From: Topham.Nathan@epamail.epa.gov

Sent: Friday, October 28, 2011 12:47 PM

To: Hanzel, Veronica; Laney, Michael N.

Subject: Fw: Primary Lead Risk Assessment

Attachments: Final Primary Lead Risk Report 10-28-11 for OMB.docx

Nathan Topham

Environmental Engineer

US EPA/OAQPS/SPPD/MIG

Phone - (919)541-0483

----- Forwarded by Nathan Topham/RTP/USEPA/US on 10/28/2011 12:46 PM -----

 From: Michael Stewart/RTP/USEPA/US 

 To: "Higgins, Cortney" <Cortney_Higgins@omb.eop.gov> 

 Cc: Elaine Manning/RTP/USEPA/US@EPA, Nathan Topham/RTP/USEPA/US@EPA, Fred 

 Talcott/DC/USEPA/US@EPA, Dave Guinnup/RTP/USEPA/US@EPA 

 Date: 10/28/2011 12:45 PM 

 Subject: Primary Lead Risk Assessment

Hi Cortney, 

This is a redline of the latest version of the Lead Risk Assessment document. 

The monitor to model comparison now contains all the monitors that Nate had sent 

you in a previous email. Also, I fixed a typo. 

Thanks, 

Mike
1

Residual Risk Assessment for the Primary Lead Smelting Source 

Category

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

October 2011
2

Table of Contents

1. Introduction................................................................................................................................. 4

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

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

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

2.3 Estimating human inhalation exposure................................................................................. 8

2.4 Acute risk screening and refined assessments...................................................................... 9

2.5 Multipathway and environmental risk screening and refined assessments ........................ 10

2.6 Dose-response assessment .................................................................................................. 12

2.6.1 Sources of chronic dose-response information............................................................ 12

2.6.2 Sources of acute dose-response information................................................................ 15

2.7 Risk characterization........................................................................................................... 18

2.7.1 General......................................................................................................................... 18

2.7.2 Mixtures....................................................................................................................... 19

2.7.3 Facility-wide risks........................................................................................................ 20

3. Risk Results for the Primary Lead Smelting Source Category............................................. 20

3.1 Source category description and results.............................................................................. 20

3.2 Baseline risk characterization ............................................................................................. 22

3.3 Assessment of emission reductions scenarios..................................................................... 27

4. General Discussion of Uncertainties and How They Have Been Addressed .......................... 28

4.1 Exposure modeling uncertainties........................................................................................ 28

4.2 Uncertainties in the dose-response relationships................................................................ 28

5. References................................................................................................................................ 37

Index of Tables

Table 2.2-1 AERMOD version 09292 model options for RTR modeling..................................... 6

Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens......... 14

Table 2.6-1 (b) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens .. 15

Table 2.6-2 Dose-Response Values for Acute Exposure............................................................. 18

Table 3.1-1 Summary of Emissions from the Primary Lead Smelting Source Category and 

Availability of Dose-Response Values......................................................................................... 22

Table 3.2-1 Summary of Source Category Level Inhalation Risks for the Primary Lead Smelting 

Facility – non-lead HAP ............................................................................................................... 24

Table 3.2-2 Summary of Lead Concentrations Relative to the NAAQS Given Actual 2009 

Emissions...................................................................................................................................... 26

Table 3.2-3 Comparison of AERMOD Modeled to Ambient Lead Concentrations Reported by 

Two Monitors in Close Proximity to the Herculaneum Primary Lead Smelting Facility. ........... 27

Appendices

Appendix 1 Technical Support Document for HEM 3 Modeling
3

Appendix 2 Analysis of data on short-term emission rates relative to long-term emission rates

Appendix 3 Technical Support Document for TRIM-Based Multipathway Screening Scenario 

for RTR: Summary of Approach and Evaluation

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

MOA Mode of action

NATA National Air Toxics Assessment

NEI National Emissions Inventory

POM Polycyclic organic matter

REL Reference exposure level

RfC Reference concentration

RfD Reference dose

TOSHI Target-organ-specific hazard index

URE Unit risk estimate
4

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 June of 2009 a 

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

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

review in May of 2010 [3]. Where appropriate, we have responded to the key messages from 

this review in developing our current risk assessments, and we will be continuing our efforts to 

improve our assessments by incorporating updates based on the SAB recommendations as they 

are developed and become available. Our responses to the key recommendations of the SAB are 

outlined in a memo entitled “EPA’s Actions in Response to Key Recommendations of the SAB

Review of RTR Risk Assessment Methodologies” [4].

In February of 2011 EPA published the notice of proposed rulemaking: National Emission 

Standards for Hazardous Air Pollutants: Primary Lead Smelting. In this proposed rulemaking, 

EPA presented a number of options for additional controls on the primary lead smelting source 

category, which includes only one facility currently operating in the United States. In this notice, 

EPA then solicited comment on these options as well as on all the analyses and data the options 

were based upon, including the risk methods and results presented in the draft document: Draft 

Residual Risk Assessment for the Primary Lead Smelting Source Category. 

During the public comment period for the proposed rule, the one facility in the source category, 

(The Doe Run Company) submitted substantially updated emissions, meteorological, facility 

boundary, and other relevant information with respect to performing a risk assessment (see 

docket number: EPA-HQ-OAR-2004-0305 for Doe Run’s public comments). Thus, the methods 

and data used to generate the baseline and post-control risk results presented in this final risk 

assessment document are largely based on the updated information received during the public 
5

comment period for the proposed rule.

1

 Methods for modeling HAP other than lead are 

discussed in sections 2.0 – 2.4, while methods used in modeling ambient lead concentrations are 

discussed in section 2.5. These discussions include 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 assess the potential for acute 

health risks, chronic non-inhalation health risks, and adverse environmental effects. 

2. Methods

2.1 Emissions and source data

Since there is only one primary lead smelter currently operating in the U.S., the data set for the 

source category is represented by the data set for this one facility. As noted above, during the 

public comment period for the proposed rule, EPA received substantially updated facility 

information, including 2009 emissions data from this facility (see docket number: EPA-HQOAR-2004-0305 for Doe Run’s public comments). Thus, the emissions dataset used to generate 

the risk results presented in this final risk report are largely based on the emissions data received 

during the public comment period for the proposed rule. 

