                                       
                                       
                                       
                                       

                       Residual Risk Assessment for the 
Leather Finishing Source Category in Support of the December, 2018 Risk and Technology Review Final Rule
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
             EPA's Office of Air Quality Planning and Standards
                          Office of Air and Radiation
                                 November 2018
                                       
                                       
                                                                               


Table of Contents 

Executive Summary	5
1	Introduction	7
2	Methods	8
2.1	Emissions and source data	9
2.2	Dispersion modeling for inhalation exposure assessment	10
2.3	Estimating chronic human inhalation exposure	13
2.4	Acute risk screening and refined assessments	13
2.5	Multipathway human health risk analysis	14
2.6	Environmental risk analysis	21
2.7	Dose-response assessment	23
2.7.1	Sources of chronic dose-response information	23
2.7.2	Sources of acute dose-response information	28
2.8	Risk characterization	30
2.8.1	General	30
2.8.2	Mixtures	32
3	Risk results for the NAME source category	32
3.1	Source category description and emissions	32
3.2	Baseline risk characterization	38
3.2.1	Risk assessment results based on actual emissions	39
3.2.2	Risk assessment results based on allowable emissions	48
3.3	Post-control risk characterization	49
4	General discussion of uncertainties in the risk assessment	50
4.1	Emissions inventory uncertainties	50
4.2	Exposure modeling uncertainties	51
4.2.1	Inhalation exposure modeling	51
4.2.2	Multipathway exposure modeling	52
4.3	Uncertainties in the dose-response relationships	53
5	References	61

Index of Tables

Table 2.2-1.  AERMOD version 16216r Model Options for RTR Modeling	11
Table 2.5-1.  Multipathway Scenarios and Ingestion Pathways	16
Table 3.1-1  Summary of Emissions from NAME Source Category and Dose-Response Values Used in the Residual Risk Assessment	35
Table 3.2-1.  Source Category Level Inhalation Risks for NAME Based on Actual Emissions	39
Table 3.2-2  Source Category Contribution to Facility-Wide Cancer Risks Based on Actual Emissions	41
Table 3.2-3.  Source Category Level Multipathway Screening Assessment Risk Results for the NAME	42
Table 3.2-4.  Source Category Level Site-Specific Multipathway Risk Results NAME Baseline Scenario	45
Table 3.2-5.  Source Category Level Environmental Risk Screen PB-HAP Results for the NAME Source Category	47
Table 3.2-6.  Source Category Level Environmental Risk Screen Acid Gases Results for the NAME Source Category	48




Appendices

Appendix 1	Emissions Inventory Support Documents
Appendix 2	Technical Support Document for HEM-3 Modeling
Appendix 3	Meteorological Data for HEM-3 Modeling	
Appendix 4	Dispersion Model Receptor Revisions and Additions 
Appendix 5	Analysis of short-term emission rates relative to long-term emission rates
Appendix 6	Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR 
Appendix 7	Protocol for Site-Specific Multipathway Risk Assessment
Appendix 8	Dose-Response Values Used in the RTR Risk Assessments
Appendix 9	Environmental Risk Screen 
Appendix 10	Detailed Risk Modeling Results 
Appendix 11	Site-Specific Human Health Multipathway Residual Risk Assessment Report	


Index of Acronyms

AERMOD	American Meteorological Society/EPA Regulatory Model
AEGL		Acute exposure guideline level
ASTDR	US Agency for Toxic Substances and Disease Registry
CalEPA	California Environmental Agency
CTE		Central Tendency Estimate
ERPG		Emergency Response Planning Guideline
HAP		Hazardous Air Pollutant
HEM		Human Exposure Model
HI		Hazard index
HQ		Hazard quotient
IRIS		Integrated Risk Information System
MACT		Maximum Achievable Control Technology
MIR		Maximum Individual Risk
MOA		Mode of action
NAC		National Advisory Committee
NAAQS	National Ambient Air Quality Standards
NATA 	National Air Toxics Assessment
NEI		National Emissions Inventory
NPRM		Notice of Proposed Rulemaking
PB-HAP	Persistent and Bioaccumulative  -  HAP
PAH		Polycyclic aromatic hydrocarbon
POM		Polycyclic organic matter
REL		Reference exposure level
RfC		Reference concentration
RfD		Reference dose
RTR		Risk and Technology Review
TOSHI		Target-organ-specific hazard index
TRIM		Total Risk Integrated Methodology
TRIM.FaTE	TRIM Environmental Fate, Transport, and Ecological Exposure
URE		Unit risk estimate
    
    Executive Summary
This document describes the risk assessment that the U.S. Environmental Protection Agency (EPA) conducted to assess the human health and environmental risks posed by hazardous air pollutant (HAP) emissions from the Portland Cement Manufacturing Industry. Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process for addressing emissions of HAP from stationary sources. In the first stage, EPA must promulgate technology-based national emission standards for hazardous air pollutants (NESHAP) for categories of sources. EPA has completed this stage. For NESHAP that require maximum achievable control technology (MACT) standards, EPA is required to complete a second stage of the regulatory process  -  the residual risk review. In this second stage, EPA is required to assess the health and environmental risks that remain after implementation of the standards. EPA must also review each of the technology-based standards at least every eight years and revise them, as necessary, taking into account developments in practices, processes and control technologies. If appropriate based on the results of the risk and technology reviews, the Agency will revise the rule. For efficiency, the Agency includes the analyses in the same regulatory package and calls the rulemakings the Risk and Technology Review (RTR).

The specific source category results contained in this document are from the leather finishing residual risk assessment, in support of EPA's 2017 proposed rule. The EPA is proposing amendments to the national emissions standards for hazardous air pollutants (NESHAP) for leather finishing facilities to address the results of the RTR review of the maximum achievable control technology (MACT) standards, required under Section 112. This source category includes 4 leather finishing facilities. Emission points include coating lines, roll coaters, lacquer mixing rooms, spray booths, and dryers. We estimate that there are a total of 24 leather finishing facilities operating in the U.S. but only 4 are identified as subject to 40 CFR part 63, subpart TTTT, three of which are major sources of hazardous air pollutants (HAP). The fourth facility was a major source, but subsequently became an area source of HAP sometime after the compliance date. Of the other 20 facilities, 5 facilities are synthetic minor sources and the other 15 are area sources. The 4 major facilities emit a total of approximately 22.5 tons per year of HAP, consisting of speciated and unspeciated glycol ethers, chromium (III) compounds, trimethylamine, ethylene glycol, toluene, and methyl isobutyl ketone. There were no emissions of persistent and biaccumulative HAP (PB-HAP). There were also no emissions of environmental risk HAP (i.e., PB-HAP, hydrogen chloride, or hydrogen fluoride). 

The below table summarizes the results of the risk assessment for the leather finishing source category.

            Risk Summary for the Leather Finishing Source Category
                                       
                            Inhalation Cancer Risk
                            Population Cancer Risk
                     Max Chronic Individual Noncancer Risk
                                  Max Acute 
                                Noncancer Risk
                             Multipathway Analysis
                                       
                            Maximum Individual Risk
                                (in 1 million)
                                  Risk Driver
                               Cancer Incidence
                               (cases per year)
                           >= 1 in 10 in 1 million
                              >= 1 in 1 million
                                 Hazard Index
                                    (TOSHI)
                                  Risk Driver
                                Hazard Quotient
                                  Risk Driver

                                  Risk Driver
                                      and
                                Screening Level
Baseline Actual Emissions
Source Category
                                       0
                                      N/A
                                       0
                                       0
                                       0
                             0.04 (Repro-ductive)
                               propyl cellosolve
                                       3
                       propyl cellosolve, glycol ethers
                                      N/A
Whole Facility
                                     0.09
                                    arsenic
                                       
                                       
                                       
                              0.1 (Respira-tory)
                                   acrolein
                                      ---
                                      ---
                                       
                                      ---
                                       
Baseline Allowable Emissions
Source Category
                                       0
                                      N/A
                                       0
                                       0
                                       0
                              0.3 (Repro-ductive)
                               propyl cellosolve
                                      ---
                                      ---
                                      ---

The results of the chronic inhalation cancer risk assessment based on actual emissions from the leather finishing source category are that there are no (0) known cancer risks posed by the 4 facilities, as the HAP emitted have no known cancer risks. 

Regarding the noncancer risk assessment, the maximum chronic noncancer hazard index value for the source category could be up to 0.04 (for the reproductive hazard index) driven by emissions of propyl cellosolve from spray booths, dryers, and coaters. Of the 4,600,000 people living within 50 kilometers of these facilities, no one is exposed to noncancer hazard index levels above 1, based on actual emissions from this source category. 

Regarding acute health hazards posed by actual baseline emissions, the highest screening acute hazard quotient value is estimated to be 3 (based on an acute dose-response value for propyl cellosolve and glycol ethers). Two facilities have an acute hazard quotient greater than 1 for the acute dose-response values examined.

No PB-HAP or environmental risk HAP were emitted from the 4 leather finishing facilities and therefore there were no multipathway or environmental risks associated with the source category.

Analysis of potential differences between actual emissions levels and the maximum emissions allowable under EPA's standards (i.e., "allowable emissions") were also calculated for the leather finishing facilities. Risk results from the inhalation risk assessment using the allowable emissions indicate that there were no known cancer risks. The maximum chronic noncancer hazard index value could be as high as 0.3 (for the reproductive target organ) at the allowable emissions level, with propyl cellosolve emissions driving this maximum hazard index value. Based on allowable emission rates for this source category, no people were estimated to have a noncancer hazard index above 1.

Finally, an assessment of whole-facility risks was performed to provide context for the source category compared to whole facility risks. The maximum cancer risks for the whole facility assessment were 0.09 in a million, driven by arsenic emissions from external combustion boilers. The maximum chronic noncancer hazard index value was 0.1 (for the reproductive target organ), driven by acrolein emissions from external combustion boilers. 

This document summarizes the methods used to conduct the risk assessment of this source category as well as the results. Section 1 discusses the relevant regulatory framework including background on the Clean Air Act sections which require the EPA to conduct these source category risk assessments. Methods described in Section 2 include those used by EPA to develop refined estimates of chronic inhalation exposures and human health risks for cancer and noncancer endpoints, as well as those used to screen for acute health risks, chronic non-inhalation (i.e., multipathway) health risks, and adverse environmental effects. The source category-specific results for the risks are presented in Section 3. Section 4 contains a discussion of the uncertainties of the risk assessment, including uncertainties in the exposure assessment and in the dose-response values. The appendices to this risk report contain detailed descriptions of the methods used and the results.
    
 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 National Emission Standards for Hazardous Air Pollutants (NESHAP) for categories of sources (e.g., petroleum refineries, pulp and paper mills, etc.). EPA has completed this stage. For NESHAP that require maximum achievable control technology (MACT) standards, EPA is required to complete a second stage of the regulatory process  -  the residual risk review. In this 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. For each source category for which EPA issued MACT standards, the residual risk stage must be completed within eight years of promulgation of the initial technology-based standard.

Also, under section 112(d)(6), EPA must review each of the technology-based standards at least every eight years and revise it, as necessary, taking into account developments in practices, processes and control technologies. If appropriate based on the results of the risk and technology reviews, the Agency will revise the rule. For efficiency, the Agency includes the 112(f) and 112(d) analyses in the same regulatory package and calls the rulemakings the Risk and Technology Review (RTR). 

In December 2006 we consulted with a panel from the EPA's Science Advisory Board (SAB) on the "Risk and Technology Review (RTR) Assessment Plan," and in June 2007 we received a letter with the results of that consultation. Subsequent to the consultation, in June 2009, EPA met with an SAB panel for a formal peer review of the "Risk and Technology Review (RTR) Assessment Methodologies" (USEPA, 2009a). We received the final SAB report on this review in May 2010 (USEPA, 2010a). Where appropriate, we have responded to the SAB's key recommendations in developing our current risk assessments and continue our efforts to improve our assessments by incorporating updates that address the SAB's recommendations as they are developed and become available. Our responses to the key recommendations of the SAB are outlined in a memo entitled, "EPA's Actions in Response to Key Recommendations from the SAB Review of RTR risk Assessment Methodologies" (USEPA, 2010b). EPA has updated several aspects of the risk assessment methodologies contained in the 2009 document. In 2017, we submitted these updated methodologies to SAB for review. The updated methodologies are described in Screening Methodologies to Support Risk and Technology Reviews (RTR): A Case Study Analysis. SAB's findings for this review will be submitted to EPA in the fall of 2017."


This document contains the methods we use to conduct the risk assessment, the results of the residual risk assessment performed for the leather finishing source category, and a description of associated uncertainties.

 Methods
A risk assessment consists of four steps: 1) hazard identification, 2) dose-response assessment, 3) exposure assessment, and 4) risk characterization. The first step, hazard identification, determines whether the pollutants of concern can be linked to the health effects in question (cancer and/or noncancer). Section 112 of the CAA identifies the HAP to be considered in the risk assessment for this source category. The second step is the dose-response assessment, which quantifies the relationship between the dose of a pollutant and the resultant health effects. Dose-response assessments are performed by EPA through the Integrated Risk Information System (IRIS) process as well as by other agencies, such as the Agency for Toxic Substances and Disease Registry (ATSDR). See Section 2.7 of this document for more information on dose-response assessments. The third and fourth steps, the exposure assessment and the risk characterization, respectively, are specific to the source category and are described throughout this report. The exposure assessment includes characterization of HAP emissions, environmental fate and transport, and population exposure for both inhalation and non-inhalation pathways. The fourth and final step, risk characterization, integrates all the information from the previous steps and describes the outcome of the assessment. This four-step approach to risk assessment was endorsed by the National Academy of Sciences in its publication "Science and Judgment in Risk Assessment" (NAS, 1994) and subsequently was adopted in the EPA's "Residual Risk Report to Congress" (USEPA, 1999).

The EPA conducts a risk assessment that provides estimates of the maximum individual risk (MIR) posed by the HAP emissions from each source in the source category, the hazard index (HI) for chronic exposures to HAP with potential to cause chronic (or long-term) noncancer health effects and the hazard quotient (HQ) for acute exposures to HAP with the potential to cause acute (or short-term) noncancer health effects. The MIR is defined as the cancer risk associated with a lifetime of exposure at the highest concentration of HAP where people are likely to live. The HQ is the ratio of the potential exposure to the HAP to the level at or below which no adverse effects are expected; the HI is the sum of HQs for HAP that affect the same target organ or organ system. The risk assessment also provides estimates of the distribution of cancer risks within the exposed populations, cancer incidence and an evaluation of the potential for adverse environmental effects. The following sections describe how we estimate HAP emissions and conduct steps three and four of the risk assessment. The methods used to assess risks are consistent with those peer reviewed by a panel of the EPA's Science Advisory Board (SAB) in 2009 and described in their peer review report issued in 2010 (USEPA 2010a).
  
