### From: Stewart.Michael@epamail.epa.gov
Sent: Thursday, November 03, 2011 3:14 PM
To: Higgins, Cortney Cc: Topham.Nathan@epamail.epa.gov; French.Chuck@epamail.epa.gov; Guinnup.Dave@epamail.epa.gov; Hanzel, Veronica Subject: Updated primary lead risk assessment Attachments: Final Primary Lead Risk Report 11

- 3

- 11 for OMB.docx

Hi Cortney,

This has the changes we talked about on today's call. Please let me know if you have any questions

Thanks, Mike.

Michael J. Stewart, Ph.D. Environmental Protection Specialist Health and Environmental Impacts Division U.S. Environmental Protection Agency Research Triangle Park, NC 27711 (919) 541

- 7524 stewart.michael@epa.gov1 

## 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 201 12 

### 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 Mixt ures

................................ ................................ ................................ ....................... 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

.......................... 27

4.1 Exposure modeling uncertainties

................................ ................................ ........................ 27 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 Concent rations Reported by Two Monitors in Close Proximity to the Herculaneum Primary Lead Smelting Facility.

.... Error! Bookmark not defined.

Appendices3 Appendix 1 Technical Support Document for HEM 3 Modeling 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 Acronym s

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 estimate4 

## 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

- bas ed 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, processe s and control technologies.” In the secon d 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 resi dual 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 Pl an , ” 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 Methodol ogies” [ 2 ]. We received the final SAB report on this review in May of 2010 [ 3 ]

. Where appropriate, we have responded to the key messages from this review in developing our current risk assessment s , and we will be continuing our efforts to improve our ass essments 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 t he 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 i n 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 pu blic comments ) . Thus, t he 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 public5 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. The se discussion s 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, ch ronic 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 s et for this one facility. As noted above, d uring 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

- HQ

OAR

- 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 o f 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 Pr imary 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 **Dispersio n 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,

an d estimation of population risks. This section focuses on the dispersion modeling component. The exposure and risk characterization components are discussed in other subsections of s ections 2 and 3.

The dispersion model in the HEM 3 system, AERMOD versi on 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 User s 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 e mission 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, h ourly emission rates used for th at simulation we re 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 thi s 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 c urrent modeling also utilized updated boundary information

, 2

, release parameters

, and information on build ing dimensions supplied by this facility in its public com ments

. The first step in the application of the HE M 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 mo del optio ns 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 th e modeling supporting the proposed rule

, site

- specific meteorological data for the one facility in this source category w ere suppli ed 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 t he facility’s boundary, and therefore removed: 0290997007002007; 02909 97006014052; 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 ye ar 1998, in our draft modeling for the proposal

. However, during the public comment period for the proposed rule , EPA r eceived site

- specific meteorological data for the year 2008. As a result, this more current meteorological data set 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) concentrati ons 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 p oints 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 ins ide 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 peak

to

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

. 2004)[ 7 ]. In that report, the Texas Environmental Research Consortium Project compared hourly and annual emission s data for volatile organic compound s 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, f ocusing 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 99 th 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, ou r 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 o f 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 Geolo gical Service 1

- degree digital elevation model (DEM) data files, which have a spatial resolution of about 90 meters. E levations of polar grid points used in estimating short

- and long

term ambient concentrations were assumed to be equal to the highest ele vation of any census block falling with in th e 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 sour ce, the model us es the average elevation of all sectors within the innermo st 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 pattern s. 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 concentration s of each HAP at each census block centroid as a surrogate f or 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 o n 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 microenvironm ent 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 estimat es 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 beha vior 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 cours e 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 populat ion 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

acu te exposure concentration for any individual.9 

### 2.4 **Acute r**  **isk s**  **creening and r**  **efined a**  **ssessments** In establishing a scientifically defensible approach for the assessment of potential health risks due to acute exposures to HAP, we f ollowed the same general approach that has been used for developing chronic health risk assessments under the residual risk program. That is, we developed a tiered, iterative approach. This approach to risk assessment was endorsed by the National Academy of Sciences in its 1993 publication “Science and Judgment in Risk Assessment” and subsequently was adopt ed 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 asse ssment. This means that we bega n 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 u s e industry

- or site

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

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

specific 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.