Details on the development of the emissions and source data for this source category are 

discussed briefly in section 3 and in greater detail in chapter 1 of the document titled: “Risk and 

Technology Review, National Emission Standards for Hazardous Air Pollutants (NESHAP) for 

the Primary Lead Smelting Source Category Technical Support Document ” (henceforth referred 

to as the TSD), which can be found in the docket supporting the Primary Lead Smelting source 

category rulemaking (see docket number: EPA-HQ-OAR-2004-0305).

2.2 Dispersion modeling for inhalation exposure assessment

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

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

in combination with the American Meteorological Society/EPA Regulatory Model dispersion 

modeling system (HEM 3). The approach used in applying this modeling system is outlined 

below, and further details are provided in Appendix 1. HEM 3 performs three main operations: 

atmospheric dispersion modeling, estimation of individual human exposures and health risks, 

and estimation of population risks. This section focuses on the dispersion modeling component. 

The exposure and risk characterization components are discussed in other subsections of sections 

2 and 3.

The dispersion model in the HEM 3 system, AERMOD version 09292, 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 

 1 Updated emission information received from the Doe Run Company during the public comment period was 

reviewed for accuracy by EPA engineers (see "Residual Risk Analysis - Development of the Revised Emissions 

Dataset for the Primary Lead Smelting Source Category" in the docket for this rulemaking for more information on 

this temporal profile). 
6

sources into the surrounding atmosphere. For the modeling done to support the proposed 

rulemaking, hourly emission rates used for that simulation were generated by evenly dividing the 

total annual emission rate from the inventory into the 8,760 hours of the year. However, during 

the public comment period for the proposed rule, EPA received a temporal profile for HAP 

emissions that varied on an hour by hour basis for some emission points at the one facility in the 

source category, and therefore, for these sources, this profile was taken into account in the 

modeling effort for the final rule, as described in this document (see Chapter 1.0 of the TSD:

"Residual Risk Analysis - Development of the Revised Emissions Dataset for the Primary Lead 

Smelting Source Category" in the docket for this rulemaking for more information on this 

temporal profile). Moreover, the current modeling also utilized updated boundary 

information,

2

,release parameters, and information on building dimensions supplied by this 

facility in its public comments.

The first step in the application of the HEM 3 modeling system is to predict ambient 

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

in Table 2.2-1 and are discussed further below.

Table 2.2-1 AERMOD version 09292 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 (20 rings and 16 radials)

Discrete (census block centroids)

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year site-specific

In the modeling supporting the proposed rule, site-specific meteorological data for the one 

facility in this source category were supplied by the state of Missouri and used for the modeling. 

The data provided by the state of Missouri were for eight quarters (i.e., eight three-month 

 2 The updated boundary information indicated that the Doe Run Company had bought an appreciable amount of land 

surrounding its facility, which in effect, extended its facility boundary outward. As a result, following census blocks 

were found to be within the facility’s boundary, and therefore removed: 0290997007002007; 0290997006014052; 

0290997006014038; 0290997006014055; 0290997006014039; 0290997006014056; 0290997006014040; 

0290997006014047; 0290997006014046; 0290997006014041; 0290997006014043. See below for a discussion of 

the use of census blocks in the HEM.
7

periods) from April 1997 through June 1999. To obtain one year of meteorological data, we 

used the middle portion of these data, the year 1998, in our draft modeling for the proposal.

However, during the public comment period for the proposed rule, EPA received site-specific 

meteorological data for the year 2008. As a result, this more current meteorological dataset was 

used as a model input for the final risk assessment presented in this document. 

The HEM 3 system estimates ambient concentrations at the geographic centroids of census 

blocks (using the 2000 Census) and at other receptor locations that can be specified by the user. 

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 since the NEI contains only 

annual emission totals, we generally apply the general assumption to all source categories that 

the maximum one-hour emission rate from any source was ten times the average annual hourly 

emission rate for that source. The average hourly emissions rate is defined as the total emissions 

for a year divided by the total number of operating hours in the year. This choice of a factor of 

ten for screening was originally based on engineering judgment. To develop a more robust peakto-mean emissions factor, and in response to one of the key messages from the SAB consultation 

on our RTR Assessment Plan, we performed an analysis using a short-term emissions dataset

from a number of sources located in Texas (originally reported by Allen et al. 2004)[7]. In that 

report, the Texas Environmental Research Consortium Project compared hourly and annual 

emissions data for volatile organic compounds for all facilities in a heavily-industrialized 4-

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

time period in 2001. We obtained the dataset and performed our own analysis, focusing on 

sources which reported emitting high quantities of HAP over short periods of time (see Appendix 

2, “Analysis of data on short-term emission rates relative to long-term emission rates”). Most 

peak emission events were less than twice the annual average, the highest was a factor of 74

times the annual average, and the 99th percentile ratio of peak hourly emission rate to the annual 

hourly emission rate was 9. Based on these results, we chose the factor of ten for all initial 

screening; it is intended to cover all possible hourly peaks associated with routinely variable 

emissions. While there have been some documented emission excursions above this level, our 

analysis of the data from the Texas Environmental Research Consortium suggests that this factor 

should cover more than 99% of the short-term peak gaseous or volatile HAP emissions from 

typical industrial sources. We have no data relating specifically to peak short-term emissions of

particulate HAP. In the absence of source category-specific data, we use this same default 

approach for particulate emissions as well.

 
8

Census block elevations for HEM 3 modeling were determined nationally from the US 

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

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

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 1. 

2.3 Estimating human inhalation exposure

We used the annual average ambient air concentrations of each HAP at each census block 

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

reside in the census block. That is, the risk analysis did not consider either the short-term or 

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

exposure.