 Emissions and source data
To conduct the exposure assessment, EPA gathers the best available data on emissions, emissions release parameters, and other relevant source category-specific parameters. EPA determines the HAP emissions levels from emission points in the source category and identifies the emissions release characteristics of these emission points (e.g., stack height). EPA often begins with the National Emissions Inventory (NEI) database as the starting point for emissions and emissions release characteristics for the source category. The NEI database contains information about sources that emit HAP and it contains annual air pollutant emissions estimates. EPA's industry experts review the source category data for consistency and completeness. This includes an evaluation of facilities contained in the source category, the emissions units expected to be included for the processes in the source category, and the HAP compounds and emissions levels typically seen. If necessary, EPA will conduct a formal information collection request (CAA, Section 114) for emissions data and other data from the industry associated with the source category under review. Following the creation of the initial data set, the EPA performs the technology review and the residual risk assessment. If appropriate, based on the results of these reviews, the EPA proposes regulatory action for the source category in a Notice of Proposed Rulemaking (NPRM) published in a Federal Register notice. The NPRM data sets are available for public review in the rulemaking docket. Industry, state and local agencies, as well as the public have an opportunity to provide comments on the data, analyses, and results used to support the proposed action. EPA incorporates the comments, as appropriate, conducts any re-assessment, and summarizes and responds to comments before finalizing the action. Through source category-specific engineering reviews, information collection efforts, and public comment, EPA ensures that the data used to conduct risk assessments in support of the RTR rulemakings are of high quality.

In order to put the source category risks in context, we also examine the risks from the entire "facility," where the facility includes all HAP-emitting operations within a contiguous area and under common control. In other words, we examine the HAP emissions not only from the source category emission points of interest, but also from all other emission sources at the facility for which we have data. Using the most current available NEI data at the time of the assessment, the EPA develops "facility-wide" emissions estimates. It is important to note that the NEI facility-wide inventory may not always reflect the level of detail or be representative of the same temporal period that is found in the source category-specific inventory. Further information on the NEI, which is developed from federal/state/local/tribal submitted data, can be found on the EPA's web site at: https://www.epa.gov/air-emissions-inventories/national-emissions-inventory.

Details on the development of the source data, emissions, associated uncertainties in the data for the leather finishing source category can be found in Appendix 1 (Emissions Inventory Support Documents). Section 3 provides a summary of the processes and emissions associated with this source category.
  
 Dispersion modeling for inhalation exposure assessment
For the residual risk analyses, we estimate both long- and short-term inhalation exposure concentrations and associated health risks from each facility in the source category. To do this, we use the Human Exposure Model 3 (HEM-3 or HEM-AERMOD) modeling system  -  which combines the Human Exposure Model (HEM) with the American Meteorological Society/EPA Regulatory Model (AERMOD) dispersion modeling system. HEM-3 performs three main operations: atmospheric dispersion modeling, estimation of individual human exposures and health risks, and estimation of population risks. The approach used in applying this modeling system is outlined below. Further details are provided in Appendix 2 to this document (Technical Support Document for HEM-3 Modeling). This section focuses on the dispersion modeling component. 

The dispersion model in the HEM-3 modeling system, AERMOD version 16216r 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 (USEPA, 2005a). Further details on AERMOD can be found in the AERMOD User's Guide (USEPA, 2016a) and the AERMOD Implementation Guide (USEPA, 2016b). The model is used to develop annual average ambient concentrations through the simulation of hour-by-hour dispersion from the emission sources into the surrounding atmosphere. Unless data are available on the hours of operation for a source category, default hourly emission rates used for this simulation are generated by evenly dividing the total annual emission rate from the inventory into the 8,760 hours of the year.

The first step in the application of the HEM-3 modeling system is to predict ambient concentrations at locations of interest. The AERMOD model options employed are summarized in Table 2.2-1 and are discussed further below.
                                       
      Table 2.2-1.  AERMOD version 16216r Model Options for RTR Modeling

Modeling Option
Selected Parameter for chronic exposure
Type of calculations
Hourly Ambient Concentration
Source types
Point                  Volume
Area                   Polygon
Line                   Buoyant Line
Receptor orientation
Polar (13 rings and 16 radials)
Discrete (census block centroids) and user-supplied receptors
Terrain characterization
Actual from USGS 1/3-arc-second DEM data
Building downwash
Not Included
Plume deposition/depletion
Not Included
Urban source option
Site Specific (See Appendix 2) 
Meteorology
1-year representative NWS from nearest site (824 stations) for year 2016

In HEM-3, meteorological data are ordinarily selected from a list of more than 800 National Weather Service (NWS) surface observation stations across the continental United States, Alaska, Hawaii, and Puerto Rico, and HEM-3 defaults to the station closest to each modeled facility. We use data from other stations in special circumstances if we have reason to believe that other data are more representative for certain facilities. In this analysis, the average distance between a modeled facility and the respective meteorological station was 16 miles (26 km). The meteorological data in HEM-3's library are for a single year, and 2016 is the most recent full year of available data. EPA's Guideline on Air Quality Models addresses the regulatory application of air quality models for assessing criteria pollutants, and requires five years of data to capture variability in weather patterns from year to year. We follow the guideline for air toxics modeling also; however, because dispersion model runtimes using five years of meteorological data would be too long for RTR source categories with many sources, we model only a single year. While the selection of a single year may result in under-prediction of long-term ambient levels at some locations, it may result in over-prediction at others. 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" (USEPA 2009a).

We use the AERMET meteorological data preprocessor and the Automated Surface Observing System (ASOS) surface data and Forecast Systems Laboratory (FSL) upper air data to generate nationwide surface and profile files for input into AERMOD. In 2016, the Agency released to the public on the EPA's Support Center for Regulatory Atmospheric Modeling (SCRAM) website both AERMET and AERMOD (version 16216r). Appendix 3 to this document (Meteorological Data for HEM-3 Modeling) provides a complete listing of meteorological stations and assumptions, along with further details used in processing the data through AERMET. EPA has posted the AERMET meteorological data (2016) used in this analysis on the EPA's Fate, Exposure, and Risk Analysis (FERA) website under the Human Exposure Model (HEM) page.

The HEM-3 modeling system estimates ambient concentrations at the geographic centroids of census blocks (using the 2010 Census) and at other receptor locations that can be specified by the user. See Appendix 4 of this document (Dispersion Model Receptor Revisions and Additions) for a discussion of user receptors and centroid location changes specific to this source category. HEM-3 accounts for the effects of multiple facilities when estimating concentration impacts at each block centroid. We typically combine the impacts of all facilities within the same source category and assess chronic exposure and risk for all census blocks with at least one resident (i.e., locations where people may reasonably be assumed to reside rather than receptor points at the fenceline of a facility). We then calculate ambient concentrations as the annual average of all estimated short-term (one-hour) concentrations at each block centroid. We do not consider possible future residential use of currently uninhabited areas.

To assess the potential impacts from short-term exposures, we estimate worst-case one-hour concentrations at the census block centroids and at points closer to the facility (using either the polar receptors or user-specified receptors) that represent locations where people may be present for short periods. Note that this is in contrast to the development of ambient concentrations for evaluating long-term exposures, which we perform only for occupied census blocks. Since short-term emission rates are needed to screen for the potential for hazard via acute exposures, and since the emission data typically 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 annual hourly emission rate for that source. However, sources may emit on a more intermittent basis and source category-specific data may support the use of engineering judgement to determine peak hourly emissions for any given process. Further information on the factor used to estimate short-term emissions for this source category is provided in Appendix 1, and further discussion of the acute risk assessment can be found in Section 2.4.

We determine census block elevations for HEM-3 nationally from the US Geological Survey 1/3 Arc Second National Elevation Dataset, which has a spatial resolution of about 10 meters. Each polar receptor is assigned the highest elevation of any census block in its neighborhood (all blocks closer to that polar receptor than any other polar receptor). If an elevation is not provided for an emission source, the model uses the average elevation of all polar receptors on the innermost polar ring. In addition to using receptor elevation to determine plume height, AERMOD adjusts the plume's flow if nearby elevated hills are expected to influence the wind patterns. For details on how hill heights are estimated and used in the AERMOD modeling, see Appendix 2 of this document.

 Estimating chronic human inhalation exposure
We use the estimated annual average ambient air concentration of each HAP at each census block centroid or user-defined receptor as a surrogate for the lifetime inhalation exposure concentration of all the people who reside in the census block. The risk assessment does not consider either the short-term or long-term behavior (mobility) of the exposed populations and its potential influence on their exposure.
  
We do not address short-term human activity, including indoor air concentrations. Our experience with the National Air Toxics Assessment (NATA), which models daily human activity using EPA's HAPEM, suggests that given our current understanding of the ratio of exposure concentrations to ambient values, including short-term human activity in RTR analyses would, on average, reduce risk estimates by up to about 25 percent for particulate HAP and typically by much less for gaseous HAPs. To ensure the risk characterization is health protective, EPA risk assessors do not include this small potential reduction in exposure concentrations when calculating risks.

We do not address long-term migration or population growth or decrease over the 70-year modeling period. Instead, we assume that each person's predicted exposure is constant over the course of their lifetime, which is assumed to be 70 years. The assumption of not considering short- or long-term population mobility does not bias the estimate of the theoretical MIR (assumes a person stays in one location for 70 years) nor does it affect the estimate of cancer incidence since the total population number remains the same. It does, however, affect the shape of the distribution of individual risks across the affected population, shifting it toward higher estimated individual risks at the upper end and reducing the number of people estimated to be at lower risks, thereby increasing the estimated number of people at higher risk levels.

 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 follow a similar approach to that for chronic health risk assessments under the residual risk program, in that we begin with a screening assessment and then, if appropriate, perform a refined assessment.

The approach for the acute health risk screening assessment is designed to eliminate from further consideration those facilities for which we have confidence that no acute adverse health effects of concern will occur. For this screening assessment, we use readily available data and conservative assumptions for emission rates, meteorology, and exposure location that, in combination, approximate a worst-case exposure.

The following are the steps we take and assumptions we make in the acute screening assessment:

 When available, we use peak 1-hour emission data obtained from data collection efforts or estimated based on the operating characteristics and engineering judgement of facility emission sources; otherwise, we use a default emission adjustment factor of 10 based on an analysis using a short-term emissions data set from a number of sources located in Texas (originally reported on by Allen et al. 2004) (see Appendix 5 of this document, Analysis of Data on Short-term Emission Rates Relative to Long-term Emission Rates). 
 We assume that the peak emissions occur at all emission points at the same time.
 For facilities with multiple emission points, 1-hour concentrations at each receptor are assumed to be the sum of the maximum concentrations due to each emission point, regardless of whether those maximum concentrations occurred during the same hour. 
 Worst-case meteorology (from one year of local meteorology) is 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 is assumed to be located downwind at the point of maximum modeled impact during this same worst-case 1-hour period, but no nearer to the source than 100 meters.
      
As a result of this screening assessment, the maximum HAP concentration is compared to multiple acute dose-response values for the HAP being assessed to determine whether a possible acute health risk might exist. The acute dose-response values are described in section 2.6 of this report. 

A facility will either be found to pose no potential acute health risks (i.e., it will "screen out") or will need to undergo a more refined assessment. When we identify levels of a HAP that exceed its acute health benchmarks, we perform a more refined assessment, if possible. Situations in which we have used engineering judgement to estimate emissions, a refinement may be to obtain facility-specific data on HAP emissions. Other refinements may include the temporal pattern of emissions (number of working hours, batch vs continuous operation), the location of emission points, the boundaries of the facility, and/or the local meteorology. In some cases, all of these site-specific data are used to refine the assessment; in others, lesser amounts of site-specific data may be used to determine that acute exposures are not a concern, and significant additional data collection is not necessary. See Section 3 of this document for the approach used for this source category.

 Multipathway human health risk assessment
Due to the potential for significant human health risks due to exposures via routes other than inhalation (e.g., ingestion), we determine whether any sources emit HAP known to be persistent and bioaccumulative in the environment (PB-HAP). The set of PB-HAP compounds or compound classes initially identified for potential screening assessment (from EPA's Air Toxics Risk Assessment (ATRA) Library) included the following: cadmium compounds, chlordane, chlorinated dibenzodioxins and furans (dioxins), 1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene (DDE), heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury compounds, methoxychlor, polychlorinated biphenyls (PCB), polycyclic organic matter (POM), toxaphene, and trifluralin. Of these, EPA identified cadmium compounds, dioxins, mercury compounds, lead, POM, as well as arsenic, as PB-HAP of primary concern, based on assessment of national emission totals, toxicity considerations, and bioaccumulation potential. We assess these six PB-HAP for human health risks due to non-inhalation exposure. 

We use a tiered approach to evaluate emissions of these PB-HAP for potential non-inhalation risks. This approach is designed to eliminate from further consideration those facilities for which we have confidence that human health risks will not occur due to non-inhalation exposure to their PB-HAP emissions. The approach was developed for use with EPA's peer-reviewed Total Risk Integrated Methodology: Fate, Transport, and Ecological Exposure (TRIM.FaTE) model. 

For each carcinogenic PB-HAP, we have derived a screening threshold emission rate at which the maximum excess lifetime cancer risk would be 1-in-1 million. For each PB-HAP that causes noncancer health effects, we have derived a screening threshold emission rate for which the maximum HQ would be 1. The ratio of facility emissions to the screening threshold emission rate is termed a "screening value;" facility emissions that exceed the screening threshold emission rate have a screening value greater than 1. A screening value greater than 1 in any of the tiered screening methods represents a high-end estimate of what the risk or hazard may be; it cannot be equated with a risk value or a HQ (or HI). For example, for a carcinogen, a screening value of 30 (i.e., facility emissions are 30 times above the screening threshold emission rate) means that we are confident that the cancer risk is lower than 30-in-1 million. Similarly, for a non-carcinogen, a screening value of 2 (i.e., facility emissions are 2 times above the screening threshold emission rate) can be interpreted to mean that we are confident that the noncancer HQ would be lower than 2.

For Tier 1, 2, and 3 assessments, we use hypothetical exposure scenarios to assess whether non-inhalation exposures pose a potential human health risk. Exposure scenarios were developed to simulate generic gardening and subsistence farming and subsistence fishing lifestyles. Each screening exposure scenario is designed to represent the upper end of the range of possible exposure levels, such that it is a conservative but not impossible scenario. The exposure scenarios were developed for use in conjunction with the TRIM.FaTE model. These hypothetical exposure scenarios and associated ingestion exposure pathways are shown in Table 2.5-1.