• P eak 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, reg ardless 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 mod el, 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 s hort

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

As mentioned above, when we identify acute impacts which exceed their rele vant benchmarks, we pursue refining our acute screening estimates. In this case , this include d 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 emi tted 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 trif luralin

. Emissions of two different PB

- HAP were identified in the emi ssions 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.FaTE 3 model. This screening scenario uses environmental media outputs from the peer

reviewed 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 th e 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 emis sions 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 μ g/m 3 based on a rolling 3

- month period with a not

- to

- be

exceeded 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/m 3 ).

The NAAQS va lue

, 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#volume111 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 ri sks 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 co nsider 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 f or 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, wou ld 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, i n childre n. (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 standa rd 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 concentrat ions 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 co mments on the proposed rulemaking

, with two modifications: 1) the main stack (ID 30001) emission rate was set to 1. 96 g/s b ased 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) r oad sources that were outside facility boundaries were excluded. In addition, we used the fenceline receptor locations provided by the facility since our previous info rmation 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 AER MOD 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 parameter s , building s , temporal emissions, and meteorological data supplied by the facility in i ts public comments on the proposed rule

. In addition to the analyses discussed above, w e also evaluated the potential for significant ecological exposures to non PB

- HAP from exceedances of chronic human health inhalation12 thresholds in the ambient air ne ar 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, the se 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 no n 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

**- r**  **esponse a**  **ssessment**

2.6.1 Sources of chronic dose

- response information

Dose

- response assessment (carcinogenic and non

- carcinogenic) for chronic exposure (either by inhalation or ingestion) for the 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 recomm endations 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 inhalat ion) 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/m 3 in air. ” The SF is “a n upper bound, approximating a 95% confidence limit, on the increased cancer risk from a lifetime exposure to an agent. This estimate, [is] usually expressed in units of proportion (of a population) affected per mg/kg

- day

... ” EPA disseminates dose

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

- response i nformation. All IRIS assessments since 1996 have also13 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 inhala tion 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 D ose (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

ne cessarily represent a threat, and MRLs are therefore not intended for use as predictors of adverse health effects or for setting cleanup levels.

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

- response assessments for many substances, based both on carcinogenicity and health effects other than cancer. The process for developing these assessments is similar to that used by EPA to develop IRIS values and incorporates significant external scientific peer review. As cited in the CalEPA Technical Support Document for developing their chronic assessments 5 : “The guidelines for developing chronic inhalation exposure levels incorporate many recommendations of the U.S. EPA [ 13 ] and NAS [ 14 ].” The non

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

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

In developing chronic risk estimates, we adjusted dose

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

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 Ep idemiology 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

c hemical category i s used as a surrogate for other compounds in the group f or which dose

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

- response values may pose an unaccep table 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 in soluble 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 c ombustion, 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

s even 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 C hronic Inhalation Exposure to Carcinogens

URE (unit risk estimate for cancer) 6 = cancer risk per μg/m 3 of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System.

Pollutant CAS Number 7 URE (1/μg/m 3 ) Source

Arsenic Compounds 174 4.3E

- 03 IRIS Cadmium Compounds 7440439 1.8E

- 03 IRIS Chromium Compounds

Chromium VI Compounds 1854029 9 0.012 IRIS Nickel Compounds 186 3.12 4.8 E

- 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/m 3 in air. URE’s are considered upper bound estimates meaning they represent a plausible upper limit to the true value.
7 Chemical Abstract Services identification number. For gro ups 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 app reciable 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/m 3 ) Source 8

