEIOHT$5677 4.64E-07 2.85E-05 3.44E-02 neurological 4.68E-07 3.45E-02 neurological 99% NEI40462 2.02E-06 6.92E-05 8.93E-02 respiratory 2.87E-06 9.39E-02 respiratory 70% NEI11681 2.00E-10 6.46E-08 4.54E-06 neurological 2.37E-06 4.49E-03 respiratory 0% NEI12988 0.00E+00 0.00E+00 1.91E-04 respiratory 2.35E-05 1.39E-01 neurological 0% NEIOK4013107 5.03E-09 3.82E-07 4.57E-05 neurological 1.28E-07 2.54E-02 neurological 4% NEI16665 2.69E-06 5.65E-05 1.36E-02 respiratory 3.40E-06 4.01E-02 respiratory 79% NEI16783 1.24E-06 2.36E-05 9.18E-03 respiratory 1.38E-06 4.85E-01 respiratory 90% NEI41416 4.27E-07 2.69E-05 5.98E-01 respiratory 4.27E-07 5.98E-01 respiratory 100% NEI11295 3.91E-09 3.77E-07 7.27E-04 respiratory 4.25E-05 4.45E+00 respiratory 0% NEI7868 2.46E-07 1.94E-05 2.32E-03 respiratory 3.67E-07 2.87E-03 respiratory 67% NEI7162 9.06E-06 1.55E-02 1.04E-02 respiratory 9.13E-06 1.60E-02 respiratory 99% NEI11200 0.00E+00 0.00E+00 1.04E-07 neurological 2.69E-06 1.29E-01 neurological 0% NEI6631 4.60E-06 1.82E-03 5.41E-03 respiratory 4.69E-06 5.93E-03 respiratory 98% NEI6481 1.69E-09 1.39E-07 3.13E-05 neurological 7.21E-07 7.54E-03 respiratory 0% NEIUT19459 6.70E-10 1.68E-09 5.91E-06 immunological 2.12E-08 3.54E-04 respiratory 3% NEI17512 2.02E-07 2.22E-06 3.30E-04 respiratory 3.45E-07 5.31E-02 neurological 59% NEI51710SEWELL 3.77E-09 3.87E-08 3.70E-05 respiratory 2.04E-06 2.41E-02 respiratory 0% NEI42539 0.00E+00 0.00E+00 4.01E-02 reproductive 8.42E-08 3.80E-02 reproductive 0% NEI18116 1.72E-07 1.98E-06 1.77E-02 neurological 2.54E-07 1.78E-02 neurological 68%
1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1 Table 2 – Maximum Predicted HEM-3 Chronic Risks Actual Emissions

Facility NEI ID Category Chronic Risk 1 Facility Chronic Risk 1 SC % of Facility-wide Cancer Risk1 of 9 REL AEGL1 AEGL2 ERPG1 ERPG2 NEI11200 Styrene 3.62E-04 8.95E-05 1.38E-05 3.62E-05 6.92E-06 NEI11295 1,2-Epoxybutane 0.00E+00 1.78E-06 9.14E-07 0.00E+00 0.00E+00 NEI11295 Acrolein 5.00E-02 1.81E-03 5.43E-04 1.14E-03 3.67E-04 NEI11295 Carbon tetrachloride 6.58E-05 4.46E-07 1.04E-07 9.61E-07 1.98E-07 NEI11295 Chloroform 2.50E-03 0.00E+00 1.21E-06 0.00E+00 1.56E-06 NEI11295 Epichlorohydrin 1.06E-02 6.25E-04 1.51E-04 7.23E-04 1.81E-04 NEI11295 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 5.00E-06 1.23E-06 NEI11295 Hexachlorobutadiene 0.00E+00 0.00E+00 0.00E+00 3.41E-05 1.17E-05 NEI11295 Methylene chloride 6.25E-05 1.27E-06 4.60E-07 8.75E-07 3.36E-07 NEI11295 Propionaldehyde 0.00E+00 1.36E-05 2.42E-06 0.00E+00 0.00E+00 NEI11295 Propylene oxide 2.42E-05 4.41E-07 1.09E-07 6.25E-07 1.27E-07 NEI11295 Styrene 2.20E-04 5.44E-05 8.41E-06 2.20E-05 4.20E-06 NEI11295 Tetrachloroethene 2.50E-05 2.08E-06 3.12E-07 7.35E-07 3.57E-07 NEI11295 Trichloroethylene 0.00E+00 3.57E-07 1.04E-07 4.63E-07 9.26E-08 NEI11681 Acetonitrile 0.00E+00 7.28E-06 2.97E-07 0.00E+00 0.00E+00 NEI11681 Acrylonitrile 0.00E+00 7.46E-08 5.74E-09 3.39E-08 9.68E-09 NEI11681 Benzene 1.63E-06 1.24E-08 8.13E-10 1.32E-08 4.40E-09 NEI11681 Dimethyl formamide 0.00E+00 0.00E+00 4.31E-10 1.94E-08 3.88E-10 NEI11681 Ethyl acrylate 0.00E+00 2.22E-08 5.04E-09 1.84E-05 6.30E-09 NEI11681 Ethyl benzene 0.00E+00 2.24E-06 6.55E-08 0.00E+00 0.00E+00 NEI11681 Methanol 6.90E-04 2.80E-05 7.16E-06 7.43E-05 1.49E-05 NEI11681 Methyl chloride 0.00E+00 0.00E+00 1.90E-06 0.00E+00 4.34E-06 NEI11681 Methyl tert-butyl ether 0.00E+00 8.75E-06 7.50E-07 0.00E+00 0.00E+00 NEI11681 Methylene chloride 1.19E-06 2.41E-08 8.76E-09 1.67E-08 6.40E-09 NEI11681 n-Hexane 0.00E+00 0.00E+00 1.57E-07 0.00E+00 0.00E+00 NEI11681 Styrene 7.48E-06 1.85E-06 2.85E-07 7.48E-07 1.43E-07 NEI11681 Tetrachloroethene 7.84E-06 6.54E-07 9.81E-08 2.31E-07 1.12E-07 NEI11681 Toluene 1.44E-04 7.13E-06 1.19E-06 2.81E-05 4.86E-06 NEI11681 Xylenes (mixed) 4.29E-05 1.68E-06 2.36E-07 0.00E+00 0.00E+00 NEI12988 Glycol Ethers 4.43E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI12988 Hydrochloric acid 2.15E-02 1.67E-02 1.37E-03 1.00E-02 1.50E-03 NEI13475 Benzene 2.47E-04 1.89E-06 1.24E-07 2.01E-06 6.69E-07 NEI13475 Chloroform 2.14E-03 0.00E+00 1.03E-06 0.00E+00 1.34E-06 NEI13475 Ethyl benzene 0.00E+00 2.91E-06 8.49E-08 0.00E+00 0.00E+00 NEI13475 Formaldehyde 1.77E-01 8.86E-03 5.73E-04 8.12E-03 8.12E-04 NEI13475 Methanol 3.04E-03 1.23E-04 3.15E-05 3.27E-04 6.55E-05 NEI13475 Methylene chloride 1.92E-04 3.89E-06 1.41E-06 2.69E-06 1.03E-06 NEI13475 Tetrachloroethene 4.86E-06 4.05E-07 6.08E-08 1.43E-07 6.94E-08 NEI13475 Toluene 1.38E-04 6.81E-06 1.14E-06 2.69E-05 4.65E-06 NEI13475 Xylenes (mixed) 2.92E-05 1.15E-06 1.61E-07 0.00E+00 0.00E+00 NEI16354 Chlorobenzene 0.00E+00 6.50E-05 4.33E-06 0.00E+00 0.00E+00 NEI16354 Ethyl benzene 0.00E+00 1.90E-03 5.53E-05 0.00E+00 0.00E+00 NEI16354 Hexachlorobutadiene 0.00E+00 0.00E+00 0.00E+00 2.06E-04 7.09E-05 NEI16354 Phenol 2.12E-03 2.12E-04 1.38E-04 3.24E-04 6.48E-05 NEI16354 Styrene 6.13E-03 1.51E-03 2.34E-04 6.13E-04 1.17E-04 NEI16354 Toluene 2.84E-02 1.40E-03 2.34E-04 5.54E-03 9.57E-04 NEI16354 Trichloroethylene 0.00E+00 1.85E-05 5.39E-06 2.40E-05 4.79E-06 NEI16355 Acetonitrile 0.00E+00 6.84E-04 2.79E-05 0.00E+00 0.00E+00 NEI16355 Benzene 2.95E-02 2.25E-04 1.47E-05 2.39E-04 7.98E-05 NEI16355 Ethyl benzene 0.00E+00 1.33E-04 3.87E-06 0.00E+00 0.00E+00 NEI16355 Methanol 9.28E-04 3.77E-05 9.62E-06 9.99E-05 2.00E-05 NEI16355 Methyl tert-butyl ether 0.00E+00 3.23E-03 2.77E-04 0.00E+00 0.00E+00 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks Actual Emissions