 

We did not address short-term human activity for two reasons. First, our experience with the 

1996, 1999, 2002, and 2005 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% for particulate HAPs; it will also reduce risk estimates for gaseous HAPs, 

but typically by much less. Second, basing exposure estimates on average ambient 

concentrations at census block centroids may underestimate or overestimate actual exposure 

concentrations at some residences. Further reducing exposure estimates for the most highly

exposed residents by modeling their short-term behavior could add a systematic low bias to these 

results.

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

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; the population risk 

distribution; and the cancer incidence. The assumption of not considering short- or long-term 

population mobility does not bias the estimate of the theoretical MIR nor does it affect the 

estimate of cancer incidence since the total population number remains the same. It does, 

however, affect the shape of the distribution of individual risks across the affected population, 

shifting it toward higher estimated individual risks at the upper end and reducing the number of 

people estimated to be at lower risks, thereby biasing the risk estimates high. 

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.
9

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 

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 

began 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 acute health risks (i.e., it “screens out”), 

or that it requires further, more refined assessment. A refined 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 would be needed to refine the assessment; in others, lesser amounts of sitespecific data could be used to determine that acute exposures are not a concern, and significant 

additional data collection would not be necessary. 

Acute health risk screening was performed 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 were assumed to equal 10 times the average 1-hour emission rates.

• Peak 1-hour emissions were assumed to occur at all emission points at the same time.

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

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

chemical being assessed to 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. In this case, this included use of a refined 

emissions multiplier to more accurately estimate the peak hourly emission rates from the average 

rates.
10

2.5 Multipathway and environmental risk screening and refined 

assessments 

The potential for significant human health risks due to exposures via routes other than inhalation 

(i.e., multipathway exposures) or adverse environmental impacts was screened by first 

determining whether any sources emitted any hazardous air pollutants known to be persistent and 

bioaccumulative in the environment (PB-HAP). The PB-HAP compounds or compound classes 

are identified for the screening from the EPA’s Air Toxics Risk Assessment Library [8], and 

they are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, 

heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury compounds, 

methoxychlor, polychlorinated biphenyls, polycyclic organic matter (POM), toxaphene, and 

trifluralin. Emissions of two different PB-HAP were identified in the emissions inventory for the 

Primary Lead Smelting source category: lead and cadmium. 

Cadmium emissions were evaluated for potential non-inhalation risks and adverse environmental 

impacts using our recently developed screening scenario which was developed for use with the 

TRIM.FaTE3 model. This screening scenario uses environmental media outputs from the peerreviewed TRIM.FaTE to estimate the maximum potential ingestion risks for any specified 

emission scenario by using a generic farming/fishing exposure scenario that simulates a 

subsistence environment. The screening scenario retains many of the ingestion and scenario 

inputs developed for EPA’s Human Health Risk Assessment Protocols (HHRAP) for hazardous 

waste combustion facilities.4

 In the development of the screening scenario a sensitivity analysis 

was conducted to ensure that its key design parameters were established such that environmental 

media concentrations were not underestimated, and to also minimize the occurrence of false 

positives for human health endpoints. See Appendix 3 for a complete discussion of the 

development and testing of the screening scenario, as well as for the values of facility-level

screening emission rates developed for screening potentially significant multi-pathway impacts. 

For the purpose of developing screening emission rates for our multi-pathway screening, we

derived emission levels for each PB HAP at which the maximum human health risk would be 1 

in a million for lifetime cancer risk or a hazard quotient of 1.0 for noncancer impacts. 

In evaluating the potential air-related multi-pathway risks from the emissions of lead compounds

from the one facility in this source category, rather than developing a screening emission rate, we 

compared its maximum modeled 3-month rolling average atmospheric lead concentration at any 

off-site location with the current primary National Ambient Air Quality Standard (NAAQS) for 

lead, which is set to a level of 0.15 μg/m3 based on a rolling 3-month period with a not-to-beexceeded form. 73 FR 66964. Notably, in making these comparisons, we estimated maximum 

rolling 3-month ambient lead concentrations taking into account all of the elements of the 

NAAQS for lead. That is, our estimated 3-month lead concentrations are calculated in a manner 

that is consistent with the indicator, averaging time, and form of the NAAQS for lead, and those 

estimates are compared to the actual level of the lead NAAQS (0.15 μg/m3

).

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

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

4 EPA’s Human Health Risk Assessment Protocol (HHRAP) for Hazardous Waste Combustion Facilities;

http://www.epa.gov/epaoswer/hazwaste/combust/riskvol.htm#volume1
11

health evaluation of air effects of lead exposure for the purposes of setting a national 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 multi-pathway risks because as mentioned above, the primary 

NAAQS is set to protect public health with an adequate margin of safety. However, ambient air 

lead concentrations above the NAAQS are considered to pose the potential for increased risk to 

public health. We consider this NAAQS assessment to be a refined analysis given 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 the site-specific 

dispersion modeling analysis performed to develop the ambient concentration estimates due to 

emissions from the one Primary Lead Processing facility being addressed in this RTR. 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.

In order to calculate three-month rolling average lead concentrations to compare to the NAAQS 

for lead, we used the inputs as supplied in the facility’s public comments on the proposed 

rulemaking, with two modifications: 1) the main stack (ID 30001) emission rate was set to 1.96

g/s based on stack test data submitted by Doe Run rather than the emission factor based number 

used in Doe Run’s submitted modeling (see the Technical Support Document in the docket for 

an explanation of this change); and 2) road sources that were outside facility boundaries were 

excluded. In addition, we used the fenceline receptor locations provided by the facility since our 

previous information was out of date. We also modeled 3-month ambient lead concentrations 

with a method that was consistent with that used in modeling submitted by the facility itself 

during the public comment period for the proposed rule. That is, we modeled with AERMOD 

followed by LEADPOST to obtain 3-month rolling average lead concentrations. Moreover, 

similar to the modeling for HAP other than lead, we used the release parameters, buildings, 

temporal emissions, and meteorological data supplied by the facility in its public comments on 

the proposed rule. 