Antimony Compounds 744036 0 0.0002 IRIS

- M Arsenic Compounds 174 0.000015 CAL Cadmium Compounds 176 0.00001 ATSDR Chromium Compounds

Chromium VI Compounds 1854029 9 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 C alifornia 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 ris ks 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 chron ic) 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,
expres sing 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 overa ll 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 ar e 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 hea lth 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 Cou ncil A s described in the Committee’s “Standing Operating Procedures (SOP) ” ( http://www.epa.gov/opptintr/aegl/pubs/sop.pdf ), AEGLs “represent threshold exposure limits for the general public and are applicable to emergency exposures ranging from 10 min to 8 h.” Their intended application is “for conducting risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real time emergency response actions, fo r 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 ai rborne 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 develop ment 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 c oncentration (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that the general population, including susceptible individuals, could experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. However, the e ffects are not disabling and are transient and reversible upon cessation of
exposure. ”

“ AEGL

- 2 is the airborne concentration (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that the general population, including susceptible individu als, 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 a nd nondisabling odor, taste, and sensory irritation17 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 concentration s 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 h our associated with effects expected to be mild or transient (ERPG

- 1), irreversible or serious (ERPG

2), and potentially life

- threatening (ERPG

- 3).

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

conce ntrations 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 emerge ncy planning and for developing comm unity emergency response plans. The emphasis is on ERPGs as planning values: When an actual chemical emergency occurs there is seldom time to measure airborne concentrations and then to take action. ”

ERPG

- 1 and ERPG

- 2 values are defined by AIHA as follows:

“ ERPG

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

“ ERPG

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

The emis sions inventory for the Primary Lead Smelting source category incl udes 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/m 3 ) AEGL

- 2 (1

- hr) (mg/m 3 ) ERPG

- 1 (mg/m 3 ) ERPG

- 2 (mg/m 3 ) REL Arsenic Compounds 7440382 0.0002 Nickel Compounds 744002 0 0.006



### 2.7 Risk **c haracterization** 

2.7.1 General The final product of the risk assessment is the risk characterization, in which the information from the previous steps is integrated and an overall conclusion about risk is synthesized that is complete, informative, and useful for decision makers. In gen eral, the nature of this risk characterization depends on the information available, the application of the risk information and the resources available. In all cases, major issues associated with determining the nature and extent of the risk are identifi ed and discussed. Further, the EPA Administrator’s March 1995 Policy for Risk Characterization [ 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/OST P 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, itera tive analytical approach, we have attempted to reduce both uncertainty and bias to the greatest degree possible in these assessments , within the limitations of available time and resources . We provide summaries of risk metrics (including maximum individua l cancer risks and noncancer hazards, as well as cancer incidence estimates) along with a discussion of the major uncertainties associated with their derivation to provide decision makers with the fullest picture of the assessment and its limitations.

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

9 Memorandum f or t he Heads o f Executive Departments a nd Agencies

- Updated Principles for Risk Analysis ( September 19, 2007 ), From Susan E. Dudley, Administrator, 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, i ncreased cancer incidence for the entire receptor population within the area of analysis wa s estimated by multiplying the estimated lifetime cancer risk for each census block by the number of people residing in that block, then summing the results for the entire modeled domain. This lifetime population incidence estimate wa s divided by 70 years to obtain an estimate of the number of cancer cases per year.

Unlike linear dose

- response assessments for cancer, nonc ancer 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 potenti al 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 incorporat e 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. W e 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 ar e 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 effec ts 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 q uotients for individual HAPs that affect the same organ or organ system. A ll TOSHI calculations presented here were based exclusively on effects occurring at the “ critical dose” (i.e., the lowest dose that produces adverse health effects). Although HQs a ssociated 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 bee n 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 m easure 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 natu re of emissions and potential exposures, acute impacts were screened on an individual pollutant basis, not using the TOSHI approach.