Maximum Hazard Quotient 12 of 9 REL AEGL1 AEGL2 ERPG1 ERPG2 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks Actual Emissions

Maximum Hazard Quotient 1

NEI16355 Styrene 6.61E-04 1.63E-04 2.52E-05 6.61E-05 1.26E-05 NEI16355 Tetrachloroethene 9.29E-04 7.74E-05 1.16E-05 2.73E-05 1.33E-05 NEI16355 Toluene 1.60E-02 7.89E-04 1.32E-04 3.12E-03 5.38E-04 NEI16355 Xylenes (mixed) 2.25E-02 8.85E-04 1.24E-04 0.00E+00 0.00E+00 NEI16356 Benzene 7.81E-03 5.97E-05 3.91E-06 6.35E-05 2.12E-05 NEI16356 Chlorobenzene 0.00E+00 9.28E-10 6.18E-11 0.00E+00 0.00E+00 NEI16356 Chloroform 4.51E-02 0.00E+00 2.18E-05 0.00E+00 2.82E-05 NEI16356 Ethyl benzene 0.00E+00 2.32E-05 6.78E-07 0.00E+00 0.00E+00 NEI16356 Formaldehyde 4.74E-01 2.37E-02 1.53E-03 2.17E-02 2.17E-03 NEI16356 Methanol 4.06E-02 1.65E-03 4.21E-04 4.37E-03 8.75E-04 NEI16356 Methyl tert-butyl ether 0.00E+00 9.18E-05 7.87E-06 0.00E+00 0.00E+00 NEI16356 Tetrachloroethene 1.04E-05 8.67E-07 1.30E-07 3.06E-07 1.49E-07 NEI16356 Toluene 1.34E-03 6.63E-05 1.10E-05 2.62E-04 4.52E-05 NEI16356 Xylenes (mixed) 5.92E-04 2.32E-05 3.25E-06 0.00E+00 0.00E+00 NEI16605 1,1,1-Trichloroethane 1.97E-05 1.03E-06 4.07E-07 7.06E-07 3.53E-07 NEI16605 Acetaldehyde 2.11E-04 1.23E-06 2.03E-07 5.52E-06 2.76E-07 NEI16605 Acetonitrile 0.00E+00 4.80E-04 1.95E-05 0.00E+00 0.00E+00 NEI16605 Aniline 0.00E+00 2.98E-05 1.94E-05 0.00E+00 0.00E+00 NEI16605 Benzene 3.81E-03 2.92E-05 1.91E-06 3.10E-05 1.03E-05 NEI16605 Carbon tetrachloride 6.27E-05 4.26E-07 9.93E-08 9.17E-07 1.89E-07 NEI16605 Chlorobenzene 0.00E+00 5.70E-05 3.80E-06 0.00E+00 0.00E+00 NEI16605 Chloroform 6.62E-04 0.00E+00 3.20E-07 0.00E+00 4.14E-07 NEI16605 Cumene 0.00E+00 9.19E-06 1.53E-06 0.00E+00 0.00E+00 NEI16605 Dimethyl formamide 0.00E+00 0.00E+00 1.51E-05 6.78E-04 1.36E-05 NEI16605 Dimethyl sulfate 0.00E+00 9.56E-04 1.85E-04 0.00E+00 0.00E+00 NEI16605 Ethyl acrylate 0.00E+00 2.34E-05 5.30E-06 1.94E-02 6.62E-06 NEI16605 Ethyl benzene 0.00E+00 9.97E-05 2.91E-06 0.00E+00 0.00E+00 NEI16605 Ethylene dibromide 0.00E+00 9.17E-07 6.62E-07 0.00E+00 0.00E+00 NEI16605 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 5.74E-07 1.42E-07 NEI16605 Ethylene glycol ethyl ether 2.15E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI16605 Ethylene glycol methyl ether 8.54E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI16605 Formaldehyde 2.31E-01 1.15E-02 7.47E-04 1.06E-02 1.06E-03 NEI16605 Glycol Ethers 1.67E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI16605 Methanol 5.69E-03 2.31E-04 5.90E-05 6.12E-04 1.22E-04 NEI16605 Methyl methacrylate 0.00E+00 3.72E-05 5.32E-06 0.00E+00 0.00E+00 NEI16605 Methyl tert-butyl ether 0.00E+00 2.06E-05 1.77E-06 0.00E+00 0.00E+00 NEI16605 Methylene chloride 1.24E-02 2.53E-04 9.17E-05 1.74E-04 6.70E-05 NEI16605 n-Hexane 0.00E+00 0.00E+00 4.28E-07 0.00E+00 0.00E+00 NEI16605 Phenol 4.17E-04 4.17E-05 2.72E-05 6.36E-05 1.27E-05 NEI16605 Styrene 1.07E-04 2.65E-05 4.10E-06 1.07E-05 2.05E-06 NEI16605 Tetrachloroethene 8.35E-05 6.96E-06 1.04E-06 2.46E-06 1.19E-06 NEI16605 Toluene 2.53E-03 1.25E-04 2.08E-05 4.93E-04 8.51E-05 NEI16605 Trichloroethylene 0.00E+00 7.82E-07 2.28E-07 1.01E-06 2.03E-07 NEI16605 Triethylamine 1.55E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI16605 Vinyl acetate 0.00E+00 3.31E-05 1.26E-06 4.41E-05 3.06E-06 NEI16605 Xylenes (mixed) 6.07E-04 2.38E-05 3.34E-06 0.00E+00 0.00E+00 NEI16665 1,1,1-Trichloroethane 5.29E-04 2.77E-05 1.09E-05 1.89E-05 9.47E-06 NEI16665 Benzene 1.22E-02 9.35E-05 6.11E-06 9.93E-05 3.31E-05 NEI16665 Benzyl chloride 2.29E-05 0.00E+00 0.00E+00 1.06E-06 1.06E-07 NEI16665 Biphenyl 0.00E+00 0.00E+00 3.42E-08 0.00E+00 0.00E+00 NEI16665 Carbon disulfide 1.58E-02 2.45E-03 1.96E-04 3.16E-02 6.12E-04 NEI16665 Carbon tetrachloride 1.87E-02 1.27E-04 2.96E-05 2.74E-04 5.64E-053 of 9 REL AEGL1 AEGL2 ERPG1 ERPG2 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks Actual Emissions

