## Analysis Plan for Human Health and Ecological

Risk Assessment

For the Review of the Lead National Ambient Air Quality Standards

Draft May 31, 2006

U.S. EPA Office of Air Quality Planning and Standards Research Triangle Park, NCThis draft scope and methods plan has b een prepared by staff from the Ambient Standards Group, Office of Air Quality Planning a nd Standards, U.S. Environmental Protection Agency, in conjunction with ICF Internati onal (through Contract No. 68-D01-052). Any opinions, findings, conclusions , or recommendations are thos e of the authors and do not necessarily reflect the reviews of the EPA or ICF International. This document is being

circulated to obtain review a nd comment from the Clean Air Scientific Advisory Committee (CASAC) and the general public. Comments on th is document should be addressed to Ginger Tennant, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, C539-01, Research Triangle Park, North Ca rolina 27711 (email: tennant.ginger@epa.gov).DRAFT – May 31, 2006 i 

### Table of Contents

1.0 INTRODUCTION ............................................................................................................ 1 1.1 Purpose and Scope of the Assessments .......................................................................... 2 1.1.1 Air Quality Scenarios.............................................................................................. 3 1.1.2 Exposure and Human Health Risk Assessment...................................................... 3 1.1.3 Ecological Exposure an d Effects Analysis ............................................................. 4 1.2 Background Regarding the Previous Review ................................................................. 4 1.3 Organization of this Document....................................................................................... 5

2.0 CASE STUDY APPROACH............................................................................................ 6 2.1 Primary Lead Smelter ..................................................................................................... 7 2.2 Other Significant Stationary Sources.............................................................................. 8 2.3 Near-Roadway re-entrainment........................................................................................ 8

HUMAN EXPOSURE AND HEALTH RISK ASSESSMENT

3.0 OVERVIEW OF ANALYSIS PLAN ............................................................................ 10 3.1 Conceptual Model for Pb Human Health Risk Assessment ......................................... 10 3.2 General Approach ......................................................................................................... 13 3.3 Spatial Scale of Analysis .............................................................................................. 17 3.4 Uncertainty and Variabil ity Characterization ............................................................... 18

4.0 EXPOSURE ASSESSMENT ......................................................................................... 20 4.1 Estimating Media Concentrations................................................................................. 20

4.1.1 Ambient Air .......................................................................................................... 20

4.1.2. Indoor Air............................................................................................................ .. 22
4.1.3 Outdoor Soil/Dust ................................................................................................. 23

4.1.4 Indoor Soil/Dust.................................................................................................... 23 4.1.5 Estimating Inhalation Exposure Concentrations................................................... 24 4.1.6 Background (Non-Air-Related) Exposure ............................................................ 24 4.2 Study Population(s) and Potential Stratificati on Based on Socioeconomic Factors 25 4.3 Estimating Blood Lead ................................................................................................. 26

4.3.1 Children................................................................................................................. 27
4.3.2 Adults................................................................................................................... . 28
4.3.3 Blood Lead Metrics............................................................................................... 28 4.3.4 Probabilistic Analysis of Exposure Parameters .................................................... 29 4.3.5 Preparation of Models and Inputs, and Evaluation of Performance ..................... 30

5.0 EFFECTS ASSESSMENT............................................................................................. 32 5.1 Endpoints to be Evaluated ............................................................................................ 32 5.2 Models for Estimating Adverse Effects........................................................................ 32 5.2.1 IQ Reduction in Children...................................................................................... 32

5.2.2 Blood Pressure Effects in Adults .......................................................................... 33

5.2.3 Renal Effects in Adults ......................................................................................... 34DRAFT – May 31, 2006 ii 6.0 RISK ASSESSMENT ..................................................................................................... 36 6.1 Defining Modeled Populations in (Assigning Exposure Concentrations).............. 36

6.2 Risk Metrics for Children ............................................................................................. 37

6.3 Risk Metrics for Adults................................................................................................. 38

7.0 UNCERTAINTY AND VARIABILITY ASSESSMENT ........................................... 39 7.1 Integrated Approach for Consider ing Uncertainty and Variability .............................. 40 7.2 Risk and Exposure Metrics Generate d with the Integrated Approach.......................... 42

ECOLOGICAL RISK ASSESSMENT

8.0 OVERVIEW OF ANALYSIS PLAN ............................................................................ 44 8.1 Conceptual Model for Lead Ecological Risk Assessment............................................ 44 8.2 General Overview of Analysis...................................................................................... 46

9.0 ECOLOGICAL RISK ANALYSIS PLAN................................................................... 50 9.1 Tier 1: Screening Level Analysis.................................................................................. 50

9.1.1 Overview............................................................................................................... 50 9.1.2 Data Sources for Determining Media Concentrations.......................................... 50 9.1.3 Case Study Selection............................................................................................. 53

9.1.4 TRIM.FaTE Model ............................................................................................... 54

9.1.5 Determination of Poten tial Adverse Effects ......................................................... 54 9.2 Tier 2: Analysis of Sensitive Receptors........................................................................ 57

9.2.1 Overview............................................................................................................... 57 9.2.2 Estimation of Exposure for Sensitive Receptor(s)................................................ 57

10.0 REFERENCES................................................................................................................ 58
10.1 Human Health Risk Assessment................................................................................... 58 10.2 Ecological Risk Assessment ......................................................................................... 63DRAFT – May 31, 2006 1 1.0 INTRODUCTION