          Table 2.5-1.  Multipathway Scenarios and Ingestion Pathways

Hypothetical
Exposure Scenario
                                     Fish
                                       
                                Breast Milk [a]
                                       
                               Beef/Pork/Chicken
                                       
                                  Dairy Milk
                                       
                                     Eggs
                                       
                                     Soil
                                       
                           Fruits and Vegetables [b]
                                       
Combined Fisher and Farmer 
(Tier 1)
                                       x
                                       x
                                       x
                                       x
                                       x
                                       x
                                       x
Fisher 
(Tier 2)
                                       x
                                       x
                                       
                                       
                                       
                                       
                                       
Gardener (urban or rural)
Tier 2)
                                       
                                       x
                                       
                                       
                                       
                                       x
                                       x
Farmer [c]
(Tier 2)

                                       
                                       x
                                       x
                                       x
                                       x
                                       x
                                       x
Pollutants of Concern [d] 

                                    Hg, Cd,
                                As, dioxin, POM
                                    dioxin
                                       
                                As, dioxin, POM
                                       
                                As, dioxin, POM
                                       
                                As, dioxin, POM
                                       
                                As, dioxin, POM
                                       
                                As, dioxin, POM
                                       
[a] Health risks from the breast milk pathway are only associated with exposure to dioxins.
[b] Both protected and unprotected fruits and vegetables are included.
[c] This scenario may be included in a Tier 2 assessment in cases where we have site-specific data   
  indicating that farming operations are present. 
[d] The health endpoint for exposure to Hg (as methylmercury) and Cd is noncancer and the health endpoint for exposure to As (as inorganic arsenic), dioxin, and POM is cancer.

For the Tier 1 screening assessment, we determine whether the facility-specific emission rates for each emitted PB - HAP are high enough to create the potential for significant non-inhalation human health risks under reasonable worst-case conditions. We do this by comparing the facility-specific emission rates to the screening threshold emission rates for each PB-HAP for a hypothetical upper-end screening exposure scenario  -  the combined fisher and farmer scenario. The subsistence fisher scenario assumes a high-end fish consumption rate of 373 g/day for adults, a 99[th] percentile ingestion rate (Burger, 2002); fish consumption rates for other age groups are presented in Appendix 6. The farmer scenario involves an individual that lives for a 70-year lifetime on a farm near the source and consumes produce grown, and meat and animal products raised, on the farm. The ingestion rates used for these food groups, and for incidental soil ingestion, are set at the 90th percentile of EPA's Exposure Factors Handbook: 2011 Edition (USEPA, 2011) and are considered upper-bound levels. The fisher and farmer exposure scenarios are combined for the Tier 1 TRIM.FaTE model application. See Appendix 6 (Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR) for a complete discussion of the development and testing of the screening scenario and the screening threshold emission rates. 

For those facilities with PB-HAP emissions that exceed the Tier 1 screening threshold emission rate, we conduct a Tier 2 multipathway screening assessment. For the Tier 2 screening assessment, we refine the assessment by using the facility locations and considering two separate exposure scenarios  -  the fisher scenario and the home gardener scenario (rural or urban, as appropriate). In some cases, if supported by site-specific information, the farmer scenario is also considered. For each facility, we use the Tier 1 PB-HAP screening threshold emission rate, but with adjustments based on the ingested media and based on an understanding of how exposure concentrations estimated for the screening scenario change with use of the local meteorology and environmental assumptions. The gardener and fisher scenarios replace the Tier 1 combined fisher and farmer scenario as more likely exposure scenarios. The gardener scenario is only evaluated for carcinogenic PB-HAP (i.e., arsenic, dioxin, and POM) because the evaluated non-carcinogens (i.e., mercury and cadmium) accumulate in soil and the farm food chain in much smaller amounts than in fish tissue. For the gardener scenario, the Tier 1 PB-HAP screening threshold emission rates are adjusted to reflect exposure only through soil and farm foods, based on the rural/urban classification of the facility site (with urban gardeners growing and ingesting less home-grown produce than rural gardeners). The gardener scenarios (rural and urban) involve an individual that maintains a garden and consumes produce from this garden for 70 years at his/her residence. The evaluated locations of the gardener correspond to the maximum impacted residential receptor according to the RTR inhalation cancer assessment for each of the 8 wind octants (N, NE, E, SE, ...) for all carcinogenic HAPs combined. The screening threshold emission rate can be different at each of these gardener locations, based on distance from the facility and based on local meteorology conditions. The ingestion rates used for the food groups are set at the 90[th] percentile and mean values for rural and urban, respectively, based on data from EPA's Exposure Factors Handbook: 2011 Edition (USEPA, 2011); both gardeners have incidental soil ingestion rates equal to those of the farmer. The largest of the gardener screening values is identified for each PB-HAP. The fisher scenario is conducted for all of the currently evaluated PB-HAP, whose Tier 1 PB-HAP screening threshold emission rates are adjusted to reflect exposure only through fish ingestion. For the Tier 2 assessment, to fulfill the adult ingestion rate for the fisher scenario, if needed, more than one lake may be included in the modeling in order to reach a cumulative total of 373 acres and achieve the 373-g/day fish ingestion rate. A complete discussion of the bioassay studies used to support the assumption that the biological productivity limitation of each lake is 1 gram of fish caught and consumed per acre of water per day is provided in Appendix 6 of this document. The screening threshold emission rate can be different at each lake location, based on distance from the facility and based on local meteorology conditions.

If we need to include more than one lake in the Tier 2 screen to achieve the 373 g/day ingestion rate, we begin with the lake with the highest modeled chemical concentration of a given PB-HAP group and "fish" up to the lake's biological productivity. We then systematically proceed to other lakes based on concentration, until the 373 g/day target is met. A maximum travel radius of 50 km relative to the facility is used to maintain a realistic scenario for the fisher. The final Tier 2 screening result for the fisher can be expressed as the sum of the screening result from each lake that is fished (which is based on the amount of fish ingested from each lake multiplied by the chemical concentration in fish). If the highest-concentration lake is at least 373 acres in size, the adult fisher catches and consumes 373 g/day of fish from that lake. If the cumulative size of multiple visited lakes exceeds 373 acres, the model includes from the final lake only the amount of fish necessary to satisfy the ingestion rate (i.e., to reach 373 g/day). If the total acreage of lakes within 50 km is less than 373, the screening result reflects a reduced ingestion rate based on the lake acreage. The order of fished lakes for a facility follows the order of PB-HAP concentration in fish from highest to lowest based on the facility's emissions. However, the resulting screening value calculations described above also potentially consider chemical inputs from emissions from multiple facilities. If a fished lake for one facility ("Facility A") is also within 50 km of another facility ("Facility B") in the source category, then the lake receives chemical input from emissions from two facilities. The order of fished lakes for Facility A considers only Facility A's chemical inputs to the lake, but the final fisher screening values for Facility A include the summed chemical inputs of Facility A and Facility B. If that lake was also fished for the Facility B scenario, then the same process would be applied to Facility B. 

The Tier 2 assessment yields a facility-specific screening value for each PB-HAP for the fisher scenario and for the gardener scenario. If information is available to identify subsistence farming operations, the Tier 2 assessment will also include a screening value for the farmer scenario. Tier 2 screening values are evaluated for the source category to determine whether further refined screening is necessary for those facilities that may pose a significant risk. A finding that a facility's emissions exceed the Tier 2 screening threshold emission rate does not necessarily mean that multipathway impacts are significant, only that we cannot rule out that possibility based on the results of the screen. See Appendix 6 of this document for a complete discussion of the Tier 2 screening assessment. 

For facilities for which the Tier 2 screening value(s) indicate a potential health risk to the public, we can conduct a Tier 3 multipathway screening assessment. The Tier 3 screening assessment has three individual stages; we progress through these stages until the facility's screening values indicate that the emissions are unlikely to pose health risks to the public, or until all three stages are complete.

The first stage of a Tier 3 screening assessment, the lake-assessment stage, is a refinement of the fisher scenario. We examine the fished lakes from Tier 2 and evaluate the existence, the potential purpose, the accessibility and fishability, and the suitability of the lakes for the models and methods used in the screening assessments. We do not reasonably expect a subsistence fisher to catch and consume fish from lakes or ponds that are for industrial or wastewater disposal; are covered in thick plant growth (e.g., swamps or marshes); are clearly closed to public use; or no longer exist (i.e., filled or drained). TRIM.FaTE is not configured to model chemical processes and environmental fate and transport mechanisms in saltwater or brackish waters, nor is it configured to model the very large watersheds and water dynamics of rivers, bays or very large lakes (e.g., larger than 100,000 acres). We use aerial imagery and web inquires to evaluate whether any Tier 2 fished lakes meet these disqualifying criteria and, if so, remove those lakes from all future screening assessments. If we remove a lake from a facility's assessment, and the total acres of fished lakes drops below the target of 373 acres, we evaluate the previously unfished lake with the highest chemical concentration, and so on, until the sizes of the qualifying lakes collectively comprise at least 373 acres or all lakes have been evaluated. We then rerun the fisher screening scenario with the revised lake data set. If the PB-HAP emissions for a facility exceed the fisher screening threshold emission rate based on the revised lake data set, we can conduct the next stage of the Tier 3 screening assessment (i.e., the plume-rise screen); otherwise, the emissions are considered unlikely to pose significant health risks in the fisher scenario.

The second stage of a Tier 3 screening assessment, the plume-rise stage, is a refinement of the previously assessed scenarios (i.e., Tier 2 farmer, Tier 2 gardener, Tier 3 lake-assessment fisher) where emissions exceeded screening threshold emission rates. We use site-specific hourly meteorology and facility-specific emission-point characteristics to estimate the fraction of annual emissions that stay within TRIM.FaTE's mixing layer where exposure occurs (i.e., that do not exit the mixing layer). In Tiers 1 and 2, all chemicals are emitted inside the mixing layer and are available for ground-level exposure. In reality, meteorological conditions and emission-point characteristics can cause emissions occasionally to reach higher than the mixing layer. In TRIM.FaTE, any emissions exiting the mixing layer do not reenter the mixing layer, resulting in no ground-level exposure for those emissions. In this Tier 3 stage, we use thermodynamic equations with local hourly meteorology and facility stack parameters to calculate hourly plume-rise heights. The fraction of annual hours during which the plume-rise height is less than the mixing-layer height equals the fraction of annual emissions available for human exposure in the screening assessment. We calculate these fractions for the location of each fished lake and for each relevant garden because lakes and gardens can be in different directions from the facility; thus, these calculations are conditional on wind direction. The results of this stage of Tier 3 are revised fisher and/or gardener screening values for each relevant PB-HAP and facility, accounting for emissions deposited above the mixing layer. If the revised screening value still indicates potential health risks to the public, we can proceed to the final stage of the Tier 3 screening assessment (i.e., the time-series screen); otherwise, the PB-HAP emissions are considered unlikely to pose significant risks.

In the third and final stage of a Tier 3 screening assessment, the time-series assessment, we can conduct new runs of TRIM.FaTE for each relevant lake and/or garden location for a facility for every PB-HAP that represents a risk concern based upon the Tier 3 plume-rise assessment. For these model runs, we start with the screening configuration corresponding to the lake and/or garden location, and we use site-specific hourly meteorology and the hourly plume-rise values calculated in the Tier 3 plume-rise assessment. Allowing TRIM.FaTE to model chemical fate and transport with hour-by-hour changes in meteorology and plume rise produces a more accurate estimate of chemical concentrations in media of interest, as compared to the static values used in Tier 2 and the post-processing adjustments made in the Tier 3 plume-rise assessment. If a facility's model-estimated PB-HAP screening-level cancer risk is below 1-in-1 million (or screening-level HQ is below 1 for non-carcinogens), the emissions are considered unlikely to pose significant risks.

If a facility's PB-HAP Tier 3 screening results still indicate a potential health risk to the public and data are available, we may elect to conduct a more refined multipathway assessment. A refined assessment replaces some of the assumptions made in the screening with site-specific data. The refined assessment also uses the TRIM.FaTE model and facility-specific emission rates for each PB-HAP. Many variables are available to consider in a refined multipathway assessment, and we have developed a protocol to maintain consistency across source categories. This protocol can be found in Appendix 7 of this document (Protocol for Site-Specific Multipathway Risk Assessment) and details of the site-specific multipathway assessment can be found in Appendix 11 of this document (Site-Specific Human Health Multipathway Residual Risk Assessment Report).

Lead
We take a different approach for assessing lead compounds. In evaluating the potential multipathway risks from emissions of lead compounds, rather than developing a screening emission rate for them, we compare maximum estimated chronic atmospheric concentrations with the current national ambient air quality standard (NAAQS) for lead. Values below the NAAQS are considered to have a low potential for multipathway risks.

The NAAQS value, a public health policy judgment, incorporates the Agency's most recent health evaluation of air effects of lead exposure for the purposes of setting a national ambient air quality standard. In setting this value, the Administrator promulgated a standard that was requisite to protect public health with an adequate margin of safety. We consider values below the level of the primary NAAQS to protect against multipathway risks because, as mentioned above, the primary NAAQS is set to protect public health with an adequate margin of safety. However, ambient air lead concentrations above the NAAQS are considered to pose the potential for increased risk to public health. We consider this NAAQS assessment to be a refined analysis given: 1) the numerous health studies, detailed risk and exposure analyses, and level of external peer and public review that went into the development of the primary NAAQS for lead, combined with 2) the site-specific dispersion modeling used in this assessment to estimated ambient lead concentrations due to the source category emissions. It should be noted, however, that this comparison does not account for possible population exposures to lead from sources other than the one being modeled; for example, via consumption of water from untreated local sources or ingestion of locally grown food. Nevertheless, the Administrator judged that such a standard would protect, with an adequate margin of safety, the health of children and other at-risk populations against an array of adverse health effects, most notably including neurological effects, particularly neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in setting the standard, also recognized that no evidence or risk-based bright line indicated a single appropriate level. Instead, a collection of scientific evidence and other information was used to select the standard from a range of reasonable values (73 FR 67006).

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

 Environmental risk assessment
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 eight environmental HAP:

 Six persistent bioaccumulative HAP (PB-HAP)  -  cadmium, dioxins, POM, mercury (both inorganic mercury and methylmercury), arsenic, and lead;
 Two acid gases  -  hydrogen chloride (HCl) and hydrogen fluoride (HF).

HAP that persist and bioaccumulate are of particular environmental concern because they accumulate in the soil, sediment, and water. The acid gases  -  HCl and HF  -  were included due to their well-documented potential to cause direct damage to terrestrial plants. See Appendix 9 of this document (Environmental Risk Screen) for a more detailed discussion of the environmental risk screen.

For the environmental risk screening assessment, EPA first determines whether any facilities in the source category emit any of the eight environmental HAP. If one or more of the environmental HAP are emitted by at least one facility in the source category, we proceed to the second step of the environmental risk screen.