Maximum Hazard Quotient 1

NEI16665 Chloroform 4.56E-03 0.00E+00 2.21E-06 0.00E+00 2.85E-06 NEI16665 Cumene 0.00E+00 4.27E-08 7.12E-09 0.00E+00 0.00E+00 NEI16665 Dimethyl formamide 0.00E+00 0.00E+00 1.50E-08 6.74E-07 1.35E-08 NEI16665 Ethyl benzene 0.00E+00 1.36E-05 3.98E-07 0.00E+00 0.00E+00 NEI16665 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 1.40E-07 3.47E-08 NEI16665 Formaldehyde 2.43E+00 1.22E-01 7.87E-03 1.12E-01 1.12E-02 NEI16665 Methanol 7.74E-04 3.14E-05 8.03E-06 8.34E-05 1.67E-05 NEI16665 Methyl tert-butyl ether 0.00E+00 6.38E-06 5.47E-07 0.00E+00 0.00E+00 NEI16665 Methylene chloride 8.88E-03 1.80E-04 6.55E-05 1.24E-04 4.78E-05 NEI16665 n-Hexane 0.00E+00 0.00E+00 7.17E-07 0.00E+00 0.00E+00 NEI16665 Phenol 2.46E-08 2.46E-09 1.60E-09 3.75E-09 7.50E-10 NEI16665 Styrene 2.27E-05 5.60E-06 8.66E-07 2.27E-06 4.33E-07 NEI16665 Tetrachloroethene 2.65E-04 2.21E-05 3.31E-06 7.80E-06 3.79E-06 NEI16665 Toluene 2.29E-04 1.13E-05 1.88E-06 4.46E-05 7.71E-06 NEI16665 Trichloroethylene 0.00E+00 2.73E-05 7.96E-06 3.54E-05 7.07E-06 NEI16665 Vinyl acetate 0.00E+00 2.88E-04 1.10E-05 3.84E-04 2.66E-05 NEI16665 Xylenes (mixed) 7.88E-05 3.10E-06 4.33E-07 0.00E+00 0.00E+00 NEI16783 1,4-Dioxane 1.64E-04 8.08E-06 4.11E-07 0.00E+00 0.00E+00 NEI16783 Acetonitrile 0.00E+00 1.07E-02 4.36E-04 0.00E+00 0.00E+00 NEI16783 Acrylonitrile 0.00E+00 2.34E-02 1.80E-03 1.06E-02 3.04E-03 NEI16783 Benzene 4.74E-03 3.63E-05 2.37E-06 3.85E-05 1.28E-05 NEI16783 Cumene 0.00E+00 9.86E-06 1.64E-06 0.00E+00 0.00E+00 NEI16783 Dimethyl formamide 0.00E+00 0.00E+00 8.22E-06 3.70E-04 7.40E-06 NEI16783 Ethyl benzene 0.00E+00 1.14E-04 3.34E-06 0.00E+00 0.00E+00 NEI16783 Formaldehyde 9.41E-02 4.71E-03 3.05E-04 4.31E-03 4.31E-04 NEI16783 Methanol 2.10E-02 8.51E-04 2.18E-04 2.26E-03 4.52E-04 NEI16783 Methyl tert-butyl ether 0.00E+00 1.55E-03 1.33E-04 0.00E+00 0.00E+00 NEI16783 Methylene chloride 6.06E-02 1.23E-03 4.46E-04 8.48E-04 3.26E-04 NEI16783 m-Xylene 9.64E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI16783 n-Hexane 0.00E+00 0.00E+00 3.44E-05 0.00E+00 0.00E+00 NEI16783 o-Xylene 4.93E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI16783 Phenol 1.28E-04 1.28E-05 8.31E-06 1.95E-05 3.89E-06 NEI16783 p-Xylene 9.86E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI16783 Styrene 1.15E-03 2.84E-04 4.39E-05 1.15E-04 2.20E-05 NEI16783 Tetrachloroethene 3.20E-04 2.67E-05 4.01E-06 9.43E-06 4.58E-06 NEI16783 Toluene 1.14E-02 5.63E-04 9.38E-05 2.22E-03 3.84E-04 NEI16783 Trichloroethylene 0.00E+00 4.23E-06 1.23E-06 5.48E-06 1.10E-06 NEI16783 Triethylamine 2.38E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI16783 Xylenes (mixed) 2.61E-03 1.03E-04 1.44E-05 0.00E+00 0.00E+00 NEI17512 Methyl tert-butyl ether 0.00E+00 3.31E-02 2.83E-03 0.00E+00 0.00E+00 NEI17512 Toluene 2.92E-03 1.44E-04 2.40E-05 5.68E-04 9.81E-05 NEI17512 Xylenes (mixed) 1.67E-03 6.57E-05 9.19E-06 0.00E+00 0.00E+00 NEI18116 Acetonitrile 0.00E+00 1.94E-04 7.91E-06 0.00E+00 0.00E+00 NEI18116 Methanol 1.02E-02 4.16E-04 1.06E-04 1.10E-03 2.21E-04 NEI18116 Methylene chloride 3.06E-02 6.21E-04 2.26E-04 4.29E-04 1.65E-04 NEI18116 n-Hexane 0.00E+00 0.00E+00 4.13E-06 0.00E+00 0.00E+00 NEI18116 Tetrachloroethene 8.96E-04 7.47E-05 1.12E-05 2.64E-05 1.28E-05 NEI18116 Toluene 1.68E-02 8.26E-04 1.38E-04 3.26E-03 5.63E-04 NEI18116 Trichloroethylene 0.00E+00 8.41E-05 2.45E-05 1.09E-04 2.18E-05 NEI18116 Xylenes (mixed) 2.28E-02 8.96E-04 1.25E-04 0.00E+00 0.00E+00 NEI32342 Acetonitrile 0.00E+00 2.25E-03 9.18E-05 0.00E+00 0.00E+00 NEI32966 Ethyl acrylate 0.00E+00 7.62E-09 1.73E-09 6.32E-06 2.16E-094 of 9 REL AEGL1 AEGL2 ERPG1 ERPG2 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks Actual Emissions