The U.S. Environmental Protection Agency (E PA) is presently conducting a review of the national ambient air quality sta ndards (NAAQS) for lead (Pb). Sections 108 and 109 of the Clean Air Act (Act) govern the establishment and periodic review of the NAAQS. These standards are established for po llutants that may reasonably be anticipated to endanger public health and welfare, and whose presence in the ambient air results from numerous or diverse mobile or stationary sources. The NAAQS are to be based on air quality criteria, which are to accurately reflect the latest sc ientific knowledge useful in in dicating the kind and extent of identifiable effects on public hea lth or welfare which may be expected from the presence of the pollutant in ambient air. The EPA Administrator is to promulga te and periodically review, at five-year intervals, “primary” (health -based) and “secondary” (welfare-based) 1 NAAQS for such pollutants. 2 Based on periodic reviews of the air quality criteria and standards, the Administrator

is to make revisions in the criteria and standard s, and promulgate any new standards, as may be appropriate. The Act also requires that an inde pendent scientific revi ew committee advise the Administrator as part of this NAAQS review pr ocess, a function now performed by the Clean Air Scientific Advisory Committee (CASAC).

EPA’s overall plan and schedule for this Pb NAAQS review is presented in the Plan for Review of the National Ambient Ai r Quality Standards for Lead (EPA, 2006a), which is available at: http://www.epa.gov/ttn/naaqs/sta ndards/pb/data/pbreviewplan_feb2006.pdf . That plan discusses the preparation of two key documents in the NAAQS review process: an Air Quality Criteria Document (AQCD) and a Staff Paper. The AQCD provides a critical assessment of the latest available scientific information upon which the NAAQS are to be based, and the Staff Paper evaluates the policy implications of the inform ation contained in the AQCD and presents staff

conclusions and identifies standa rd-setting options for the Administ rator to consider in reaching decisions on the NAAQS. 3 In conjunction with preparation of the Staff Paper, staff in EPA’s

1 Welfare effects, as defined in section 302(h ) of the Act include, but are not limited to, A effects on soils, water, crops, vegetation, man-made materials, animals, wild life, weather, visibility and climate, damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal comfort and well-being. @

2 Section 109(b)(1) [42 U.S.C. 7409] of the Act defines a primary standard as one “the attainment and maintenance of which in the judgment of the Administrator, based on su ch criteria and allowing an ad equate margin of safety, are requisite to protect the public health.” Section 109(b)(2 ) of the Act directs that a secondary standard is to A specify a level of air quality the attainment and maintenance of whic h, in the judgment of the Administrator, based on such criteria, is requisite to protect the public welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the ambient air. @

3 NAAQS decisions involve consideration of the four basic elements of a standard: indicator, averaging time, form, and level. The indicator defines the pollutant to be measured in the ambient air for the purpose of determining compliance with the standard. The averaging time defines the time period over which air quality measurements are to be obtained and averaged, considering evidence of eff ects associated with various time periods of exposure. The form of a standard defines the air quality statistic that is to be compared to the level of the standard (i.e., an ambient concentration of the indicator pollutant) in determining whether an area attains the standard. The form of the standard specifies the air quality measurements that are to be used for compliance purposes (e.g., the mean average over 90 days), the monitors from which the measurements are to be obtained (e.g., one or more population-oriented monitors in an area), and whether the statistic is to be averaged across multiple years.DRAFT – May 31, 2006 2 Office of Air Quality Planning and Standard s (OAQPS) conduct various policy-relevant assessments, including for this Pb NAAQS review , a quantitative human exposure analysis and a human health risk assessment, as well as an ecological exposure and effects analysis.

The purpose of this analysis plan is to outline the scope, approaches, and methods that staff is planning to use for the human health and ecological risk assessments. This plan also highlights key issues in the estimation of human health and ecological exposure and risks posed by Pb under existing air quality levels (“as is” exposur es and health risks), upon attainment of the

current Pb NAAQS, and upon meeting various alternative st andards in selected sample areas.

This analysis plan is intended to facilitate consultation with the CASAC, as well as public review, for the purpose of obtaining advice on the ove rall scope, approaches, and key issues in advance of the completion of such analyses and pres entation of results in the first draft of the Pb Staff Paper. The assessment approaches described in this plan are intended to build upon and extend the exposure and risk assessment approaches employed for the last review, 4 and on Agency experience with Pb expos ure and risk assessment since th at time, while also drawing from information presented in the Decembe r 2005 and May 2006 drafts of the AQCD for the current review (EPA 2005a, 2006b). The final asse ssments will reflect information in the final AQCD, which will be completed later this year.

1.1 Purpose and Scope of the Assessments

The focus of these assessments is the estimation of risk resulting from exposure to lead released into ambient air. To help inform future policy co nsiderations, this assessment will further attempt to distinguish between Pb exposures associat ed with currently emitting sources (e.g., primary and secondary Pb smelters) and historical (no longer operative) sources (e.g., emissions associated with leaded gasoline).

This plan recognizes several distin ctions with regard to the scope of the risk assessments for Pb. First, because exposure to atmospheric Pb particle s occurs not only via direct inhalation, but also via ingestion of deposited particles, the proble m being assessed is multimedia in nature, with exposure occurring via both the inhalation and in gestion routes. And, for human health exposure assessment of Pb, in contrast to ozone or partic ulate matter, the dose metric or biomarker most commonly used and associated with health eff ects information is internal (i.e., blood Pb). Additionally, the exposure duration of interest (i.e ., that influencing inte rnal dose pertinent to health effects of interest) may span months to potentially years, as doe s the time scale of the environmental processes influenc ing Pb deposition and fate.