In addition to the analyses discussed above, we also evaluated the potential for significant 

ecological exposures to non PB-HAP from exceedances of chronic human health inhalation 
12

thresholds in the ambient air near these facilities. Human health dose-response threshold values 

are generally derived from studies conducted on laboratory animals (such as rodents) and 

developed with the inclusions of uncertainty factors that could be as high as 3000. As a result, 

these human threshold values are often significantly lower than the level expected to cause an 

adverse effect in an exposed rodent. It should be noted that there is a scarcity of data on the 

direct atmospheric impact of these HAPs on other receptors, such as plants, birds, and wildlife. 

Thus, if the maximum inhalation hazard in an ecosystem is below the level of concern for 

humans, we have generally concluded that mammalian receptors should be at no risk of adverse 

effects due to inhalation exposures from non PB-HAP, and have assurance that other ecological 

receptors are also not at any significant risk from direct atmospheric impact. In some isolated 

cases where we have data indicating potential adverse impacts on plants, birds, or other wildlife 

due to the direct atmospheric impacts of specific HAPs, we note that as an uncertainty and, 

where possible, refine our analysis by comparing our modeled impacts to available threshold 

values from the scientific literature.

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 inventory for the one facility in 

the Primary Lead Smelting source category was based on the EPA Office of Air Quality 

Planning and Standards’ existing recommendations for HAPs [9], also used for NATA 1999, 

2002, and 2005 [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, 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 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/m3 in air.” The SF is “an upper 

bound, approximating a 95% confidence limit, on the increased cancer risk from a lifetime 

exposure to an agent. This estimate, [is] usually expressed in units of proportion (of a 

population) affected per mg/kg-day...” EPA disseminates dose-response assessment 

information in several forms, based on the level of review. The Integrated Risk Information 

System (IRIS) [11] is an EPA database that contains scientific health assessment 

information, including dose-response information. All IRIS assessments since 1996 have also 
13

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 CalEPA 

Technical Support Document for developing their chronic assessments5: “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 doseresponse 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: 

 5 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)
14

1) In the case of antimony compounds, the most conservative dose-response value of the 

chemical category is used as a surrogate for other compounds in the group for which doseresponse values are not available. This is done in order to examine, under conservative 

assumptions, whether the HAP that lack dose-response values may pose an unacceptable risk 

and require further examination, or screen out from further assessment. 

2) The IRIS URE for nickel inhalation was derived from evidence of the carcinogenic effects of 

insoluble nickel compounds in crystalline form. Soluble nickel species, and insoluble species 

in amorphous form, do not appear to produce genotoxic effects by the same toxic mode of 

action as insoluble crystalline nickel. Nickel speciation information for some of the largest 

nickel-emitting sources (including oil combustion, coal combustion, and others) suggests that 

at least 35% of total nickel emissions may be soluble compounds. The remaining insoluble 

nickel emissions are not well-characterized. Consistent with this limited information, this 

assessment will conservatively assume that 65% of emitted nickel is insoluble and that all 

insoluble nickel is crystalline. The nickel URE (based on nickel subsulfide, and 

representative of pure insoluble crystalline nickel) was adjusted to reflect an assumption that 

65% of the total mass of nickel may be carcinogenic. The noncancer dose-response values 

were not adjusted because noncancer effects of nickel are not thought to be limited to the 

crystalline, insoluble form.

The emissions inventory for the primary lead smelting source category includes emissions of 

seven 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).

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

Carcinogens

URE (unit risk estimate for cancer)6 = cancer risk per μg/m3 of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System.

Pollutant CAS Number7 URE

(1/μg/m3)

Source

Arsenic Compounds 174 4.3E-03 IRIS

Cadmium Compounds 7440439 1.8E-03 IRIS

Chromium Compounds 

 Chromium VI Compounds 18540299 0.012 IRIS

Nickel Compounds 186 3.124.8E-04 IRIS

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

at a concentration of 1 μg/m3 in air. URE’s are considered upper bound estimates meaning they represent a 

plausible upper limit to the true value. 7 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have 

used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAPs.
15

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 and Disease Registry, NAAQS = National Ambient Air Quality Standard.

Pollutant CAS Number RfC 

(mg/m3)

Source8

Antimony Compounds 7440360 0.0002 IRIS-M

Arsenic Compounds 174 0.000015 CAL

Cadmium Compounds 176 0.00001 ATSDR

Chromium Compounds

Chromium VI Compounds 18540299 0.0001 IRIS-M

Cobalt Compounds 178 0.0001 ATSDR

Lead & Compounds 182 0.00015 NAAQS

Nickel Compounds 186 0.00009 ATSDR

2.6.2 Sources of acute dose-response information 

Hazard identification and dose-response assessment information for the acute inhalation 

exposure assessment are based on the existing recommendations of OAQPS for HAP [17]. 

Depending on availability, the results from screening acute assessments are compared to both 

“no effects” reference levels for the general public, such as the California Reference Exposure 

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

(AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the recognition that the 

ultimate interpretation of any potential risks associated with an estimated exceedance of a 

particular reference level depends on the definition of that level and any limitations expressed 

therein. Comparisons among different available inhalation health effect reference values (both 

acute and chronic) for selected HAPs can be found in the EPA document (see: U.S. EPA. 

Graphical Arrays of Chemical-Specific Health Effect Reference Values for Inhalation Exposures 

[Final Report]. (U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-09/061, 

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

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). 

 8 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 the IRIS file.
16

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. [18]. 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 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.”

“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 
17

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) [19] for 

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

for exposure of the general population (but not particularly sensitive persons) for up to 1 hour 

associated with effects expected to be mild or transient (ERPG-1), irreversible or serious (ERPG2), and potentially life-threatening (ERPG-3). 

ERPG values (http://www.aiha.org/1documents/Committees/ERP-erpglevels.pdf) are described 

in their supporting documentation as follows: “Emergency Response Planning Guidelines 

(ERPGs) were developed for emergency planning and are intended as health based guideline 

concentrations for single exposures to chemicals. These guidelines (i.e., the ERPG Documents 

and ERPG values) are intended for use as planning tools for assessing the adequacy of accident

prevention and emergency response plans, including transportation emergency planning and for 

developing community emergency response plans. The emphasis is on ERPGs as planning 

values: When an actual chemical emergency occurs there is seldom time to measure airborne 

concentrations and then to take action.” 