### 2.7.3 Facility

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

- wide” risks

us ing facility

- wide and source category data files developed by EPA thru emission data updates from industry and refined EPA profiles for th e source category . In this instance, there are no other HAP emission sources present

-- all HAP sources have been incl uded 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 c**  **ategory d**  **escription and r**  **esul ts**  The primary lead smelti ng source category includes one facility that smelt s 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 lea d 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 oxide21 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, t here 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 ambi ent 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 rep resents a base year of 200 9 (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, Missouri22 The emissions in the primary lead smelting sourc e catego ry NPRM data set are summarized in Table 3.1

- 1. The total HAP emissions for the sour ce category are approximately 84 tons per year. Based on these data, lead is the HAP emitted in the largest quantity and constitutes about 9 6 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 Summar y of Emissions from the Primary Lead Smelting Source Category and Availability of Dose

- Response Values

HAP a Emissions (tpy) Number of Facilities Reporting

HAP (1 facility in ANPRM data set) Prioritized Inhalation Dose

- Response Value Identified by OAQPS b

PB

HAP? Unit Risk Estimate for

Cancer? Reference Concentration for Noncancer? Health Benchmark Values for Acute

Noncancer?
Lead Compounds 80.8 1 Y c 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 : F o r most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g., “cadmium” emissions are includ ed in the class “cadmium compounds”). In the absence of speciation information, we assume the reported mass is 100 per cent 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 Tr ansfer 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 (h ealth

- based) standard is 0.15 μg/m 3 , 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 **r isk**  **c haracterization** 

This se ction presents the results of the preliminary risk a ssessment for the Primary Lead S melting s ource category. The basic chronic inhalation risk estimates presented here are the maximum individual lifetime cancer risk, the maximum chronic hazard index, and the cancer23 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 su mmarize s the preliminary chronic and acute inhalation risk results for this source category for all HAP except for lead

. T he results of the chronic inhalation cancer risk assessment indicate the maximum lifetime individual cancer risk could be as high as 2 0 i n a

m illion with fugitive dust emissions of cadmium dominating the risk. The total estimated cancer incidence from th is source category based on actual emission levels is 0.0 08 e xcess cancer cases per year, or one case in eve ry 125 years. A pproximately 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 2 0 in a millio n

. 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 I nhalation Risks for the Primary Lead Smelting Facilit y

– non - lead HAP 10

Result HAP “Drivers” Cancer Risks Maximum Individual Lifetime Cancer Risk (in 1 million) 2 0 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

. W hen considering MACT allowable emissions, for the reason provided above, the maximum chronic noncancer TOSHI value remains 1 . Taken together, these results indicat e 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 th is 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 comme nts 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 st ack 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 em ission rates). Results of this worst

case 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 p otential 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
exposu res 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 cate gory.
Table 3.2

- 2 presents the results of our lead impact analysis broken down by emission point considering actual 200 9 emissions. R esults indicate that estimat ed 3

- month rolling average lead
concentrat ions resulting from this facility ’s refining stack and

, furnace area , and fugitive dust emissions c ould exceed the primary NAAQS for lead by as much as 8 7.7

- fold

, and 2.0

- fold , and 1 .1

- fold respectively

. In addition, the highest 3

- month rol ling average lead concentration resulting from this facility’s f ugitive dust emissions w as estimated to be very close to the NAAQS for lead ( 0.17 μg/m 3 )

. T T his analysis also indicate s that actual emissions from the main stack do not res ult in estimat ed ambient lead concentrations that exceed the primary NAAQS anywhere within 50 km of the facility (likely d u e 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 conc entrat ions c ould exceed the primary NAAQS for lead by as much as 11

- fold (driven primarily by emissions from stacks)

. T his 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 2 009 Emissions 11

Emission Point Actual 200 9 Emissions (tpy) Maximum Impact

Main stack 2 68.3 0

. 9 85 times the NAAQS
Refining stacks 9.1 8 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 200 9

, we also compared maximum AERMOD estimates of ambient lead concentrations with those measured at 6 monitors in close proximity to the Doe Run Primary Lead Smelting Facility. More specifically, we compared maximum 3

- month rolling average lead concentrations (for calendar year 200 9 ) calculated from data reported at the 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. Results of this a nalysis are presented in Table 3.2

- 3 and indicate that at these six locations, model predictions are well wi thin 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 Impleme ntation 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 concentration s provide a reasonable representation of the meas ured 3

- month maximum lead concentrations present in the ambient air near this facility.