There are a variety of situations involving exposure to Pb in am bient air (and Pb deposited from air to non-air media). These incl ude populations living in the vici nity of a primary Pb smelter, 5
those in areas where multiple significant point so urces are concentrated, and those in areas near roadways where anthropogenic Pb contained in roadway dust may become suspended in ambient air and undergo deposition and transport to non-air media. To the extent that the internal biomarker, blood Pb, is used in exposure assessmen t, the separation of c ontributions related to

4 The last Pb NAAQS review was comp leted in 1991 with no revisions made to the standards at that time.

5 Currently only one primary Pb smelter (in He rculaneum, MO) is operating in the U.S..DRAFT – May 31, 2006 3 new Pb emissions to ambient air from those refl ecting inhalation and inge stion of historically deposited ambient Pb poses a particular challenge.

1.1.1 Air Quality Scenarios

We intend to assess human exposure and health ri sks, and ecological risks for the following air quality scenarios.

• Current (baseline) air quality

• Attainment of the current NAAQS (i.e., any reduced ambient air concentrations necessary to meet the current NAAQS)

• Attainment of alternate NAAQS

As exposures associated with deposited Pb are of interest to this review , the assessment of each of these air quality scenarios wi ll include estimates of future Pb concentrations in other media (e.g., outdoor soil/dust) asso ciated with deposition.

In modeling scenarios for alternate NAAQSs, the 1990 Staff Paper, with concurrence from CASAC, relied on the assumption that the soil Pb level would remain constant for at least six years after a new standard would be implemented, while they projected cha nges in indoor air and dust concentrations with the changing air con centration. The plan presented here includes

consideration of the use of current methods for estimating the relationship of soil Pb levels to changes in air concentrations (i.e ., projection of outdoor soil/dust c oncentrations for future points in time resulting from changes in ambient ai r Pb reflecting specific NAAQS scenarios).

As feasible, we are consideri ng utilizing 2005 air quali ty data as the basis for the baseline scenario. We are investigating the available information on Pb emissions estimates and Pb

concentrations in other media to identify that most appropriate for use with 2005 air quality data.

1.1.2 Exposure and Human Health Risk Assessment

The planned Pb exposure analysis and health risk assessment address exposures (of multiple years) to Pb and associated health effects. The risk assessment will characterize health risks associated with Pb exposure pathways that in clude ambient air (i.e., "NAAQS-relevant") and put those risks into context with other Pb exposure pa thways that do not include ambient air, such as indoor paint and drinking wa ter (i.e., “background”).

This assessment will focus on health endpoints asso ciated with the range of exposures expected to most closely reflect current levels and for which there is adequate information to develop quantitative risk assessments. There are some health endpo ints, however, for which there currently is insufficient information to develop qu antitative risk estimates. We plan to discuss these additional health endpoints qual itatively in the Pb Staff Paper.DRAFT – May 31, 2006 4 The risk assessment is intended as a tool that, together with other info rmation on these health endpoints and other health e ffects evaluated in the Pb AQCD and Pb Staff Paper, can aid the Administrator in judging whethe r the current primary standard is adequate to protect public health with an adequate margin of safety, or wh ether revisions to the st andard are appropriate.

The goals of the Pb risk assessment are: (1) to characterize the risks, in cluding estimation of the potential magnitude of risk, associated with curr ent Pb levels and with attaining the current Pb NAAQS (as well as the reduced effects associated with attaining alternat ive Pb standards), and (2) to develop a better understa nding of the influence of vari ous inputs and assumptions on the

risk estimates

.

1.1.3 Ecological Exposure and Effects Analysis

The planned Pb ecological exposure and risk analyses address the risk to sensitive receptors from exposure to media concentrations resulting from am bient air deposition of Pb over time. In the first tier of this analysis, we will attempt to de scribe the current media Pb concentrations in the

environment and model what those concentrations ar e likely to be in the future. These future concentrations can then be compared to screening va lues for effect in soil, water, and sediment to focus the analysis further on those receptors that may be most sens itive to Pb. The second tier of the analysis will focus more detailed modeling on those receptors which seem most likely to be affected to determine intake values or body/tissue concentrations which can then be compared to

available concentration effects data.

1.2 Background Regarding the Previous Review

The current primary and secondary standard fo r Pb is 1.5 micrograms per cubic meter (μg/m 3 ) in total suspended particulate matter (TSP), as the maximum arithmetic mean averaged over a calendar quarter.

As part of its last review, EPA’s Office of Air Quality Pl anning and Standards (OAQPS) performed an exposure assessment to estimate bl ood Pb levels among populations exposed under the current NAAQS, as well as alternate Pb re gulations in the future (EPA 1989). For the different regulatory scenarios, the focus of the quantitative risk assessment was primarily on estimating the percentages of children with blood Pb levels above 10 and above 15 μg/dL. The

available data at that time indicated impaired neurobehavi oral function and development associated with levels of 10-15 ug/ d. The available data did not i ndicate a clear threshold at this range, rather suggesting a continuum of health risks down to the lowest levels measured, although effects below this range were increasingly difficult to detect and their significance more difficult to determine (EPA 1990). The last risk assessment also developed estimates of the percentages of middle-aged men with blood Pb levels above 10 and 12 μg/dL. The dose-response information on blood pressure changes available at th e time of the last review was less clear than the information on children, however, the same a pproximate range of blood Pb levels as for children were also considered for assessing risks among adult men.