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

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

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

adverse health effects or without perceiving a clearly defined, objectionable odor.”

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

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

or other serious health effects or symptoms which could impair an individual's ability to take 

protective action.”

The emissions inventory for the Primary Lead Smelting source category includes emissions of 

two HAP with relevant and available quantitative acute dose-response threshold values. 

Specifically, REL values for arsenic and nickel are listed below in Table 2.6-2.
18

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)

ERPG-1

(mg/m3)

ERPG-2

(mg/m3) REL 

Arsenic Compounds 7440382 0.0002

Nickel Compounds 7440020 0.006

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 [20] 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 [21], in 2002 

by the Agency’s information quality guidelines [22], and in the OMB/OSTP September 2007 

Memorandum on Updated Principles for Risk Analysis,

9 and are incorporated in these 

assessments.

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

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

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

limitations of available time and resources. We provide summaries of risk metrics (including 

maximum individual cancer risks and noncancer hazards, as well as cancer incidence estimates) 

along with a discussion of the major uncertainties associated with their derivation to provide 

decision makers with the fullest picture of the assessment and its limitations.

For each carcinogenic HAP included in an assessment that has a potency estimate available, 

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 

 9

Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis

(September 19, 2007), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs, Office 

of Management and Budget; and Sharon L. Hays, Associate Director and Deputy Director for Science, Office of 

Science and Technology Policy (http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf)
19

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.

In addition, 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.

Unlike linear dose-response assessments for cancer, noncancer health hazards generally are not 

expressed as a probability of an adverse occurrence. Instead, “risk” for noncancer effects is 

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

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

exposures at or below the reference level (HQ≤1) are not likely to cause adverse health effects. 

As exposures increase above the reference 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 

whether there is truly 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.
20

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

[23,24] 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 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.

2.7.3 Facility-wide risks

To help place the source category risks in context, we typically examine “facility-wide” risks 

using facility-wide and source category data files developed by EPA thru emission data updates 

from industry and refined EPA profiles for the source category. In this instance, there are no 

other HAP emission sources present-- all HAP sources have been included in the risk analysis. 

Therefore, the facility-wide risk is the same as the source category risk; no separate facility-wide 

analysis was necessary.

3. Risk Results for the Primary Lead Smelting Source Category

3.1 Source category description and results

The primary lead smelting source category includes one facility that smelts lead-bearing ore 

concentrates to produce lead metal. This metal is used to make various construction and 

consumer products such as batteries, paint, glass, piping, and filler. Lead sulfide (PbS) ore 

concentrates are the main feed material to primary lead smelters. The primary lead smelting 

process consists of PbS concentrate storage and handling, sintering of ore concentrates, sinter 

crushing and handling, smelting of sinter to lead metal, drossing (i.e., removing the solid oxide 
21

deposits), refining and alloying of lead metal, and smelting of the drosses. HAP can be emitted 

during these steps as process emissions, process fugitive emissions, and fugitive dust emissions. 

Across all three of these types of emission points, lead and lead compounds are the main HAP 

emitted. Other emissions include other metal HAP, such as arsenic, antimony, and cadmium. 

As noted above, there is only one primary lead smelter currently operating in the U.S., and the 

data set for this source category contains data for this one facility. Figure 3.1-1 shows the 

facility, modeling receptor locations, as well as the locations of two nearby ambient lead 

monitors. Figure 3.1-1 also identifies the census block MIR, and the location of the highest 

modeled ambient lead concentration (see results in section 3.2). The data set for this facility was 

developed based on site-specific information and represents a base year of 2009 (see section 2.1).

Figure 3.1-1 Discrete Receptors, Maximum Risk Receptors, and Ambient Monitors in 

Close Proximity to the Doe Run Primary Lead Smelter in Herculaneum, Missouri
22

The emissions in the primary lead smelting source category NPRM data set are summarized in 

Table 3.1-1. The total HAP emissions for the source category are approximately 84 tons per 

year. Based on these data, lead is the HAP emitted in the largest quantity and constitutes about 

96 percent of the total HAP mass emitted. Persistent and bioaccumulative HAP (PB-HAP) 

emissions reported in the NPRM dataset include lead and cadmium. 

Table 3.1-1 Summary of Emissions from the Primary Lead Smelting Source Category and 

Availability of Dose-Response Values

HAPa Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (1 

facility in 

ANPRM 

data set)

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSb

PBHAP? Unit Risk 

Estimate for 

Cancer?

Reference 

Concentration 

for Noncancer?

Health 

Benchmark 

Values for Acute 

Noncancer?

Lead Compounds 80.8 1 Yc Υ

Cadmium Compounds 1.9 1 Υ Υ Υ

Antimony Compounds 0.23 1 Υ

Arsenic Compounds 0.48 1 Υ Υ Υ

Nickel Compounds 0.22 1 Υ Υ Υ

Chromium Compounds

Chromium III Compounds 0.14 1

Chromium VI Compounds 0.0 1 Υ Υ

Cobalt Compounds 0.07 1 Υ

a Note for how metal HAP were speciated for risk assessment: For most metals, emissions reported as the elemental metal are 

combined with metal compound emissions (e.g., “cadmium” emissions are included in the class “cadmium compounds”). In the 

absence of speciation information, we assume the reported mass is 100 percent of the most toxic metal compound in the class. In 

the case of lead, there are no elemental lead emissions, so only lead compounds are included in our assessment.

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. See tables 2.6-1 and 2.6-2 above.

c In considering the air-related multipathway hazards for lead in this assessment, we used the National Ambient Air Quality 

Standards (NAAQS) for lead, which were adopted in October 2008 (http://www.epa.gov/air/lead/actions/html). The primary 

(health-based) standard is 0.15 μg/m3

, as a maximum or not-to-be-exceeded, rolling three-month average, measured as total 

suspended particles (TSP). The secondary (welfare-based) standard is equal to the primary.