For cadmium, mercury, POM, arsenic, and dioxins, the environmental screening assessment consists of the same three tiers used in the multipathway human health risk assessment (see Section 2.5). In the first tier, the same TRIM.FaTE modeling used in human health risk assessment is conducted, using reasonable worst-case environmental conditions to identify screening threshold emission rates corresponding to ecological benchmarks for soil, fish, surface water, and sediment. For each facility and PB-HAP, facility emissions are compared to theses screening threshold emission rates to determine the potential for significant impacts on off-site ecological receptors. The ratio of facility emissions to the screening threshold emission rate is termed a "screening value." Facility emissions that exceed the screening threshold emission rate have a screening value greater than 1, and risks above levels of concern for ecological receptors are possible. Screening values below 1 indicate that risks to ecological receptors are likely below levels of concern. 
For those facilities with PB-HAP emissions that exceed a Tier 1 screening threshold emission rate, we conduct a Tier 2 screening assessment. In Tier 2, the Tier 1 screening threshold emission rates are adjusted to account for local meteorology and environmental assumptions. For lake-related ecological receptors, actual locations of lakes within 50 km of the facility are identified, and the screening threshold emission rate can be different at each lake location based on distance from the facility and based on local meteorology conditions. After the screening value (i.e., ratio of facility emissions to screening threshold emission rate) is calculated at each lake, the largest screening value is identified. Screening threshold emission rates for soil receptors are evaluated at many locations surrounding the facility and are also impacted by distance from facility and local meteorology. For soil receptors in Tier 2, we are interested in the overall average screening value across all soil receptors (for a given facility and PB-HAP), and we are also interested in the total area in the vicinity of the facility where screening values are above 1 (for a given facility and PB-HAP). If a lake-related screening value is above 1, or the soil screening value is above 1 at any location, or the overall average soil screening value is above 1, it does not necessarily mean that the ecological effects are significant, but only that we cannot rule out that possibility. For facilities with Tier 2 screening values above 1, we can evaluate their emissions further in Tier 3.
Like in the multipathway human health risk assessment, in Tier 3 of the environmental screening assessment, we examine the suitability of the lakes around the facilities to support life and remove those that are not (e.g., lakes that have been filled in or are industrial ponds), adjust emissions for plume-rise, and conduct hour-by-hour time-series assessments. For the lake assessment, we remove from the screening any lakes that appear to be industrial, for wastewater disposal, or no longer exist. TRIM.FaTE is not configured to model chemical processes and environmental fate and transport mechanisms in saltwater or brackish waters, nor is it configured to model the very large watersheds and water dynamics of rivers or very large lakes (e.g., larger than 100,000 acres); these types of water bodies are also removed from the screening assessment. Unlike the multipathway human health risk assessment, we assume that if lakes that are swampy or are not publicly accessible, they still can support ecological life and some animals will still eat from them. After lakes are removed that meet these disqualifying criteria, lake-related receptors are rescreened. For the plume-rise assessment, as in the human health assessment, we adjust the facility's previously calculated screening value based the fraction of facility emissions that remain in the mixing layer where exposure occurs, after accounting for plume rise (which is based on site-specific meteorology and facility-specific emission-point characteristics). If these Tier 3 adjustments still indicate that ecological risks could be above levels of concern (i.e., screening values are above 1), as in the human health assessment, we can conduct new TRIM.FaTE modeling using the screening configuration corresponding to the relevant lake and/or soil locations, site-specific hourly meteorology, and hourly plume-rise values. If such modeling results in screening-level media concentrations or doses above benchmark levels, we may elect to conduct a more refined assessment using more site-specific information. If, after additional refinement, the media concentrations or doses are above benchmark levels, the facility may have the potential to cause adverse environmental effects.

For acid gases, the environmental screening assessment evaluates the potential phytotoxicity and reduced productivity of plants due to chronic exposure to acid gases. The environmental risk screening methodology for acid gases is a single-tier screen that compares the average off-site ambient air concentration over the modeling domain to ecological benchmarks for each of the acid gases. For purposes of an ecological risk assessment, EPA identifies a potential for adverse environmental effects to plant communities from exposure to acid gases when the average off-site ambient air concentration over the modeling domain for a facility exceeds the ecological benchmark for that acid gas. In such cases, we further investigate factors such as the magnitude of the exceedance and the characteristics of the area of exceedance (e.g., land use of exceedance area, size of exceedance area) to determine whether the facility's emissions have the potential to cause adverse environmental effects.


Lead
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 assessment using HEM-3. To evaluate the potential for adverse environmental effects from lead, we compare the average modeled air concentrations of lead around each facility in the source category to the level of the secondary NAAQS for lead. The secondary lead NAAQS is a reasonable means of evaluating environmental risk because it is set to provide substantial protection against adverse welfare effects which can include "effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal comfort and well-being."  We investigate any modeled exceedances of the lead NAAQS in a manner similar to that noted above for acid gases.

 Dose-response assessment
 Sources of chronic dose-response information 
Dose-response assessments (carcinogenic and non-carcinogenic) for chronic exposure (either by inhalation or ingestion) for the HAP reported in the emissions inventory for this source category are based on the EPA Office of Air Quality Planning and Standards' (OAQPS) existing recommendations for HAP (USEPA, 2014a). This information has been obtained from various sources and prioritized according to (1) conceptual consistency with EPA risk assessment guidelines and (2) level of peer review received. The prioritization process was aimed at incorporating into our assessments the best available science with respect to dose-response information. The recommendations are based on the following sources, in order of priority: 

 U.S. Environmental Protection Agency (EPA).  EPA has developed dose-response assessments for chronic exposure for many HAP. These assessments typically provide a qualitative statement regarding the strength of scientific data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, for ingestion) to protect against effects other than cancer and/or a unit risk estimate (URE, for inhalation) or slope factor (SF, for ingestion) to estimate the probability of developing cancer. The RfC is defined as an "estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime." The RfD is "an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime." The URE is defined as "the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an agent at a concentration of 1 ug/m[3] in air." The SF is "an upper bound, approximating a 95 percent confidence limit, on the increased cancer risk from a lifetime exposure to an agent. This estimate, [is] usually expressed in units of proportion (of a population) affected per mg/kg-day..."  
   
      EPA disseminates dose-response assessment information in several forms, based on the level of review. The Integrated Risk Information System (IRIS) is an EPA database that contains scientific health assessment information, including dose-response information. All IRIS assessments since 1996 have also undergone independent external peer review. The current IRIS process includes review by EPA scientists, interagency reviewers from other federal agencies, and the public, as well as peer review by independent scientists external to EPA. New IRIS values are developed and old IRIS values are updated as new health effects data become available. Refer to the IRIS Agenda for detailed information on status and scheduling of current individual IRIS assessments and updates. EPA's science policy approach, under the current carcinogen guidelines, is to use linear low-dose extrapolation as a default option for carcinogens for which the mode of action (MOA) has not been identified. We expect future EPA dose-response assessments to identify nonlinear MOAs where appropriate, and we will use those analyses (once they are peer reviewed) in our risk assessments. At this time, however, there are no available carcinogen dose-response assessments for inhalation exposure that are based on a nonlinear MOA.

 U.S. Agency for Toxic Substances and Disease Registry (ATSDR).  ATSDR, which is part of the US Department of Health and Human Services, develops and publishes Minimal Risk Levels (MRLs) for inhalation and oral exposure to many toxic substances. As stated on the ATSDR web site: "Following discussions with scientists within the Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a practice similar to that of the EPA's Reference Dose (RfD) and Reference Concentration (RfC) for deriving substance specific health guidance levels for non-neoplastic endpoints."  The MRL is defined as "an estimate of daily human exposure to a substance that is likely to be without an appreciable risk of adverse effects (other than cancer) over a specified duration of exposure."  ATSDR describes MRLs as substance-specific estimates to be used by health assessors to select environmental contaminants for further evaluation.
   
 California Environmental Protection Agency (CalEPA).  The CalEPA Office of Environmental Health Hazard Assessment has developed dose-response assessments for many substances, based both on carcinogenicity and health effects other than cancer. The process for developing these assessments is similar to that used by EPA to develop IRIS values and incorporates significant external scientific peer review. As stated in the CalEPA Technical Support Document for developing their chronic assessments, the guidelines for developing chronic inhalation exposure levels incorporate many recommendations of the U.S. EPA (USEPA, 1994a) and NAS (NAS, 1994). The noncancer information includes available inhalation health risk guidance values expressed as chronic inhalation reference exposure levels (RELs). CalEPA defines the REL as "the concentration level at or below which no health effects are anticipated in the general human population."  CalEPA's quantitative dose-response information on carcinogenicity by inhalation exposure is expressed in terms of the URE, defined similarly to EPA's URE. 
  
For certain HAP, the dose-response information, based on this prioritization, is limited. To address data gaps, increase accuracy, and avoid underestimating risk, we made additional changes to some of the chronic inhalation exposure values. These important changes, outlined below and reflected in Appendix 8 (Dose-Response Values Used in the RTR Risk Assessments) to this document, are as follows: 
   
 Manganese.  The EPA considers the ATSDR MRL for manganese (Mn) the most appropriate chronic inhalation reference value to be used in RTR assessments. There is an existing IRIS RfC for Mn (USEPA, 1993a), and ATSDR published an assessment of Mn toxicity which includes a chronic inhalation reference value (i.e., an ATSDR Minimal Risk Level, MRL). (ATSDR, 2012). Both the 1993 IRIS RfC and the 2012 ATSDR MRL were based on the same study (Roels et al., 1992); however, ATSDR used updated dose-response modeling methodology (benchmark dose approach) and considered recent pharmacokinetic findings to support their MRL derivation. Because of the updated methods, EPA has determined that the ATSDR MRL is the appropriate health reference value to use in RTR risk assessments.

 Polycyclic Organic Matter.  EPA has identified appropriate UREs for many individual compounds of POM, published in the sources used for RTR risk assessments. When an individual POM compound is reported in the emission inventory for the source category, we use the appropriate URE for that compound. However, if in the emission inventory for the source category a POM compound is reported for which EPA has not identified a URE, or when POM are not speciated into individual compounds, then EPA applies simplifying assumptions so that cancer risk can be quantitatively evaluated without substantially under- or over-estimating risk (which can occur if all reported POM emissions were assigned the same URE). To accomplish this, EPA places each POM compound into one of eight POM groups, generally defined by toxicity and the estimated emission profile of POM compounds. POM Groups 1 and 2 include unspeciated POM (emissions reported as "polycyclic organic matter") and individual POM compounds with no URE assigned from the sources used in RTR risk assessments. With two exceptions, both Groups 1 and 2 are assigned a URE equal to 5 percent of that for pure benzo[a]pyrene; the two exceptions are benzo[a]fluoranthene and generic "benzofluoranthenes", which received the URE of benzo[b]fluoranthene. POM Groups 3 through 7 comprise POM compounds for which UREs are available from the sources used for RTR risk assessments, except for benzo[b+k]fluoranthene and benzo[g,h,i]fluoranthene which receive the URE of benzo[b]fluoranthene. If reported emissions are for a specific compound in these groups, then EPA evaluates the cancer risk of the compound using its unique URE if one has been derived or its group URE if one has not been specifically derived. If the reported emissions are for a specific POM group rather than a compound within the group, then EPA evaluates the cancer risk of the POM group using a URE value that is close to the average of the UREs of the individual compounds within the group. POM Group 8 is composed of unspeciated polycyclic aromatic hydrocarbons (PAH) reported as 7-PAH and are assigned a URE equal to approximately 18 percent of that for pure benzo[a]pyrene. In addition, we have concluded that three PAHs -- anthracene, phenanthrene and pyrene -- are not carcinogenic and therefore no URE is assigned. Details of the analysis that led to this conclusion can be found in the document titled Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic Hydrocarbon (PAH) Mixtures: In Support of Summary Information of the Integrated Risk Information System (IRIS).

 Glycol Ethers.  Often in an emission inventory, the glycol ethers are reported only as the total mass for the entire group without distinguishing among individual glycol ether compounds. In other cases, emissions of individual glycol ether compounds that had not been assigned dose-response values were reported. To avoid underestimating the health hazard associated with glycol ethers, we protectively apply the RfC for ethylene glycol methyl ether (the most toxic glycol ether for which an assessment exists) to glycol ether emissions of unspecified composition.

 Lead.  We consider the primary NAAQS for lead, which incorporates an adequate margin of safety, to be protective of all potential health effects for the most susceptible populations. The NAAQS, developed using the EPA Integrated Exposure, Uptake, Biokinetic Model, was preferred over the RfC for noncancer adverse effects because the NAAQS for lead was developed using more recent toxicity and dose-response information on the noncancer adverse impacts of lead. The NAAQS for lead was set to protect the health of the most susceptible children and other potentially at-risk populations against an array of adverse health effects, most notably including neurological effects, particularly neurobehavioral and neurocognitive effects (which are the effects to which children are most sensitive). The lead NAAQS, a rolling 3-month average level of lead in total suspended particles, is used as a long-term value in the RTR risk assessment. 

 Nickel compounds.  To provide a conservative estimate of the potential cancer risks, the EPA considers the IRIS URE value for nickel subsulfide (which is considered the most potent carcinogen among all nickel compounds) to be the most appropriate value to be used in RTR assessments. Based on consistent views of major scientific bodies, such as the National Toxicology Program (NTP) in their 14[th] Report of the Carcinogens (RoC) (NTP, 2016), the International Agency for Research on Cancer (IARC, 1990), and other international agencies (WHO, 1991) that consider all nickel compounds to be carcinogenic, we currently consider all nickel compounds to have the potential of being carcinogenic to humans. The 14th RoC states that "the combined results of epidemiological studies, mechanistic studies, and carcinogenic studies in rodents support the concept that nickel compounds generate nickel ions in target cells at sites critical for carcinogenesis, thus allowing consideration and evaluation of these compounds as a single group." Although the precise nickel compound (or compounds) responsible for carcinogenic effects in humans is not always clear, studies indicate that nickel sulfate and the combinations of nickel sulfides and oxides encountered in industrial emissions of nickel mixtures cause cancer in humans (these studies are summarized in a review by Grimsrud et al., 2010). The major scientific bodies mentioned above have also recognized that there may be differences in the toxicity and/or carcinogenic potential across the different nickel compounds. For this reason, and given that there are two additional URE values derived for exposure to mixtures of nickel compounds (as a group) that are 2-3 fold lower than the IRIS URE for nickel subsulfide, the EPA considers it reasonable, in some instances (e.g., when high quality data are available on the composition of nickel emissions from a specific source category), to use a value that is 50 percent of the IRIS URE for nickel subsulfide for providing an estimate of the lower end of the plausible range of cancer potency values for different mixtures of nickel compounds.
 