The consideration of environmental effects durin g the last Pb NAAQS re view did not include a quantitative risk assessment (EPA 1990). Rather, critical aspects of th e evidence regarding PbDRAFT – May 31, 2006 5 effects on terrestrial and aquatic ecosystems that was presented in the AQCD (EPA 1986) were summarized to provide a basis for staff c onclusions and recommendations. Based on this information, EPA concluded that, under ambient Pb exposure conditions existing at the time, the primary standard provided adequate protection to the environment.

1.3 Organization of this Document

The remainder of this document is generally orga nized into four parts. The first includes a discussion of the case study approach (Section 2) , which, like the discussion of air quality scenarios in Section 1.1.1, pertains to both the human health and ecological risk assessments. Next is the plan for the human exposure and he alth risk assessment (Sections 3 through 7), followed by the plan for the ecological risk assess ment (Sections 8 through 9). References for both plans are provided in Section 10. While separate plans are described for the ecological and human health risk assessments, the assessments will draw, to a large extent, from common environmental characterizations (e .g., air quality scenarios with a ssociated media concentrations, and some case studies).DRAFT – May 31, 2006 6 2.0 CASE STUDY APPROACH

A case study approach will be used for both the human exposure and risk assessment (described in Sections 3 through 7) and the ecological risk assessment (descr ibed in Sections 8 and 9). Some case studies will be “shared” by the two assessments (e.g., with one characterization of environmental conditions serving the purposes of bot h assessments), while we plan to also have

some cases studies that are used only by either the human health or ecological risk assessment. A description of such a case study th at is particular to ecological risk assessment is discussed in Chapter 9. The discussion provided here is in tended to address case studies common to both types of assessment as well as those that may serve only the purposes of the human health

assessment.

The general strategy for assessm ent of each case study will consider characteristics of the source(s) of interest, as well as the availability of measurement and source characterization data. Selection of case study locations wi ll reflect the following factors:

• Variety of ambient air sources of Pb emissions : Pb emissions sources ranging from the primary Pb smelter, to other significant st ationary sources (e.g., metal refinishing and ceramics manufacturing) and emitters of histor ically deposited Pb (e.g., urban roadways) will be considered. In addition to the magnitude of Pb emissions, other attributes related

to emissions that influence exposure (e.g., effective stack he ight, particle size profiles, etc.) will be considered.

• Available monitoring da ta for ambient air: As discussed below, characterization of ambient air concentrations and soil deposition will rely on (a) monitoring data (if a case study location has sufficient monitor cove rage) and/or (b) dispersion modeling, supported by monitoring data, particularly those that are appropriate for sourceapportionment. Consequently, selection of case studies may favor locations which have local Pb monitoring data that can be sour ce-apportioned. In add ition to national Pb monitoring networks (see Chapter 3 of th e AQCD), state and local sources will be

considered.

• Available soil monitoring data and, for purpos es of the human health assessment, biomonitoring (blood Pb) data : When available, measurements of Pb soil concentrations for a study area together with blood Pb le vels for the residential population can considerably increase confidence levels associ ated with characterizing human health risk

associated with baseline conditions. Theref ore, locations where relevant biomonitoring

and/or collection of Pb soil concentrations has been conducted will be favored in the selection of case study locations. Note, that such data heavily influenced by sources unrelated to air (e.g., Pb paint) will be less useful for our purposes.

• For purposes of the human health assessment, socioeconomic status (SES) and other demographic attributes related to Pb exposure and risk: If specific demographic and/or SES factors are linked quantitatively to incr eased exposure or concentration-response for Pb effects being modeled, then those factors may also be considered in selecting case study locations. Potential factors currently under consideration include: (a) generalDRAFT – May 31, 2006 7 population density (would favor locations w ith higher population densities since will have larger modeled population and more fully characterized population risk distributions), (b) housing age (for background exposure levels to Pb paint), and (c) SES (race and income) to inform estimation of risk characterist ics (e.g., baseline blood pressure levels, which could result in gr eater blood pressure impacts following Pb exposure).

• Available water quality and sediment monitori ng data, for purposes of ecological risk

assessment: Locations which include the presen ce of a surface water body for which

measurements of Pb are available for water a nd sediment will be favored in selection of case study locations.

We also plan to select case st udies that reflect particular ty pes of ambient Pb emissions and exposure situations, such as the following:

• Primary lead smelter : This case study would be expected to reflect Pb contamination of the surrounding study area which is dominated by emissions from this facility, with other sources being of relati vely lesser importance.

• Other significant stationary sources : This category of case study would reflect a location impacted by Pb emissions from multiple point and area sources, with emissions from any particular source being significantly lower th an those for the primary Pb smelter, but where combined impacts from these multiple sources (including re-entrainment of historically deposited near-roadway Pb) may be significant.

• Near roadway re-entrainment : This category of case study reflects environmental Pb contamination dominated by re-emission of histor ically deposited Pb near roadways (i.e., re-entrainment of near-roadway Pb). This may include considerati on of a general urban scenario with heavy road traffic, historic dust (e.g., reflecting old housing sources) and proximal residential areas, but without large local sources of industrial Pb emissions.

It is anticipated that one or mo re locations will be modeled fo r each of the above categories of case studies described in the following secti on, the final number for each category will reflect

consideration of a number of factors including a desire to characterize po pulation risk variability

associated with these situations within the US . The following subsection focuses on distinctions among these categories with regard to the appr oaches we intend to employ to characterize exposure and risk.