3.2 Baseline risk characterization

This section presents the results of the preliminary risk assessment for the Primary Lead 

Smelting 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 
23

incidence. We also present results from our acute inhalation impact assessments in the form of 

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

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

contribute 90 percent of the maximum cancer risk or maximum hazard at the highest exposure 

location, as well as a summary of the results of the maximum allowed emissions under the 

current MACT standards.

Table 3.2-1 summarizes the preliminary chronic and acute inhalation risk results for this source 

category for all HAP except for lead. The results of the chronic inhalation cancer risk 

assessment indicate the maximum lifetime individual cancer risk could be as high as 20 in a

million with fugitive dust emissions of cadmium dominating the risk. The total estimated cancer 

incidence from this source category based on actual emission levels is 0.008 excess cancer cases 

per year, or one case in every 125 years. Approximately 33 people were estimated to have 

cancer risks above 10 in a million, and approximately 110,000 people were estimated to have 

cancer risks above 1 in a million. Finally, when considering MACT allowable emissions, the 

MIR remains 20 in a million. This is because fugitive dust emissions, which are driving the risk, 

are covered by a work practice standard under the MACT and therefore, MACT-allowable and 

actual fugitive dust emissions are equal.
24

Table 3.2-1 Summary of Source Category Level Inhalation Risks for the Primary 

Lead Smelting Facility – non-lead HAP10

Result HAP “Drivers”

Cancer Risks

Maximum Individual Lifetime Cancer Risk 

(in 1 million) 20 cadmium compounds

Chronic Noncancer Risks

Maximum Kidney Hazard Index 1 cadmium compounds

Acute Noncancer Risks

Maximum Acute Hazard Quotient 2 arsenic compounds (REL))

Refined Acute Noncancer Risk

Maximum Refined Acute Hazard Quotient 2 arsenic compounds (REL)

Population Exposure

Number of People Living Within 50 

Kilometers of the Facility Modeled 1,509,000 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 33 n/a

Greater than or equal to 1 in 1 

million 110,000 n/a

Number of People Exposed to Noncancer Kidney Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.008 n/a

Contribution of HAP to Cancer Incidence

cadmium compounds 66% n/a

arsenic compounds 32% n/a

nickel compounds 2% n/a

The maximum chronic noncancer TOSHI value is 1, with fugitive dust emissions of cadmium

dominating those impacts. When considering MACT allowable emissions, for the reason 

provided above, the maximum chronic noncancer TOSHI value remains 1. Taken together, these 

results indicate no significant potential for chronic noncancer impacts. 

Based on the acute REL value for arsenic, an off-site screening-level acute HQ value from this 

facility could be as high as 2 resulting from emissions from the main stack. 

 10 Results presented in this table, as well as those presented with respect to lead risk (see below) consider the main 

stack modeled at the good engineering practice (GEP) stack height of 330 feet (as was done in the SIP modeling 

submitted by the Doe Run Company in its public comments on the proposed rulemaking). The actual height of the 

main stack is approximately 500 feet and thus, it is very likely that in instances where the main stack is contributing 

an appreciable portion of risk, that risk would have been lower had actual stack height been modeled.
25

In addition to the analyses presented above, to screen for potential multipathway effects from 

emissions of cadmium, we compared the cadmium emission rate from the one facility in this 

source category to the screening threshold value for cadmium described in section 2.5 (also see 

Appendix 3 for a more detailed discussion of screening emission rates). Results of this worstcase screen estimate that cadmium emissions from this facility could exceed the cadmium 

screening emission rate by about 50-fold. Notably, the cadmium screening emission rate is not 

based on cancer risk, but on the potential to pose a noncancer ingestion exposure equal to the 

chronic reference dose, or RfD, for cadmium. Thus, the results of this worst-case multipathway 

screen indicate that with respect to cadmium, there is at least some potential for ingestion 

exposures as high as 50 times the RfD, but this result is biased high for purposes of screening 

and subject to significant uncertainties. Additional data were not available to develop a more 

refined analysis of potential multi-pathway risks for this source category. 

Table 3.2-2 presents the results of our lead impact analysis broken down by emission point 

considering actual 2009 emissions. Results indicate that estimated 3-month rolling average lead 

concentrations resulting from this facility’s refining stack, furnace area, and fugitive dust 

emissions could exceed the primary NAAQS for lead by as much as 7.7-fold, 2.0-fold, and 1.1-

fold respectively. This analysis also indicates that actual emissions from the main stack do not 

result in estimated ambient lead concentrations that exceed the primary NAAQS anywhere 

within 50 km of the facility (likely due to stack height, which was modeled at 330 feet). 

In addition to the information presented in Table 3.2-2, we note that considering all emission 

points combined, estimated 3-month rolling average lead concentrations could exceed the 

primary NAAQS for lead by as much as 11-fold (driven primarily by emissions from stacks). 

This analysis further indicates that within 50 km of this facility, approximately 1,550 people 

could be exposed to ambient lead concentrations exceeding the primary NAAQS for lead.

As mentioned above, to evaluate the potential for adverse environmental effects, we also 

compared maximum 3-month rolling average atmospheric concentrations with the current 

secondary NAAQS for lead (which is identical to the primary, health-based standard). Thus, the 

analyses just discussed above also indicate the potential for adverse environmental effects from 

emissions of lead. 
26

Table 3.2-2 Summary of Lead Concentrations 

Relative to the NAAQS Given Actual 2009

Emissions11

Emission Point

Actual 2009

Emissions (tpy)

Maximum 

Impact

Main stack2 68.3 0.85 times the 

NAAQS

Refining stacks 9.1 7.7 times the 

NAAQS

Furnace area: 

(controlled blast and 

drossing fugitives)

2.47 2 times the 

NAAQS

Fugitive dust 1.1 1.1 times the 

NAAQS

In addition to the results presented above, for calendar year 2009, we also compared maximum 