Maximum Hazard Quotient 1

NEI32966 Ethyl benzene 0.00E+00 7.97E-10 2.33E-11 0.00E+00 0.00E+00 NEI32966 Hydrochloric acid 6.91E-05 5.37E-05 4.40E-06 3.22E-05 4.84E-06 NEI32966 Methyl methacrylate 0.00E+00 1.91E-06 2.73E-07 0.00E+00 0.00E+00 NEI32966 Toluene 1.81E-06 8.93E-08 1.49E-08 3.52E-07 6.09E-08 NEI37022 Carbon disulfide 5.14E-04 7.96E-05 6.37E-06 1.03E-03 1.99E-05 NEI37022 Methanol 3.49E-03 1.42E-04 3.62E-05 3.76E-04 7.53E-05 NEI40462 1,1,1-Trichloroethane 3.64E-04 1.91E-05 7.51E-06 1.30E-05 6.52E-06 NEI40462 2,4-Toluene diisocyanate 0.00E+00 2.47E-01 5.86E-02 4.87E-01 3.14E-02 NEI40462 Acetonitrile 0.00E+00 1.17E-03 4.76E-05 0.00E+00 0.00E+00 NEI40462 Acrylonitrile 0.00E+00 1.27E-03 9.74E-05 5.75E-04 1.64E-04 NEI40462 Aniline 0.00E+00 4.70E-05 3.07E-05 0.00E+00 0.00E+00 NEI40462 Benzene 2.48E-02 1.90E-04 1.24E-05 2.02E-04 6.72E-05 NEI40462 Benzyl chloride 4.43E-03 0.00E+00 0.00E+00 2.04E-04 2.04E-05 NEI40462 Carbon disulfide 3.79E-05 5.88E-06 4.71E-07 7.59E-05 1.47E-06 NEI40462 Carbon tetrachloride 7.54E-03 5.12E-05 1.19E-05 1.10E-04 2.27E-05 NEI40462 Chlorobenzene 0.00E+00 2.85E-04 1.90E-05 0.00E+00 0.00E+00 NEI40462 Chloroform 1.21E-01 0.00E+00 5.87E-05 0.00E+00 7.58E-05 NEI40462 Cumene 0.00E+00 5.99E-06 9.98E-07 0.00E+00 0.00E+00 NEI40462 Dimethyl formamide 0.00E+00 0.00E+00 1.02E-05 4.57E-04 9.14E-06 NEI40462 Dimethyl sulfate 0.00E+00 1.28E-03 2.47E-04 0.00E+00 0.00E+00 NEI40462 Ethyl acrylate 0.00E+00 3.66E-04 8.30E-05 3.04E-01 1.04E-04 NEI40462 Ethyl benzene 0.00E+00 7.54E-04 2.20E-05 0.00E+00 0.00E+00 NEI40462 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 6.30E-04 1.56E-04 NEI40462 Formaldehyde 3.71E-01 1.86E-02 1.20E-03 1.70E-02 1.70E-03 NEI40462 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 2.56E-03 2.56E-04 NEI40462 Methanol 6.43E-03 2.61E-04 6.67E-05 6.93E-04 1.39E-04 NEI40462 Methyl methacrylate 0.00E+00 8.74E-04 1.25E-04 0.00E+00 0.00E+00 NEI40462 Methyl tert-butyl ether 0.00E+00 1.09E-05 9.37E-07 0.00E+00 0.00E+00 NEI40462 Methylene chloride 2.98E-03 6.04E-05 2.19E-05 4.17E-05 1.60E-05 NEI40462 m-Xylene 9.26E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI40462 n-Hexane 0.00E+00 0.00E+00 3.20E-06 0.00E+00 0.00E+00 NEI40462 o-Xylene 4.22E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI40462 Phenol 1.84E-03 1.84E-04 1.20E-04 2.80E-04 5.61E-05 NEI40462 p-Xylene 4.07E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI40462 Styrene 3.95E-03 9.76E-04 1.51E-04 3.95E-04 7.54E-05 NEI40462 Tetrachloroethene 1.42E-03 1.18E-04 1.77E-05 4.17E-05 2.02E-05 NEI40462 Titanium tetrachloride 0.00E+00 3.66E-03 2.53E-04 3.95E-04 9.87E-05 NEI40462 Toluene 7.98E-03 3.94E-04 6.56E-05 1.55E-03 2.68E-04 NEI40462 Trichloroethylene 0.00E+00 3.96E-05 1.15E-05 5.13E-05 1.03E-05 NEI40462 Triethylamine 1.69E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI40462 Vinyl acetate 0.00E+00 3.00E-04 1.14E-05 4.01E-04 2.77E-05 NEI40462 Vinyl chloride 1.10E-05 3.11E-06 6.41E-07 1.53E-06 1.53E-07 NEI40462 Xylenes (mixed) 1.06E-02 4.16E-04 5.83E-05 0.00E+00 0.00E+00 NEI41416 Chlorine 1.14E+00 1.60E-01 4.14E-02 8.27E-02 2.76E-02 NEI41416 Hydrazine 0.00E+00 2.52E-04 1.93E-06 5.04E-05 5.04E-06 NEI41416 Hydrochloric acid 3.45E-01 2.68E-01 2.20E-02 1.61E-01 2.42E-02 NEI42539 Glycol Ethers 2.20E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI42539 Methanol 2.15E-03 8.73E-05 2.23E-05 2.32E-04 4.63E-05 NEI42539 n-Hexane 0.00E+00 0.00E+00 1.78E-08 0.00E+00 0.00E+00 NEI42539 Toluene 1.49E-02 7.34E-04 1.22E-04 2.90E-03 5.01E-04 NEI42539 Xylenes (mixed) 2.25E-02 8.84E-04 1.24E-04 0.00E+00 0.00E+00 NEI6481 Benzene 2.85E-04 2.18E-06 1.43E-07 2.32E-06 7.73E-075 of 9 REL AEGL1 AEGL2 ERPG1 ERPG2 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks Actual Emissions

Maximum Hazard Quotient 1

NEI6481 Tetrachloroethene 1.82E-04 1.52E-05 2.28E-06 5.36E-06 2.60E-06 NEI6481 Vinyl chloride 6.87E-07 1.93E-07 3.99E-08 9.51E-08 9.51E-09 NEI6631 1,1,1-Trichloroethane 2.34E-03 1.22E-04 4.82E-05 8.37E-05 4.19E-05 NEI6631 1,3-Butadiene 0.00E+00 3.73E-05 4.66E-06 2.54E-03 1.27E-04 NEI6631 1,4-Dioxane 1.69E-03 8.29E-05 4.21E-06 0.00E+00 0.00E+00 NEI6631 Acetonitrile 0.00E+00 3.82E-03 1.56E-04 0.00E+00 0.00E+00 NEI6631 Acrylic acid 8.73E-04 1.19E-03 3.74E-05 1.75E-03 3.49E-05 NEI6631 Aniline 0.00E+00 1.99E-03 1.30E-03 0.00E+00 0.00E+00 NEI6631 Benzene 4.89E-02 3.74E-04 2.45E-05 3.97E-04 1.32E-04 NEI6631 Biphenyl 0.00E+00 0.00E+00 1.19E-04 0.00E+00 0.00E+00 NEI6631 Carbon disulfide 6.47E-04 1.00E-04 8.02E-06 1.29E-03 2.51E-05 NEI6631 Carbon tetrachloride 4.49E-02 3.04E-04 7.10E-05 6.56E-04 1.35E-04 NEI6631 Chlorobenzene 0.00E+00 1.87E-03 1.24E-04 0.00E+00 0.00E+00 NEI6631 Chloroform 3.90E-01 0.00E+00 1.89E-04 0.00E+00 2.44E-04 NEI6631 Cumene 0.00E+00 1.79E-05 2.98E-06 0.00E+00 0.00E+00 NEI6631 Dimethyl formamide 0.00E+00 0.00E+00 6.56E-05 2.95E-03 5.90E-05 NEI6631 Epichlorohydrin 6.25E-03 3.69E-04 8.93E-05 4.27E-04 1.07E-04 NEI6631 Ethyl benzene 0.00E+00 1.61E-04 4.69E-06 0.00E+00 0.00E+00 NEI6631 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 2.62E-04 6.46E-05 NEI6631 Formaldehyde 2.06E-01 1.03E-02 6.67E-04 9.45E-03 9.45E-04 NEI6631 Glycol Ethers 4.98E-01 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI6631 Hydrochloric acid 3.72E-02 2.90E-02 2.37E-03 1.74E-02 2.61E-03 NEI6631 Hydrofluoric acid 4.41E-02 1.29E-02 5.29E-04 6.62E-03 6.62E-04 NEI6631 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 2.45E-02 2.45E-03 NEI6631 Methanol 1.05E-02 4.28E-04 1.09E-04 1.14E-03 2.27E-04 NEI6631 Methyl methacrylate 0.00E+00 8.83E-05 1.26E-05 0.00E+00 0.00E+00 NEI6631 Methyl tert-butyl ether 0.00E+00 3.64E-05 3.12E-06 0.00E+00 0.00E+00 NEI6631 Methylene chloride 1.01E-02 2.04E-04 7.41E-05 1.41E-04 5.42E-05 NEI6631 n-Hexane 0.00E+00 0.00E+00 2.67E-05 0.00E+00 0.00E+00 NEI6631 Phenol 7.16E-03 7.16E-04 4.67E-04 1.09E-03 2.19E-04 NEI6631 Propylene oxide 1.30E-03 2.37E-05 5.85E-06 3.36E-05 6.84E-06 NEI6631 Styrene 2.52E-04 6.22E-05 9.62E-06 2.52E-05 4.81E-06 NEI6631 Tetrachloroethene 7.92E-03 6.60E-04 9.90E-05 2.33E-04 1.13E-04 NEI6631 Titanium tetrachloride 0.00E+00 1.09E-01 7.54E-03 1.18E-02 2.94E-03 NEI6631 Toluene 1.49E-02 7.33E-04 1.22E-04 2.89E-03 5.00E-04 NEI6631 Trichloroethylene 0.00E+00 5.67E-05 1.65E-05 7.35E-05 1.47E-05 NEI6631 Xylenes (mixed) 1.36E-02 5.35E-04 7.48E-05 0.00E+00 0.00E+00 NEI7162 1,1,1-Trichloroethane 2.11E-04 1.10E-05 4.34E-06 7.54E-06 3.77E-06 NEI7162 2,4-Toluene diisocyanate 0.00E+00 1.29E-01 3.06E-02 2.54E-01 1.64E-02 NEI7162 Acetonitrile 0.00E+00 1.78E-04 7.24E-06 0.00E+00 0.00E+00 NEI7162 Acrylic acid 7.92E-04 1.08E-03 3.40E-05 1.58E-03 3.17E-05 NEI7162 Acrylonitrile 0.00E+00 3.06E-04 2.35E-05 1.39E-04 3.97E-05 NEI7162 Allyl chloride 0.00E+00 2.40E-04 1.24E-05 2.25E-04 1.63E-05 NEI7162 Aniline 0.00E+00 6.49E-04 4.24E-04 0.00E+00 0.00E+00 NEI7162 Benzene 2.84E-02 2.17E-04 1.42E-05 2.31E-04 7.69E-05 NEI7162 Benzyl chloride 3.35E-03 0.00E+00 0.00E+00 1.55E-04 1.55E-05 NEI7162 Biphenyl 0.00E+00 0.00E+00 4.33E-05 0.00E+00 0.00E+00 NEI7162 Carbon disulfide 2.18E-04 3.39E-05 2.71E-06 4.37E-04 8.46E-06 NEI7162 Carbon tetrachloride 1.58E-02 1.07E-04 2.50E-05 2.31E-04 4.77E-05 NEI7162 Chlorobenzene 0.00E+00 5.91E-04 3.94E-05 0.00E+00 0.00E+00 NEI7162 Chloroform 9.54E-02 0.00E+00 4.62E-05 0.00E+00 5.96E-05 NEI7162 Cumene 0.00E+00 2.11E-06 3.52E-07 0.00E+00 0.00E+006 of 9 REL AEGL1 AEGL2 ERPG1 ERPG2 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks Actual Emissions