2.1 Primary Lead Smelter

There is presently only one primary Pb smelter operating in the U.S. Source characterization information is available from past regulatory analyses, EPA Regional Office permit files, and state/local offices. The Staff anticipates that th ere will be more available site-specific ambient air, soil, and blood Pb level measurement data fo r areas near this facility than for any other facility in the U.S. As a result, it is expect ed that measurement data (e.g., ambient air monitoringDRAFT – May 31, 2006 8 data and soil sampling data) will play a majo r role in characterizing exposures and risks associated with this facility.

The primary Pb smelter case study is anticipated to ha ve the most comprehensive site-specific blood Pb monitoring data among the three categorie s of case studies, a feat ure relevant to the human health assessment. This may support the s ite-specific characteriza tion of blood Pb levels for key target populations. These data will also be useful in evaluating performance of the blood Pb modeling approach applied to this case study location. For example, we may be able to compare model predicted blood Pb distributions for the study area to site-specific blood Pb data in order to (a) evaluate the overall representati veness of the blood Pb modeling approach and (b) guide refinement of that modeli ng approach as appropriate.

2.2 Other Significant Stationary Sources

As described in the Pb AQCD (EPA 2006b), there ar e numerous stationary sources of Pb to air, including secondary Pb smelters, metal finish ing facilities, and cer amic manufacturers. Assessments for this category of case study will rely on locations and emissions characterization

information for these facilities from EPA’s Nati onal Emissions Inventory (NEI), supplemented by available data from Regional Office permit fi les, emissions standards (MACT and residual risk) files, and state/local offices. This case study category will addre ss co-location of these types of facilities to th e extent feasible. We anticipate that air, soil and blood Pb measurements in locations for this category of case studies may be limited relative to site-specific data available for the primary Pb smelter, necessitating a greater reliance on modeling to estimate environmental concentrations (as de scribed in sections 4.1.2 and 4.1.3).

Because this case study category is expected to have limited (or no) site-specific blood Pb monitoring data, the degree to which blood Pb m odeling performance can be evaluated will be limited. Therefore, more general regional or na tional-scale blood Pb data obtained form NHANES (possibly differentiated by relevant SES factors) may be used for performance evaluation. Note, however, that locations for wh ich local blood Pb data are available will be given preference in selection.

2.3 Near-Roadway re-entrainment

The use of leaded gasoline in the U.S. resulted in accumulation of Pb in soils near roadways and the resuspension of this Pb may ha ve the potential to contribute si gnificantly to concentrations of airborne Pb (Harris et al. 2005) . Besides historic combusti on, there are othe r auto-related releases of Pb near roadways (see EPA 2006b). Measurements of concentrations near some roadways have been reported (Filippelli et al . 2005; Teichman et al. 1993). This case study category is intended to provide some characteriza tion of associated exposur es and risks for this type of air emissions source. Consequently, sele ction of locations will favor areas that are not significantly influenced by ot her types of air sources.

To develop estimates of associated media concentr ations for this case study category, we intend to consider the available literature, as well as potential use of m odeling tools, ambient measurements and source apportionment meth ods (discussed in Section 4.1.1 and 4.1.2). TheDRAFT – May 31, 2006 9 use of multiple methods may inform characterization of uncertainty in estimated concentrations. As with the last case study cat egory discussed, locations for wh ich blood Pb measurements are

available will be favored. As available such da ta will be used, in preference to NHANES data, and in conjunction with socioeconomic and de mographics data, to evaluate blood Pb model performance.DRAFT – May 31, 2006 10 

## HUMAN EXPOSURE AND HEALTH RISK ASSESSMENT

3.0 OVERVIEW OF ANALYSIS PLAN

This risk assessment is designed to estimate various health effects associated with current, and some alternate, exposures to Pb emitted into ambient air in the U.S. This includes new emissions

of Pb into ambient air as well as emissions of historically deposited Pb. As recognized in Section 1.1, the exposure assessment will not only includ e inhalation but also th e ingestion route (e.g., incidental ingestion of ambient Pb deposited to residential soils a nd that transported indoors). In addition to ambient air associated Pb, exposure to Pb from other (i.e., “background”) sources will be assessed. “Background" for th e purposes of this assessment refers to sources, pathways and exposures that do not involve ambient air (e.g., Pb in drinking water and Pb paint-related dust). As described in Section 2.0, we will use a case study approach wherein a small set of locations intended to illustrate the variety of exposure conditions associated with ambient air associated Pb

that occur in the U.S. This assessment will focu s on health endpoints, associated with the range of exposures expected to most closely reflect current levels, an d for which there is adequate information to develop quantitative risk assessments. For example, we intend to assess neurological effects in young child ren, and we are also considering other endpoints for adults. In the risk characterization, we will characterize th e magnitude and distribution of Pb-related risk within the case study populations.

The risk assessment will be completed in two stages . The first stage, referred to as the Pilot, will be completed by the end of this year and will be described in the first draft of the exposure/risk report and summarized in the first draft of th e Staff Paper, both of which will undergo CASAC review. We anticipate including 3-4 case studies for the Pilot, with a primary focus being on testing and refining the assessment approaches . The methodology for the Full-Scale assessment will build on our experience with and CASAC comments on the Pilot assessment. The FullScale risk assessment will be described in the se cond draft exposure/risk report, which we intend to complete in mid-2007. We anticipate expandin g the number of case studies for the Full-Scale analysis (e.g., 5-10).

