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Draft Residual Risk Assessment for the Mineral Wool Production 

and Wool Fiberglass Manufacturing Source Categories

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

October 2014
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Table of Contents

1 Introduction........................................................................................................................ 5

2 Methods.............................................................................................................................. 6

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

2.2 Dispersion modeling for inhalation exposure assessment........................................... 7

2.3 Estimating human inhalation exposure ..................................................................... 10

2.4 Acute Risk Screening and Refined Assessments ...................................................... 10

2.5 Multipathway human health and environmental risk screening................................ 12

2.6 Dose-Response Assessment ...................................................................................... 15

2.6.1 Sources of chronic dose-response information .................................................. 15

2.6.2 Sources of acute dose-response information...................................................... 21

2.7 Risk Characterization ................................................................................................ 24

2.7.1 General ............................................................................................................... 24

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

3 Risk Results for the Mineral Wool Production............................................................ 27

3.1 Source Category Description and Results................................................................. 27

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

4 Risk Results for the Wool Fiberglass Manufacturing Source Category ...................... 34

4.1 Source Category Description and Results................................................................. 34

4.2 Risk Characterization ................................................................................................ 36

4.3 Auxiliary Risk Characterization................................................................................ 43

5 General Discussion of Uncertainties and How They Have Been Addressed................... 45

5.1 Exposure Modeling Uncertainties............................................................................. 45

5.2 Uncertainties in the Dose-Response Relationships................................................... 46

5 References........................................................................................................................ 55

Index of Tables

Table 2.2-1 AERMOD version 11103 model options for RTR modeling....Error! Bookmark 

not defined.

Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens.... 20

Table 2.6-1 (b) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

.................................................................................................................................................. 21

Table 2.6-2 Dose-Response Values for Acute Exposure........................................................ 24

Table 3.1-1 Summary of Emissions from the Mineral Wool Production Category and 

Availability of Dose-Response Values .................................................................................... 28

Table 3.2-1 Summary of Source Category Level Inhalation Risks for Mineral Wool 

Production ................................................................................................................................ 32

Table 4.2-1 Summary of Source Category Level Inhalation Risks for Wool Fiberglass 

Manufacturing.......................................................................................................................... 39

Table 4.2-2 Summary of Refined Acute Results for Wool Fiberglass Manufacturing Facilities

.................................................................................................................................................. 40

Table 4.2-3 Summary of Source Category Level Cancer Risks for the Wool Fiberglass 

Manufacturing Auxiliary Analysis........................................................................................... 44
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Appendices

Appendix 1 Emissions Inventory Support Memorandum

Appendix 1a - Emissions Data Used in Residual Risk Modeling for Mineral 

Wool Production

Appendix 1b - Emissions Data Used in Residual Risk Modeling for Wool 

Fiberglass Manufacturing

Appendix 2 Technical Support Document for HEM3 Modeling

Appendix 3 Meteorological Data for HEM3 Modeling

Appendix 4 Analysis of data on short-term emission rates relative to long-term emission 

rates

Appendix 5 Technical Support Document for TRIM-Based Multipathway Screening 

Methodology for RTR

Appendix 6 Detailed Risk Modeling Results

Appendix 7 Acute Impacts Refined Analysis 

Appendix 8 Development of the Chronic Screening Level for Carbonyl Sulfide

Appendix 9 Environmental Risk Screen
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Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model

AEGL Acute exposure guideline level

ASTDR US Agency for Toxic Substances and Disease Registry

CalEPA California Environmental Agency

ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant

HEM Human Exposure Model

HI Hazard index

HQ Hazard quotient

IRIS Integrated Risk Information System

MACT Maximum Achievable Control Technology

MIR Maximum Individual Risk

MOA Mode of action

NAC National Advisory Committee

NAAQS National Ambient Air Quality Standard

NATA National Air Toxics Assessment

NEI National Emissions Inventory

NPRM Notice of Proposed Rulemaking

PB-HAP Persistent and Bioaccumulative - HAP

POM Polycyclic organic matter

REL Reference exposure level

RfC Reference concentration

RfD Reference dose

RTR Risk and Technology

TOSHI Target-organ-specific hazard index

URE Unit risk estimate
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1 Introduction

Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process for 

addressing emissions of hazardous air pollutants (HAPs) from stationary sources. In the first 

stage, section 112(d) requires the Environmental Protection Agency (EPA, or the Agency) to 

develop technology-based standards for categories of sources (e.g., petroleum refineries, pulp 

and paper mills, etc.) [1]. EPA has largely completed the initial Maximum Achievable 

Control Technology (MACT) standards as required under this provision. Under section 

112(d)(6), EPA must review each of these technology-based standards at least every eight 

years and revise a standard, as necessary, “taking into account developments in practices, 

processes and control technologies.” In the second stage, EPA is required under section 

112(f)(2) to assess the health and environmental risks that remain after implementation of the 

MACT standards. If additional risk reductions are necessary to protect public health with an 

ample margin of safety or to prevent an adverse environmental effect, EPA must develop 

standards to address these remaining risks. This second stage of the regulatory process is 

known as the residual risk stage. For each source category for which EPA issued MACT 

standards, the residual risk stage must be completed within eight years of promulgation of the 

initial technology-based standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board 

(SAB) on the “Risk and Technology Review (RTR) Assessment Plan” and in June of 2007, 

we received a letter with the results of that consultation. Subsequent to the consultation, in 

July of 2009 a meeting was held with an SAB panel for a formal peer review of the “Risk and 

Technology Review (RTR) Assessment Methodologies” [2]. We received the final SAB 

report on this review in May of 2010 [3]. Where appropriate, we have responded to the key 

messages from this review in developing our current risk assessments and we will be 

continuing our efforts to improve our assessments by incorporating updates based on the SAB 

recommendations as they are developed and become available. Our responses to the key 

recommendations of the SAB are outlined in the memo entitled, “EPA’s Actions in Response 

to Key Recommendations from the SAB Review of RTR risk Assessment Methodologies” 

[4].

This document contains the methods and the results of baseline risk assessments (i.e., after the 

implementation of the respective MACT standards) performed for the Wool Fiberglass 

Manufacturing and Mineral Wool Production source categories. The methods discussion 

includes descriptions of the methods used to develop refined estimates of chronic inhalation 

exposures and human health risks for cancer and noncancer endpoints, as well as descriptions 

of the methods used to screen for acute health risks, chronic non-inhalation health risks, and 

adverse environmental effects. 
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2 Methods

2.1 Emissions and source data

Mineral Wool

The 2002 National Emissions Inventory (NEI) data served as the starting point for the mineral 

wool assessment. Using the process MACT code,1a subset of the NEI was developed that 

contained facility, process, and emissions data for each facility in the Mineral Wool 

Production source category. Those data were based on an emission factor developed from 

one source test; emissions were estimated for all facilities based on their production data 

reported in NEI, and were supplemented with other emissions estimates in NEI. In the 2008 

proposal EPA requested comment and new data that could inform that proposal. After 

proposal, EPA learned that the one plant for which we had test data had closed, some facilities 

were missing from the NEI and others, listed in NEI, had since closed. As a result, EPA 

decided to collect 2010 industry process and source test data under an information collection 

request that would inform the risk review, the technology review (under 112(d)(6)), start up 

and shutdown issues that emerged after the General Provisions vacature2, and a petition3

raised to the court following the vacature of the Brick MACT4. These data underwent an 

extensive QA/QC procedure, and revisions were made to the dataset as necessary. The 

Mineral Wool ICR emissions data and modifications made to the NEI data are discussed in 

the Appendix 1a, entitled “Emissions Data Used in Residual Risk Modeling for Mineral Wool 

Production.” 

Wool Fiberglass

A voluntary ICR was sent out in March 2010 to the 29 wool fiberglass facilities currently 

operating in the US. ICR responses from all the facilities were compiled in the project 

database and included information on each facility, process, process equipment, control 

equipment, costs, and HAP emissions. Facility-specific production rate information was also 

included and was claimed as confidential business information (CBI), which resulted in much 

of the project database being treated as CBI.

Emissions test data provided by the wool fiberglass industry varied widely by facility. 

Companies were allowed to, in lieu of new testing, submit existing and well documented test 

data that was representative of current operations and used the recommended test methods. A 

number of facilities submitted existing test reports on one or more of the following emission 

sources: glass melting furnaces, curing ovens, forming, and collection operations. In order to 

fill data gaps in existing data, new test data was also required. Companies were, however, 

allowed to test a subset of facility operations, if the facilities were able to demonstrate that the 

tested subset was representative of the untested subset. Test results were typically presented 

 

1 Assigning data with MACT codes allows EPA to determine reductions attributable to the MACT program. The 

NEI associates MACT codes corresponding to MACT source categories with stationary major and area source 

data.

2

 SierraClub v. EPA, 551 F. 3d 1019 (D.C. Cir. 2008), cert. denied, 130 S. Ct. 1735 (2010).

3 SierraClub v. Stephen L. Johnson, Petition for Rulemaking, January 14, 2009

4 SierraClub v. EPA, 479 F. 3d 875 (D.C. Cir. March 13, 2007)
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as three test runs and provided in units of mass of pollutant per mass of production (e.g., lbs 

of HAP/ton of glass pulled). A combination of new and existing test data was used to 

populate the wool fiberglass residual risk modeling file. Using the glass pull rates (generally 

given in tons of glass pulled/day) that each facility provided in its ICR response and the 

source emission rates (lb/ton) from emission testing, annual emissions (tpy) were calculated 

for each pollutant and emission source. For each source, stack heights, diameters and 

coordinates were compiled from ICR responses and then sent to each facility for 

confirmation. The Wool Fiberglass ICR emissions data and modifications made to the NEI 

data are discussed in the Appendix 1b, entitled “Emissions Data Used in Residual Risk 

Modeling for Wool Fiberglass Manufacturing.” 

2.2 Dispersion modeling for inhalation exposure assessment

Both long- and short-term inhalation exposure concentrations and associated health risks from 

each facility in the source category were estimated using the Human Exposure Model in 

combination with the American Meteorological Society/EPA Regulatory Model dispersion 

modeling system (HEM-AERMOD, or HEM3). The approach used in applying this modeling 

system is outlined below, and further details are provided in Appendix 2 to this document 

(Technical Support Document for HEM3 Modeling). The HEM3 performs three main 

operations: atmospheric dispersion modeling, estimation of individual human exposures and 

health risks, and estimation of population risks. This section focuses on the dispersion 

modeling component. The exposure and risk characterization components are discussed in 

other subsections of Sections 2 and 3.

The dispersion model in the HEM3 system, AERMOD version 14134 (the version of 

AERMOD that was available at the time of the risk analysis), is a state-of-the-science 

Gaussian plume dispersion model that is preferred by EPA for modeling point, area, and 

volume sources of continuous air emissions from facility applications [5]. Further details on 

AERMOD can be found in the AERMOD Users Guide [6]. The model is used to develop 

annual average ambient concentrations through the simulation of hour-by-hour dispersion 

from the emission sources into the surrounding atmosphere. Hourly emission rates used for 

this simulation are generated by evenly dividing the total annual emission rate from the 

inventory into the 8,760 hours of the year.

The first step in the application of the HEM3 modeling system is to predict ambient 

concentrations at locations of interest. The AERMOD model options employed are 

summarized in Table 2.2-1 and are discussed further below.
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Table 2.2-1 AERMOD version 14134 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, area represented as pseudo point source

Receptor orientation Polar (13 rings and 16 radials)

Discrete (census block centroids) and user-supplied receptors

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site ( 824 stations)

In HEM3, meteorological data are ordinarily selected from a list of over 800 National 

Weather Service (NWS) surface observation stations across the continental United States, 

Alaska, Hawaii, and Puerto Rico. In most cases the nearest station is selected as 

representative of the conditions at the subject facility. When considering off-site 

meteorological data most site specific dispersion, EPA modeling guidelines suggest that 

modeling efforts employ up to five years of data to capture variability in weather patterns 

from year to year. However, because dispersion model runtimes using five years of 

meteorological data would be too long for RTR source categories with many sources, we 

modeled only a single year, 2011. While the selection of a single year may result in underprediction of long-term ambient levels at some locations, likewise it may result in overprediction at others. For each facility identified by its characteristic latitude and longitude 

coordinates, the closest meteorological station was used in the dispersion modeling. The 

average distance between a modeled facility and the applicable meteorological station was 22 

miles. Appendix 3 to this document (Meteorological Data Processing Using AERMET for 

HEM3) provides a complete listing of stations and assumptions along with further details used 

in processing the data through AERMET, the AERMOD meteorological data processing 

program. The document “Risk and Technology Review (RTR) Risk Assessment 

Methodologies: For Review by the EPA’s Science Advisory Board with Case Studies –

MACT I Petroleum Refining Sources and Portland Cement Manufacturing” (US EPA, 2009) 

provides more details on these modeling approaches.

Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to be 

utilized when processing monitoring stations that are equipped with sonic anemometers and 

for an adjustment to the surface friction velocity (u*). The public version of AERMET 

available at the time we conducted the modeling did not include the surface friction velocity 

adjustment. Also at the time, an updated version of AERMET (and AERMOD) was under 

development that incorporated further surface friction velocity adjustments to AERMET 

version 12345. In anticipation of the update, EPA processed meteorological data using a pre-
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public release version of AERMET and incorporated the updated surface friction velocity 

adjustment in the output. It was EPA’s judgement that the pre-public release version of 

AERMET would generate AERMOD ready meteorological data very close to that generated 

with the final public release version. 

The HEM3 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. The model accounts for the effects of multiple facilities when estimating concentration 

impacts at each block centroid. When evaluating the risks associated with a particular source 

category we combined the impacts of all facilities within the same source category, and 

assessed chronic exposure and risk for all census blocks with at least one resident (i.e., 

locations where people may reasonably be assumed to reside rather than receptor points at the 

fenceline of a facility). Chronic ambient concentrations were calculated as the annual average 

of all estimated short-term (one-hour) concentrations at each block centroid. Possible future 

residential use of currently uninhabited areas was not considered. Census blocks, the finest 

resolution available in the census data, are typically comprised of approximately 40 people or 

about ten households. 

In contrast to the development of ambient concentrations for evaluating long-term exposures, 

which was performed only for occupied census blocks, worst-case short-term (one-hour) 

concentrations were estimated both at the census block centroids and at points nearer the 

facility that represent locations where people may be present for short periods, but generally 

no nearer than 100 meters from the center of the facility (note that for large facilities, this 

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

emission rates were needed to screen for the potential for hazard via acute exposures, and 

where ICR data only contains annual emission totals.

The average hourly emissions rate used for chronic risk modeling is defined as the total 

emissions for a year divided by the total number of operating hours in the year. If a source 

operated 8,760 hours per year this emissions rate would be appropriate for predicting potential 

acute impacts. While hourly emissions data for each source are not available, in this 

assessment, we utilized source category specific engineering judgment to determine what the 

peak hourly emissions for any given process could be. For processes in which no 

“engineering judgment” information was available we utilized a generic factor of ten times 

actual annual emissions to estimate peak hourly emissions. The generic factor of ten is 

discussed in Appendix 4, (Analysis of data on short-term emission rates relative to long-term 

emission rates) of this document. 

Census block elevations for HEM3 modeling were determined nationally from the US 

Geological Service 1-degree digital elevation model (DEM) data files, which have a spatial 

resolution of about 90 meters. Elevations of polar grid points used in estimating short- and 

long-term ambient concentrations were assumed to be equal to the highest elevation of any 

census block falling within the polar grid sector corresponding to the grid point. If a sector 

does not contain any blocks, the model defaults the elevation to that of the nearest block. If 

an elevation is not provided for the emission source, the model uses the average elevation of 

all sectors within the innermost model ring. 
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In addition to using receptor elevation to determine plume height, AERMOD adjusts the 

plume’s flow if nearby elevated hills are expected to influence the wind patterns. For details 

on how hill heights were estimated and used in the AERMOD modeling see Appendix 2 of 

this document. 

2.3 Estimating human inhalation exposure

We used the estimated annual average ambient air concentration of each HAP at each census 

block centroid as a surrogate for the lifetime inhalation exposure concentration of all the 

people who reside in the census block. That is, the risk analysis did not consider either the 

short-term or long-term behavior (mobility) of the exposed populations and its potential 

influence on their exposure.

We did not address short-term human activity, including indoor air concentrations, for two 

reasons. First, our experience with the NATA assessments (which modeled daily activity 

using EPA’s HAPEM model) suggests that, given our current understanding of 

microenvironment concentrations and daily activities, modeling short-term activity would, on 

average, reduce risk estimates about 25 percent for particulate HAP; it will also reduce risk 

estimates for gaseous HAP, but typically by much less. Second, basing exposure estimates on 

average ambient concentrations at census block centroids may underestimate or overestimate 

actual exposure concentrations at some residences. 

We did not address long-term migration nor population growth or decrease over 70 years, 

instead basing the assessment on the assumption that each person’s predicted exposure is 

constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer 

risk, we generally estimated three metrics; the maximum individual risk (MIR), which is 

defined as the risk associated with a lifetime of exposure at the highest concentration where 

people are likely to live; the population risk distribution; and the cancer incidence. The 

assumption of not considering short or long-term population mobility does not bias the 

estimate of the theoretical MIR (assumes a person stays in one location for 70 years) nor does 

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 specific risk levels. 

When screening for potentially significant acute exposures, we used an estimate of the highest 

hourly ambient concentration at any off-site location as the surrogate for the maximum 

potential acute exposure concentration for any individual. This was generally the facility 

fenceline.

2.4 Acute Risk Screening and Refined Assessments

In establishing a scientifically defensible approach for the assessment of potential health risks 

due to acute exposures to HAP, we followed the same general approach that has been used for 
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developing chronic health risk assessments under the residual risk program. That is, we 

developed a tiered, iterative approach. This approach to risk assessment was endorsed by the 

National Academy of Sciences in its 1993 publication “Science and Judgment in Risk 

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

in 1999. 

The assessment methodology is designed to eliminate from further consideration those 

facilities for which we have confidence that no acute adverse health effects of concern will 

occur. To do so, we use what is called a tiered, iterative approach to the assessment. This 

means that we begin with a screening assessment, which relies on readily available data and 

uses conservative assumptions that in combination approximate a worst-case exposure. The 

result of this screening process is that either the facility being assessed poses no potential 

acute health risks (i.e., it “screens out”), or that it requires further, more refined assessment. 

A more refined acute assessment could use industry- or site-specific data on the temporal 

pattern of emissions, the layout of emission points at the facility, the boundaries of the 

facility, and/or the local meteorology. In some cases, all of these site-specific data are used to 

refine the assessment; in others, lesser amounts of site-specific data can be used to determine 

that acute exposures are not a concern, and significant additional data collection is not 

necessary. 

Acute health risk screening was performed for each facility as the first step. We used 

conservative assumptions for emission rates, meteorology, and exposure location. We used 

the following worst-case assumptions in our screening approach:

 Peak 1-hour emissions for the Wool Fiberglass Manufacturing category were assumed 

to equal 10 times the average 1-hour emission rates. Peak 1-hour emissions for the 

Mineral Wool Production category were assumed to equal 3 times the average 1-hour 

emission rates. (See discussion below.)

 For facilities with multiple emission points, peak 1-hour emissions were assumed to 

occur at all emission points at the same time.

 For facilities with multiple emission points, 1-hour concentrations at each receptor 

were assumed to be the sum of the maximum concentrations due to each emission 

point, regardless of whether those maximum concentrations occurred during the same 

hour. 

 Worst-case meteorology (from one year of local meteorology) was assumed to occur 

at the same time the peak emission rates occur. The recommended EPA local-scale 

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

 A person was assumed to be located downwind at the point of maximum impact 

during this same worst-case 1-hour period, but no nearer to the source than 100 

meters.

 The maximum impact was compared to multiple short-term health benchmarks for the 

HAP being assessed to determine if a possible acute health risk might exist. These 

benchmarks are described in section 2.6 of this report.

As mentioned above, when we identify acute impacts which exceed their relevant 

benchmarks, we pursue refining our acute screening estimates to the extent possible. In some 
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cases, this may entail determining the actual physical layout and boundaries of a facility to 

more accurately gauge where people might reasonably be exposed for an hour.

For the Mineral Wool Production source category we used a refined emissions multiplier of 3 

to estimate the peak hourly emission rates from the average rates. Refer to a separate 

memorandum in the docket for a detailed description of how the refined emissions multiplier 

was developed for the Mineral Wool Production source category. For the Wool Fiberglass 

Manufacturing source category data were not available to develop a refined emissions 

multiplier, therefore, the default emissions multiplier of 10 was used.

2.5 Multipathway human health and environmental risk screening

Multipathway Human Health

The EPA conducted a screening analysis examining the potential for significant human health 

risks due to exposures via routes other than inhalation (i.e., ingestion). We first determined 

whether any sources in the source categories emitted any hazardous air pollutants known to be 

persistent and bio-accumulative in environment (PB-HAP)5. The PB-HAP compounds or 

compound classes are identified for the screening from the EPA’s Air Toxics Risk 

Assessment Library [7]. With respect to PB-HAP emissions other than lead, emissions were 

evaluated for potential non-inhalation risks impacts using a Tiered screening approach. We 

first determined whether the facility-specific emission rates of each of the emitted PB–HAP 

were large enough to create the potential for significant non-inhalation human health risks 

under reasonable worst-case conditions. To facilitate this step, we developed emissions rate 

thresholds for each PB–HAP using a hypothetical upper-end screening exposure scenario 

developed for use in conjunction with the EPA’s Total Risk Integrated Methodology. Fate, 

Transport, and Ecological Exposure (TRIM.FaTE[8]) model. We conducted a sensitivity 

analysis on the screening scenario to ensure that its key design parameters would represent the 

upper end of the range of possible values, such that it would represent a conservative but not 

impossible scenario. The facility-specific emissions rates of each of the PB–HAP were 

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

the potential for significant human health risks via non-inhalation pathways. We call this 

application of the TRIM.FaTE model the Tier I TRIM- Screen.

For the purpose of developing emissions rates for our Tier I TRIM-Screen, we derived 

emission levels for each PB-HAP (other than lead) at which the maximum excess lifetime 

cancer risk would be 1-in-1 million or, for HAP that cause non-cancer health effects, the 

maximum HQ would be 1. If the emissions rate of any PB-HAP exceeds the Tier I screening 

emissions rate for any facility, we conduct a Tier II multi-pathway screen. In the Tier II 

screen, the location of each facility that exceeded the Tier I emission rate is used to refine the 

assumptions associated with the environmental scenario while maintaining the exposure 

scenario assumptions. We then adjust the risk-based Tier I screening level for each PB-HAP 

for each facility based on an understanding of how exposure concentrations estimated for the 

 

5 Although the two-letter chemical symbol for lead is Pb, in this assessment PB-HAP refers to the many air 

pollutants known to be persistent and bioaccumulative in the environment. In instances where the report is 

specifically referring to lead, it is spelled out (i.e., the two-letter chemical symbol for lead is not used in this 

document). 
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screening scenario change with meteorology and environmental assumptions. PB-HAP 

emissions that do not exceed these new Tier II screening levels are considered to pose no 

unacceptable risks. When facilities exceed the Tier II screening levels, it does not mean that 

multi-pathway impacts are significant, only that we cannot rule out that possibility based on 

the results of the screen. These facilities may be further evaluated for multi-pathway risks 

using the TRIM.FaTE model and more site specific information. Appendix 5 of this 

document (Technical Support Document for TRIM-Based Multipathway Tiered Screening 

Methodology for RTR) contains a complete discussion of the multipathway Tier I and II 

screening approach. 

In evaluating the potential multipathway risks from emissions of lead compounds, rather than 

developing a screening emission rate for them, we compared maximum estimated chronic 

atmospheric concentrations with the current National Ambient Air Quality Standard 

(NAAQS) for lead. Values below the NAAQS were considered to have a low potential for 

multipathway risks of any significance.

The NAAQS value, a public health policy judgment, incorporated the Agency’s most recent 

health evaluation of air effects of lead exposure for the purposes of setting a national ambient 

air quality standard. In setting this value, the Administrator promulgated a standard that was 

requisite to protect public health with an adequate margin of safety. We consider values 

below the level of the primary NAAQS to protect against multipathway risks because as 

mentioned above, the primary NAAQS is set as to protect public health with an adequate 

margin of safety. However, ambient air lead concentrations above the NAAQS are 

considered to pose the potential for increased risk to public health. We 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.
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Adverse Environmental Effects

The EPA has developed a screening approach to examine the potential for adverse 

environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental 

screen focuses on the following seven environmental HAP: 

 Five persistent bioaccumulative HAP (PB-HAP) – cadmium, dioxins/furans, 

polycyclic organic matter (POM), mercury (both inorganic mercury and 

methyl mercury) and lead.

 Two acid gases - hydrogen chloride (HCl), and hydrogen fluoride (HF). 