Maximum Hazard Quotient 1

NEI7162 Dimethyl formamide 0.00E+00 0.00E+00 5.87E-06 2.64E-04 5.28E-06 NEI7162 Epichlorohydrin 8.13E-04 4.80E-05 1.16E-05 5.56E-05 1.39E-05 NEI7162 Ethyl acrylate 0.00E+00 5.08E-05 1.15E-05 4.21E-02 1.44E-05 NEI7162 Ethyl benzene 0.00E+00 5.96E-04 1.74E-05 0.00E+00 0.00E+00 NEI7162 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 2.76E-04 6.82E-05 NEI7162 Ethylene glycol ethyl ether 1.43E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI7162 Formaldehyde 2.70E-02 1.35E-03 8.74E-05 1.24E-03 1.24E-04 NEI7162 Glycol Ethers 1.14E-02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI7162 Hexachlorobutadiene 0.00E+00 0.00E+00 0.00E+00 3.36E-04 1.15E-04 NEI7162 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 6.60E-04 6.60E-05 NEI7162 Methanol 6.86E-03 2.78E-04 7.11E-05 7.39E-04 1.48E-04 NEI7162 Methyl bromide 9.08E-04 0.00E+00 4.32E-06 0.00E+00 1.86E-05 NEI7162 Methyl chloride 0.00E+00 0.00E+00 1.11E-06 0.00E+00 2.54E-06 NEI7162 Methyl methacrylate 0.00E+00 1.33E-05 1.90E-06 0.00E+00 0.00E+00 NEI7162 Methylene chloride 8.85E-03 1.80E-04 6.52E-05 1.24E-04 4.76E-05 NEI7162 Methylene diphenyl diisocyanate 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.07E-05 NEI7162 m-Xylene 5.60E-05 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEI7162 n-Hexane 0.00E+00 0.00E+00 5.03E-08 0.00E+00 0.00E+00 NEI7162 Phenol 1.17E-03 1.17E-04 7.63E-05 1.79E-04 3.57E-05 NEI7162 Styrene 1.10E-03 2.73E-04 4.21E-05 1.10E-04 2.11E-05 NEI7162 Tetrachloroethene 1.32E-03 1.10E-04 1.65E-05 3.87E-05 1.88E-05 NEI7162 Titanium tetrachloride 0.00E+00 1.12E-02 7.79E-04 1.21E-03 3.04E-04 NEI7162 Toluene 8.01E-03 3.95E-04 6.59E-05 1.56E-03 2.69E-04 NEI7162 Trichloroethylene 0.00E+00 2.40E-05 7.00E-06 3.11E-05 6.22E-06 NEI7162 Vinyl acetate 0.00E+00 4.51E-03 1.72E-04 6.01E-03 4.16E-04 NEI7162 Vinyl chloride 2.93E-06 8.25E-07 1.70E-07 4.06E-07 4.06E-08 NEI7162 Xylenes (mixed) 7.26E-03 2.85E-04 3.99E-05 0.00E+00 0.00E+00 NEI7868 Ethyl benzene 0.00E+00 1.07E-04 3.13E-06 0.00E+00 0.00E+00 NEI7868 Hydrochloric acid 6.48E-03 5.04E-03 4.13E-04 3.03E-03 4.54E-04 NEI7868 Hydrofluoric acid 1.16E-03 3.41E-04 1.40E-05 1.75E-04 1.75E-05 NEI7868 Methanol 5.36E-04 2.18E-05 5.56E-06 5.77E-05 1.15E-05 NEI7868 Methylene chloride 6.98E-03 1.42E-04 5.14E-05 9.77E-05 3.76E-05 NEI7868 Toluene 4.90E-03 2.42E-04 4.03E-05 9.53E-04 1.65E-04 NEI7868 Xylenes (mixed) 2.06E-03 8.10E-05 1.13E-05 0.00E+00 0.00E+00 NEI880 Hydrazine 0.00E+00 9.95E-03 7.61E-05 1.99E-03 1.99E-04 NEI880 Hydrofluoric acid 5.39E-03 1.58E-03 6.46E-05 8.08E-04 8.08E-05 NEI880 Methylene chloride 9.57E-02 1.94E-03 7.05E-04 1.34E-03 5.15E-04 NEI880 Tetrachloroethene 1.55E-02 1.29E-03 1.93E-04 4.55E-04 2.21E-04 NEI880 Titanium tetrachloride 0.00E+00 6.09E-04 4.22E-05 6.58E-05 1.65E-05 NEIIL031069A Ethyl benzene 0.00E+00 5.82E-05 1.70E-06 0.00E+00 0.00E+00 NEIIL031069A Methanol 2.71E-04 1.10E-05 2.81E-06 2.92E-05 5.84E-06 NEIIL031069A Methylene chloride 3.35E-06 6.80E-08 2.47E-08 4.69E-08 1.81E-08 NEIIL031069A Tetrachloroethene 9.72E-06 8.10E-07 1.21E-07 2.86E-07 1.39E-07 NEIIL031069A Toluene 2.18E-03 1.08E-04 1.80E-05 4.25E-04 7.35E-05 NEIIL031069A Trichloroethylene 0.00E+00 4.55E-09 1.33E-09 5.90E-09 1.18E-09 NEIIL031069A Xylenes (mixed) 1.45E-03 5.71E-05 7.99E-06 0.00E+00 0.00E+00 NEIIL03153 Acetonitrile 0.00E+00 1.54E-04 6.27E-06 0.00E+00 0.00E+00 NEIIL03153 Dimethyl formamide 0.00E+00 0.00E+00 9.59E-10 4.32E-08 8.63E-10 NEIIL03153 Ethyl benzene 0.00E+00 3.09E-07 9.01E-09 0.00E+00 0.00E+00 NEIIL03153 Methanol 1.27E-02 5.17E-04 1.32E-04 1.37E-03 2.74E-04 NEIIL03153 Methylene chloride 8.67E-06 1.76E-07 6.39E-08 1.21E-07 4.67E-08 NEIIL03153 m-Xylene 5.31E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+007 of 9 REL AEGL1 AEGL2 ERPG1 ERPG2 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks Actual Emissions