The following subsections provide an overview of the analysis plan. Section 3.1 presents the conceptual model for Pb human health risk as sessment, describing both the elements generally pertinent to the assessment of health risk associat ed with Pb, and identifying those elements to be

included and a focus of this asse ssment. Section 3.2 expands on the elements of this assessment, although with a grouping of conceptual model elem ents that reflects basic steps of the general approach for this assessment. Section 3.3 desc ribes issues related to spatial scale and the definition of the GIS-based spatial template us ed as the basis for modeling in the analysis. Lastly, Section 3.4 generally describes how we will characterize uncertainty and variability associated with those steps.

3.1 Conceptual Model for Pb Hu man Health Risk Assessment

This section presents the conceptual model (Fig ure 3-1) intended to illustrate the elements pertinent to assessment of public health risks associated with environmental Pb exposures.DRAFT – May 31, 2006 11 For purposes of this risk assessment, “background” is intended to refer to sources of, exposures to, etc, Pb associated with pathways that do not involve ambient air. Included among these would be pathways associated with i ndoor Pb paint, Pb in drinking wa ter, Pb in food, etc. Background elements are shown in Figure 3-1 in non-bold (regular) type. As s hown in Figure 3-1, the

assessment will include contributions from all so urces. We intend, however, to characterize risks

associated with ambient air associated sources separately from background sources.

Sources: The focus of this review is on sources to ambient air (e.g., current sources of new Pb emissions and re-emission of historically depos ited Pb). Other (“background”) sources of environmental Pb that are significant contribut ors to population Pb expos ures (e.g., indoor Pb paint) will also be considered in estimating bl ood Pb levels associated with all sources.

Pathways: Figure 3-1 is intended to generally illu strate the many pathways by which Pb emitted

into the environment becomes available for human exposure. Those not passing through ambient air are considered “background” for the purposes of this assessment. For those case studies in which modeling is employed for exposure assessment (in lieu of adequate measurements), those pathways shown with white background in Fi gure 3-1 will not be explicitly modeled.

Routes: The ingestion and inhalation routes are considered the primary routes of human exposures to environmental Pb, a nd the ingestion route (e.g., inclusi on of incidental ingestion) is expected to be the more significant. Both routes will be included in this assessment.

Exposed Populations: The Pb exposed populations can be characterized and subset based on a variety of characteristics. Figur e 3-1 identifies groups based primar ily on age or lifestage, which

has an influence on behaviors that can influence e xposure or susceptibility. It is recognized that more specific factors (e.g., calcium deficiency, to name one) also influence susceptibility. Such characteristics will be addressed as feasible in the assessment, given limitations of the currently available information (e.g., differing exposure/do se – response functions). Where quantitative assessment of particular subgroups (e.g., children w ith calcium deficiencies) may not be feasible, qualitative characterization will be important. In this assessment, we intend to quantify risks to young children, and are considering also quantifying risks fo r adult populations.

Internal Disposition: While Pb is distribut ed throughout the body, bone is an established site of internal accumulation of Pb, while blood is an es tablished internal dose metric for purposes of both exposure and risk assessment. This risk assessment will rely primarily on blood Pb with corresponding dose-response functions. The tools employed will recognize the role of bone as a reservoir with the potential to act as source and storage site.

Endpoints: Figure 3-1 generally identif ies the wide variety of h ealth endpoints recognized in the draft AQCD (EPA, 2006b) as associated with Pb exposures. As mentioned previously, the endpoints of interest for this assessment are t hose associated with the range of exposures expected to most closely reflect current levels, and for which there is adequate information to develop quantitative risk asse ssments. Recognizing that, the primary endpoint we currently

intend to include in the assessmen t (indicated in Figure 3-1 via bol d outlined box) is neurological effects in children, while we are considering the additional endpoints of cardiovascular and renal

effects in adults.DRAFT – May 31, 2006 12 Figure 3-1. Conceptual Model for Pb Human Health Risk Assessment

Soil/Dust -

Outdoors 2 Other

Miscellaneous

SourcesSOURCES

Ambient Air Stationary and
Mobile SourcesPATHWAYS

Inhalation IngestionROUTES

Adult

Men Adult

WomenPOPULATIONS

Central

Nervous System

(for children)ENDPOINTSRISK METRICS Lead Paint -

Indoors Drinking

Water Diet

Air - Indoor Soil/Dust -

Outdoors REENTRAINMENT

Soil/Dust -

Indoors Soil/Dust -
Outdoors

Air - Indoor Soil/Dust -

Outdoors

Soil/Dust -

Indoors Air - Indoor

Soil/Dust -

Indoors Soil/Dust -
Outdoors Soil/Dust - Indoors

Pregnant women Young
Children Older

children Post-menopausal

womenINTERNALDISPOSITION

Bone Blood Other

Other (genotoxic effects/cancers, reproductive/developmental effects, blood effects, immune system effects, bone and teeth effects) Cardiovascular System

(for adults)

Intelligence Quotient (IQ)

Decrement Blood Pressure Change Hypertension

Incidence Other DEPOSITION DEPOSITION DEPOSITION

DEPOSITION

NOTES

1 Components with gray background and solid borders will be included in this assessment. Components depicted in gray but without borders are being considered for inclusion in the assessment. Further, a distinction is made between components linked to exposure pathways involving ambient ai r (shown in bold) and components involving other pathways (i.e., background).