HAP that persist and bioaccumulate are of particular environmental concern because they 

accumulate in the soil, sediment and water. The acid gases, HCl and HF, were included due to 

their well-documented potential to cause direct damage to terrestrial plants.

For the environmental risk screening analysis, EPA first determined whether any facilities in 

the source category emitted any of the seven environmental HAP. If one or more of the seven 

environmental HAP evaluated are emitted by at least one facility in the source category we 

proceed to the second step of the evaluation.

For cadmium, mercury, POM and dioxins/furans, the environmental screening analysis 

consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst-cast 

environmental conditionals to determine whether the facility-specific off-site emission rates 

of each of the emitted environmental HAP exceeded ecological benchmarks represented as 

emissions threshold values. If off-site emissions from a facility do not exceed the Tier I

thresholds the facility “passes” the screen, and therefore, is not evaluated further under the 

screening approach. If off-site emissions from a facility exceed the Tier I thresholds, we 

evaluate the facility further in Tier II.

In Tier II of the environmental screening analysis, the screening emission thresholds are 

adjusted to account for local meteorology and the actual location of lakes in the vicinity of 

facilities that did not pass the Tier I screen. If off-site emissions from a facility do not exceed 

the Tier II thresholds, the facility passes the screen, and is typically not evaluated further. If 

off-site emissions from a facility exceed the Tier II thresholds, the facility does not pass the 

screen and, therefore, may have the potential to cause adverse environmental effects. Such 

facilities are evaluated further to investigate factors such as the magnitude and characteristics 

of the area of exceedance. 

For acid gases, the environmental screening analysis 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 ecological risk screening, EPA identifies a potential for adverse 

environmental effects to plant communities from exposure to acid gases when the average 

concentration of the HAP around a facility exceeds the LOAEL ecological benchmark. In 

such cases, we further investigate factors such as the magnitude and characteristics of the area 
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of exceedance (e.g., land use of exceedance area, size of exceedance area) to determine if

there is an adverse environmental effect. 

For lead compounds, we currently do not have the ability to calculate media concentrations 

using the TRIM.FaTE model. However, air concentrations of lead are already calculated as 

part of the human health exposure and risk analysis using the HEM-3 AERMOD air 

dispersion model. To evaluate the potential for adverse environmental effects from lead, we 

compare the average modeled air emission concentrations of lead from each facility in the 

source category emissions to the level of the secondary National Ambient Air Quality 

Standard (NAAQS) for lead. The secondary lead NAAQS is a reasonable means of evaluating 

environmental risk because it is set to provide substantial protection against adverse welfare 

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.”6

Ecological benchmarks included in the environmental risk screen are presented in Appendix 

9.

2.6 Dose-Response Assessment

2.6.1 Sources of chronic dose-response information 

Dose-response assessment (carcinogenic and non-carcinogenic) for chronic exposure (either 

by inhalation or ingestion) for the HAPs reported in the emissions inventories for Wool 

Fiberglass Manufacturing and for Mineral Wool Production sources were based on the EPA 

Office of Air Quality Planning and Standards’ existing recommendations for HAP [9], which 

were also used for NATA [10]. This information has been obtained from various sources and 

prioritized according to (1) 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: 

1) US Environmental Protection Agency (EPA). EPA has developed dose-response 

assessments for chronic exposure for many of the pollutants in this study. These 

assessments typically provide a qualitative statement regarding the strength of scientific 

data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, 

for ingestion) to protect against effects other than cancer and/or a unit risk estimate (URE, 

 

6 A secondary standard, as defined in Section 109(b)(2), must “specify a level of air quality the attainment and 

maintenance of which, in the judgment of the Administrator, based on criteria, is requisite to protect the public 

welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the 

ambient air.” Welfare effects as defined in section 302(h) (42 U.S.C. 7602(h)) include, but are not limited to, 

“effects on soils, 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.”
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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 μg/m3in air.” The 

SF is “an upper bound, approximating a 95percent 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 doseresponse assessment information in several forms, based on the level of review. The 

Integrated Risk Information System (IRIS) [11] is an EPA database that contains scientific 

health assessment information, including dose-response information. All IRIS assessments 

since 1996 have also undergone independent external peer review. The current IRIS 

process includes review by EPA scientists, interagency reviewers from other federal 

agencies, and the public, and peer review by independent scientists external to EPA. 

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

low-dose extrapolation as a default option for carcinogens for which the mode of action 

(MOA) has not been identified. We expect future EPA dose-response assessments to 

identify nonlinear MOAs where appropriate, and we will use those analyses (once they are 

peer reviewed) in our risk assessments. At this time, however, there are no available 

carcinogen dose-response assessments for inhalation exposure that are based on a 

nonlinear MOA.

2) US Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part 

of the US Department of Health and Human Services, develops and publishes Minimum 

Risk Levels (MRLs) [12] for inhalation and 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. 

Exposures above an MRL do not necessarily represent a threat, and MRLs are therefore 

not intended for use as predictors of adverse health effects or for setting cleanup levels.

3) California Environmental Protection Agency (CalEPA). The CalEPA Office of 

Environmental Health Hazard Assessment has developed dose-response assessments for 

many substances, based both on carcinogenicity and health effects other than cancer. The 

process for developing these assessments is similar to that used by EPA to develop IRIS 

values and incorporates significant external scientific peer review. As cited in the 
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CalEPA Technical Support Document for developing their chronic assessments7: “The 

guidelines for developing chronic inhalation exposure levels incorporate many 

recommendations of the U.S. EPA [13] and NAS [14].” The non-cancer information 

includes available inhalation health risk guidance values expressed as chronic inhalation 

reference exposure levels (RELs) [15]. CalEPA defines the REL as “the concentration 

level at or below which no health effects are anticipated in the general human population.” 

CalEPA's quantitative dose-response information on carcinogenicity by inhalation 

exposure is expressed in terms of the URE [16], defined similarly to EPA's URE. 

 

In developing chronic risk estimates, we adjusted dose-response values for some HAPs based 

on professional judgment, as follows: 

1) In the case of HAP categories such as glycol ethers and cyanide compounds, the most 

conservative dose-response value of the chemical category is used as a surrogate for other 

compounds in the group for which dose-response values are not available. This is done in 

order to examine, under conservative assumptions, whether these HAPs that lack doseresponse values may pose an unacceptable risk and require further examination, or screen 

out from further assessment. 

2) Where possible for emissions of unspecified mixtures of HAP categories such as metal 

compounds and POM, we apply category-specific chemical speciation profiles appropriate 

to the source category to develop a composite dose-response value for the category. 

3) In 2004, the EPA determined that the Chemical Industry Institute of Toxicology (CIIT) 

cancer dose-response value for formaldehyde (5.5 x 10-9per μg/m3) was based on better 

science than the IRIS cancer dose-response value (1.3 x 10-5per μg/m3), and we switched 

from using the IRIS value to the CIIT value in risk assessments supporting regulatory 

actions. Subsequent research published by EPA suggested that the CIIT model was not 

appropriate and in 2010 EPA returned to using 1991 IRIS value. The EPA has been 

working on revising the formaldehyde IRIS assessment and the National Academy of 

Sciences (NAS) completed its review of the EPA’s draft assessment in April of 2011.

8

The EPA will follow the NAS Report recommendations and will present results obtained 

by implementing the biologically-based dose-response (BBDR) model for formaldehyde. 

The EPA will compare these estimates with those currently presented in the External 

Review draft of the assessment and will discuss their strengths and weaknesses. As 

recommended by the NAS committee, appropriate sensitivity and uncertainty analyses 

will be an integral component of implementing the BBDR model. In the interim, we will 

present findings using the 1991 IRIS value as a primary estimate, and EPA may also 

consider other information as the science evolves.

 

7 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document

for the Determination of Non-cancer Chronic Reference Exposure Levels. Air Toxicology and Epidemiology 

Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency. 

February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf)

8 http://www.nap.edu/catalog.php?record_id=13142
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4) In the case of nickel compounds, to provide a conservative estimate of potential cancer 

risks, we used the IRIS URE value for nickel subsulfide in this assessment. Based on past 

scientific and technical considerations, the determination of the percent of nickel 

subsulfide was considered a major factor for estimating the extent and magnitude of the 

risks of cancer due to nickel-containing emissions. Nickel speciation information for 

some of the largest nickel-emitting sources (including oil combustion, coal combustion, 

and others) suggested that at least 35 percent of total nickel emissions may be soluble 

compounds and that the URE for the mixture of inhaled nickel compounds (based on 

nickel subsulfide, and representative of pure insoluble crystalline nickel) could be derived 

to reflect the assumption that 65 percent of the total mass of nickel may be carcinogenic. 

Based on consistent views of major scientific bodies (i.e., National Toxicology Program 

(NTP) in their 12th Report of the Carcinogens (ROC)9, International Agency for Research 

on Cancer (IARC)10, and other international agencies)11 that consider all nickel 

compounds to be carcinogenic, we currently consider all nickel compounds to have the 

potential of being as carcinogenic to humans. The 12th Report of the Carcinogens states 

that the “combined results of epidemiological studies, mechanistic studies, and 

carcinogenic studies in rodents support the concept that nickel compounds generate nickel 

ions in target cells at sites critical for carcinogenesis, thus allowing consideration and 

evaluation of these compounds as a single group.” Although the precise nickel compound 

(or compounds) responsible for the carcinogenic effects in humans is not always clear, 

studies indicate that nickel sulfate and the combinations of nickel sulfides and oxides 

encountered in the nickel refining industries cause cancer in humans (these studies are 

summarized in a review by Grimsrud et al., 201012). The major scientific bodies 

mentioned above have also recognized that there are differences in toxicity and/or 

carcinogenic potential across the different nickel compounds. 

In this inhalation risk assessment, to take a conservative approach, we considered all 

nickel compounds to have the same carcinogenic potential as nickel subsulfide and used 

the IRIS URE for nickel subsulfide to estimate risks due to all nickel emissions from the 

source category. without a factor to reflect the assumption that 100 percent of the total 

mass of nickel may be as carcinogenic as pure nickel subsulfide. In addition, given that 

there are two additional URE13 values derived for exposure to mixtures of nickel 

 

9 National Toxicology Program (NTP), 2011. Report on carcinogens. 12th ed. Research Triangle Park, NC: US 

Department of Health and Human Services (DHHS), Public Health Service. Available online at 

http://ntp.niehs.nih.gov/ntp/roc/twelfth/roc12.pdf

10 International Agency for Research on Cancer (IARC), 1990. IARC monographs on the evaluation of 

carcinogenic risks to humans. Chromium, nickel, and welding. Vol. 49. Lyons, France: International Agency 

for Research on Cancer, World Health Organization Vol. 49:256.

11 World Health Organization (WHO, 1991) and the European Union’s Scientific Committee on Health and 

Environmental Risks (SCHER, 2006).

12Grimsrud TK and Andersen A. Evidence of carcinogenicity in humans of water-soluble nickel salts. J Occup 

Med Toxicol 2010, 5:1-7. Available online at 

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2868037/?tool=pubmed

13 Two UREs (other than the current IRIS values) have been derived for nickel compounds as a group: one 

developed by the California Department of Health Services 
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compounds, as a group, that are 2-3 fold lower than the IRIS URE for nickel subsulfide, 

the EPA also considers it reasonable to use a value that is 50 percent of the IRIS URE for 

nickel subsulfide for providing an estimate of the lower end of the plausible range of 

cancer potency values for different mixtures of nickel compounds. 

5) Where POM emissions were not speciated into individual compounds, we applied the 

same simplifying assumptions to assessments that are used in NATA [17]. The NATA 

approach partitions POM into eight different non-overlapping “groups” that are modeled 

as separate pollutants. Each POM group comprises POM species of similar carcinogenic 

potency, for which we can apply the same URE.

6) A chronic screening level of 163 ug/m3 was developed by EPA for carbonyl sulfide (COS) 

from a No Observed Adverse Effects Level (NOAEL) of 200 ppm based on brain lesions 

and neurophysiological alterations in rodents. A more detailed discussion of the studies 

used to develop the COS chronic screening level is provided in Appendix 8. The 

screening level includes a total uncertainty factor (UF) of 3,000: 10 times for 

extrapolation for interspecies differences, 10 times for consideration of intraspecies 

variability, 10 times for extrapolation from subchronic to chronic duration, and 3 times for 

database insufficiencies. See section 5 of this document for a detailed discussion of 

exposure modeling uncertainties. 

The emissions inventory for the Wool Fiberglass Manufacturing and for the Mineral Wool 

Production source categories includes emissions of 14 HAP with available chronic 

quantitative inhalation dose-response values. These HAP, their dose-response values, and the 

source of the values are listed in Tables 2.6-1 (a) and (b).