Maximum Hazard Quotient 1

NEIIL03153 n-Hexane 0.00E+00 0.00E+00 1.01E-11 0.00E+00 0.00E+00 NEIIL03153 Phenol 3.20E-07 3.20E-08 2.08E-08 4.88E-08 9.76E-09 NEIIL03153 Tetrachloroethene 8.25E-06 6.87E-07 1.03E-07 2.43E-07 1.18E-07 NEIIL03153 Toluene 1.69E-05 8.33E-07 1.39E-07 3.29E-06 5.68E-07 NEIIN00345 Acetonitrile 0.00E+00 8.65E-05 3.52E-06 0.00E+00 0.00E+00 NEIIN00345 Ethyl benzene 0.00E+00 1.99E-05 5.82E-07 0.00E+00 0.00E+00 NEIIN00345 Formaldehyde 7.51E-03 3.76E-04 2.43E-05 3.44E-04 3.44E-05 NEIIN00345 Methanol 6.97E-04 2.83E-05 7.22E-06 7.50E-05 1.50E-05 NEIIN00345 Methylene chloride 1.64E-03 3.34E-05 1.21E-05 2.30E-05 8.85E-06 NEIIN00345 Styrene 1.10E-04 2.71E-05 4.20E-06 1.10E-05 2.10E-06 NEIIN00345 Tetrachloroethene 1.35E-04 1.12E-05 1.68E-06 3.96E-06 1.92E-06 NEIIN00345 Toluene 2.43E-03 1.20E-04 2.00E-05 4.74E-04 8.19E-05 NEIIN00345 Trichloroethylene 0.00E+00 9.49E-07 2.77E-07 1.23E-06 2.46E-07 NEIIN00345 Vinyl acetate 0.00E+00 3.85E-05 1.47E-06 5.14E-05 3.56E-06 NEIIN00345 Xylenes (mixed) 1.37E-03 5.40E-05 7.56E-06 0.00E+00 0.00E+00 NEIIN423 Ethyl benzene 0.00E+00 1.25E-05 3.65E-07 0.00E+00 0.00E+00 NEIIN423 Glycol Ethers 1.01E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEIIN423 Methanol 9.07E-04 3.68E-05 9.40E-06 9.77E-05 1.95E-05 NEIIN423 Methylene chloride 2.08E-05 4.23E-07 1.54E-07 2.92E-07 1.12E-07 NEIIN423 n-Hexane 0.00E+00 0.00E+00 3.00E-06 0.00E+00 0.00E+00 NEIIN423 Styrene 1.04E-04 2.58E-05 3.98E-06 1.04E-05 1.99E-06 NEIIN423 Tetrachloroethene 6.57E-05 5.47E-06 8.21E-07 1.93E-06 9.38E-07 NEIIN423 Toluene 1.00E-03 4.94E-05 8.24E-06 1.95E-04 3.37E-05 NEIIN423 Trichloroethylene 0.00E+00 2.21E-05 6.45E-06 2.86E-05 5.73E-06 NEIIN423 Xylenes (mixed) 1.19E-03 4.66E-05 6.53E-06 0.00E+00 0.00E+00 NEIKY103236 1,2-Epoxybutane 0.00E+00 1.85E-04 9.47E-05 0.00E+00 0.00E+00 NEIKY103236 Acetaldehyde 4.52E-01 2.62E-03 4.34E-04 1.18E-02 5.90E-04 NEIKY103236 Benzene 4.50E-06 3.44E-08 2.25E-09 3.66E-08 1.22E-08 NEIKY103236 Carbon tetrachloride 4.51E-04 3.06E-06 7.15E-07 6.60E-06 1.36E-06 NEIKY103236 Chloroform 1.02E-02 0.00E+00 4.92E-06 0.00E+00 6.35E-06 NEIKY103236 Ethyl benzene 0.00E+00 2.72E-04 7.93E-06 0.00E+00 0.00E+00 NEIKY103236 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 8.41E-07 2.08E-07 NEIKY103236 Methanol 1.09E-03 4.43E-05 1.13E-05 1.17E-04 2.35E-05 NEIKY103236 Methylene chloride 4.47E-05 9.07E-07 3.29E-07 6.26E-07 2.41E-07 NEIKY103236 n-Hexane 0.00E+00 0.00E+00 1.60E-06 0.00E+00 0.00E+00 NEIKY103236 Propionaldehyde 0.00E+00 5.52E-04 9.79E-05 0.00E+00 0.00E+00 NEIKY103236 Styrene 1.15E-03 2.83E-04 4.37E-05 1.15E-04 2.19E-05 NEIKY103236 Toluene 1.25E-02 6.17E-04 1.03E-04 2.44E-03 4.21E-04 NEIKY103236 Trichloroethylene 0.00E+00 6.69E-09 1.95E-09 8.67E-09 1.73E-09 NEIKY103236 Vinyl acetate 0.00E+00 2.80E-07 1.07E-08 3.73E-07 2.58E-08 NEIKY103236 Xylenes (mixed) 4.29E-03 1.69E-04 2.36E-05 0.00E+00 0.00E+00 NEILA0930002 Chlorine 4.01E-02 5.62E-03 1.45E-03 2.90E-03 9.68E-04 NEIMILF450 Glycol Ethers 1.41E-04 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEIMILF450 Xylenes (mixed) 1.79E-06 7.05E-08 9.87E-09 0.00E+00 0.00E+00 NEIMIN0731 Methanol 1.49E-05 6.04E-07 1.54E-07 1.60E-06 3.20E-07 NEIMIN0731 Methylene chloride 2.43E-06 4.93E-08 1.79E-08 3.40E-08 1.31E-08 NEIMIN0731 Tetrachloroethene 5.67E-07 4.72E-08 7.08E-09 1.67E-08 8.10E-09 NEIMIN0731 Xylenes (mixed) 1.89E-05 7.44E-07 1.04E-07 0.00E+00 0.00E+00 NEINE00009 Formaldehyde 1.14E+00 5.70E-02 3.69E-03 5.23E-02 5.23E-03 NEINE00009 Methanol 5.27E-02 2.14E-03 5.47E-04 5.68E-03 1.14E-03 NEINE00009 Methyl tert-butyl ether 0.00E+00 1.19E-03 1.02E-04 0.00E+00 0.00E+00 NEINE00009 Phenol 9.77E-03 9.77E-04 6.37E-04 1.49E-03 2.98E-048 of 9 REL AEGL1 AEGL2 ERPG1 ERPG2 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks Actual Emissions