AERMOD estimates of ambient lead concentrations with those measured at 62 monitors in close 

proximity to the Doe Run Primary Lead Smelting Facility12. More specifically, we compared 

maximum 3-month rolling average lead concentrations (for calendar year 2009) calculated from 

data reported at the Main Street and Church Street monitors listed in Table 3.2-3 to the 

maximum 3-month rolling average lead concentrations at model receptor locations in close 

proximity to these monitoring sites (see Figure 3.1-1 for location of monitors and AERMOD 

receptors). Results of this analysis are presented in Table 3.2-3 and indicate that at these six two

locations, model predictions are well within 50 percent when compared to monitored 

concentrations at these sites. Moreover, EPA notes that these results are very similar to the 

results of a model to monitor comparison submitted by the Doe Run facility based on its own 

modeling for State Implementation Plan (SIP) purposes (see comments provided by Doe Run 

Company in docket number: EPA-HQ-OAR-2004-0305). Taken together, these results indicate 

that AERMOD estimates of ambient air lead concentrations provide a reasonable representation 

of the measured 3-month maximum lead concentrations present in the ambient air near this 

facility.

 11 We note that modeling allowable emissions for the main stack was not possible. The allowable emissions are 

calculated based on emissions, production, and operating hours. It was not possible to produce a single value for 

modeling purposes that would provide a truly representative estimate of allowable emissions. Moreover, the other 

emission points listed in Table 3.2-2 were not covered under the MACT standard, and thus, we were also unable to 

calculate allowable emissions for these emission points. 12 A more detailed model to monitor comparison which includes comparisons with additional monitors surrounding 

the Doe Run facility can be found in the docket for this rulemaking (see docket number: EPA-HQ-OAR-2004-

0305).
27

Table 3.2-3 Comparison of AERMOD Modeled to Ambient Lead Concentrations Reported 

by Two Monitors in Close Proximity to the Herculaneum Primary Lead Smelting Facility.

Location Maximum AERMOD 

Modeled 3-Month Lead 

Concentration (μg/m3)

Maximum Monitored 3-

Month Lead 

Concentration 13 (μg/m3)

Model to 

Monitor Ratio14

Main Street 0.85 1.24 -1.46

Church Street 0.6 0.45 1.33

Table 3.2-3 Comparison of AERMOD Modeled to Ambient Lead Concentrations Reported by Two 

Monitors in Close Proximity to the Herculaneum Primary Lead Smelting Facility.

Monitor Name

AERMOD 

Modeled Lead 

Concentration2 

(μg/m3)

Maximum 

Monitored Lead 

Concentration2 

(μg/m3)

Max Modeled/

Max Monitored

Percent 

Difference 3

City Hall/Main St. 0.78 1.2 64.67% -35.33%

Sherman 0.19 0.16 118.75% 18.75%

North Cross 0.34 0.59 57.63% -42.37%

High School/Dunklin 0.34 0.39 87.95% -12.05%

Church St. 0.60 0.45 133.56% 33.56%

Ursuline1 0.07 0.08 91.25% -8.75%

1 There is no 2009 data for this monitor, therefore monitored values from 2010 were used. 2 Maximum 3-month rolling average

3 Negative value indicates a model underestimation

3.3 Assessment of emission reductions scenarios

To inform possible risk management actions, we considered specific emission limits for those 

emission points that were modeled to result in lead concentrations in excess of the NAAQS (see 

Table 3-2.2). We chose this level because we believe that the level of the NAAQS is the level 

which represents the upper limit of acceptable risk in the context of the section 112(f) decision 

framework. In so doing, we found that in order to meet the NAAQS based solely on emissions 

from the Doe Run facility at those sites of maximum impact refinery stack emissions and furnace 

area stack emissions would have to be reduced to a combined total of approximately 1.2 TPY 

(this takes into account that the maximum impacts of refinery and furnace emission points occur 

at the same location). With respect to fugitive emissions, we estimate that in order to meet the 

NAAQS at the site of maximum impact by fugitives, these emissions would have to be reduced 

to a total of approximately 1 TPY. Emissions from the main stack (i.e., emission point for sinter 

machine, blast furnace and drossing operations) did not result in ambient lead concentrations in 

 13 Maximum 3-month monitored concentrations were calculated for the year 2009 based on data submitted to EPA’s 

Air Quality System (AQS). 

14 Negative sign denotes an underestimation of AERMOD modeled ambient lead concentrations, relative to 

monitored concentrations. AERMOD estimated concentrations were based on the 2009 emissions estimates 

described in section 2.1.

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excess of the lead NAAQS at modeled locations within 50 km of the fenceline and thus, we did 

not calculate any reductions at the main stack in order to meet the NAAQS for lead. 

4. General Discussion of Uncertainties and How They Have 

Been Addressed

4.1 Exposure modeling uncertainties

Although every effort has been made to identify all the relevant 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 modeling estimates. 

The chronic exposure modeling uncertainties are considered relatively small in comparison, 

since we are using EPA’s refined local dispersion model with site-specific parameters and 

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. 

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. 

4.2 Uncertainties in the dose-response relationships

In the sections that follow, separate discussions are provided on uncertainty associated with 

cancer potency factors and noncancer reference values. Cancer potency values are derived for 

chronic (lifetime) exposures. Noncancer reference values are generally derived for chronic 

exposures (up to a lifetime), but may also be derived for acute (<24 hours), short-term (>24 

hours up to 30 days), and subchronic (>30 days up to 10% of lifetime) exposure durations, all of 

which are derived based on an assumption of continuous 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
29

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 [25] (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 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).15 In some circumstances, the true risk could be as low as zero; 

however, in other circumstances the risk could also be greater.16 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.17 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).

 15 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm). 16 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.

17 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.
30

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 [26]. Some of the major sources of uncertainty and 

variability in deriving cancer risk values are described more fully below. 