 

(http://www.arb.ca.gov/toxics/id/summary/nickel_tech_b.pdf) and the other by the Texas Commission on 

Environmental Quality (http://www.epa.gov/ttn/atw/nata1999/99pdfs/healtheffectsinfo.pdf).
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Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to 

Carcinogens

URE (unit risk estimate for cancer)14 = cancer risk per μg/m3of average lifetime 

exposure. Sources: IRIS = EPA Integrated Risk Information System, CAL = California 

EPA Office of Environmental Health Hazard Assessment.

Pollutant CAS Number15 URE16

(1/μg/m3)

Source

Arsenic compounds 7440382 4.3E-03 IRIS

Beryllium compounds 7440417 2.4E-03 IRIS

Cadmium compounds 7440439 1.8E-03 IRIS

Chromium (VI) compounds 18540299 1.2E-02 IRIS

Formaldehydea50000 1.3E-05 IRIS

a- The EPA has used the CIIT URE value, 5.5X10-9 per ug/m3, to characterize formaldehyde cancer risk in 

some instances.

 

14 The URE is the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an 

agent at a concentration of 1 μg/m3in air. URE’s are considered upper bound estimates meaning they represent 

a plausible upper limit to the true value. 

15 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we 

have used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAPs.
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Table 2.6-1 (b) Dose-Response Values for Chronic Inhalation Exposure to 

Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an 

order of magnitude) of a continuous inhalation exposure to the human population (including 

sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects 

during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = 

California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency 

for Toxic Substances Disease Registry, EPA OAQPS = EPA Office of Air Quality Planning 

and Standards.

Pollutant CAS Number6 RfC 

(mg/m3)

Source16

Arsenic compounds 7440382 1.5E-05 CAL

Beryllium compounds 7440417 2.0E-05 IRIS - M

Cadmium compounds 7440439 1.0E-05 ATSDR-D

Carbonyl sulfide 463581 0.163 ORD Screening

Chromium (VI) compounds 18540299 1.0E-04 IRIS - M

Formaldehyde 50000 0.0098 ATSDR

Hydrochloric acid 7647010 0.02 IRIS - L

Lead compounds 7439921 1.5E-04 EPA OAQPS

Manganese 7439965 5.0E-05 IRIS - M

Methanol 67561 4 CAL

Mercury (elemental) 7439976 3.0E-4 IRIS - M

Nickel compounds 7440020 0.00048 EPA OAQPS17

Phenol 10952 0.2 CAL

Selenium compounds 7782492 0.02 CAL

2.6.2 Sources of acute dose-response information 

Hazard identification and dose-response assessment information for preliminary acute 

inhalation exposure assessments are based on the existing recommendations of OAQPS for 

HAPs [18]. Depending on availability, the results from screening acute assessments are 

compared to both “no effects” reference levels for the general public, such as the California 

Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute 

Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines 

(ERPGs), with the recognition that the ultimate interpretation of any potential risks associated 

with an estimated exceedance of a particular reference level depends on the definition of that 

level and any limitations expressed therein. Comparisons among different available 

 

16 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the 

overall level of confidence in the RfC value, as reported in IRIS.

17 The EPA IRIS assessments for nickel compounds provide a range of plausible UREs. This assessment used 

the highest value in that range which is equal to the URE for nickel subsulfide, 4.8E-04 per ug/m3. The low end 

of the range is equal to 50% of the URE for nickel subsulfide, 2.4E-04 per ug/m3.
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inhalation health effect reference values (both acute and chronic) for selected HAPs can be 

found in an EPA document [19].

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 Reference Exposure Levels (RELs). 

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

“the concentration level at or below which no adverse health effects are anticipated for a 

specified exposure duration [20]. RELs are based on the most sensitive, relevant, adverse 

health effect reported in the medical and toxicological literature. RELs are designed to 

protect the most sensitive individuals in the population by the inclusion of margins of 

safety. Since 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 Reference 

Concentrations (RfCs) for chronic exposures (and, in fact, California also has developed 

chronic RELs).

Acute Exposure Guideline Levels (AEGLs). AEGLs are developed by the National Advisory 

Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) for Hazardous 

Substances, and then reviewed and published by the National Research Council As described 

in the Committee’s “Standing Operating Procedures (SOP)”

(http://www.epa.gov/opptintr/aegl/pubs/sop.pdf), AEGLs “represent threshold exposure limits 

for the general public and are applicable to emergency exposures ranging from 10 min to 8 h.” 

Their intended application is “for conducting risk assessments to aid in the development of 

emergency preparedness and prevention plans, as well as real time emergency response 

actions, for accidental chemical releases at fixed facilities and from transport carriers.” The 

document states that “the primary purpose of the 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 that, “It is anticipated that the AEGL values 

will be used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, 

and possibly the international community in conjunction with chemical emergency response, 

planning, and prevention programs. More specifically, the AEGL values will be used for 

conducting various risk assessments to aid in the development of emergency preparedness 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/m3) 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.”
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“AEGL-2 is the airborne concentration (expressed as ppm or mg/m3) of a substance above 

which it is predicted that the general population, including susceptible individuals, could 

experience irreversible or other serious, long-lasting adverse health effects or an impaired 

ability to escape.”

“Airborne concentrations below AEGL-1 represent exposure levels that can produce mild 

and progressively increasing but transient and nondisabling odor, taste, and sensory 

irritation or certain asymptomatic, nonsensory effects. With increasing airborne 

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 Emergency Response Planning Guidelines (ERPGs) [21] 

for acute exposures at three different levels of severity. These guidelines represent 

concentrations for once in a lifetime 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

(http://www.aiha.org/insideaiha/GuidelineDevelopment/ERPG/Pages/default.aspx) are 

described in their supporting documentation as follows: “ERPGs are air concentration 

guidelines for single exposures to agents and are intended for use as tools to assess the 

adequacy of accident prevention and emergency response plans, including transportation 

emergency planning, community emergency response plans and incident prevention and 

mitigation.”

ERPG-1 and ERPG-2 values are defined by AIHA as follows:

“ERPG-1 is the maximum airborne concentration below which it is believed that nearly all 

individuals could be exposed for up to 1 hour without experiencing other than mild 

transient adverse health effects or without perceiving a clearly defined, objectionable 

odor.”

“ERPG-2 is the maximum airborne concentration below which it is believed that nearly all 

individuals could be exposed for up to 1 hour without experiencing or developing 

irreversible or other serious health effects or symptoms which could impair an individual's 

ability to take protective action.”

The emissions inventory for the Wool Fiberglass Manufacturing and for the Mineral Wool 

Production source categories includes emissions of 9 HAP with relevant and available 
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quantitative acute dose-response threshold values. These HAPs, their acute threshold values, 

and the source of the value are listed below in Table 2.6-2.

Table 2.6-2 Dose-Response Values for Acute Exposure

Pollutant

CAS 

Number

AEGL-1

(1-hr)

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG1

(mg/m3)

ERPG2

(mg/m3) REL 

Arsenic compounds 7440382 0.0002

Beryllium compounds 7440417 0.025

Carbonyl sulfide 463581 140

Formaldehyde 50000 1.1 17 1.2 12 0.055

Hydrochloric acid 7647010 2.7 33 4.5 30 2.1

Mercury (elemental) 7439976 1.7 2 0.0006

Methanol 67561 690 2700 260 1300 28

Nickel 7440020 0.0002

Phenol 108952 58 89 38 190 5.8

2.7 Risk Characterization

2.7.1 General

The final product of the risk assessment is the risk characterization, in which the information 

from the previous steps is integrated and an overall conclusion about risk is synthesized that is 

complete, informative, and useful for decision makers. In 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, the EPA Administrator’s March 

1995 Policy for Risk Characterization [22] specifies that a risk characterization “be prepared 

in a manner that is clear, transparent, reasonable, and consistent with other risk 

characterizations of similar scope prepared across programs in the Agency.” These principles 

of transparency and consistency have been reinforced by the Agency’s Risk Characterization 

Handbook [23], in 2002 by the Agency’s information quality guidelines [24], and in the 

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis18, and 

are incorporated in these assessments.

Estimates of health risk are presented in the context of uncertainties and limitations in the data 

and methodology. Through our tiered, iterative analytical approach, we have attempted to 

reduce both uncertainty and bias to the greatest degree possible in these assessments, within 

 

18Memorandum 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 

(http://www.whitehouse.gov/sites/default/omb/assets/omb/memoranda/fy2007/m07-24.pdf)
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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 the assessment that has a potency estimate available, 

individual and population cancer risks were 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-yr period (i.e., the 

assumed human lifespan) at that exposure. Because UREs for most HAPs are upper-bound 

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

zero.

For EPA’s list of carcinogenic HAPs that act by a mutagenic mode-of-action [25], we applied 

EPA’s Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to 

Carcinogens [26]. This guidance has the effect of adjusting the URE by factors of 10 (for 

children aged 0-1), 3 (for children aged 2-15), or 1.6 (for 70 years of exposure beginning at 

birth), as needed in risk assessments. In this case, this has the effect of increasing the 

estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a 

small fraction of the total POM emissions may be reported as individual compounds, EPA 

expresses carcinogenic potency for compounds in this group in terms of benzo[a]pyrene 

equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism 

of action as does benzo[a]pyrene. For this reason, EPA implementation policy [27] 

recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk 

estimates are based on relative potency. Accordingly, we applied the Supplemental Guidance 

to all unspeciated POM mixtures.

Increased cancer incidence for the entire receptor population within the area of analysis was 

estimated by multiplying the estimated lifetime cancer risk for each census block by the 

number of people residing in that block, then summing the results for the entire modeled 

domain. This lifetime population incidence estimate was divided by 70 years to obtain an 

estimate of the number of cancer cases per year.

In the case of benzene, the high end of the reported cancer URE range was used in our 

assessments to provide a conservative estimate of potential cancer risks. Use of the high end 

of the range provides risk estimates that are approximately 3.5 times higher than use of the 

equally plausible low end value. If the estimated benzene–associated risks exceed 1 in a 

million, we also evaluated the impact of using the low end of the URE range on our risk 

results.

Unlike linear dose-response assessments for cancer, noncancer health hazards generally are 

not expressed as a probability of an adverse occurrence. Instead, “risk” for noncancer effects 

is expressed by comparing an exposure to a reference level as a ratio. The “hazard quotient” 

(HQ) is the estimated exposure divided by a reference level (e.g., the RfC). For a given HAP, 

exposures at or below the reference level (HQ≤1) are not likely to cause adverse health 
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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 expressed in terms of the HQ 

for inhalation, calculated for each HAP at each census block centroid. As discussed above, 

RfCs incorporate generally conservative uncertainty factors in the face of uncertain 

extrapolations, such that an HQ greater than one does not necessarily suggest the onset of 

adverse effects. The HQ cannot be translated to a probability that adverse effects will occur, 

and is unlikely to be proportional to adverse health effect outcomes in a population.

Screening for potentially significant acute inhalation exposures also followed the HQ 

approach. We divided the maximum estimated acute exposure by each available short-term 

threshold value to develop an array of HQ values relative to the various acute endpoints and 

thresholds. In general, when none of these HQ values are greater than one, there is no 

potential for acute risk. In those cases where HQ values above one are seen, additional 

information is used to determine if there is a potential for significant acute risks.

2.7.2 Mixtures

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

rather than a single pollutant, we estimated the aggregate health risks associated with all the 

exposures from a particular source category combined.

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

[28,29] default assumption of additivity of effects, and combine risks by summing them using 

the independence formula in the mixtures guidelines.

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 aggregated non-cancer HQs of HAPs that act by similar 

toxic modes of action, or (where this information is absent) that affect the same target organ. 

This process creates, for each target organ, a target-organ-specific hazard index (TOSHI), 

defined as the sum of hazard quotients for individual HAPs that affect the same organ or 

organ system. All TOSHI calculations presented here were based exclusively on effects 

occurring at the “critical dose” (i.e., the lowest dose that produces adverse health effects). 

Although HQs associated with some pollutants have been aggregated into more than one 

TOSHI, this has been done only in cases where the critical dose affects more than one target 

organ. Because impacts on organs or systems that occur 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 
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potential for adverse health outcomes associated with pollutant exposure, and needs to be 

interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the acute inhalation screening and the variable nature of 

emissions and potential exposures, acute impacts were screened on an individual pollutant 

basis, not using the TOSHI approach.