 Carbonyl Sulfide.  Although the health effects data for carbonyl sulfide (COS) are very limited, a series of studies (Morgan et. al., 2004; Herr et. al., 2007; Sills et. al., 2004) conducted by the National Toxicology Program have shown that the major concern regarding exposure to COS is its potential for neurotoxicity. These studies have shown consistently and at the same range of COS concentrations that the brain is a target organ for COS toxicity. Since appropriate health effects benchmarks have not been derived by our preferred sources of dose-response data including IRIS, ATSDR, and Cal EPA, the EPA has used the data from the above referenced studies to derive a chronic screening benchmark level for COS. A chronic screening level of 163 ug/m[3] was developed for COS from a No Observed Adverse Effects Level (NOAEL) of 200 ppm based on brain lesions and neurophysiological alterations in rodents. Additional details on the derivation of the chronic screening level for COS can be found in Appendix 8.

 Pollutant Groups.  In the case of HAP groups such as cyanide compounds, mercury compounds, antimony compounds and others, the most conservative dose-response value in the chemical group is used as a surrogate for other compounds in the group for which dose-response values are not available. This is done to examine, under conservative assumptions, whether those HAP that lack dose-response values may pose an unacceptable risk and require further examination.

 Mutagenic Mode of Action.  For carcinogenic chemicals acting via a mutagenic mode of action (i.e., chemicals that cause cancer by damaging genes), we estimate risks to reflect the increased carcinogenicity of such chemicals during childhood. This approach is explained in detail in the Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens. Where available data do not support a chemical-specific evaluation of differences between adults and children, the Supplemental Guidance recommends using the following default adjustment factors for early-life exposures: increase the carcinogenic potency by 10-fold for children up to 2 years old and by 3-fold for children 2 to 15 years old. These adjustments have the aggregate effects of increasing by about 60 percent the estimated risk (a 1.6-fold increase) for a lifetime of constant inhalation exposure. EPA uses these default adjustments only for carcinogens known to be mutagenic for which data to evaluate adult and juvenile differences in toxicity are not available. The UREs for several HAP (see Appendix 8) were adjusted upward, by multiplying by a factor of 1.6, to account for the increased risk during childhood exposures. Although trichloroethylene is carcinogenic by a mutagenic mode of action, the age-dependent adjustment factor for the URE only applies to the portion of the slope factor reflecting risk of kidney cancer. For full lifetime exposure to a constant level of trichloroethylene exposure, the URE is adjusted upward by a factor of 1.12 (rather than 1.6 as discussed above). For more information on applying age-dependent adjustment factors in cases where exposure varies over the lifetime, see Toxicological Review of Trichloroethylene. The URE for vinyl chloride includes exposure from birth, although the IRIS assessment contains UREs for both exposure from birth and exposure during adulthood. This value already accounts for childhood exposure; thus, no additional factor is applied.

 Sources of acute dose-response information 
Hazard identification and dose-response assessment information for preliminary acute inhalation exposure assessments is based on the existing recommendations of OAQPS for HAP (USEPA, 2014b). When the benchmarks are available, the results from acute screening assessments are compared to both "no effects" reference levels for the general public, such as the California Reference Exposure Levels (RELs), and to emergency response levels, such as Acute Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the recognition that the ultimate interpretation of any potential risks associated with an estimated exceedance of a particular reference level depends on the definition of that level and any limitations expressed therein. Comparisons among different available inhalation health effect reference values (both acute and chronic) for selected HAP can be found in an EPA document of graphical arrays (USEPA, 2009b).

California Acute Reference Exposure Levels (RELs).  The California Environmental Protection Agency (CalEPA) has developed acute dose-response reference values for many substances, expressing the results as acute inhalation RELs.

    The acute REL is defined by CalEPA as "the concentration level at or below which no adverse health effects are anticipated for a specified exposure duration (OEHHA, 2015). RELs are based on the most sensitive, relevant, adverse health effect reported in the medical and toxicological literature. RELs are designed to protect the most sensitive individuals in the population by the inclusion of margins of safety. Since margins of safety are incorporated to address data gaps and uncertainties, exceeding the REL does not automatically indicate an adverse health impact." Acute RELs are developed for 1-hour (and 8-hour) exposures. The values incorporate uncertainty factors similar to those used in deriving EPA's inhalation RfCs for chronic exposures.
    
Acute Exposure Guideline Levels (AEGLs).  AEGLs are developed by the National Advisory Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) for Hazardous Substances and then reviewed and published by the National Research Council. As described in the Committee's Standing Operating Procedures, AEGLs "represent threshold exposure limits for the general public and are applicable to emergency exposures ranging from 10 min to 8 h."  Their intended application is "for conducting risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real time emergency response actions, for accidental chemical releases at fixed facilities and from transport carriers."  The document states that "the primary purpose of the AEGL program and the NAC/AEGL Committee is to develop guideline levels for once-in-a-lifetime, short-term exposures to airborne concentrations of acutely toxic, high-priority chemicals."  In detailing the intended application of AEGL values, the document states, "It is anticipated that the AEGL values will be used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, and possibly the international community in conjunction with chemical emergency response, planning, and prevention programs. More specifically, the AEGL values will be used for conducting various risk assessments to aid in the development of emergency preparedness and 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 individuals, could experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. However, the effects are not disabling and are transient and reversible upon cessation of exposure."
   
   "AEGL-2 is the airborne concentration (expressed as ppm or mg/m[3]) of a substance above which it is predicted that the general population, including susceptible individuals, could experience irreversible or other serious, long-lasting adverse health effects or an impaired ability to escape."
   
    "Airborne concentrations above AEGL-1 represent exposure levels that can produce mild and progressively increasing but transient and nondisabling odor, taste, and sensory irritation or certain asymptomatic, nonsensory effects. With increasing airborne concentrations above each AEGL, there is a progressive increase in the likelihood of occurrence and the severity of effects described for each corresponding AEGL. Although the AEGL values represent threshold levels for the general public, including susceptible subpopulations, such as infants, children, the elderly, persons with asthma, and those with other illnesses, it is recognized that individuals, subject to unique or idiosyncratic responses, could experience the effects described at concentrations below the corresponding AEGL."

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

ERPG values are described in their supporting documentation as follows: "ERPGs are air concentration guidelines for single exposures to agents and are intended for use as tools to assess the adequacy of accident prevention and emergency response plans, including transportation emergency planning, community emergency response plans, and incident prevention and mitigation."  

ERPG-1 and ERPG-2 values are defined by AIHA's Standard Operating Procedures as follows:
   
   "ERPG-1 is the maximum airborne concentration below which nearly all individuals could be exposed for up to 1 hour without experiencing more than mild, transient health effects or without perceiving a clearly defined objectionable odor." 
   
   "ERPG-2 is the maximum airborne concentration below which nearly all individuals could be exposed for up to 1 hour without experiencing or developing irreversible or other serious adverse health effects or symptoms that could impair an individual's ability to take protective action."

 Risk characterization
 General
The final product of the risk assessment is the risk characterization, in which the information from the previous steps is integrated and an overall conclusion about risk is synthesized that is complete, informative, and useful for decision makers. In general, the nature of this risk characterization depends on the information available, the application of the risk information and the resources available. In all cases, major issues associated with determining the nature and extent of the risk are identified and discussed. Further, it is EPA's policy 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 (USEPA, 2000a), in the Agency's information quality guidelines (USEPA, 2002a), and in the Office of Management and Budget (OMB) Memorandum on Updated Principles for Risk Analysis (OMB, 2007), and they are incorporated in these assessments.

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

For each carcinogenic HAP included in an assessment for which a potency estimate is available, individual and population cancer risks are calculated by multiplying the corresponding lifetime average exposure estimate by the appropriate URE. This calculated cancer risk is defined as the upper-bound probability of developing cancer over a 70-year 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.

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

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

The risk characterization for chronic effects other than cancer is developed using the HQ for inhalation, calculated for each HAP at each census block centroid. As discussed above, RfCs incorporate generally conservative uncertainty factors in the face of uncertain extrapolations, such that an HQ greater than 1 does not necessarily suggest the onset of adverse effects. The Hazard Index (HI) is the sum of hazard quotients for substances that affect the same target organ or organ system and is an approximation of the aggregate effect on a specific target organ (e.g., the lungs). The HQ and HI cannot be translated to a probability that adverse effects will occur, and it is unlikely to be proportional to adverse health effect outcomes in a population.

Screening for potentially significant acute inhalation exposures also follows the HQ approach. We divide the maximum estimated acute exposure by each available acute dose-response value to develop an array of HQs. In general, when none of these HQs are greater than one, there is no potential for acute risk. When one or more HQs are above 1, we evaluate additional information (e.g., proximity of the facility to potential exposure locations) to determine whether there is a potential for significant acute risks.

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

To combine risks across multiple carcinogens, our assessments use the mixtures guidelines' default assumption of additivity of effects and combine risks by summing them using the independence formula in the mixtures guidelines (USEPA, 1986; USEPA, 2000b).

In assessing noncancer hazard from chronic exposures, in cases where different pollutants cause adverse health effects via completely different modes of action, it may be inappropriate to aggregate HQs. In consideration of these mode-of-action differences, the mixtures guidelines support aggregating effects of different substances in specific and limited ways. To conform to these guidelines, we aggregate noncancer 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 HQs for individual HAP that affect the same organ or organ system. For the RTRs, TOSHI calculations are based exclusively on effects occurring at the "critical dose" (i.e., the lowest dose that produces adverse health effects). Although HQs associated with some pollutants have been aggregated into more than one TOSHI, this has been done only in cases where the critical dose affects more than one target organ. Because impacts on organs or systems that occur above the critical dose have not been included in the TOSHI calculations, some TOSHIs may have been underestimated. As with the HQ, the TOSHI should not be interpreted as a probability of adverse effects or as strict delineation of "safe" and "unsafe" levels. Rather, the TOSHI is another measure of the potential for adverse health outcomes associated with pollutant exposure and needs to be interpreted carefully by health scientists and risk managers.

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


3   Risk results for the Leather Finishing source category
 Source category description and emissions 

The leather finishing operations NESHAP (40 CFR part 63, subpart TTTT) defines leather finishing as a single process or group of processes used to adjust and improve the physical and aesthetic characteristics of the leather surface through the multistage application of a coating comprised of dyes, pigments, film-forming materials, and performance modifiers dissolved or suspended in liquid carriers.

In the overall process of leather products manufacturing, leather finishing is considered a dry operation as opposed to the "wet-end" operations associated with leather tanning. Leather finishing operations can be co-located with a tannery, or performed in stand-alone operations. Coatings are typically applied to the leather substrate using spray, roll, and flow coating techniques. Emissions of HAP occur from volatilization during the application of the coating, drying or curing of the coating, and from handling, storage, and clean-up of the finishing materials. The current leather finishing operations MACT standards were promulgated on February 27, 2002 and apply to any leather finishing operation that has been a major source of HAP emissions after the compliance date of the rule or that has been located at, or part of, a source that has been a major source of HAP emissions after the compliance date.[,] 

The emissions data for this source category were obtained from the 2014 National Emissions Inventory (2014 NEI), facility operating permits and permit applications, facility annual emission reports, Google Earth satellite and street view imagery, and communication with facility representatives. We estimate that there are four facilities with leather finishing operations in the U.S. that are subject to 40 CFR part 63, subpart TTTT. Emissions from the leather finishing source category are summarized in Table 3.1-1. The total HAP emissions for the source category are approximately 22.5 tons per year. Based on these data, HAP emissions include propyl cellosolve, glycol ethers, diethylene glycol monobutyl ether, chromium (III) compounds, trimethylamine, diethylene glycol monomethyl ether, ethylene glycol, toluene, and methyl isobutyl ketone. No PB-HAP or environmental HAP are emitted from these facilities. 

Emissions data and other modeling parameters for the facility-wide assessment came from version 1 of the 2014 National Emissions Inventory (NEI).  The data from the NEI was used "as-is" without further investigation of source category or non-source category records. The source category records in the NEI may be different than those reviewed and quality assured for the source category assessments for this source category. Facility-wide data were available in the NEI for 3 of the 4 facilities. A total of 21.6 tons per year were emitted, with propyl cellosolve emitting the most mass with 11.2 tons.
 
The emissions for this source category are estimates of actual emissions on an annual basis. The risk results presented in the following sections are based on these actual emissions. MACT-allowable and facility-wide emissions were also estimated and the risk results based on those emissions are presented below as well. Details on the development of the actual and allowable emissions and the source of the data for this source category can be found in Appendix 1.

As detailed in Appendix 1, the data sources for emission characterization varied by facility and could present slight inconsistencies in the emissions between facilities. For example, speciated emissions of glycol ethers was not available for all 4 facilities. However, this likely had little to no impact on risk results as detailed in Section 3.2.

Operating hours of each facility were available and used to develop a facility-specific temporal profile. This temporal profile was applied to annual emissions to better represent hours when emissions actually occur based on when the facility operates. This approach was used for both chronic and acute risk estimates. See Appendix 1 to this document for more information on the temporal profiles.

For the chronic inhalation risk assessment, the emissions inventory for the leather finishing category includes emissions of 9 HAP with available chronic inhalation dose-response values. Of these, none are classified as known, probable, or possible carcinogens, with quantitative cancer dose-response values available and 8 HAP have quantitative noncancer dose-response values available. These HAP, their emissions and toxicity values are listed in Table 3.1-1 and the source of each toxicity value is listed in Appendix 8.

For the acute inhalation risk assessment, for the leather finishing category, an acute emissions multiplier of 1.8 was used in place of the default emissions multiplier of 10 as described in Section 2.4. Emissions from leather finishing facilities are primarily from coatings operations. For such operations the maximum production rate is similar to the actual production rate due to limited ability to increase operation time. Consequently, actual emissions and acute emissions will be similar. The multiplier was based on U.S. census data on the production capacity utilization factor of leather finishing facilities over 2013-2017. The multiplier was calculated based on the ratio of the highest production rate capacity utilization factor (87.5) to the lowest (46.6). 

The emissions inventory for the leather finishing category includes emissions of 6 HAP (or 9 HAP when accounting for speciated glycol ethers) with relevant and available quantitative acute dose-response values. These HAP, their emissions and acute and chronic dose-response values are listed in Table 3.1-1 and the source of each dose-response value is listed in Appendix 8.

As mentioned previously, when we identify acute impacts which exceed their relevant benchmarks, we pursue refining our acute screening estimates to the extent possible. The acute results for the source category are summarized in the following section, and detailed information is contained in Appendix 10 to this document (Detailed Risk Modeling Results).

Also note that the 2011 NEI (version 2) was also used to fill in some data gaps for some stack parameter and operating schedule information. Details about these 2011 NEI data can be found in Appendix 1.