11 We note that m odeling 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 the se emission points.27 Table 3.2

- 3 Comparison of AERMOD Modeled to Ambient Lead Concentrations Reported by Six Two Monitors in Close Proximity to the Herculaneum Primary Lead Smelting Facility.

Monitor Name AERMOD Modeled Lead Concentration 2 (μg/m 3 ) Maximum Monitored Lead Concentration 2 (μg/m 3 ) Max Modeled/ Max Monitored Percent Difference 3 City Hall/Main St. 0.7 8 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%
Ursuline 1 0.07 0.08 91.25%

- 8.75%

1 There i s 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 **e mission**  **r eductions**  **s cenarios** 

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 acce ptable 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 the estimated fugitive emissions, we e stimate that ambient lead in order to meet the concentration at the site of maximum impact from fugitive dust emissions, th is concentration was estimated to be very close to th e NAAQS for lead at the site of maximum impact by fugitives

, and thus, only minimal reductions (if any) are needed to meet the lead NAAQ S

. these emissions would have to be reduced to a total of approximately 1 TPY. E missions from the main stack (i.e., emission point for sinter machine, blast furnace and drossing operations ) did not result in ambient lead concentrations in 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 Uncer tainties and How They Have Been Addressed

**4.1 Exposure**  **m odeling**  **u ncertainties**  Formatted: Font: 10 pt, Complex Script Font: 10 pt

Formatted: Font: 10 pt, Complex Script Font: 10 pt, Not Superscript/ Subscript28 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 mod el 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 de gradation.

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**  **d ose**  **- r**  **esponse r**  **elationships**

In the sections that follow, separate discussions are provided on uncertainty associated with cancer potency factors and noncancer reference values. Cancer potency values are derived for chronic (lifetime) exposures. Noncancer re ference values are generally derived for chronic exposures (up to a lifetime), bu t may also be derived for acute ( <24 hours), short

- term (>24 hours up to 30 days), and subchronic (> 30 days up to 10% 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 wit h the emissions included in an assessment, we rely on both chronic (cancer and noncancer) and acute (noncancer) benchmarks, which are described in more detail below.

Although every effort is made to identify peer

- review ed dose

- response values for all HAPs emitted by the source cate gory 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 estim ate, 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 determi ne that these pollutants are more or less potent than currently thought. We will re

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

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

Cancer assessment

The discussion of dose

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

- reviewed cancer potency factors used where EPA

- developed values are not yet available. To place this discussion in context, we provide a quote from the EPA’ s Guidelines for Carcinogen Risk Assessment [ 25 ] (herein referred to as Cancer Guidelines ). “The primary goal of EPA actions is protection of human health;29 accordingly, as an Agency policy, risk assessment procedures, including default options that are

use d 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

unav ailable.

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 Guidel ines 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 est imate 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). 12 In some circumstances, the true risk could be as low as zero; how ever, in other circumstances the risk could also be greater. 13 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. 14 EPA also uses the upper bou nd (rather than lower bound or central) estimates in its assessments, although it is noted that this approach can have limitations for some uses (e.g. priority setting, expected benefits analysis).

Such health risk assessments have associated uncertaintie s, 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 diff erent 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 characterizat ions present estimates demonstrating the impact on the assessment of alternative

12 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm).
13 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 maxim um likelihood estimates.
14 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 ris k

- 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 a ppears 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 ri sk). 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 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. 15 However, unless scientific support is available to show otherwise, EPA assumes that tumors in animals are relevant in humans, rega rdless 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 mo del 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 o ver 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 introduc e 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 t he correctness of scaled estimates.