3 Risk Results for the Mineral Wool Production

3.1 Source Category Description and Results

Mineral wool is a fibrous, glassy substance made from natural rock (such as basalt), blast 

furnace slag, or other similar materials and consisting of silicate fibers typically 4 to 7 

micrometers in diameter. In the mineral wool manufacturing process, rock and/or blast 

furnace slag and other raw materials (e.g., gravel) are melted in a furnace (cupola) using coke 

as fuel. The molten material is then formed into fiber. Mineral wool is manufactured as 

either a “bonded” product that incorporates a binder to increase structural rigidity or a less 

rigid “nonbonded” product. The production of bonded mineral wool involves the application 

of a HAP-based binder to the fiber, where the binder is typically composed of a phenolformaldehyde resin, water, urea, silane, ammonia, and ammonium sulfate. The application of 

binder is followed by thermosetting in a curing oven and cooling of the finished product. 

Sources that manufacture only nonbonded fibers would not include the processes of binder 

application and curing. Emission sources at mineral wool production facilities include the 

cupola furnace where the mineral charge is melted; a blow chamber, in which air or a binder 

is drawn over the fibers, forming them into a screen; a curing oven that bonds the fibers (for 

bonded products); and a cooling oven.

In 1997 when the mineral wool MACT rule was proposed, EPA estimated that there were 16 

major source mineral wool production facilities in the United States that would be subject to 

the rule. Currently, there are seven mineral wool production facilities nationwide. Based on 

emissions data collected from the industry, these 7 facilities emitted a reported total of 

400tons/year of HAP into the atmosphere. Refer to Appendix 1 for details on how the 

emissions inventory was developed. 

Table 3.1-1 summarizes the emissions for the Mineral Wool Production source category. 

Based on these data, the HAP emitted in largest quantities in total from these facilities are 

carbonyl sulfide, formaldehyde, phenol, hydrochloric acid, methanol, and hydrogen fluoride; 

emissions of these 6 pollutants account for 99.9 percent of the total HAP emissions by mass 

from the data set. Carbonyl sulfide, hydrogen chloride, and hydrogen fluoride were reported 

as an emission for all of the facilities. Formaldehyde, methanol and phenol are also emitted in 

large quantities, but from fewer facilities; no more than three facilities report emissions of any 

one of these HAP. Formaldehyde, methanol and phenol are emitted only from bonded 

mineral wool fiber manufacturing, which includes emissions from the application of the 

binder, curing, and cooling. Raw material feed rates in mineral wool processes are essentially 

constant with minimal fluctuation (approximately ± 10 percent). Consequently, emissions 
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also have minimal fluctuation [30]. In refining the acute risk assessment, a short-term 

emissions multiplier of 3 was used to estimate the maximum hazard from acute exposures. 

See separate memo on the Mineral Wool Acute Factor in the project docket for a detailed 

description of how the refined emissions multiplier was developed for the Mineral Wool 

Production source category. 

Emissions of PB HAP reported in the data set for the mineral wool production source 

category include lead, cadmium, and mercury compounds. 

The following environmental HAP are emitted from the Mineral Wool Production source 

category and are included in the environmental risk screen: lead, mercury (mercuric chloride 

and methyl mercury), cadmium, hydrogen chloride and hydrogen fluoride.

Table 3.1-1 Summary of Emissions from the Mineral Wool Production Category and Availability of 

Dose-Response Values

HAP Emissions 

(tpy)

Number 

of 

Facilities 

Reporting 

HAP (7 

facilities 

in data 

set) 

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSa

PBUnit Risk HAP?

Estimate for 

Cancer?

Reference 

Concentration 

for Noncancer?

Health 

Benchmark 

Values for Acute 

Noncancer?

Carbonyl Sulfide 264.5 7 

Formaldehyde 47.9 3   

Phenol 36.2 3  

Hydrochloric acid 18.5 7  

Methanol 18.2 3  

Hydrogen Fluoride 14.6 7

Selenium 0.28 7 

Manganese 0.14 7 

Chromium III 0.04 7

Lead 0.03 7  

Arsenic 0.03 7   

Nickel 0.02 7  

Antimony 0.002 7

Cadmium 0.002 7   

Cobalt 0.0013 7 

Elemental Gaseous Mercury 0.0005 6   

Chromium (VI) 0.0003 7  

Beryllium 0.0002 7   

Gaseous Divalent Mercury 0.0001 6 

Particulate Divalent Mercury 0.0001 6 

a Tables 2.6-1 (a) and (b) of this report contain the URE and RfC values for the HAP emitted from mineral wool production 

facilities. Table 2.6-2 of this report contains the acute noncancer values for the HAP emitted from mineral wool production 

facilities. Specific dose-response values for each chemical are identified on EPA’s Technology Transfer Network website for 

air toxics at http://www.epa.gov/ttn/atw/toxsource/summary.html. 
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3.2 Risk Characterization

This section presents the results of the risk assessment for the Mineral Wool Production

source category. The basic chronic inhalation risk estimates presented here are the maximum 

individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. 

We also present results from our acute inhalation impact screening in the form of maximum 

hazard quotients, as well as the results of our preliminary screen for potential non-inhalation 

risks from PB HAP. Also presented are the HAP “drivers,” which are the HAP that 

collectively contribute 90 percent of the maximum cancer risk or maximum hazard index at 

the highest exposure location. In addition, we present the results for a screening-level 

evaluation of the potential adverse environmental risks associated with actual emissions.

Actual Emissions

Tables 3.2-1 and 3.2-2 summarize the chronic and acute inhalation risk results for this source 

category based on actual emissions. The results for the Mineral Wool Production source 

category indicate that maximum lifetime individual cancer risks could be as high as 10 in a 

million. The major contributor to this risk is formaldehyde. Approximately 11,600 people 

were estimated to have cancer risks above 1 in a million as a result of the emissions from 2

facilities. The maximum chronic non-cancer TOSHI value for the source category could be 

up to 0.1 with emissions of formaldehyde dominating those impacts, indicating no significant 

potential for chronic noncancer impacts. 

Worst-case screening acute hazard quotients (HQs) were calculated for every HAP shown in 

Table 3.1-1 that has an acute benchmark, and the highest acute HQ (REL of 20 for 

formaldehyde) is shown in Table 3.2-1. A refined emissions multiplier of 3 was used to 

estimate the peak hourly emission rates from the average rates. Refer to a separate 

memorandum in the docket for a detailed description of how the refined emissions multiplier 

was developed for the Mineral Wool Production source category. For cases where the 

screening acute HQ was greater than 1, we further refined the estimates by determining the 

highest HQ value that is outside facility boundaries. In this case, the unrefined acute HQ 

value was 34. During refinement values located on site or in areas where the public would 

not be found for at least an hour were removed, yielding a refined acute HQ of 20. Table 3.2-

2 provides more information on the acute risk estimates for formaldehyde, the only HAP that 

had a worst-case screening acute HQ greater than 1 for any benchmark. 

To better characterize the potential health risks associated with estimated worst-case acute 

exposures to HAP, and in response to a key recommendation from the Science Advisory 

Board’s peer review of EPA’s RTR risk assessment methodologies19, we examine a wider 

range of available acute health metrics than we do for our chronic risk assessments. This is in 

response to the acknowledgement that there are generally more data gaps and inconsistencies 

in acute reference values than there are in chronic reference values. By definition, the acute 

CA-REL represents a health-protective level of exposure, with no risk anticipated below those 

 

19 The SAB peer review of RTR Risk Assessment Methodologies is available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EPA-SAB-10-

007-unsigned.pdf
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levels, even for repeated exposures; however, the health risk from higher-level exposures is 

unknown. Therefore, when a CA-REL is exceeded and an AEGL-1 or ERPG-1 level is 

available (i.e., levels at which mild effects are anticipated in the general public for a single 

exposure), we have used them as a second comparative measure. Historically, comparisons of 

the estimated maximum off-site one-hour exposure levels have not been typically made to 

occupational levels for the purpose of characterizing public health risks in RTR assessments. 

This is because occupational ceiling values are not generally considered protective for the 

general public since they are designed to protect the worker population (presumed healthy 

adults) for short duration (<15 minute) increases in exposure20. As a result, for most 

chemicals, the 15-minute occupational ceiling values are set at levels higher than a one-hour 

AEGL-1, making comparisons to them irrelevant unless the AEGL-1 or ERPG-1 levels are 

exceeded13. Such is not the case when comparing the available acute inhalation health effect 

reference values for formaldehyde13.

The worst-case maximum estimated 1-hour exposure to formaldehyde outside the facility 

fence line for the mineral wool production source category is 1.06 mg/m3. This estimated 

worst-case exposure exceeds the 1-hour REL by a factor of 20 (HQREL = 20) and exceeds the 

1-hour AEGL-1by a factor of 1 (HQAEGL-1 = 1). This exposure estimate exceeds the 

workplace ceiling level guideline for the formaldehyde value developed by National Institutes 

for Occupational Safety and Health (NIOSH)21 “for any 15 minute period in a work day” by a 

factor of 9 (NIOSH REL-ceiling value of 0.12 mg/m3; HQNIOSH = 9). The estimate exceeds

the value developed by the American Conference of Governmental Industrial Hygienists 

(ACGIH)

22 as “not to be exceeded at any time” by a factor of 3 (ACGIH TLV-ceiling value of 

0.37 mg/m3; HQACGIH = 3). Additionally, the estimated maximum acute exposure exceeds the 

Air Quality Guideline value that was developed by the World Health Organization23 for 30-

minute exposures by a factor of 11 (0.1 mg/m3; HQWHO = 11).

Chronic noncancer target organ specific hazard indices (HIs) were calculated for every HAP 

shown in Table 3.1-1 that has a chronic benchmark, and the highest target organ specific HI 

(0.1 for formaldehyde) is shown in Table 3.2-1. There were no HIs greater than 1 for this 

source category. 

 

20 U.S. EPA. (2009) Chapter 2.9 Chemical Specific Reference Values for Formaldehyde in Graphical Arrays of 

Chemical-Specific Health Effect Reference Values for Inhalation Exposures (Final Report). U.S. 

Environmental Protection Agency, Washington, DC, EPA/600/R-09/061, and available on-line at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003

21 National Institutes for Occupational Safety and Health (NIOSH). Occupational Safety and Health Guideline 

for Formaldehyde; http://www.cdc.gov/niosh/docs/81-123/pdfs/0293.pdf

22 ACGIH (2001) Formaldehyde. In Documentation of the TLVs® and BEIs® with Other Worldwide 

Occupational Exposure Values. ACGIH, 1300 Kemper Meadow Drive, Cincinnati, OH 45240 (ISBN: 978-1-

882417-74-2) and available on-line at http://www.acgih.org.

23 WHO (2000). Chapter 5.8 Formaldehyde, in Air Quality Guidelines for Europe, second edition. World Health 

Organization Regional Publications, European Series, No. 91. Copenhagen, Denmark. Available on-line at 

http://www.euro.who.int/__data/assets/pdf_file/0005/74732/E71922.pdf. 
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We conducted a screening-level evaluation of the potential human health risks associated with 

emissions of PB HAP. Reported emissions of PB HAP were compared to emission thresholds 

established by EPA for the purposes of the RTR risk assessments. 24 The PB HAPs emitted 

by facilities in this category are lead, cadmium, and mercury. All lead, cadmium, and 

mercury emissions were below the threshold levels, indicating no potential for significant 

multi-pathway risks from these facilities.

We conducted a screening-level evaluation of the potential adverse environmental risks 

associated with emissions of the following environmental HAP from the Mineral Wool

Production source category: lead, mercury, cadmium, hydrogen chloride and hydrogen 

fluoride. The results of the environmental screening analysis are summarized in Table 3.2-3. 

In the Tier 1 screening analysis for PB-HAP (other than lead, which was evaluated differently 

as noted below) the individual modeled Tier 1 concentrations for mercury and cadmium did 

not exceed any ecological benchmark for any facility in the source category. For lead, we did 

not estimate any exceedances of the secondary lead NAAQS.

For hydrogen fluoride and hydrogen chloride, the average modeled concentration around each 

facility (i.e., the average concentration of all off-site data points in the modeling domain) did 

not exceed the ecological benchmarks. In addition, each individual modeled concentration of 

hydrogen chloride and hydrogen fluoride (i.e., each off-site data point in the modeling 

domain) was below the ecological benchmarks for all facilities. 

MACT-Allowable Emissions

Analysis of potential differences between actual emissions levels and the maximum emissions 

allowable under the MACT standards indicated that MACT allowable emission levels may be 

up to 3 times greater than actual emissions levels. Considering this difference, the risk results 

from the inhalation risk assessment indicate that the maximum excess lifetime individual 

cancer risk could be up to 30 in a million, and that the maximum chronic noncancer TOSHI 

value could be as high as 0.4 at the MACT-allowable emissions level. 