Table 3.1-1  Summary of Emissions from Leather Finishing Source Category and Dose-Response Values Used in the Residual Risk Assessment
                                       
                                      HAP
                                Emissions (tpy)
         Number of Facilities Reporting HAP (4 facilities in data set)
        Prioritized Inhalation Dose-Response Value Identified by OAQPS
PB-HAP Oral Benchmark Values for  Cancer (1/(mg/kg/d)) and/or Noncancer (mg/kg/d)[a]
                                       
                                       
                                       
                 Unit Risk Estimate for Cancer (1/(ug/m[3]))
                Reference Concentration for Noncancer (mg/m[3])
             Health Benchmark Values for Acute Noncancer (mg/m[3])
                                       
Propyl Cellosolve
                                                                             12
                                       2
                                       
                                     0.02
                                  0.093 (REL)
                                       
Glycol Ethers
                                                                              4
                                       1
                                       
                                     0.02
                                  0.093 (REL)
                                       
Diethylene Glycol Monobutyl Ether
                                                                              3
                                       1
                                       
                                     0.02
                                  0.093 (REL)
                                       
Chromium (III) Compounds
                                                                              1
                                       3
                                       
                                       
                                       
                                       
Triethylamine
                                                                              1
                                       1
                                       
                                     0.007
                                   2.8 (REL)
                                       
Diethylene Glycol Monomethyl Ether
                                                                            0.7
                                       1
                                       
                                     0.02
                                  0.093 (REL)
                                       
Ethylene Glycol
                                                                            0.7
                                       2
                                       
                                      0.4
                                       
                                       
Toluene
                                                                            0.2
                                       1
                                       
                                       5
                                   37 (REL)
                                       
Methyl Isobutyl Ketone
                                                                            0.2
                                       1
                                       
                                       3
                                       
                                       

Notes: 
[a] Benchmark values are provided only for those PB-HAPs for which multipathway risk is assessed (via TRIM). There may be other PB-HAPs in this table, even though no benchmark is presented.

 Baseline risk characterization
This section presents the results of the risk assessment for the leather finishing source category based on the modeling methods described in the previous sections. All baseline risk results are developed using the best estimates of actual HAP emissions summarized in the previous section. The basic chronic inhalation risk estimates presented here are the maximum individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. We also present results from our acute inhalation impact screening assessment in the form of maximum hazard quotients, as well as the results of our preliminary screening assessment for potential non-inhalation risks and environmental risk from PB-HAP. Also presented are the HAP "drivers," which are the HAP that collectively contribute 90 percent of the maximum cancer risk or maximum hazard at the highest exposure location. A detailed summary of the facility-specific inhalation results is available in Appendix 10 of this document.

Regarding potential differences in risks estimates using speciated and unspeciated glycol ether emissions from the 4 facilities, it should be mentioned we apply health conservative unit risk estimates, reference concentrations, and health benchmark values for unspeciated emissions. For example, the health benchmark value used here for unspeciated glycol ethers (0.093 REL) was identical to the value for speciated emissions of propyl cellosolve (0.093 REL), diethylene glycol monobutyl ether (0.093 REL), and diethylene glycol monomethyl ether (0.093 REL) (Table 3.1-1) and would therefore had little to no impact on risk results. 

3.2.1	Risk assessment results based on actual emissions

Inhalation
Table 3.2-1 summarizes the chronic and acute inhalation risk results for this source category based upon baseline actual emissions. The results of the chronic inhalation cancer risk assessment indicate there is no known cancer risk posed by the 4 facilities. The maximum chronic noncancer hazard index value for the source category could be up to 0.04 (reproductive) driven by emissions of propyl cellosolve from spray booths, dryers, and coaters and no one is exposed to TOSHI levels above 1.

Worst-case acute HQs were calculated for every HAP that has an acute benchmark, as shown in Table 3.1-1. For cases where the screening HQ was greater than 1, we further refined the estimates by determining the highest HQ value that might occur outside facility boundaries. Based on actual baseline emissions, the highest refined screening acute HQ value of 3 (based on the acute REL for glycol ethers and propyl cellosolve) is shown in Table 3.2-2. This value includes a refinement of determining the highest HQ value that is outside facility boundaries. It is also important to note that the highest HQ assumes that the primary source of the glycol ether and propyl cellosolve emissions from spray machine operations was modeled with an hourly emissions rate based on annual actual emissions applied only during hours the facility operated and with an acute emissions multiplier of 1.8 times the annual emission rate. No facilities are estimated to have an AEGL or an EPRG greater than 1. Acute estimates for each plant and pollutant are provided in Appendix 10 of this document.


Table 3.2-1.  Source Category Level Inhalation Risks for Leather Finishing Based on Actual Emissions

                                    Result
                               HAP "Drivers"
Facilities in Source Category
Number of Facilities Estimated to be in Source Category
                                       4
                                      n/a
Number of Facilities Modeled in Risk Assessment
                                       4
                                      n/a
Cancer Risks
Maximum Individual Lifetime Cancer Risk (in 1 million)
                                      n/a
                                      n/a
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
                                       0
                                      n/a
Chronic Noncancer Risks
Maximum Reproductive Hazard Index
                                     0.04
                               propyl cellosolve
Number of Facilities with Maximum Kidney Hazard Index:
	Greater than 1
                                       0
                                      n/a
Acute Noncancer Screening Results
                                                  Maximum Acute Hazard Quotient
                                       3
                 glycol ethers (REL), propyl cellosolve (REL)
                          Number of Facilities With Potential for Acute Effects
                                       2
                 glycol ethers (REL), propyl cellosolve (REL)
Population Exposure
             Number of People Living Within 50 Kilometers of Facilities Modeled
                                   4,600,000
                                      n/a
Number of People Exposed to Cancer Risk:
                                     	Greater than or equal to 100 in 1 million
                                       0
                                      n/a
                                      	Greater than or equal to 10 in 1 million
                                       0
                                      n/a
                                       	Greater than or equal to 1 in 1 million
                                       0
                                      n/a
Number of People Exposed to Noncancer Respiratory Hazard Index:
                                                                	Greater than 1
                                       0
                                      n/a
Estimated Cancer Incidence (excess cancer cases per year)
                                       0
                                      n/a


Facility-wide Inhalation
The facility-wide chronic MIR and TOSHI, available in Appendix 10, are based on emissions from all sources at the identified facilities (both MACT and non-MACT sources). The results of the facility-wide assessment for cancer risks, as compared to the source category assessment, are summarized in Table 3.2-2. The results indicate that facility-wide cancer MIR was less than 1-in-1 million for all 4 facilities. The maximum facility-wide TOSHI for the source category is estimated to be 0.1., mainly driven by emissions of acrolein from external combustion boilers.

Table 3.2-2  Source Category Contribution to Facility-Wide Cancer Risks Based on Actual Emissions
                                       
                               Leather Finishing
        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%
                                       0
                                       0
                                       0
                                       0
                                       0
                                    50-90%
                                       0
                                       0
                                       0
                                       0
                                       0
                                    10-50%
                                       0
                                       0
                                       0
                                       0
                                       0
                                   < 10%
                                       4
                                       0
                                       0
                                       0
                                       4
Total
                                       4
                                       0
                                       0
                                       0
                                       0


Multipathway
No PB-HAP were emitted from this source category, therefore a multipathway analysis was not warranted.


Environmental
There are no environmetnal HAP emitted by this source category, therefore, an environmental risk screen was not conducted.

3.2.2	Risk assessment results based on allowable emissions

Inhalation
Potential differences between actual emissions levels and the maximum emissions allowable under the MACT standards (i.e., MACT-allowable emissions) were also calculated for the facilities. See Appendix 1 of this document for a discussion of the development of the allowable factors. Risk results from the inhalation risk assessment using the MACT-allowable emissions indicate that there is no known cancer risk, and that the maximum chronic noncancer TOSHI value could be as high as 0.3 at the MACT-allowable emissions level with propyl cellosolve emissions driving the TOSHI value. 

 Post-control risk characterization
In light of the low cancer and noncancer risk posed to individuals exposed to HAP emitted from this source category, no post-control risk assessment was warranted.

 General discussion of uncertainties in the risk assessment
The uncertainties in virtually all of the RTR risk assessments can be divided into three areas: 1) uncertainties in the emission data sets, 2) exposure modeling uncertainties, and 3) uncertainties in the dose-response relationships. Uncertainties in the emission estimates and in the air quality models lead to uncertainty in air concentrations. Uncertainty in exposure modeling can arise due to uncertain activity patterns, the locations of individuals within a census tract, and the microenvironmental concentrations as reflected in the exposure model. Finally, uncertainty in the shape of the relationship between exposure and effects, the URE and the RfC, also contributes to uncertainties in the risk assessment. These three areas of uncertainty are discussed below.
 Emissions inventory uncertainties
Although the development of the RTR emissions data set involves an extensive quality assurance/quality control process, the accuracy of emission values will vary depending on certain factors, for example, the source of the data, the degree to which data are incomplete or missing, the degree to which assumptions made to complete the data sets are accurate, and the extent to which there are errors in these emission estimates. The emission estimates used in the risk assessment generally are annual totals for certain years, and they do not reflect short-term fluctuations during the course of a year or variations from year to year. 

For the acute effects screening assessment, therefore, in the absence of available specific estimates or measurements, we use estimates of peak hourly emission rates. These estimates typically are calculated by first estimating the average annual hourly emissions rates by evenly dividing the total annual emission rate from the inventory into the 8,760 hours of the year. However, in the case of the leather finishing category, operating hours for every facility were available, and therefore the facility-specific operating hours was applied to estimate acute emissions in place of 8,760 hours. An emission adjustment factor that is intended to account for emission fluctuations during normal facility operations is then applied to these average annual hourly emission rates. The adjustment factor can be based on actual fluctuations seen in the available emission data for sources in a category or on engineering judgment; in the absence of such information, a default factor is applied.

To prepare the emissions data set, EPA gathers the best available data on emissions, emission release parameters, and other relevant source category-specific parameters. EPA often begins with its National Emissions Inventory (NEI) database as the starting point for emission rates, emissions release characteristics, and locations of the emission release points for each facility in the source category. The NEI is a composite of emission measurements and estimates produced by state and local regulatory agencies, industry, and EPA. EPA's industry experts then review the data for consistency and completeness and conduct an extensive quality assurance/quality control checks. Available information, which may include compliance data, information from project files, permits, and other sources regarding facilities and emission sources, also is incorporated into the data set. This additional information may be incorporated in addition to the NEI data or in place of the NEI data, depending on EPA's evaluation of the quality of the various sources of data. In order to fill data gaps, EPA may conduct a formal information collection request (ICR) under the authority of section 114 of the Clean Air Act to obtain current, complete emissions data and other data from the facility owners and operators associated with the source category under review. 

Uncertainty in the emissions data set stems from data gaps, default assumptions, and the emission models used to develop emissions inventory estimates. A variety of methods, such as emission factors, material balances, engineering judgement, air permit information and source testing, are used to develop emission estimates. Other parameters that are part of the emissions data set, including facility location and emission point parameters, may also be a source of uncertainty. Some release point locations use an average facility location instead of the location of each specific unit within the facility. In some instances, default release point parameters may be in the inventory. Where fugitive release parameters are not available, default values are included. Another potential source of emission estimate uncertainty may be low or poor quality data (e.g., out-of-date parameter values). For more information on the uncertainties in the emission estimates for this source category see Appendix 1 (Emissions Inventory Support Documents) of this document.
 Exposure modeling uncertainties
4.2.1	Inhalation exposure modeling
Although every effort is made to identify all of the relevant facilities and emission points, as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the uncertainties in our emission inventory likely dominate the uncertainties in the exposure assessment. The ambient air modeling uncertainties are considered relatively small in comparison, since we are using EPA's refined local dispersion model with site-specific parameters and reasonably representative meteorology. If anything, the population exposure estimates are biased high by not accounting for short- or long-term population mobility and by not addressing processes like deposition, plume depletion, and atmospheric degradation. Additionally, estimates of maximum individual risk (MIR) contain uncertainty because they are derived at census block centroid locations rather than actual residences. This uncertainty is known to create potential underestimates and overestimates of the actual MIR values for individual facilities; however, overall, it is not thought to have a significant impact on the estimated MIR for a source category. We also do 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 do we factor in the possibility of population growth during the 70-year chronic exposure period, which could lead to a potential downward bias in both the MIR and population risk estimates. Finally, we do not factor in time an individual spends indoors.

We did not include the effects of human mobility on exposures in the assessment. Specifically, short-term mobility and long-term mobility between census blocks in the modeling domain were not considered. (Short-term mobility is movement from one micro-environment to another over the course of hours or days. Long-term mobility is movement from one residence to another over the course of a lifetime.) The approach of not considering short or long-term population mobility does not bias the estimate of the theoretical MIR (by definition), nor does it affect the estimate of cancer incidence because the total population number remains the same. It does, however, affect the shape of the distribution of individual risks across the affected population, shifting it toward higher estimated individual risks at the upper end and reducing the number of people estimated to be at lower risks, thereby increasing the estimated number of people at specific high risk levels (e.g., 1-in-10 thousand or 1-in-1 million).

In addition, the assessment predicted the chronic exposures at the centroid of each populated census block as surrogates for the exposure concentrations for all people living in that block. Using the census block centroid to predict chronic exposures tends to over-predict exposures for people in the census block who live farther from the facility and under-predict exposures for people in the census block who live closer to the facility. Thus, using the census block centroid to predict chronic exposures may lead to a potential understatement or overstatement of the true maximum impact, but is an unbiased estimate of average risk and incidence. We reduce this uncertainty by analyzing large census blocks near facilities using aerial imagery and adjusting the location of the block centroid to better represent the population in the block, as well as adding additional receptor locations where the block population is not well represented by a single location.

The assessment evaluates the cancer inhalation risks associated with pollutant exposures over a 70-year period, which is the assumed lifetime of an individual. In reality, both the length of time that modeled emission sources at facilities actually operate (i.e., more or less than 70 years) and the domestic growth or decline of the modeled industry (i.e., the increase or decrease in the number or size of domestic facilities) will influence the future risks posed by a given source or source category. Depending on the characteristics of the industry, these factors will, in most cases, result in an overestimate both in individual risk levels and in the total estimated number of cancer cases. However, in the unlikely scenario where a facility maintains, or even increases, its emissions levels over a period of more than 70 years, residents live beyond 70 years at the same location, and the residents spend more of their days at that location, then the cancer inhalation risks could potentially be underestimated. However, annual cancer incidence estimates from exposures to emissions from these sources would not be affected by the length of time an emissions source operates.

The exposure estimates used in these analyses assume chronic exposures to ambient (outdoor) levels of pollutants. Because most people spend the majority of their time indoors, actual exposures may not be as high, depending on the characteristics of the pollutants modeled. For many of the HAP, indoor levels are roughly equivalent to ambient levels, but for very reactive pollutants or larger particles, indoor levels are typically lower. This factor has the potential to result in an overestimate of 25 to 30 percent of exposures (USEPA, 2001).