Maximum Hazard Quotient 1

NEINE00009 Styrene 2.70E-03 6.67E-04 1.03E-04 2.70E-04 5.15E-05 NEINE00009 Xylenes (mixed) 4.51E-02 1.77E-03 2.48E-04 0.00E+00 0.00E+00 NEIOHT$5677 1,1,1-Trichloroethane 1.80E-03 9.43E-05 3.71E-05 6.45E-05 3.22E-05 NEIOHT$5677 Ethyl benzene 0.00E+00 2.39E-04 6.97E-06 0.00E+00 0.00E+00 NEIOHT$5677 Methanol 2.73E-04 1.11E-05 2.83E-06 2.94E-05 5.88E-06 NEIOHT$5677 Methylene chloride 8.33E-02 1.69E-03 6.14E-04 1.17E-03 4.49E-04 NEIOHT$5677 Tetrachloroethene 6.23E-02 5.19E-03 7.78E-04 1.83E-03 8.90E-04 NEIOHT$5677 Toluene 1.75E-03 8.63E-05 1.44E-05 3.41E-04 5.89E-05 NEIOHT$5677 Trichloroethylene 0.00E+00 9.53E-05 2.78E-05 1.24E-04 2.47E-05 NEIOHT$5677 Xylenes (mixed) 1.49E-03 5.85E-05 8.20E-06 0.00E+00 0.00E+00 NEIOHT$5915 1,1,1-Trichloroethane 2.74E-05 1.44E-06 5.66E-07 9.82E-07 4.91E-07 NEIOHT$5915 1,1-Dimethylhydrazine 0.00E+00 0.00E+00 2.97E-06 0.00E+00 0.00E+00 NEIOHT$5915 1,3-Butadiene 0.00E+00 1.69E-08 2.12E-09 1.15E-06 5.77E-08 NEIOHT$5915 1,4-Dioxane 1.17E-04 5.77E-06 2.93E-07 0.00E+00 0.00E+00 NEIOHT$5915 2,4-Toluene diisocyanate 0.00E+00 2.54E-04 6.02E-05 5.01E-04 3.23E-05 NEIOHT$5915 Acetaldehyde 3.10E-04 1.80E-06 2.97E-07 8.09E-06 4.04E-07 NEIOHT$5915 Acetonitrile 0.00E+00 9.65E-05 3.93E-06 0.00E+00 0.00E+00 NEIOHT$5915 Acrolein 6.77E-04 2.45E-05 7.36E-06 1.54E-05 4.98E-06 NEIOHT$5915 Acrylic acid 8.46E-07 1.15E-06 3.63E-08 1.69E-06 3.38E-08 NEIOHT$5915 Acrylonitrile 0.00E+00 3.38E-06 2.60E-07 1.54E-06 4.40E-07 NEIOHT$5915 Aniline 0.00E+00 3.22E-06 2.10E-06 0.00E+00 0.00E+00 NEIOHT$5915 Benzene 6.24E-04 4.77E-06 3.12E-07 5.07E-06 1.69E-06 NEIOHT$5915 Carbon disulfide 1.11E-04 1.71E-05 1.37E-06 2.21E-04 4.28E-06 NEIOHT$5915 Carbon tetrachloride 4.18E-04 2.83E-06 6.61E-07 6.11E-06 1.26E-06 NEIOHT$5915 Chlorobenzene 0.00E+00 1.30E-05 8.66E-07 0.00E+00 0.00E+00 NEIOHT$5915 Chloroform 4.43E-03 0.00E+00 2.15E-06 0.00E+00 2.77E-06 NEIOHT$5915 Chloromethyl methyl ether 0.00E+00 0.00E+00 6.35E-06 0.00E+00 3.08E-06 NEIOHT$5915 Cumene 0.00E+00 3.72E-07 6.20E-08 0.00E+00 0.00E+00 NEIOHT$5915 Dichlorvos 0.00E+00 1.37E-05 2.65E-06 0.00E+00 0.00E+00 NEIOHT$5915 Dimethyl formamide 0.00E+00 0.00E+00 8.46E-07 3.81E-05 7.62E-07 NEIOHT$5915 Dimethyl sulfate 0.00E+00 2.82E-04 5.46E-05 0.00E+00 0.00E+00 NEIOHT$5915 Epichlorohydrin 5.86E-05 3.46E-06 8.37E-07 4.01E-06 1.00E-06 NEIOHT$5915 Ethyl acrylate 0.00E+00 9.95E-07 2.26E-07 8.25E-04 2.82E-07 NEIOHT$5915 Ethyl benzene 0.00E+00 6.07E-06 1.77E-07 0.00E+00 0.00E+00 NEIOHT$5915 Ethylene dibromide 0.00E+00 5.47E-07 3.95E-07 0.00E+00 0.00E+00 NEIOHT$5915 Ethylene dichloride 0.00E+00 0.00E+00 0.00E+00 2.90E-06 7.17E-07 NEIOHT$5915 Ethylene oxide 0.00E+00 0.00E+00 2.88E-05 0.00E+00 2.59E-05 NEIOHT$5915 Formaldehyde 6.71E-03 3.35E-04 2.17E-05 3.07E-04 3.07E-05 NEIOHT$5915 Glycol Ethers 4.40E-03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEIOHT$5915 Hexachlorobutadiene 0.00E+00 0.00E+00 0.00E+00 2.74E-05 9.41E-06 NEIOHT$5915 Hydrazine 0.00E+00 3.91E-05 2.99E-07 7.81E-06 7.81E-07 NEIOHT$5915 Hydrochloric acid 5.64E-06 4.39E-06 3.59E-07 2.63E-06 3.95E-07 NEIOHT$5915 Hydrofluoric acid 9.17E-05 2.68E-05 1.10E-06 1.37E-05 1.37E-06 NEIOHT$5915 Maleic anhydride 0.00E+00 0.00E+00 0.00E+00 1.16E-04 1.16E-05 NEIOHT$5915 Methanol 1.50E-04 6.07E-06 1.55E-06 1.61E-05 3.22E-06 NEIOHT$5915 Methyl bromide 7.81E-06 0.00E+00 3.71E-08 0.00E+00 1.60E-07 NEIOHT$5915 Methyl chloride 0.00E+00 0.00E+00 2.40E-08 0.00E+00 5.50E-08 NEIOHT$5915 Methyl hydrazine 0.00E+00 0.00E+00 1.73E-07 0.00E+00 0.00E+00 NEIOHT$5915 Methyl iodide 0.00E+00 0.00E+00 0.00E+00 2.26E-07 1.17E-07 NEIOHT$5915 Methyl methacrylate 0.00E+00 1.33E-06 1.90E-07 0.00E+00 0.00E+00 NEIOHT$5915 Methylene chloride 5.55E-05 1.13E-06 4.09E-07 7.77E-07 2.99E-07 NEIOHT$5915 n-Hexane 0.00E+00 0.00E+00 1.62E-08 0.00E+00 0.00E+009 of 9 REL AEGL1 AEGL2 ERPG1 ERPG2 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks Actual Emissions