Post-RTR Emissions

Post-RTR emissions of formaldehyde are the same as actual emissions. Therefore, the postRTR risks will be the same as those described above for actual emissions.

 

24 ICF International. TRIM-Based Multipathway Screening Scenario. Prepared for U.S. Environmental 

Protection Agency, Research Triangle Park, NC. October 2008.
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Table 3.2-1 Summary of Source Category Level Inhalation Risks for Mineral Wool Production

Based on Actual Emissions

Result HAP 

“Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source Category 7 n/a

Number of Facilities Identified in the NEI and Modeled in Preliminary 

Risk Assessment 7 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 million) 10

25 formaldehyde

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 1 formaldehyde

Greater than or equal to 1 in 1 million 2 formaldehyde

Chronic Noncancer Risks

Maximum Hazard Index 0.1 formaldehyde

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Refined Acute Noncancer Screening Results

Maximum Acute Hazard Quotient [using scaling factor of 3]

20

1

formaldehyde

(REL)

formaldehyde

(AEGL-1, 

ERPG-1))

Number of Facilities With Potential for Acute Effects 1 formaldehyde

Population Exposure

Number of People Living Within 50 Kilometers of Facilities Modeled 4,100,000 n/a

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 1 n/a

Greater than or equal to 1 in 1 million 11,600 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) 0.00118 n/a

Contribution of HAP to Cancer Incidence:

Formaldehyde

Arsenic compounds

Cr (VI)

Arsenic Compounds

82%

16%

1%

1%

n/a

 

25 We note that the MIR for this source category would be 0.001-in-1 million if the CIIT URE for formaldehyde 

had been used in the assessment. There is an ongoing IRIS reassessment for formaldehyde, and future RTR risk 

assessments will use the cancer potency for formaldehyde that results from that reassessment. As a result, the 

current results may not match those of future assessments.
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Table 3.2-2 Summary of Refined Acute Results for Mineral Wool Production Facilities

Based on Actual Emissions

 

Refined Results

MAXIMUM ACUTE HAZARD 

QUOTIENTS

ACUTE DOSE-RESPONSE VALUES

Based 

on 

REL

Based on 

AEGL-1/

ERPG-1

Based on 

AEGL-2/

ERPG-2

REL

(mg/m3)

AEGL-1 (1-

hr) (mg/m3)

ERPG-1 

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG2 

(mg/m3)

HAP Max. 1-hr. 

Air Conc. 

(mg/m3)

Formaldehyde 1.06 20 1 0.06/0.09 0.055 1.1 1.2 17 12

Notes on Refined Process:

1) The screening was performed for all emitted HAP with available acute dose-response values. Only those pollutants whose 

screening HQs were greater than 1 for at least one acute threshold value are shown in the table.

2) HAP with available acute dose-response values which are not in the table do not carry any potential for posing acute health risks, 

based on an analysis of currently available emissions data.

Notes on Acute Dose-Response Values:

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

AEGL – Acute exposure guideline levels represent emergency exposure (1-hour) limits for the general public.

AEGL-1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could 

experience effects that are notable discomfort, but which are transient and reversible upon cessation of exposure.

AEGL-2 is the exposure level above which it is predicted that the general population, including susceptible individuals, could 

experience irreversible or other serious, long-lasting adverse health effects of an impaired ability to escape.

EPRG – Emergency Removal Program guidelines represent emergency exposure (1-hour) limits for the general public.

ERPG-1 is the maximum level below which it is believed that nearly all individuals could be exposed for up to 1 hour without 

experiencing other than mild, transient adverse health effects.

ERPG-2 is the maximum exposure below which it is believed that nearly all individuals could be exposed for up to 1 hour without 

experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual’s ability to 

take protective action.

Table 3.2-3 Summary of Environmental Risk Screen Results for the Mineral Wool 

Production Source Category Based on Actual Emissions

Environmental 

HAP

Number of Facilities In Category 

Exceeding

Percent of Modeled 

Area in Category 

Exceeding2

Tier 1 Screen Tier 2 Screen1

NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PBHAP

Pb None None NA NA 0% 0%

Hg NA None NA NA 0% 0%

Cd None None NA NA 0% 0%

Acid 

Gases

HF3 NA None - - NA 0%

HCL4 NA None - - NA 0%

NA – Not Applicable. 

1- Tier 2 screen is performed for PB-HAP when there are exceedances of the Tier 1 screen. The acid gas screen 

is a one tier screen.

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

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

on the Tier 1 results. 

3 – For HF, we evaluated two benchmarks, one from Canada and the other from the State of Washington. 

Although, they are both considered to be LOELs - the level between a NOAEL and a LOAEL, we have listed the 

results under the LOAEL column.

4 – For HCL, we evaluated one benchmark at the LOAEL level.
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4 Risk Results for the Wool Fiberglass Manufacturing Source Category

4.1 Source Category Description and Results

Wool fiberglass is manufactured in a process that forms thin fibers from molten glass. A 

typical wool fiberglass manufacturing line consists of the following processes: (1) Preparation 

of molten glass, (2) formation of fibers into a wool fiberglass mat, (3) curing the bindercoated fiberglass mat, (4) cooling the mat (not always present), and (5) backing, cutting, and 

packaging. Typically, the binder consists of a solution of phenol-formaldehyde resin, water, 

urea, lignin, silane, and ammonia. Wool fiberglass products are primarily used as thermal and 

acoustical insulation for buildings, automobiles, aircraft, appliances, ductwork, and pipes. 

Other uses include liquid and air filtration. Approximately 90 percent of the wool fiberglass 

currently produced is for building insulation products.

The NESHAP for the Wool Fiberglass Manufacturing source category was promulgated on 

June 14, 1999 (62 FR 31695) and codified at 40 CFR part 63, subpart DDD. The 1999 

NESHAP (40 CFR 63.1381) defines a “wool fiberglass manufacturing facility” as “any 

facility manufacturing wool fiberglass on a rotary spin manufacturing line or on a flame 

attenuation manufacturing line.” The MACT rule for the Wool Fiberglass Manufacturing 

source category does not apply to facilities that manufacture mineral wool from rock, slag, 

and other similar materials. Wool fiberglass is currently manufactured in the United States by 

6 companies operating 30 plants in 16 States. Ten of these facilities are major sources and 19 

are area sources.

HAP are emitted from wool fiberglass glass-melting furnaces, including arsenic, chromium, 

and lead. Organic HAP (formaldehyde, phenol, and methanol) are released from RS forming, 

curing, and cooling processes and FA forming and curing processes. Formaldehyde, phenol, 

and methanol are emitted from wool fiberglass plants that use HAP in their binders. Some 

furnaces also emit high risk metals including hexavalent chromium. 

The emissions for the Wool Fiberglass Manufacturing source category data set (of 29 

facilities, 1 facility did not have emissions data) are summarized in Table 4.1-1. The total 

HAP emissions for the source category are approximately 1,874 tons per year. Based on these 

data, the HAP emitted in the largest quantities across these 29 facilities are: methanol, 

formaldehyde, phenol, hydrogen fluoride, and hydrogen chloride. Emissions of these 5 HAP 

make up 99.9 percent of the total emissions by mass. For the Wool Fiberglass Manufacturing 

source category, data were not available to develop a refined emissions multiplier. Therefore, 

the default emissions multiplier of 10 was used for the acute risk analysis. 
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Persistent and bioaccumulative HAP (PB HAP) reported as emissions from these facilities 

include lead, cadmium, and mercury compounds.

26

 

The following environmental HAP are emitted from the Wool Fiberglass Manufacturing 

source category and are included in the environmental risk screen: lead, mercury (mercuric 

chloride and methyl mercury), cadmium, hydrogen chloride and hydrogen fluoride.

Table 4.1-1 Summary of Emissions from the Wool Fiberglass Manufacturing Category and 

Availability of Dose-Response Values

HAP Emissions 

(tpy)

Number 

of 

Facilities 

Reporting 

HAP (29 

facilities 

in data 

set) 

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSa

PBUnit Risk HAP?

Estimate for 

Cancer?

Reference 

Concentration 

for Noncancer?

Health 

Benchmark 

Values for Acute 

Noncancer?

Methanol 820 26 Y Y

Formaldehyde 618 26 Y Y Y

Phenol 403 26 Y Y

Hydrogen Fluoride 20 29

Hydrochloric acid 12 29 Y Y

Chromium (VI) 0.36 29 Y Y

Selenium 0.15 29 Y

Lead 0.14 29 Y Y

Manganese 0.13 29 Y

Nickel 0.12 29 Y Y

Chromium III 0.05 28

Arsenic 0.04 29 Y Y Y

Elemental Gaseous Mercury 0.04 29 Y Y Y

Antimony 0.04 29

Cadmium 0.02 29 Y Y Y

Cobalt 0.02 29 Y

Berylium 0.01 29 Y Y Y

Gaseous Divalent Mercury 0.005 29 Y

Particulate Divalent Mercury 0.005 29 Y

a Notes for how HAP were speciated for risk assessment:

 For emissions of any chemicals or chemical groups classified as polycyclic organic matter (POM), emissions were grouped into 

POM subgroups as found on EPA’s Technology Transfer Network website for the 2002 National-Scale Air Toxics Assessment at 

http://www.epa.gov/nata2002/methods.html#pom (Approach to Modeling POM).

b Specific dose-response values for each chemical are identified on EPA’s Technology Transfer Network website for air toxics at 

http://www.epa.gov/ttn/atw/toxsource/summary.html.

 

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

EPa-453K-04-001A, as referenced in the ANPRM and provided on the EPA’s Technology Transfer Network 

website for Fate, Exposure, and Risk Assessment at http://www.epa.gov/ttn/fera/risk_atra_vol1.html.
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4.2 Risk Characterization

This section presents the results of the risk assessment for the Wool Fiberglass Manufacturing 

source category for three emissions scenarios – actual, MACT-Allowable, and post-RTR 

control. The basic chronic inhalation risk estimates presented here are the maximum 

individual lifetime cancer risk, the maximum chronic hazard index, and the cancer incidence. 

We also present results from our acute inhalation impact screening in the form of maximum 

hazard quotients, as well as the results of our preliminary screen for potential non-inhalation 

risks from PB HAP. Also presented are the HAP “drivers,” which are the HAP that 

collectively contribute 90 percent of the maximum cancer risk or maximum hazard index at 

the highest exposure location. In addition, we present the results for a screening-level 

evaluation of the potential adverse environmental risks associated with actual emissions.

Actual Emissions

Tables 4.2-1 and 4.2-2 summarize the chronic and acute inhalation risk results for this source 

category based on actual emissions. The results for the Wool Fiberglass Manufacturing source 

category indicate that maximum lifetime individual cancer risks could be as high as 20 in a 

million. The major contributor to the risk is formaldehyde. Approximately 650,000 people 

were estimated to have cancer risks above 1 in a million as a result of the emissions from 15

facilities. The maximum chronic non-cancer TOSHI value for the source category could be 

up to 0.2 with emissions of formaldehyde dominating those impacts, indicating no significant 

potential for chronic noncancer impacts. 

Worst-case acute hazard quotients (HQs) were calculated for every HAP shown in Table 4.1-

1 that has an acute benchmark, and the highest acute HQ REL of 30 for formaldehyde is 

shown in Table 4.2-1. For cases where the screening acute HQ was greater than 1, we further 

refined the estimates by determining the highest HQ value that is outside facility boundaries. 

In this case, the refined analysis did not change the acute analysis results. Table 4.2-2 

provides more information on the acute risk estimates for formaldehyde, the only HAP that 

had a worst-case screening acute HQ greater than 1 for any benchmark. 

To better characterize the potential health risks associated with estimated worst-case acute 

exposures to HAP, and in response to a key recommendation from the Science Advisory 

Board’s peer review of EPA’s RTR risk assessment methodologies27, we examine a wider 

range of available acute health metrics than we do for our chronic risk assessments. This is in 

response to the acknowledgement that there are generally more data gaps and inconsistencies 

in acute reference values than there are in chronic reference values. By definition, the acute 

CA-REL represents a health-protective level of exposure, with no risk anticipated below those 

levels, even for repeated exposures; however, the health risk from higher-level exposures is 

unknown. Therefore, when a CA-REL is exceeded and an AEGL-1 or ERPG-1 level is 

 

27 The SAB peer review of RTR Risk Assessment Methodologies is available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EPA-SAB-10-

007-unsigned.pdf
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37

available (i.e., levels at which mild effects are anticipated in the general public for a single 

exposure), we have used them as a second comparative measure. Historically, comparisons of 

the estimated maximum off-site one-hour exposure levels have not been typically made to 

occupational levels for the purpose of characterizing public health risks in RTR assessments. 