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

15 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer stu dies 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 (5) Extrapola tion 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 a gent 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 pr eference 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 i n 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) app roach 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 b roader 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 ext rapolation approach as a baseline science

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

- specific determination. This approach is designed to not underestimate risk in the face of uncertainty and variability. EPA believes that li near 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 characterizat ion 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 wi thin 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 th resholds 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 rode nts). Note also that low dose linearity in risk can arise for reasons separate from population variability: due to the nature of a mode of action and additivity of a chemical’s effect on top of background chemical exposures and biological processes.

As n oted 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 to32 uncertainty in risk assessment (Subramaniam et al., 2 006) [ 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

s ensitive 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 e stimates 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 an d 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 g aps in the available information, uncertainty factors (UFs) are applied to derive reference values that are intended to be protective against appreciable risk of deleterious effects

. Uncertainty factors are commonly default values 16 e.g., factors of 10 or 3, used in the absence of compound

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

- specific information. When data are limited, more assumptions are needed and more default factors are used. Thus there ma y 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

16 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 o r 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 prot ecting 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 a nd Practices, EPA/100/B

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

- final.pdf.33 potent) because fewer default assumptions are needed. However, for some pollutants it is possible that r isks 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 uncerta inty 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 l ifetime. 17 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 downw ard 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 d ata 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 selec ted 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 us ed when the data do not support the use of a 10

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

1) Heterogeneity among humans is a key source of variability as well as uncertainty. Uncertainty related to human variation is considered in extrapolating doses from a subset or smaller

- sized population, often of one sex or of a narrow range of life stage s (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

17 See IRIS glossary34 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 est imate 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 app roach 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 le ading 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 consist ent with these differences. For a s pecific chemical, differences in species responses may be greater or less than this value

. Pharmacokinetic models are useful to examine species differences in pharmacokinetic processing and associated uncertainties; how ever, 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 toxi cokinetic 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

c omponents 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 su bchronic studies in rodents) or when such data are judged more appropriate for development of an RfC, an additional UF of 3 or 10

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

4) Toxicity data are typically limited as to the dose or exposure levels that have been tested in individual studies; in an animal study, for example, treatment groups may differ in exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use BMD analysis; however, this approach requires adequate quantitative results for a meaningful analysis, which is not always possible. Use of a NOAEL is the next preferred approach after BMD analysis in determining a POD for deriving a health effect referen ce 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 acc ount for the potential for deriving an underprotective RfD/RfC due to a data gap preventing complete characterization of the35 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 app lied.

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 compare d 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 cons idered 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 informati on, 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/m 3 , 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 E PA’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 f or 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 criter ia 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 environmenta l toxicants, the degree of uncertainty about the health effects of lead is quite low. It appears that some of these effects, particularly changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral development, may
occur at blood lead levels so low as to be essentially without a threshold. The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two meetings (07/08/1985 and 07/22/1985) and considered it inappropriate to develop an RfD for inorganic lead.” EPA’s IRIS assessment for Lead and compounds (inorganic) (CASRN 7439

- 92

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

.36 We note further that because of the multi

- pathway, multi

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

related multi

- pathway exposures from industrial sources like primary and secondary lead smelting operations. It also considered background lead exposures from oth er 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, alt hough 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 mar gin 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 ba sed on chemical

- specific or health effect

- specific information (e.g., simple irritation effects do not vary appreciably between human individuals, hence a value of 3 is typically used), or based on the purpose for the reference value (see the following par agraph). The UFs applied in acute reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. Additional adjustments are often applied to account for uncertainty in extrapolation from observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an acute reference value

a t another exposure duration (e.g., 1 hour).