A sensitivity analysis, discussed in "Risk and Technology Review (RTR) Risk Assessment Methodologies" (USEPA 2009a), found that the selection of the meteorology data set location could have an impact on the risk estimates. The analysis found that cancer MIR derived using different meteorological stations varied by as much as 63 percent below to 51 percent above the value derived using the nearest meteorological station. Cancer incidence estimated using different meteorological stations varied by as much as 68 percent below to 120 percent above the value estimated using the nearest meteorological station. Similarly, air concentrations estimated using different meteorological stations varied by as much as 49 percent below to 21 percent above the value estimated using the nearest meteorological station. Since this analysis was performed EPA has increased the number of meteorological stations used in our risk assessments; thus, we expect variability to be reduced. 

For the acute screening assessment, the results are intentionally biased high, and thus health-protective, by assuming the co-occurrence of independent factors, such as hourly emission rates, meteorology and human activity patterns. Furthermore, in cases where multiple acute dose-response values for a pollutant are considered scientifically acceptable, we choose the most conservative of these dose-response values, erring on the side of overestimating potential health risks from acute exposures. In cases where these results indicate the potential for exceeding acute HQs, we refine our assessment by developing a better understanding of the geography of the facility relative to potential exposure locations.

4.2.2	Multipathway exposure modeling
In modeling the fate and transport of pollutants through the environment and the non-inhalation exposure (i.e., ingestion) to these pollutants, TRIM.FaTE uses simplified representations of many complex real-world processes. This simplified representation introduces uncertainty. Uncertainties arise from model assumptions and structure, as reflected in the algorithms that describe the environmental movement of pollutants, and in the input values for numerous environmental parameters. 

Uncertainty in the algorithms is inherent to any model attempting to represent complex processes in the real world. How persistent, bioaccumulative chemicals such as mercury, cadmium, arsenic, PAHs, and dioxins behave in the environment is highly complex, and many natural processes are represented in a simplified manner by TRIM.FaTE, including, for example:

 gaseous and particulate deposition from air; 
 biogeochemical cycling in the aquatic environment, particularly mercury transformations through methylation and demethylation at the sediment-surface interface; 
 mixing processes in air, water, and sediment;
 suspended and benthic sediment dynamics in lakes; and 
 biotic processes such as growth, reproduction, and predation. 

Even though some processes, such as diffusion, are known to follow second-order dynamics, the TRIM.FaTE model represents all fate and transport processes in terms of first-order differential equations. TRIM.FaTE also does not explicitly deal with lateral or vertical dispersion in the air compartments. Some algorithms, such as those addressing methylation and sediment transport, for example, do not consider all of the factors known to affect the process. Biotic processes including chemical absorption, chemical elimination, growth, reproduction, predation, and death have been represented relatively simplistically in the model. Although the model's algorithms have been validated and are based on professional judgment, some level of uncertainty results from such simplifications.

The input values for parameters are also associated with uncertainty. Algorithms that describe the environmental movement of pollutants depend on numerous environmental parameters for which the values might be naturally variable and for which available data are often limited. Examples of parameters for which input values are variable and uncertain include aquatic food web structure (e.g., diet of each fish species), biokinetic parameters that influence bioaccumulation (e.g., assimilation efficiencies and elimination rates), topographic characteristics (e.g., lake depth, runoff rates, and erosion rates), meteorological parameters (e.g., evaporation and precipitation rates), chemical transformation rates (e.g., methylation and demethylation rates, in the case of mercury), and human exposure parameters (especially fish consumption rates). 

For TRIM.FaTE modeling, we use central tendency values and combinations of values that would lead to estimates of reasonable maximum exposures to bound risk estimates. We have conducted analyses of the sensitivity of risk estimates to parameter input values. For those parameters to which the model is particularly sensitive, we have continued to collect additional data to better quantify the variability and distribution of input values. 
A more comprehensive explanation of the uncertainties related to fate, transport, and exposure modeling using TRIM.FaTE is provided in Appendix 6 (Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR) of this report for the tiered assessments and Appendix 11 (Site-Specific Human Health Multipathway Residual Risk Assessment Report) of this report for a site-specific assessment if one was conducted.

4.2.3	Environmental risk screening assessment
For each source category, we generally rely on site-specific levels of environmental HAP emissions to perform an environmental screening assessment. The environmental screening assessment is based on the outputs from models that estimate environmental HAP concentrations. The same models, specifically the TRIM.FaTE multipathway model and the AERMOD air dispersion model, are used to estimate environmental HAP concentrations for both the human multipathway screening analysis and for the environmental screening analysis. Therefore, both screening assessments have similar modeling uncertainties. Two important types of uncertainty associated with the use of these models in RTR environmental screening assessments (and inherent to any assessment that relies on environmental modeling) are model uncertainty and input uncertainty.

Model uncertainty concerns whether the selected models are appropriate for the assessment being conducted and whether they adequately represent the movement and accumulation of environmental HAP emissions in the environment. For example, does the model adequately describe the movement of the pollutant through the soil?  This type of uncertainty is difficult to quantify. However, based on feedback received from previous EPA SAB reviews and other reviews, we are confident that the models used in the screen are appropriate and state-of-the-art for the environmental risk assessments conducted in support of our RTR analyses.

Input uncertainty is concerned with how accurately the models have been configured and parameterized for the assessment at hand. For Tier 1 of the environmental screen for PB-HAP, we configured the models to avoid underestimating exposure and risk to reduce the likelihood that the results indicate the risks are lower than they actually are. This was accomplished by selecting upper-end values from nationally-representative datasets for the more influential parameters in the environmental model, including selection and spatial configuration of the area of interest, the location and size of any bodies of water, meteorology, surface water and soil characteristics, and structure of the aquatic food web. In Tier 1, we used the maximum facility-specific emissions for the PB-HAP (other than lead compounds, which were evaluated by comparison to the Secondary Lead NAAQS) that were included in the environmental screening assessment and each of the media when comparing to ecological benchmarks. This is consistent with the conservative design of the Tier 1 screen. In Tier 2 of the environmental screening assessment for PB-HAP, we refine the model inputs to account for meteorological patterns in the vicinity of the facility versus using upper-end national values, and we identify the locations of water bodies near the facility location. By refining the screening approach in Tier 2 to account for local geographical and meteorological data, we decrease the likelihood that concentrations in environmental media are overestimated, thereby increasing the usefulness of the screen. To better represent widespread impacts, the modeled soil concentrations are averaged in Tier 2 to obtain one average soil concentration value for each facility and for each PB-HAP. For PB-HAP concentrations in water, sediment, and fish tissue, the highest value for each facility for each pollutant is used.

For the environmental screening assessment for acid gases, we employ a single-tiered approach. We use the modeled air concentrations and compare those with ecological benchmarks.

For both Tiers 1 and 2 of the environmental screening assessment, our approach to addressing model input uncertainty is generally cautious. We choose model inputs from the upper end of the range of possible values for the influential parameters used in the models, and we assume that the exposed individual exhibits ingestion behavior that would lead to a high total exposure. This approach reduces the likelihood of not identifying potential risks for adverse environmental impacts.

 Uncertainties in the dose-response relationships
 In the sections that follow, separate discussions are provided on uncertainty associated with cancer potency factors and for noncancer reference values. Cancer potency values are derived for chronic (lifetime) exposures. Noncancer dose-response values are generally derived for chronic exposures (up to a lifetime) but may also be derived for acute (less than 24 hours), short-term (from 24 hours up to 30 days), and subchronic (30 days up to 10 percent of lifetime) exposure durations, all of which are derived based on an assumption of continuous exposure throughout the duration specified. For the purposes of assessing all potential health risks associated with the emissions included in an assessment, we rely on both chronic (cancer and noncancer) and acute (noncancer) dose-response values, which are described in more detail below.
 
Although every effort is made to identify peer-reviewed dose-response values for all HAP emitted by the source category included in an assessment, some HAP have no peer-reviewed values. Since exposures to these pollutants cannot be included in a quantitative risk estimate, an understatement of risk for these pollutants at environmental exposure levels is possible. To help alleviate this potential underestimate, where we conclude similarity with a HAP for which a dose-response assessment value is available, we use that value as a surrogate for the assessment of the HAP for which no value is available. To the extent use of surrogates indicates appreciable risk, we may identify a need to increase priority for a new IRIS assessment of that substance. We additionally note that, generally speaking, HAP of greatest concern due to environmental exposures and hazards are those for which dose-response assessments have been performed, reducing the likelihood of understating risk. Further, HAP not included in the quantitative assessment as assessed qualitatively and considered in the risk characterization that informs the risk management decisions, including with regard to consideration of HAP reductions achieved by various control options.

Additionally, chronic dose-response values for certain compounds included in the assessment may be under EPA IRIS review. In those cases, revised assessments may determine in the future that these pollutants are more or less potent than currently thought. 

For a group of compounds that are unspeciated (e.g., glycol ethers), we conservatively use the most protective reference value of an individual compound in that group to estimate risk. Similarly, for an individual compound in a group (e.g., ethylene glycol diethyl ether) that does not have a specified reference value, we apply the most protective reference value from the other compounds in the group to estimate risk.

Cancer assessment
The discussion of dose-response uncertainties in the estimation of cancer risk below focuses on the uncertainties associated with the specific approach currently used by the EPA to develop cancer potency factors. In general, these same uncertainties attend the development of cancer potency factors by CalEPA, the source of peer-reviewed cancer potency factors used where EPA-developed values are not yet available. To place this discussion in context, we provide a quote from the EPA's Guidelines for Carcinogen Risk Assessment (herein referred to as Cancer Guidelines). (USEPA, 2005d) "The primary goal of EPA actions is protection of human health; accordingly, as an Agency policy, risk assessment procedures, including default options that are used in the absence of scientific data to the contrary, should be health protective." The approach adopted in this document is consistent with this approach as described in the Cancer Guidelines.

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

Extrapolation of study data to estimate potential risks to human populations is based upon EPA's assessment of the scientific database for a pollutant using EPA's guidance documents and other peer-reviewed methodologies. The EPA Cancer Guidelines describes the Agency's recommendations for methodologies for cancer risk assessment. EPA believes that cancer risk estimates developed following the procedures described in the Cancer Guidelines and outlined below generally provide an upper bound estimate of risk. That is, EPA's upper bound estimates represent a plausible upper limit to the true value of a quantity (although this is usually not a true statistical confidence limit). In some circumstances, the true risk could be as low as zero; however, in other circumstances the risk could also be greater. When developing an upper bound estimate of risk and to provide risk values that do not underestimate risk, EPA generally relies on conservative default approaches.   EPA also uses the upper bound (rather than lower bound or central tendency) estimates in its assessments, although it is noted that this approach can have limitations for some uses (e.g. priority setting, expected benefits analysis).

Such health risk assessments have associated uncertainties, some which may be considered quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary substantially among cancer risk assessments associated with exposures to different pollutants, since the assessments employ different databases with different strengths and limitations and the procedures employed may differ in how well they represent actual biological processes for the assessed substance. Some of the major sources of uncertainty and variability in deriving cancer risk values are described more fully below.

(1) The qualitative similarities or differences between tumor responses observed in experimental animal bioassays and those which would occur in humans are a source of uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites observed in an experimental animal bioassay are necessarily predictive of the sites at which tumors would occur in humans. However, unless scientific support is available to show otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target organ concordance. For a specific pollutant, qualitative differences in species responses can lead to either under-estimation or over-estimation of human cancer risks.

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

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to humans, EPA uses scaling methodologies (relating expected response to differences in physical size of the species), which introduce another source of uncertainty. These methodologies are based on both biological data on differences in rates of process according to species size and empirical comparisons of toxicity between experimental animals and humans. For a particular pollutant, the quantitative difference in cancer potency between experimental animals and humans may be either greater than or less than that estimated by baseline scientific scaling predictions due to uncertainties associated with limitations in the test data and the correctness of scaled estimates.

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

(5) Extrapolation from high to low doses is an important source of uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk assessment (i.e., developing estimates of risk for exposures to environmental doses of an agent from observations in experimental or epidemiological studies at higher dose) depending on the available data and understanding of a pollutant's mode of action (i.e., the manner in which a pollutant causes cancer). EPA's Cancer Guidelines express a preference for the use of reliable, compound-specific, biologically-based risk models when feasible; however, such models are rarely available. The mode of action for a pollutant (i.e., the manner in which a pollutant causes cancer) is a key consideration in determining how risks should be estimated for low-dose exposure. A reference value is calculated when the available mode of action data show the response to be nonlinear (e.g., as in a threshold response). A linear low-dose (straight line from POD) approach is used when available mode of action data support a linear (e.g., nonthreshold) response or as the most common default approach when a compound's mode of action is unknown. Linear extrapolation can be supported by both pollutant-specific data and broader scientific considerations. For example, EPA's Cancer Guidelines generally consider a linear dose-response to be appropriate for pollutants that interact with DNA and induce mutations. Pollutants whose effects are additive to background biological processes in cancer development can also be predicted to have low-dose linear responses, although the slope of this relationship may not be the same as the slope estimated by the straight line approach.

EPA most frequently utilizes a linear low-dose extrapolation approach as a baseline science-policy choice (a "default") when available data do not allow a compound-specific determination. This approach is designed to not underestimate risk in the face of uncertainty and variability. EPA believes that linear dose-response models, when appropriately applied as part of EPA's cancer risk assessment process, provide an upper bound estimate of risk and generally provide a health protective approach. Note that another source of uncertainty is the characterization of low-dose nonlinear, non-threshold relationships. The National Academy of Sciences (NAS, 1994) has encouraged the exploration of sigmoidal type functions (e.g., log-probit models) in representing dose-response relationships due to the variability in response within human populations. Another National Research Council report (NRC, 2006) suggests that models based on distributions of individual thresholds are likely to lead to sigmoidal-shaped dose-response functions for a population. This report notes sources of variability in the human population:  "One might expect these individual tolerances to vary extensively in humans depending on genetics, coincident exposures, nutritional status, and various other susceptibility factors..." Thus, if a distribution of thresholds approach is considered for a carcinogen risk assessment, application would depend on ability of modeling to reflect the degree of variability in response in human populations (as opposed to responses in bioassays with genetically more uniform rodents). Note also that low dose linearity in risk can arise for reasons separate from population variability: due to the nature of a mode of action and additivity of a chemical's effect on top of background chemical exposures and biological processes.

As noted above, EPA's current approach to cancer risk assessment typically utilizes a straight line approach from the BMDL. This is equivalent to using an upper confidence limit on the slope of the straight line extrapolation. The impact of the choice of the BMDL on bottom line risk estimates can be quantified by comparing risk estimates using the BMDL value to central estimate BMD values, although these differences are generally not a large contributor to uncertainty in risk assessment (Subramaniam et. al., 2006). It is important to note that earlier EPA assessments, including the majority of those for which risk values exist today, were generally developed using the multistage model to extrapolate down to environmental dose levels and did not involve the use of a POD. Subramaniam et. al. (2006) also provide comparisons indicating that slopes based on straight line extrapolation from a POD do not show large differences from those based on the upper confidence limit of the multistage model.