This is because occupational ceiling values are not generally considered protective for the 

general public since they are designed to protect the worker population (presumed healthy 

adults) for short duration (<15 minute) increases in exposure28. As a result, for most 

chemicals, the 15-minute occupational ceiling values are set at levels higher than a one-hour 

AEGL-1, making comparisons to them irrelevant unless the AEGL-1 or ERPG-1 levels are 

exceeded21. Such is not the case when comparing the available acute inhalation health effect 

reference values for formaldehyde21.

The worst-case maximum estimated 1-hour exposure to formaldehyde outside the facility 

fence line for the wool fiberglass manufacturing source category is 1.5 mg/m3. This estimated 

worst-case exposure exceeds the 1-hour REL by a factor of 30 (HQREL = 30) and the 1-hour 

AEGL-1 by 1 (HQAEGL-1 = 1). This exposure estimate also exceeds multiple workplace 

ceiling level guidelines for formaldehyde, including the value developed by the American 

Conference of Governmental Industrial Hygienists (ACGIH)29 as “not to be exceeded at any 

time” (ACGIH TLV-ceiling value of 0.37 mg/m3; HQACGIH = 4), and the value developed by 

the National Institutes for Occupational Safety and Health (NIOSH)30 “for any 15 minute 

period in a work day” (NIOSH REL-ceiling value of 0.12 mg/m3; HQNIOSH = 13). 

Additionally, the estimated maximum acute exposure exceeds the Air Quality Guideline value 

that was developed by the World Health Organization31 for 30-minute exposures (0.1 mg/m3; 

HQWHO = 15).

Chronic noncancer target organ specific hazard indices (HIs) were calculated for every HAP 

shown in Table 4.1-1 that has a chronic benchmark, and the highest target organ specific HI 

(0.1 for formaldehyde) is shown in Table 4.2-1. There were no HIs greater than 1 for this 

source category. 

For this source category, EPA conducted a screening-level evaluation of the potential human 

health risks associated with emissions of PB HAP. Reported emissions of PB HAP were 

compared to emission thresholds established by EPA for the purposes of the RTR risk 

 

28 U.S. EPA. (2009) Chapter 2.9 Chemical Specific Reference Values for Formaldehyde in Graphical Arrays of 

Chemical-Specific Health Effect Reference Values for Inhalation Exposures (Final Report). U.S. 

Environmental Protection Agency, Washington, DC, EPA/600/R-09/061, and available on-line at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003

29 ACGIH (2001) Formaldehyde. In Documentation of the TLVs® and BEIs® with Other Worldwide 

Occupational Exposure Values. ACGIH, 1300 Kemper Meadow Drive, Cincinnati, OH 45240 (ISBN: 978-1-

882417-74-2) and available on-line at http://www.acgih.org.

30 National Institutes for Occupational Safety and Health (NIOSH). Occupational Safety and Health Guideline 

for Formaldehyde; http://www.cdc.gov/niosh/docs/81-123/pdfs/0293.pdf

31 WHO (2000). Chapter 5.8 Formaldehyde, in Air Quality Guidelines for Europe, second edition. World Health 

Organization Regional Publications, European Series, No. 91. Copenhagen, Denmark. Available on-line at 

http://www.euro.who.int/__data/assets/pdf_file/0005/74732/E71922.pdf. 
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assessments.32 The PB HAP emitted by facilities in this category are lead, cadmium, and 

mercury. All lead, cadmium, and mercury emissions were below the threshold levels,

indicating no potential for significant multi-pathway risks from these facilities.

We conducted a screening-level evaluation of the potential adverse environmental risks 

associated with emissions of the following environmental HAP from the Wool Fiberglass 

Manufacturing source category: lead, mercury, cadmium, hydrogen chloride and hydrogen 

fluoride. The results of the environmental screening analysis are summarized in Table 4.2-3. 

In the Tier 1 screening analysis for PB-HAP (other than lead, which was evaluated differently 

as noted below) the individual modeled Tier 1 concentrations for mercury and cadmium did 

not exceed any ecological benchmark for any facility in the source category. For lead, we did 

not estimate any exceedances of the secondary lead NAAQS.

For hydrogen fluoride and hydrogen chloride, the average modeled concentration around each 

facility (i.e., the average concentration of all off-site data points in the modeling domain) did 

not exceed the ecological benchmarks. In addition, each individual modeled concentration of 

hydrogen chloride and hydrogen fluoride (i.e., each off-site data point in the modeling 

domain) was below the ecological benchmarks for all facilities. 

MACT-Allowable Emissions

Analysis of potential differences between actual emissions levels and the maximum emissions 

allowable under the MACT standards indicated that MACT allowable emission levels may be 

up to 3 times greater than actual emissions levels for phenol, methanol, and formaldehyde 

emissions. For hexavalent chromium emissions the allowable emissions are equal to actual 

emissions since there is no hexavalent chromium emission limit in the MACT standard. 

Considering this difference, the risk results from the inhalation risk assessment indicate that 

the maximum excess lifetime individual cancer risk could be up to 60 in a million, and that 

the maximum chronic noncancer TOSHI value could be as high as 0.5 at the MACTallowable emissions level. 

 

32 ICF International. TRIM-Based Multipathway Screening Scenario. Prepared for U.S. Environmental 

Protection Agency, Research Triangle Park, NC. October 2008.
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Table 4.2-1 Summary of Source Category Level Inhalation Risks for Wool Fiberglass 

Manufacturing Based on Actual Emissions

Result HAP “Drivers”

Number of Facilities Estimated to be in Source 

Category 30 n/a

Number of Facilities Identified in NEI and 

Modeled in Preliminary Risk Assessment 29 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 

million) 2033 formaldehyde

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 3 formaldehyde, Cr (VI)

Greater than or equal to 1 in 1 million 12 formaldehyde, Cr (VI), 

Ni, As 

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 0.1 formaldehyde

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Screening Results

Maximum Acute Hazard Quotient

30

1

formaldehyde (REL)

formaldehyde (AEGL-1, 

ERPG-1)

Number of Facilities With Potential for Acute 

Effects 7 formaldehyde

Population Exposure

Number of People Living Within 50 Kilometers 

of Facilities Modeled 30,000,000 n/a

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 6,900 n/a

Greater than or equal to 1 in 1 million 1,200,000 n/a

Number of People Exposed to Noncancer Respiratory Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.07

26 n/a

Contribution of HAP to Cancer Incidence

 Formaldehyde

 Cr (VI)

 Arsenic Compounds

75%

24%

1%

n/a

 

33 We note that the MIR for this source category would change to 10 in a million (driven by Cr(VI)) if the CIIT 

URE for formaldehyde had been used in the assessment, and the total cancer incidence would decrease by 64%. 

There is an ongoing IRIS reassessment for formaldehyde, and future RTR risk assessments will use the cancer 

potency for formaldehyde that results from that reassessment. As a result, the current results may not match 

those of future assessments.
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Table 4.2-2 Summary of Refined Acute Results for Wool Fiberglass Manufacturing 

Facilities Based on Actual Emissions

Refined Results

MAXIMUM ACUTE HAZARD 

QUOTIENTS

ACUTE DOSE-RESPONSE VALUES

Based on 

REL

Based on 

AEGL-1/

ERPG-1

Based on 

AEGL-2/

ERPG-2

REL

(mg/m3)

AEGL-1 

(1-hr) 

(mg/m3)

ERPG-1 

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG-2 

(mg/m3)

HAP Max. 1-hr. 

Air Conc. 

(mg/m3)

Formaldehyde 1.5 30 1 0.1 0.055 1.1 1.2 17 12

Notes on Refined Process:

1) The screening was performed for all emitted HAP with available acute dose-response values. Only those pollutants whose 

screening HQs were greater than 1 for at least one acute threshold value are shown in the table.

2) HAP with available acute dose-response values which are not in the table do not carry any potential for posing acute health risks, 

based on an analysis of currently available emissions data.

Notes on Acute Dose-Response Values:

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

AEGL – Acute exposure guideline levels represent emergency exposure (1-hour) limits for the general public.

AEGL-1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could 

experience effects that are notable discomfort, but which are transient and reversible upon cessation of exposure.

AEGL-2 is the exposure level above which it is predicted that the general population, including susceptible individuals, could 

experience irreversible or other serious, long-lasting adverse health effects of an impaired ability to escape.

EPRG – Emergency Removal Program guidelines represent emergency exposure (1-hour) limits for the general public.

ERPG-1 is the maximum level below which it is believed that nearly all individuals could be exposed for up to 1 hour without 

experiencing other than mild, transient adverse health effects.

ERPG-2 is the maximum exposure below which it is believed that nearly all individuals could be exposed for up to 1 hour without 

experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual’s ability to 

take protective action.
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Table 4.2-3 Summary of Environmental Risk Screen Results for the Wool Fiberglass 

Manufacturing Source Category Based on Actual Emissions

Environmental 

HAP

Number of Facilities In Category 

Exceeding

Percent of Modeled 

Area in Category 

Exceeding2

Tier 1 Screen Tier 2 Screen1

NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PBHAP

Pb None None NA NA 0% 0%

Hg NA None NA NA 0% 0%

Cd None None NA NA 0% 0%

Acid 

Gases

HF3 NA None - - NA 0%

HCL4 NA None - - NA 0%

NA – Not Applicable. 

1- Tier 2 screen is performed for PB-HAP when there are exceedances of the Tier 1 screen. The acid gas screen 

is a one tier screen.

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

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

on the Tier 1 results. 

3 – For HF, we evaluated two benchmarks, one from Canada and the other from the State of Washington. 

Although, they are both considered to be LOELs - the level between a NOAEL and a LOAEL, we have listed the 

results under the LOAEL column.

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

Post-RTR Emissions

Table 4.2-4 summarizes the chronic and acute inhalation risk results for this source category

based on post-RTR control emissions. The MIR from Wool Fiberglass Manufacturing 

emissions will be reduced from 20-in-1 million to 5-in-1 million after implementation of this 

proposal. In addition, the acute noncancer HQ for formaldehyde based on the REL will be 

reduced from 30 to 7. The number of individuals exposed to cancer risks above 10-in-1 

million will be reduced from 6,900 for actual emissions to 0 for this proposal, and the number 

of individuals exposed to cancer risks above 1-in-1 million will be reduced from 1.2 million 

for actual emissions to 21,000 for this proposal. 
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Table 4.2-4 Summary of Source Category Level Inhalation Risks for Wool Fiberglass 

Manufacturing – Post-RTR Control

Result HAP “Drivers”

Number of Facilities Estimated to be in Source 

Category 30 n/a

Number of Facilities Identified in NEI and 

Modeled in Preliminary Risk Assessment 29 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 

million) 5 Formaldehyde, Cr (VI)

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 9

formaldehyde, Cr (VI), 

Ni, As 

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 0.03 formaldehyde

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Screening Results

Maximum Acute Hazard Quotient

7

0.3

formaldehyde (REL)

formaldehyde (AEGL-1, 

ERPG-1)

Number of Facilities With Potential for Acute 

Effects 5 formaldehyde

Population Exposure

Number of People Living Within 50 Kilometers 

of Facilities Modeled 30,000,000 n/a

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 0 n/a

Greater than or equal to 1 in 1 million 21,000 n/a

Number of People Exposed to Noncancer Respiratory Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.0126 n/a

Contribution of HAP to Cancer Incidence

 Formaldehyde

 Cr (VI)

 Arsenic Compounds

 Nickel

 Cadmium

73%

18%

5%

3%

1%

n/a
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4.3 Auxiliary Risk Characterization

As indicated in Section 4.2, the maximum lifetime individual cancer risks for the Wool 

Fiberglass Manufacturing source category could be as high as 20 in a million based on actual 

emissions. The major contributor to this cancer risk is formaldehyde, although chromium 

(VI) is also a risk driver. Chromium is emitted from the furnace refractory brick as it breaks 

down over time. The greatest amount of chromium (VI) is emitted from the Certainteed 

Facility in Kansas City, Kansas that uses a type of refractory brick that is almost 100% 

chromium. 

Furnace refractory brick are essential to the structure and function of the furnace, providing 

thermal insulation and corrosion protection. Refractory bricks in the glass melting industry 

are typically composed of Al2O3, Al203-SiO2, ZrO2, with or without Cr2O3 added, and dense 

Cr2O3.[31, 32] Refractory brick breaks down over time, and therefore, must be replaced 

periodically (approximately every 7-14 years depending on the type of brick).