Not all acute reference values are developed for the same purpose

, and care must be taken when interpreting the results of an acute assessment of human health effects relative to the reference value or values being exceeded. W here relevant to the estimated exposures, the lack of threshold values at different levels of severity should be factored into the risk characterization as potential uncertainties.37 

## 5. References

1 . US EPA, 2006. Emission Standards for Hazardous Air Pollutants

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

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

– MACT I Petroleum Refining Sources and Portland Cement Manufacturing

. EPA

- 4 52/R

- 09

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

3 . US EPA, 2010. SAB’s Response to EPA’s RTR Risk Assessment Methodologies. http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EPA

SAB

- 10

- 007

- unsigned.pdf

.
4 . US EPA, 2010. Memor andum from Dave Guinnup to the RTR Wood Furniture Docket (Docket ID Number EPA

- HQ

- OAR

- 2010

- 0786) , entitled, “EPA’s Actions in Response to Key Recommendations of the SAB Review of RTR Risk Assessment Methodologies”.

5

. US EPA, 2005. Revision to the Guidel ine on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 40 CFR Part 51. http://www.epa.gov/EP A

- AIR/2005/November/Day

- 09/a21627.htm

6 . US EPA, 2004. Users’ guide for the AMS/EPA regulatory model

– AERMOD. EPA

- 454/B

- 03

- 001

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

.
7

. Allen , D. , C

. Murphy, Y

. Kimura, W

. Vizuete, T

. Edgar

, H

. Jeffries, B

. - U

. Kim, M

. Webster

, and M

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

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

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

- 453

- K

- 04

- 001A. http://www.epa.gov/ttn/fera/risk_atra_vol1.html.

9

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

- Response Values (2/28/05). Office of Air Quality Planning and Standards. http://www.epa.gov/ttn/atw/toxsource/table1.pdf

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

11

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

.
12

. US Agency for Toxic Substances and Disease Registry. 2006. Minimum Risk Levels (MRLs) for Hazardous Substances

. http://www.atsdr.cdc.gov/mrls/index.html

.
13

. US EPA, 1994

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

- 90/066F. Office of Research and Development. Washington, DC: U.S.EPA.38 14

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

15

. CA Office of Environmental Health Hazard Assessme nt, 2005. Chronic Reference Exposure Levels Adopted by OEHHA as of December 2008. http://www.oehha.ca.gov/air/chronic_rels/AllChrels.html

.

16

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

. http://www.oehha.ca.gov/air/hot_spots/may2005tsd.html

.
17 . US EPA, 2005. Ta ble 2. Acute Dose

- Response Values for Screening Risk Assessments (6/02/2005). Office of Air Quality Planning and Standards. http://www.epa.gov/ttn/atw/toxsource/table2.pdf

18 . CA Office of Environmental Health Hazard Assessment, 2000. All Acute Reference Exposure Levels developed by OEHHA as of May 2000. http://www.oehha.ca.gov/air/acute_rels/allAcRELs.html

19 . American In dustrial Hygiene Association , 2009. Current AIHA ERPG Values. http://www.aiha.org/insideaiha/volunteergroups/Documents/ERP

- erpglevels.pdf

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

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

- B

- 00

- 002.

22 . US EPA, 2002. EPA’s Guide lines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by the Environmental Protection Agency. EPA Office of Environmental Information. EPA/260R

- 02

- 008

. http://www.epa.gov/quality/informationguidelines/documents/EPA_InfoQualityGuidelines.pdf

23

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

- 630

- R

98

- 002. http://www.epa.gov/NCEA/raf/pdfs/chem_mix/chemmix_1986.pdf

24

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

- 630/R

- 00

- 002. http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf

.
25

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

- 03/001F, 2005. http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=116283

26

. US EPA, 2000

. Risk Characterization Handbook. EPA 100

- B

- 00

- 002.

27

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

.
28

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

– 830.39 29

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

30

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

.

31

. US EPA. 2002. A Review of the Reference Dose and Reference Concentration Processes. http://www.epa.gov/ncea/iris/RFD_FINAL1.pdf

.