2 Includes contributions of hi storical sources, including (but not limited to) emissions from the use of leaded gasoline,
historical emissions from stationar y sources, and exterior leaded paints.EXPOSUREEFFECTSRISK Kidney

(for adults)

Creatinine
Clearance

DecrementDRAFT – May 31, 2006 13 Metrics: Figure 3-1 generally recogni zes that there are many metric s that might be considered for risk assessment. Recognizing the need for the metrics used in this assessment to have sufficient support for use in quantifying population health risk, we currently intend to use IQ decrement in children, and are c onsidering the inclusion of blood pressure and kidney effects in

adults. We will use these metrics to characte rize the magnitude and distribution of Pb-related risk within the exposed populations.

3.2 General Approach

This section presents the genera l approach for the exposure and risk assessment methodology for generation of population-level risk estimates.

The specific approach used to estimate exposure a nd risk for individual case studies is expected to differ depending on the amount of site-specific monitoring data available for characterizing Pb

concentrations in ambient air and outdoor soil/dust. For example, where there is adequate monitoring coverage, we will rely primarily on meas urements to establish baseline conditions for both ambient air and soil Pb concentrations. By contrast, for those case study sites lacking in ambient monitoring data, we will depend primarily on modeling to characterize baseline soil and ambient air Pb conditions. In all cases, future pr ojections of soil Pb concentrations (and related

media concentrations such as indoor dust), designed to reflec t conditions under various NAAQS scenarios, will rely on modeling.

The general approach for a case study location at which modeling is used to characterize baseline

ambient air Pb concentrations or soil Pb con centrations is illustrated in Figure 3-2. This approach is divided into four co mponents, the first three of whic h comprise steps in the exposure assessment (intended to cover the components indicated in bold in the conceptual model, Figure 3-1) and the last of which repres ents the effects and risk assessme nt. The four components are: (a) estimating ambient air concen trations and deposition to soil, (b) estimating soil and indoor dust concentrations, (c) estimating blood Pb levels and (d) health effects incidence estimation (risk characterization). Each of these components is discussed below. This discussion also highlights ways in which the fully-modeled appro ach presented in Figure 3-2 would be modified

for those case studies where there are sufficient am bient air and/or soil measurements to support

characterization of baseline conditi ons without or with lesser use of modeling. The approach for characterizing uncertainty and variability associat ed with the steps in th is approach, while not illustrated in Figure 3-2, is described in section 3.4.

Estimating Ambient Air Concentrations and Deposition to Soil

We intend to rely on air monitoring data, wher e available, in preference to modeling, to

characterize baseline conditions across the study area. For those case studies lacking sufficient ambient air monitoring data to characterize base line conditions across th e study area, we intend to model the dispersion of air emissions in or der to characterize the spatial and temporal

distribution of ambient air c oncentrations across the study ar ea, and also to estimate Pb deposition to soil. If a case study area has so me ambient monitors (but not enough to fully characterize baseline conditions), then we w ill consider source-apportioning those monitoring data and using the results to performance-evaluate the ambien t air concentration surfaceDRAFT – May 31, 2006 14 Exhibit 3-2. Overview of Analys is Approach relying on Modeling. Where measurement data are available and sufficient, they will be used in preference to, or in combination with, modeling steps shown here to characterize baseline conditions. The lower two boxes are to be implemented fo r each population and endpoint assessed.

Dispersion modeling for
fresh emissions sources Ambient air monitoring data

Source apportionment

modeling (of monitored levels)

Ambient air
concentrations for study area Deposition rates across study area Estimating

re-entrained lead (near road way) Emissions from

current sources

Estimating

residential soil

concentrations Characterizing Ambient Air Levels and Deposition to Soil

Estimating indoor

residential lead

dust levels Background

residential soil

concentrations Background

indoor dust

concentrations (paint-sourced)

Indoor residential dust

concentrations Outdoor

residential soil

concentrations

Background levels for

additional lead

exposures (drinking water, diet) Estimating Soil and

Indoor Dust
Concentrations

Calibration of

blood lead level models using
NHANES data

and additional

data sources Estimating Blood Lead Levels

Concentration-response functions for key endpoints:

• IQ (child) • Blood pressure (adults) Health effects

incidence estimation Health Effects

Incidence Estimation (Risk Characterization) EPA’s 1999 NATA
National Scale

Assessment Used to

evaluate performance of

source modeling and

characterize uncertainty Soil profiles

Pharmacokinetic

models Empirical slope

models Blood lead level modeling

(children and adults)

Pathway-specific exposure rates (where relevant) Ambient air

concentrations

(see above)

Demographics

Key: Input dataset Modeling step Modeled output Health effects incidence estimates for each endpoint/study population

combinationESTIMATING MEDIA CONCENTRATIONSEXPOSURE ASSESSMENTRISK CHARACTERIZATION Dispersion modeling for
fresh emissions sources Ambient air monitoring data

Source apportionment

modeling (of monitored levels)

Ambient air
concentrations for study area Deposition rates across study area Estimating

re-entrained lead (near road way) Emissions from

current sources

Estimating

residential soil

concentrations Characterizing Ambient Air Levels and Deposition to Soil

Estimating indoor

residential lead

dust levels Background

residential soil

concentrations Background

indoor dust

concentrations (paint-sourced)