Since there are currently no regulations preventing the use of high chromium refractory brick 

or limiting emissions of chromium in the Wool Fiberglass Manufacturing Industry, it is 

reasonable to assume that another Wool Fiberglass Manufacturing facility might replace its 

current furnace refractory brick with high chromium refractory brick sometime in the future. 

Therefore, we performed an auxiliary risk characterization analysis to assess the maximum 

individual lifetime cancer risks of the other 28 Wool Fiberglass facilities switching to high 

chromium brick. For the auxiliary risk characterization analysis it was assumed that the Cr 

(VI) would be emitted from the other facilities at the same as rate (in lbs Cr (VI) emitted per 

ton of glass pulled) as for the K2 furnace at the Certainteed Facility in Kansas City, Kansas. 

The results of the auxiliary analysis (Table 4.3-1) show that 4 facilities would have maximum 

individual lifetime cancer risks greater than 100 in a million, with the highest facility at 400 in 

a million. Under this scenario, 54,000 people would be exposed to risks of greater than 10 in 

a million and over 2.2 million people would be exposed to cancer risks of greater than 1 in a 

million. 
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Table 4.3-1 Summary of Source Category Level Cancer Risks for the Wool Fiberglass 

Manufacturing Auxiliary Analysis

Result HAP “Drivers”

Number of Facilities Estimated to be in Source 

Category 29 n/a

Number of Facilities Identified in NEI and

Modeled in Auxiliary Risk Assessment 29 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 

million) 400 Cr (VI)

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 million 4 Cr (VI), formaldehyde

Greater than or equal to 10 in 1 million 24 Cr (VI), formaldehyde

Greater than or equal to 1 in 1 million 29 Cr (VI), formaldehyde

Population Exposure

Number of People Living Within 50 Kilometers 

of Facilities Modeled 27,000,000 n/a

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1 million 200 n/a

Greater than or equal to 10 in 1 million 54,000 n/a

Greater than or equal to 1 in 1 million 2,200,000 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.14 n/a

Contribution of HAP to Cancer Incidence

 Formaldehyde

 Cr (VI) 

18%

82%

n/a

 
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5 General Discussion of Uncertainties and How They Have 

Been Addressed

5.1 Exposure Modeling Uncertainties

Although every effort has been made to identify all the relevant facilities and emission points, 

as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the 

uncertainties in our emission inventory likely dominate the uncertainties in our exposure 

analysis. The chronic ambient modeling uncertainties are considered relatively small in 

comparison, since we are using EPA’s refined local dispersion model with site-specific 

parameters and reasonably representative meteorology. If anything, the population exposure 

estimates are biased high by not accounting for short- or long-term population mobility, and 

by neglecting processes like deposition, plume depletion, and atmospheric degradation. 

Additionally, estimates of the MIR 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, 

but, overall, it is not thought to have a significant impact on the estimated MIR for a source 

category. Finally, we did not factor in the possibility of a source closure occurring during the 

70-year chronic exposure period, leading to a potential upward bias in both the MIR and 

population risk estimates; nor did we factor in the possibility of population growth during the 

70-year chronic exposure period, leading to a potential downward bias in both the MIR and 

population risk estimates.

A sensitivity analysis performed for the 1999 NATA found that the selection of the 

meteorology dataset location could result in a range of chronic ambient concentrations which 

varied from as much as 17 percent below the predicted value to as much as 84 percent higher 

than the predicted value. This variability translates directly to the predicted exposures and 

risks in our assessment, indicating that the actual risks could vary from 17% lower to 84% 

higher than the predicted values.

We have purposely biased the acute screening results high, considering that they depend upon 

the joint occurrence of independent factors, such as hourly emissions rates, meteorology and 

human activity patterns. Furthermore, in cases where multiple acute threshold values are 

considered scientifically acceptable we have chosen the most conservative of these threshold 

values, erring on the side of overestimating potential health risks from acute exposures. In the 

cases where these results indicated the potential for exceeding short-term health thresholds, 

we have refined our assessment by developing a better understanding of the geography of the 

facility relative to potential exposure locations. 
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5.2 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 reference values are generally derived for 

chronic exposures (up to a lifetime), but may also be derived for acute ( rtterm (>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) benchmarks, which are described in more detail below.

Although every effort is made to identify peer-reviewed dose-response values for all HAPs

emitted by the source category included in an assessment, some HAP have no peer-reviewed 

cancer potency values or reference values for chronic non-cancer or acute effects (inhalation 

or ingestion). Since exposures to these pollutants cannot be included in a quantitative risk 

estimate, an understatement of risk for these pollutants at environmental exposure levels is 

possible.

Additionally, chronic dose-response values for certain compounds included in the assessment 

may be under EPA IRIS review and revised assessments may determine that these pollutants 

are more or less potent than currently thought. We will re-evaluate risks if, as a result of these 

reviews, a dose-response metric changes enough to indicate that the risk assessment may 

significantly mischaracterize human health risk

Cancer assessment

The discussion of dose-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 [33] (herein 

referred to as Cancer Guidelines). “The primary goal of EPA actions is protection of human 

health; accordingly, as an Agency policy, risk assessment procedures, including default 

options that are used in the absence of scientific data to the contrary, should be health 

protective.” The approach adopted in this document is consistent with this approach as 

described in the Cancer Guidelines.

For cancer endpoints EPA usually derives an oral slope factor for ingestion and a unit risk 

value for inhalation exposures. These values allow 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 
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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).34 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.35 

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.36 EPA also 

uses the upper bound (rather than lower bound or central) estimates in its assessments, 

although it is noted that this approach can have limitations for some uses (e.g. priority setting, 

expected benefits analysis).

Such health risk assessments have associated uncertainties, some which may be considered 

quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary 

substantially among cancer risk assessments associated with exposures to different pollutants, 

since the assessments employ different databases with different strengths and limitations and 

the procedures employed may differ in how well they represent actual biological processes for 

the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk 

characterizations present estimates demonstrating the impact on the assessment of alternative 

choices, data, models and assumptions [34]. 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 is a source of 

uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites 

observed in an experimental animal bioassay are necessarily predictive of the sites at which 

 

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

35 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of 

which is considered to be equally plausible, and which is based on maximum likelihood estimates.

36 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are 

generic approaches, based on general scientific knowledge and policy judgment, that are applied to various 

elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 

NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the 

option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to 

the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the 

agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be 

appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions 

are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly 

overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Principles and Practices, 

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf.
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tumors would occur in humans.37 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% 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, and potentially large, source of 

uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk 

assessment (i.e., developing estimates of risk for exposures to environmental doses 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 

 

37 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer studies indicate 

that the agent under study can have carcinogenic potential in humans.” and “Target organ concordance is not a 

prerequisite for evaluating the implications of animal study results for humans.”
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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 sciencepolicy choice (a “default”) when available data do not allow a compound-specific 

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

and variability. EPA believes that linear dose-response models, when appropriately applied 

as part of EPA’s cancer risk assessment process, provide an upper bound estimate of risk and 

generally provide a health protective approach. Note that another source of uncertainty is the 

characterization of low-dose nonlinear, non-threshold relationships. The National Academy 

of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit 

models) in representing dose response relationships due to the variability in response within 

human populations. Another National Research Council (NRC) report [35] 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) [36]. 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 
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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 which develop noncancer reference 

values (e.g., the Agency for Toxic Substances and Disease Registry – ATSDR) utilize an 

approach that is intended not to underestimate risk in the face of uncertainty and variability. 

When there are gaps in the available information, uncertainty factors (UFs) are applied to 

derive reference values that are intended to be protective against appreciable risk of 

deleterious effects. Uncertainty factors are commonly default values38 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 non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) 

for exposures via ingestion, and a Reference Concentration (RfC) for inhalation exposures. 

 

38

 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are 

generic approaches, based on general scientific knowledge and policy judgment, that are applied to various 

elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 

NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the 

option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to 

the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the 

agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be 

appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions 

are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly 

overestimate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practices,

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf. 
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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.39 To derive values that are intended to be “without 

appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach

[37],[38] 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 point of 

departure (POD) for derivation of the reference value. A POD is determined by (in order of 

preference): (1) a statistical estimation using the benchmark dose (BMD) approach; (2) use of 

the dose or concentration at which the toxic response was not significantly elevated (no 

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

level (LOAEL).

A series of downward adjustments using default UFs is then applied to the POD to estimate 

the reference value [39]. 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 

 

39 See IRIS glossary
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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.
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There is no RfD or other comparable chronic health benchmark value for lead compounds. 

Thus, to address multipathway human health and environmental risks associated with 

emissions of lead from these facilities, ambient lead concentrations were compared to the 

NAAQS for lead. In developing the NAAQS for lead, EPA considered human health 

evidence reporting adverse health effects associated with lead exposure, as well as an EPA 

conducted multipathway risk assessment that applied models to estimate human exposures to 

air-related lead and the associated risk (73FR at 66979). EPA also explicitly considered the 

uncertainties associated with both the human health evidence and the exposure and risk 

analyses when developing the NAAQS for lead. For example, EPA considered uncertainties 

in the relationship between ambient air lead and blood lead levels (73FR at 66974), as well as 

uncertainties between blood lead levels and loss of IQ points in children (73FR at 66981). In 

considering the evidence and risk analyses and their associated uncertainties, the EPA 

Administrator noted his view that there is no evidence- or risk-based bright line that indicates 

a single appropriate level. Instead, he noted, there is a collection of scientific evidence and 

judgments and other information, including information about the uncertainties inherent in 

many relevant factors, which needs to be considered together in making this public health 

policy judgment and in selecting a standard level from a range of reasonable values (73FR at 

66998). In so doing, the Administrator decided that, a level for the primary lead standard of 

0.15 μg/m3, in combination with the specified choice of indicator, averaging time, and form, 

is requisite to protect public health, including the health of sensitive groups, with an adequate 

margin of safety (73FR at 67006). A thorough discussion of the health evidence, risk and 

exposure analyses, and their associated uncertainties can be found in EPA’s final rule revising 

the lead NAAQS (73 FR 66970-66981, November 12, 2008). 

We also note the uncertainties associated with the health-based (i.e., primary) NAAQS are 

likely less than the uncertainties associated with dose-response values developed for many of 

the other HAP, particularly those HAP for which no human health data exist. In 1988, EPA’s 

IRIS program reviewed the health effects data regarding lead and its inorganic compounds 

and determined that it would be inappropriate to develop an RfD for these compounds, 

saying, “A great deal of information on the health effects of lead has been obtained through 

decades of medical observation and scientific research. This information has been assessed in 

the development of air and water quality criteria by the Agency's Office of Health and 

Environmental Assessment (OHEA) in support of regulatory decision-making by the Office 

of Air Quality Planning and Standards (OAQPS) and by the Office of Drinking Water 

(ODW). By comparison to most other environmental toxicants, the degree of uncertainty 

about the health effects of lead is quite low. It appears that some of these effects, particularly 

changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral 

development, may occur at blood lead levels so low as to be essentially without a threshold. 

The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two 

meetings (07/08/1985 and 07/22/1985) and considered it inappropriate to develop an RfD for 

inorganic lead.” EPA’s IRIS assessment for Lead and compounds (inorganic) (CASRN 7439-

92-1), http://www.epa.gov/iris/subst/0277.htm. 

We note further that because of the multi-pathway, multi-media impacts of lead, the risk 

assessment supporting the NAAQS considered direct inhalation exposures and indirect airrelated multi-pathway exposures from industrial sources like primary and secondary lead 
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smelting operations. It also considered background lead exposures from other sources (like 

contaminated drinking water and exposure to lead-based paints). In revising the NAAQS for 

lead, we note that the Administrator placed more weight on the evidence-based framework 

and less weight on the results from the risk assessment, although he did find the risk estimates 

to be roughly consistent with and generally supportive of the evidence-based framework 

applied in the NAAQS determination (73FR at 67004). Thus, when revising the NAAQS for 

lead to protect public health with an adequate margin of safety, EPA considered both the 

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

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute 

reference values are quite similar to those developed for chronic durations, but more often 

using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific information (e.g., simple irritation effects do not vary 

appreciably between human individuals, hence a value of 3 is typically used), or based on the 

purpose for the reference value (see the following paragraph). The UFs applied in acute 

reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in 

extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 

4) 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 reference values are developed for the same purpose and care must be taken 

when interpreting the results of an acute assessment of human health effects relative to the 

reference value or values being exceeded. Where relevant to the estimated exposures, the 

lack of threshold values at different levels of severity should be factored into the risk 

characterization as potential uncertainties. 
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5 References

 

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