(6) Cancer risk estimates do not generally make specific adjustments to reflect the variability in response within the human population  --  resulting in another source of uncertainty in assessments. In the diverse human population, some individuals are likely to be more sensitive to the action of a carcinogen than the typical individual, although compound-specific data to evaluate this variability are generally not available. There may also be important life stage differences in the quantitative potency of carcinogens and, with the exception of the recommendations in EPA's Supplemental Cancer Guidance for carcinogens with a mutagenic mode of action, risk assessments do not generally quantitatively address life stage differences. However, one approach used commonly in EPA assessments that may help address variability in response is to extrapolate human response from results observed in the most sensitive species and sex tested, resulting typically in the highest URE which can be supported by reliable data, thus supporting estimates that are designed not to underestimate risk in the face of uncertainty and variability.

Chronic noncancer assessment
Chronic noncancer reference values represent chronic exposure levels that are intended to be health-protective. That is, EPA and other organizations, such as the Agency for Toxic substances and disease Registry (ATSDR), which develop noncancer dose-response values use 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 (e.g., factors of 10 or 3) used in the absence of compound-specific data; where data are available, uncertainty factors may also be developed using compound-specific information. When data are limited, more assumptions are needed and more default factors are used. Thus, there may be a greater tendency to overestimate risk -- in the sense that further study might support development of reference values that are higher (i.e., less potent) because fewer default assumptions are needed. However, for some pollutants it is possible that risks may be underestimated.
      
For noncancer endpoints related to chronic exposures, EPA derives a reference dose (RfD) for exposures via ingestion, and a reference concentration (RfC) for inhalation exposures. As stated in the IRIS Glossary, these values provide an estimate (with uncertainty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a continuous inhalation exposure (RfC) to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. To derive values that are intended to be "without appreciable risk," EPA's methodology relies upon an uncertainty factor (UF) approach (USEPA, 1993b; USEPA, 1994b) which includes consideration of both uncertainty and variability.
   
EPA begins by evaluating all of the available peer-reviewed literature to determine noncancer endpoints of concern, evaluating the quality, strengths and limitations of the available studies. EPA typically chooses the relevant endpoint that occurs at the lowest dose, often using statistical modeling of the available data, and then determines the appropriate POD for derivation of the reference value. A POD is determined by (in order of preference): (1) a statistical estimation using the BMD approach; (2) use of the dose or concentration at which the toxic response was not significantly elevated (no observed adverse effect level --  NOAEL); or (3) use of the lowest observed adverse effect level (LOAEL).

A series of downward adjustments using default UFs is then applied to the POD to estimate the reference value (USEPA, 2002b). While collectively termed "UFs", these factors account for a number of different quantitative considerations when utilizing observed animal (usually rodent) or human toxicity data in a risk assessment. The UFs are intended to account for: (1) variation in susceptibility among the members of the human population (i.e., inter-individual variability); (2) uncertainty in extrapolating from experimental animal data to humans (i.e., interspecies differences); (3) uncertainty in extrapolating from data obtained in a study with less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and (5) uncertainty when the database is incomplete or there are problems with applicability of available studies. When scientifically sound, peer-reviewed assessment-specific data are not available, default adjustment values are selected for the individual UFs. For each type of uncertainty (when relevant to the assessment), EPA typically applies an UF value of 10 or 3 with the cumulative UF value leading to a downward adjustment of 10-3000 fold from the selected POD. An UF of 3 is used when the data do not support the use of a 10-fold factor. If an extrapolation step or adjustment is not relevant to an assessment (e.g., if applying human toxicity data and an interspecies extrapolation is not required) the associated UF is not used. The major adjustment steps are described more fully below.

	1) Heterogeneity among humans is a key source of variability as well as uncertainty. Uncertainty related to human variation is considered in extrapolating doses from a subset or smaller-sized population, often of one sex or of a narrow range of life stages (typical of occupational epidemiologic studies), to a larger, more diverse population. In the absence of pollutant-specific data on human variation, a 10-fold UF is used to account for uncertainty associated with human variation. Human variation may be larger or smaller; however, data to examine the potential magnitude of human variability are often unavailable. In some situations, a smaller UF of 3 may be applied to reflect a known lack of significant variability among humans.

	2) Extrapolation from results of studies in experimental animals to humans is a necessary step for the majority of chemical risk assessments. When interpreting animal data, the concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. rodents) is extrapolated to estimate the human response. While there is long-standing scientific support for the use of animal studies as indicators of potential toxicity to humans, there are uncertainties in such extrapolations. In the absence of data to the contrary, the typical approach is to use the most relevant endpoint from the most sensitive species and the most sensitive sex in assessing risks to the average human. Typically, compound specific data to evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty in this extrapolation. Size-related differences (allometric relationships) indicate that typically humans are more sensitive than rodents when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF is consistent with these differences. For a specific chemical, differences in species responses may be greater or less than this value.

      Pharmacokinetic models are useful to examine species differences in pharmacokinetic processing and associated uncertainties; however, such dosimetric adjustments are not always possible. Information may not be available to quantitatively assess toxicokinetic or toxicodynamic differences between animals and humans, and in many cases a 10-fold UF (with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to account for expected species differences and associated uncertainty in extrapolating from laboratory animals to humans in the derivation of a reference value. If information on one or the other of these components is available and accounted for in the cross-species extrapolation, a UF of 3 may be used for the remaining component.

	3) In the case of reference values for chronic exposures where only data from shorter durations are available (e.g., 90-day subchronic studies in rodents) or when such data are judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is typically applied unless the available scientific information supports use of a different value.

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

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

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. For acute reference values, though, individual UF values that may be less than 10. UFs are applied based on chemical- or health effect-specific information or based on the purpose of the reference value. The UFs applied in acute reference value derivation include:  1) heterogeneity among humans; 2) uncertainty in extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. Additional adjustments are often applied to account for uncertainty in extrapolation from observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an acute reference value at another exposure duration (e.g., 1 hour). 
	
Not all acute dose-response values are developed for the same purpose and care must be taken when interpreting the results of an acute assessment of human health effects relative to the reference value or values being exceeded. Where relevant to the estimated exposures, the lack of dose-response values at different levels of severity should be factored into the risk characterization as potential uncertainties.

Environmental Risk Screening Assessment
Uncertainty also exists in the ecological benchmarks for the environmental risk screening assessment. We established a hierarchy of preferred benchmark sources to allow selection of benchmarks for each environmental HAP at each ecological assessment endpoint. In general, EPA benchmarks used at a programmatic level (e.g., Office of Water, Superfund Program) were used if available. If not, we used EPA benchmarks used in regional programs (e.g., Superfund Program). If benchmarks were not available at a programmatic or regional level, we used benchmarks developed by other agencies (e.g., NOAA) or by state agencies.

In all cases (except for lead compounds, which were evaluated through a comparison to the NAAQS), we searched for benchmarks at the following three effect levels, as described in Section 2.6 of this report and in Appendix 9 (Environmental Risk Screen) of this report:  a no-effect level (i.e., NOAEL), threshold-effect level (i.e., LOAEL), and probable-effect level (i.e., PEL).

For some ecological assessment endpoint/environmental HAP combinations, we could identify benchmarks for all three effect levels, but for most we could not. In one case, where different agencies derived significantly different numbers to represent a threshold for effect, we included both. In several cases, only a single benchmark was available. In cases where multiple effect levels were available for a particular PB-HAP and assessment endpoint, we used all of the available effect levels to help us determine whether risk exists if risks could be considered significant and widespread.

 References

Allen, et al., 2004. Variable Industrial VOC Emissions and their impact on ozone formation in the Houston Galveston Area. Texas Environmental Research Consortium. https://www.researchgate.net/publication/237593060_Variable_Industrial_VOC_Emissions and_their_Impact_on_Ozone_Formation_in_the_Houston_Galveston_Area 

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

Burger, J. 2002. Daily consumption of wild fish and game: Exposures of high end recreationalists. Environmental Health Research. 12(4):343 - 354.

Grimsrud TK and Andersen A., 2010. Evidence of carcinogenicity in humans of water-soluble nickel salts. J Occup Med Toxicol 2010, 5:1-7. Available online at http://www.occup-med.com/content/5/1/7

Herr, D.W., Graff, J.E., Moser, V.C., Crofton, K.M., Little, P.B., Morgan, D.L., and Sills, R.C., 2007. Inhalational exposure to carbonyl sulfide produced altered brainstem auditory and somatosensory-evoked potentials in Fischer 344N rats. Toxicol. Sci. 95(1):118-135, 2007.

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

Morgan, D.L., Little, P.B., Herr, D.W., Moser, V.C., Collins, B., Herbert, R., Johnson, G.A., Maronpot, R.R., Harry, G.J., and Sills, R.C., 2004. Neurotoxicity of carbonyl sulfide in F344 rats following inhalation exposure for up to 12 weeks. Toxicol. Appl. Pharmacol. 200(2):131- 145, 2004.

National Academy of Sciences, 1994. National Research Council. Science and Judgement in Risk Assessment. Washington, DC: National Academy Press.

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

NTP, 2016. National Toxicology Program. 2016. Report on Carcinogens, Fourteenth Edition.; Research Triangle Park, NC: U.S. Department of Health and Human Services, Public Health Service. https://ntp.niehs.nih.gov/pubhealth/roc/index-1.html

OEHHA, 2015. California Office of Environmental Health Hazard Assessment. All Acute Reference Exposure Levels developed by OEHHA as of June 2014. http://oehha.ca.gov/air/allrels.html

OMB, 2007. Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis (September 19, 2007), from Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs, Office of Management and Budget; and Sharon L. Hays, Associate Director and Deputy Director for Science, Office of Science and Technology Policy.
https://georgewbush-whitehouse.archives.gov/omb/memoranda/fy2007/m07-24.pdf

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.

Roels HA, Ghyselen P, Buchet JP, et al. 1992. Assessment of the permissible exposure level to manganese in workers exposed to manganese dioxide dust. Br J Ind Med 49:25-34. 

Sills, R.C., Morgan, D.L., Herr, D.W., Little, P.B., George, N.M., Ton, T.V., Love, N.E., Maronpot, R.R., and Johnson, G.A., 2004. Contribution of magnetic resonance microscopy in the 12-week neurotoxicity evaluation of carbonyl sulfide in Fischer 344 rats. Toxicol. Pathol. 32:501-510, 2004.

USEPA, 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. EPA-630-R-98-002. https://www.epa.gov/risk/guidelines-health-risk-assessment-chemical-mixtures

USEPA, 1993a. Integrated Risk Information system Review of Manganese. https://cfpub.epa.gov/ncea/iris2/chemicalLanding.cfm?substance_nmbr=373 

USEPA, 1993b. Reference Dose (RfC):  Description and Use in Health Risk Assessments. https://www.epa.gov/iris/reference-dose-rfd-description-and-use-health-risk-assessments 

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

USEPA, 1994b. Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetry. https://www.epa.gov/risk/methods-derivation-inhalation-reference-concentrations-and-application-inhalation-dosimetry 

USEPA, 1999. Residual Risk Report to Congress. 453R-99-001. https://www3.epa.gov/airtoxics/rrisk/risk_rep.pdf

USEPA, 2000a. Risk Characterization Handbook. EPA 100-B-00-002. https://www.epa.gov/sites/production/files/2015-10/documents/osp_risk_characterization_handbook_2000.pdf 

USEPA, 2000b. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures. EPA-630/R-00-002. https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=20533 

USEPA, 2001. National-Scale Air Toxics Assessment for 1996. EPA 453/R - 01 - 003.  January 2001. page 85.

USEPA, 2002a. 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. https://www.epa.gov/quality/guidelines-ensuring-and-maximizing-quality-objectivity-utility-and-integrity-information 

USEPA, 2002b. A Review of the Reference Dose and Reference Concentration Processes. https://www.epa.gov/osa/review-reference-dose-and-reference-concentration-processes 


USEPA, 2005a. 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. https://www3.epa.gov/scram001/guidance/guide/appw_05.pdf 

USEPA, 2005b. Supplemental guidance for assessing early-life exposure to carcinogens. EPA/630/R-03003F. https://www3.epa.gov/ttn/atw/childrens_supplement_final.pdf 

USEPA, 2005c. Science Policy Council Cancer Guidelines Implementation Workgroup Communication I: Memo from W.H. Farland dated 4 October 2005 to Science Policy Council. https://www.epa.gov/sites/production/files/2015-01/documents/cgiwgcommuniation_i.pdf 

USEPA, 2005d. Guidelines for Carcinogen Risk Assessment. U.S. Environmental Protection Agency, Washington, DC, EPA/630/P-03/001F. https://www.epa.gov/risk/guidelines-carcinogen-risk-assessment 

USEPA, 2006. Performing risk assessments that include carcinogens described in the Supplemental Guidance as having a mutagenic mode of action. Science Policy Council Cancer Guidelines Implementation Workgroup Communication II: Memo from W.H. Farland dated 14 June 2006. https://www.epa.gov/sites/production/files/2015-01/documents/cgiwg-communication_ii.pdf 

USEPA, 2009a. 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-452/R-09-006. https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryID=238928 

USEPA, 2009b. 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

USEPA, 2010a. SAB's Response to EPA's RTR Risk Assessment Methodologies. https://yosemite.epa.gov/sab/sabproduct.nsf/0/b031ddf79cffded38525734f00649caf!OpenDocument&TableRow=2.3#2 

US EPA, 2010b. Memorandum 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". https://yosemite.epa.gov/sab/sabproduct.nsf/3BE2C36A4ADDC85A85257B48006C88D7/$File/EPA+resp+to+SAB+on+RTR+memo.pdf 

USEPA, 2011. Exposure Factors Handbook: 2011 Edition (Final). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-09/052F. https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252 

USEPA, 2014a. Table 1. Prioritized Chronic Dose-Response Values (5/9/14). Office of Air Quality Planning and Standards. https://www.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants 

USEPA, 2014b. Table 2. Acute Dose-Response Values for Screening Risk Assessments (9/18/2014). Office of Air Quality Planning and Standards. https://www.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants 

USEPA, 2016a User's Guide for the AMS/EPA Regulatory Model (AERMOD). EPA-454/B-16-011, U.S. Environmental Protection Agency, Research Triangle Park, NC.
https://www3.epa.gov/ttn/scram/models/aermod/aermod_userguide.pdf 

USEPA, 2016b. AERMOD Implementation Guide. EPA-454/B-16-013, U.S. Environmental Protection Agency, Research Triangle Park, NC.
https://www3.epa.gov/ttn/scram/models/aermod/aermod_implementation_guide.pdf

WHO, 1991. World Health Organization and the European Union's Scientific Committee on Health and Environmental Risks (SCHER, 2006).