(1) The qualitative similarities or differences between tumor responses observed in 

experimental animal bioassays and those which would occur in humans are a source of 

uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites 

observed in an experimental animal bioassay are necessarily predictive of the sites at which 

tumors would occur in humans.18 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 

 18 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.”
31

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 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. A 

recent National Research Council (NRC) report [27] 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 
32

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) [28]. It is important to note that 

earlier EPA assessments, including the majority of those for which risk values exist today, were 

generally developed using the multistage model to extrapolate down to environmental dose 

levels and did not involve the use of a POD. Subramaniam et al. (2006) also provide 

comparisons indicating that slopes based on straight line extrapolation from a POD do not show 

large differences from those based on the upper confidence limit of the multistage model.

(6) Cancer risk estimates do not generally make specific adjustments to reflect the 

variability in response within the human population — resulting in another source of uncertainty 

in assessments. In the diverse human population, some individuals are likely to be more 

sensitive to the action of a carcinogen than the typical individual, although compound-specific 

data to evaluate this variability are generally not available. There may also be important life 

stage differences in the quantitative potency of carcinogens and, with the exception of the 

recommendations in EPA’s Supplemental Cancer Guidance for carcinogens with a mutagenic 

mode of action, risk assessments do not generally quantitatively address life stage differences. 

However, one approach used commonly in EPA assessments that may help address variability in 

response is to extrapolate human response from results observed in the most sensitive species 

and sex tested, resulting typically in the highest URE which can be supported by reliable data, 

thus supporting estimates that are designed not to underestimate risk in the face of uncertainty 

and variability.

Chronic noncancer assessment

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

health-protective. That is, EPA and other organizations 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 values19 e.g., factors of 10 or 3, used in the absence of 

 19 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 
33

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. These 

values provide an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily 

oral exposure (RfD) or a continuous inhalation exposure (RfC) to the human population 

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

effects during a lifetime.20 To derive values that are intended to be “without appreciable risk,” 

EPA’s methodology relies upon an uncertainty factor (UF) approach [29],[30] which includes 

consideration of both uncertainty and variability.

EPA begins by evaluating all of the available peer-reviewed literature to determine non-cancer 

endpoints of concern, evaluating the quality, strengths and limitations of the available studies. 

EPA typically chooses the relevant endpoint that occurs at the lowest dose, often using statistical 

modeling of the available data, and then 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 [31] . 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 

 

of EPA Risk Assessment Principles and Practices, EPA/100/B-04/001 available at: 

http://www.epa.gov/osa/pdfs/ratf-final.pdf. 

20 See IRIS glossary
34

required) the associated UF is not used. The major adjustment steps are described more fully 

below.

1) Heterogeneity among humans is a key source of variability as well as uncertainty. 

Uncertainty related to human variation is considered in extrapolating doses from a subset or 

smaller-sized population, often of one sex or of a narrow range of life stages (typical of 

occupational epidemiologic studies), to a larger, more diverse population. In the absence of 

pollutant-specific data on human variation, a 10-fold UF is used to account for uncertainty 

associated with human variation. Human variation may be larger or smaller; however, data to 

examine the potential magnitude of human variability are often unavailable. In some situations, 

a smaller UF of 3 may be applied to reflect a known lack of significant variability among 

humans.

2) Extrapolation from results of studies in experimental animals to humans is a necessary 

step for the majority of chemical risk assessments. When interpreting animal data, the 

concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. rodents) is 

extrapolated to estimate the human response. While there is long-standing scientific support for 

the use of animal studies as indicators of potential toxicity to humans, there are uncertainties in 

such extrapolations. In the absence of data to the contrary, the typical approach is to use the 

most relevant endpoint from the most sensitive species and the most sensitive sex in assessing 

risks to the average human. Typically, compound specific data to evaluate relative sensitivity in 

humans versus rodents are lacking, thus leading to uncertainty in this extrapolation. Size-related 

differences (allometric relationships) indicate that typically humans are more sensitive than 

rodents when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF 

is consistent with these differences. For a specific chemical, differences in species responses 

may be greater or less than this value.

Pharmacokinetic models are useful to examine species differences in pharmacokinetic 

processing and associated uncertainties; however, such dosimetric adjustments are not always 

possible. Information may not be available to quantitatively assess toxicokinetic or 

toxicodynamic differences between animals and humans, and in many cases a 10-fold UF (with 

separate factors of 3 for toxicokinetic and toxicodynamic components) is used to account for 

expected species differences and associated uncertainty in extrapolating from laboratory animals 

to humans in the derivation of a reference value. If information on one or the other of these 

components is available and accounted for in the cross-species extrapolation, a UF of 3 may be 

used for the remaining component.

3) In the case of reference values for chronic exposures where only data from shorter 

durations are available (e.g., 90-day subchronic studies in rodents) or when such data are judged 

more appropriate for development of an RfC, an additional UF of 3 or 10-fold is typically 

applied unless the available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the dose or exposure levels that have been 

tested in individual studies; in an animal study, for example, treatment groups may differ in 

exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use 

BMD analysis; however, this approach requires adequate quantitative results for a meaningful 
35

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.

There is no RfD or other comparable chronic health benchmark value for lead compounds. 

Thus, to address multipathway human health and environmental risks associated with emissions 

of lead from this facility, ambient lead concentrations were compared to the NAAQS for lead. In 

developing the NAAQS for lead, EPA considered human health evidence reporting adverse 

health effects associated with lead exposure, as well as an EPA conducted multipathway risk 

assessment that applied models to estimate human exposures to air-related lead and the 

associated risk (73FR66979). 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 (73FR66974), as well as uncertainties between blood lead 

levels and loss of IQ points in children (73FR66981). 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 (73FR66998). 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 (73FR67006). 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 
36

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 

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 (73FR67004). 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 chemical-specific or 

health effect-specific information (e.g., simple irritation effects do not vary appreciably between 

human individuals, hence a value of 3 is typically used), or based on the purpose for the 

reference value (see the following paragraph). The UFs applied in acute reference value 

derivation include: 1) heterogeneity among humans; 2) uncertainty in extrapolating from 

animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 4) 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. 
37

5. References

 

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38

 

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different statistical approaches, Risk Anal. Vol 26, p. 825–830.
39

 

29. US EPA. 1993. Reference Dose (RfD): Description and Use in Health Risk Assessments. 

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