Maximum Hazard Quotient 1

NEIOHT$5915 Phenol 2.05E-04 2.05E-05 1.33E-05 3.12E-05 6.24E-06 NEIOHT$5915 Styrene 3.47E-06 8.56E-07 1.32E-07 3.47E-07 6.62E-08 NEIOHT$5915 Tetrachloroethene 4.13E-05 3.44E-06 5.16E-07 1.21E-06 5.90E-07 NEIOHT$5915 Toluene 1.87E-04 9.23E-06 1.54E-06 3.64E-05 6.30E-06 NEIOHT$5915 Trichloroethylene 0.00E+00 1.23E-06 3.59E-07 1.60E-06 3.19E-07 NEIOHT$5915 Triethylamine 3.02E-06 0.00E+00 0.00E+00 0.00E+00 0.00E+00 NEIOHT$5915 Vinyl chloride 9.12E-07 2.56E-07 5.30E-08 1.26E-07 1.26E-08 NEIOK4013107 Acetonitrile 0.00E+00 9.65E-05 3.93E-06 0.00E+00 0.00E+00 NEIOK4013107 Benzene 2.45E-03 1.87E-05 1.23E-06 1.99E-05 6.64E-06 NEIOK4013107 Ethyl benzene 0.00E+00 1.52E-05 4.42E-07 0.00E+00 0.00E+00 NEIOK4013107 Methanol 1.52E-04 6.16E-06 1.57E-06 1.63E-05 3.27E-06 NEIOK4013107 Methyl tert-butyl ether 0.00E+00 3.60E-04 3.08E-05 0.00E+00 0.00E+00 NEIOK4013107 Styrene 5.06E-05 1.25E-05 1.93E-06 5.06E-06 9.65E-07 NEIOK4013107 Tetrachloroethene 1.06E-04 8.85E-06 1.33E-06 3.12E-06 1.52E-06 NEIOK4013107 Toluene 2.44E-03 1.20E-04 2.01E-05 4.75E-04 8.21E-05 NEIOK4013107 Xylenes (mixed) 3.14E-03 1.23E-04 1.73E-05 0.00E+00 0.00E+00 NEIUT19459 1,1,1-Trichloroethane 1.20E-02 6.28E-04 2.48E-04 4.30E-04 2.15E-04 NEIUT19459 Benzene 6.61E-02 5.06E-04 3.31E-05 5.37E-04 1.79E-04 NEIUT19459 Chloroform 2.29E-01 0.00E+00 1.11E-04 0.00E+00 1.43E-04 NEIUT19459 Methanol 3.72E-02 1.51E-03 3.85E-04 4.00E-03 8.00E-04 NEIUT19459 Methylene chloride 5.53E-02 1.12E-03 4.07E-04 7.74E-04 2.98E-04 NEIUT19459 n-Hexane 0.00E+00 0.00E+00 7.88E-06 0.00E+00 0.00E+00 NEIUT19459 Toluene 6.97E-04 3.44E-05 5.73E-06 1.36E-04 2.34E-05 NEIUT19459 Trichloroethylene 0.00E+00 1.23E-05 3.58E-06 1.59E-05 3.18E-06 NEIUT19459 Vinyl acetate 0.00E+00 7.16E-04 2.73E-05 9.55E-04 6.61E-05 NEIUT19459 Xylenes (mixed) 8.60E-03 3.38E-04 4.73E-05 0.00E+00 0.00E+00

Note: BOLD indicates acute risks greater than 1 1 Some maximum acute impacts may be at onsite locations.1 of 1 Facility NEI ID Pollutant Tier 1 1 Tier 2 1
NEIOHT$5915 PAH Below Tier 1 Threshold Below Tier 1 Threshold Table 4 – Multipathway Screening Analysis Results Actual Emissions

1 Numeric value indicates number of times facility level emissions above screening threshold.

### Appendix 6 Acute Impacts Refined Analysis

### Refined Acute Modeling Approach
Initial acute screening risk calculati ons were performed with the HEM-3 model. HEM-3 estimates acute (1-hour) im pacts at both polar and census block receptors. It is assumed for this shor t 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 th e facilities evaluated made it difficult to determine whether receptors were on- or off-site. In the absence of such information, the first ring of polar recept ors was placed 100 meters from the plant center for many facilities. However, these polar rings often transected on-site locations, restricting public access to exposures at these levels and thereby overestimating exposures.
The screening approach used by HEM-3 to estimate maximum 1-hour exposures also likely overestimate s. To estimate maximum 1-hour concentrations at each receptor, HEM-3 sums the maximum concentrations attributed to each source, regardless of whether those maximum concentrations occurred during the same hour. In ot her words, HEM-3 assumes that the maximum impact from each source at each re ceptor occurs at the same time. In actuality, maximum impacts from different sources may occur at different times.
This appendix includes the resu lts of refined acute assessments performed for facilities that exceeded short-term health benchmarks using the HEM-3 approach. The refinements can addr ess both areas of conservatism described above. The simplest refinement is to plot the HEM-3 polar grid results on aerial photographs of the facilities; this allows the assessment of off-site locations that may be accessible to the public ( e.g., roadways and public buildings.). The attached figure present the estimated hazard quotient values (the modeled 1-hour concentration of a pollu tant divided by its short-term health benchmark) at the polar rec eptors near the facilities.HEM‐3(Screening)RefinedResultsNEI16665 FormaldehydeREL2.40.5Examined facility for maximum off-site impactNEI42539GlycolEthersREL2.21.0Examined facility for maximum off-site impactTable 1 – Maximum Predicted Acute Risks Greater than 1 (Refined Approach)Off-Site Waste and Recovery Operations1Indicatesmodelingtechniqueusedtorefinedestimates;seeAppendix6forfiguresdepictingoff‐siteimpactsFacilityNEIIDPollutant CriteriaRefinedModelingApproach1Baseline‐Actuals

# Figure 1 – NEI16665 Acute Formaldehyde HQ (REL)

# Figure 2 – NEI42539 Acute Glycol Ethers HQ (REL)

### Appendix 7 Dispersion Model Receptor Revisions and Additions

### Dispersion Model Receptor Revi sions and Additions for the Off-Site Waste and Recovery Operations To estimate ambient concentrations for evaluating long-term exposures, the HEM-3 model uses the geographic centroids of census bl ocks (currently utiliz ing the 2010 Census) as dispersion model receptors. The census block ce ntroids are generally go od surrogates for where people live within a census bloc k. A census block generally encompasses about 40 people or 10- 15 households. However, in cases where a block ce ntroid is located on in dustrial property, or where a census block is large and the centroid le ss likely to be representative of the block’s residential locations, the block centroid may not be an appropriate surrogate.
Census block centroids that are on facility property can sometimes be identified by their proximity to emission sources. In cases where a census block centroid was within 300 meters of any emission source, we viewed aerial images of the facility to dete rmine whether the block centroid was likely located on facil ity property. The selection of th e 300-meter distance reflects a compromise between too few and too many blocks identified as being potentially on facility property. Distances smaller th an 300 meters woul d identify only block centroids near the emission sources and could exclude some block cen troids that are still within facility boundaries, particularly for large facilities. Distances si gnificantly larger than 300 meters would identify many block centroids that are outside facility bounda ries, particularly for small facilities. Where we confirmed a block centroid on facility prope rty, we moved the block centroid to a location that best represents the residential locations in the block.
In addition, census block centroids for blocks with large areas may not be representative of residential locations. Risk estimates based on such centroids can be understated if there are residences nearer to a facility than the centroid, and overstated if the residences are farther from the facility than the centroid. To avoid understating the maximu m individual risk associated with a facility, in some cases we relocated bl ock centroids, or added di spersion model receptors other than the block centroid. We examined aerial images of all large cens us blocks within one kilometer of any emission source. Experience from previous risks characteri zations show that in most cases the MIR is generally located within 1 km of the facility boundary. If the block centroid did not represent the residential locations, we relocated it to better represent them. If residential locations could not be represented by a single receptor (that is, the residences were spread out over the block), we added additional r eceptors for residences nearer to the facility than the centroid.
For these source categories, the table below contains each census block for which we changed the centroid location because it was on facility property or was otherwise not representative of the residential locations in the block. The table also contains the locations of additional receptors that were incl uded to represent residential locations nearer to the facility than the block centroid.

### Revised Census Block Centroid Locations and A dditional Receptors for Off-Site Waste and Recovery Operations

Recommended Corr.

Facility ID Latitude Longitude Note NEIKS325 37.081590 -95.587250 Add User Defined Receptor

NEIKY103236 38.437410 -85.2367 70 Add User Defined Receptor

NEIKY103236 38.43216 -85.238493

NEI32966 38.210103 -85.841025 Add User Defined Receptor

NEIMIN0731 42.35858 -82.9621 59 Add User Defined Receptor

NEIOHT$5677 39.984220 -82.4667 50 Add User Defined Receptor

NEI40462 41.322633 -82.037665 Add User Defined Receptor

NEI40462 41.322409 -82.039941 Add User Defined Receptor

NEI42539 43.078957 -89.194301 Add User Defined Receptor

NEI18116 44.754340 -91.452468 Add User Defined Receptor

NEI18116 44.757789 -91.453563 Add User Defined Receptor