Indoor residential dust

concentrations Outdoor

residential soil

concentrations

Background levels for

additional lead

exposures (drinking water, diet) Estimating Soil and

Indoor Dust
Concentrations

Calibration of

blood lead level models using
NHANES data

and additional

data sources Estimating Blood Lead Levels

Concentration-response functions for key endpoints:

• IQ (child) • Blood pressure (adults) Health effects

incidence estimation Health Effects

Incidence Estimation (Risk Characterization) EPA’s 1999 NATA
National Scale

Assessment Used to

evaluate performance of

source modeling and

characterize uncertainty Soil profiles

Pharmacokinetic

models Empirical slope

models Blood lead level modeling

(children and adults)

Pathway-specific exposure rates (where relevant) Ambient air

concentrations

(see above)

Demographics

Key: Input dataset Modeling step Modeled output Health effects incidence estimates for each endpoint/study population

combinationESTIMATING MEDIA CONCENTRATIONSEXPOSURE ASSESSMENTRISK CHARACTERIZATION Exhibit 3-2. Overview of Analys is Approach relying on Modeling. Where measurement data are available and sufficient, they will be used in preference to, or in combination with, modeling steps shown here to characterize baseline conditions. The lower two boxes are to be implemented fo r each population and endpoint assessed.

Dispersion modeling for
fresh emissions sources Ambient air monitoring data

Source apportionment

modeling (of monitored levels)

Ambient air
concentrations for study area Deposition rates across study area Estimating

re-entrained lead (near road way) Emissions from

current sources

Estimating

residential soil

concentrations Characterizing Ambient Air Levels and Deposition to Soil

Estimating indoor

residential lead

dust levels Background

residential soil

concentrations Background

indoor dust

concentrations (paint-sourced)

Indoor residential dust

concentrations Outdoor

residential soil

concentrations

Background levels for

additional lead

exposures (drinking water, diet) Estimating Soil and

Indoor Dust
Concentrations

Calibration of

blood lead level models using
NHANES data

and additional

data sources Estimating Blood Lead Levels

Concentration-response functions for key endpoints:

• IQ (child) • Blood pressure (adults) Health effects

incidence estimation Health Effects

Incidence Estimation (Risk Characterization) EPA’s 1999 NATA
National Scale

Assessment Used to

evaluate performance of

source modeling and

characterize uncertainty Soil profiles

Pharmacokinetic

models Empirical slope

models Blood lead level modeling

(children and adults)

Pathway-specific exposure rates (where relevant) Ambient air

concentrations

(see above)

Demographics

Key: Input dataset Modeling step Modeled output Health effects incidence estimates for each endpoint/study population

combinationESTIMATING MEDIA CONCENTRATIONSEXPOSURE ASSESSMENTRISK CHARACTERIZATION Dispersion modeling for
fresh emissions sources Ambient air monitoring data

Source apportionment

modeling (of monitored levels)

Ambient air
concentrations for study area Deposition rates across study area Estimating

re-entrained lead (near road way) Emissions from

current sources

Estimating

residential soil

concentrations Characterizing Ambient Air Levels and Deposition to Soil

Estimating indoor

residential lead

dust levels Background

residential soil

concentrations Background

indoor dust

concentrations (paint-sourced)

Indoor residential dust

concentrations Outdoor

residential soil

concentrations

Background levels for

additional lead

exposures (drinking water, diet) Estimating Soil and

Indoor Dust
Concentrations

Calibration of

blood lead level models using
NHANES data

and additional

data sources Estimating Blood Lead Levels

Concentration-response functions for key endpoints:

• IQ (child) • Blood pressure (adults) Health effects

incidence estimation Health Effects

Incidence Estimation (Risk Characterization) EPA’s 1999 NATA
National Scale

Assessment Used to

evaluate performance of

source modeling and

characterize uncertainty Soil profiles

Pharmacokinetic

models Empirical slope

models Blood lead level modeling

(children and adults)

Pathway-specific exposure rates (where relevant) Ambient air

concentrations

(see above)

Demographics

Key: Input dataset Modeling step Modeled output Health effects incidence estimates for each endpoint/study population

combinationESTIMATING MEDIA CONCENTRATIONSEXPOSURE ASSESSMENTRISK CHARACTERIZATIONDRAFT – May 31, 2006 15 projected by modeling. If the modeled ambient ai r surface differs significantly from the sourceapportioned monitor values (in the vicinity of the monitor) then key elements of the dispersion modeling, including emissions profiles for modeled sources, would be reevaluated in order to reduce uncertainty and improve model performance.

For those case studies with sufficient monitor coverage for the st udy area, we are considering using those monitor data to char acterize the spatial and temporal distribution of ambient air Pb concentrations across the study area without relying on dispersion modeling. We are additionally considering the use of source-apportionment to dete rmine which fraction of each

monitored concentration is associated with a particular source groupi ng in order to obtain separate estimates of ambient air concentration for different source groupi ngs of interest. In these case study locations, modeling will still be required to project deposition to soil for

characterization of future conditions.

Estimating Soil and Indoor Dust Concentrations

We intend to rely on soil measurements, wher e available, in preference to modeling, to

characterize baseline conditions across the study area. For thos e case study locations in areas impacted by both emissions of new Pb as well as historically depos ited Pb arising from combustion of leaded fuels, but lacking sufficient soil concentration da ta to characterize baselineconditions, a hybrid modeling approach which uses background soil Pb data together with multimedia fate and transport modeling is being considered to characterize baseline soil Pb concentrations. This hybrid approach has two steps:

• Estimation of soil concentrations associated with historically depos ited Pb associated

with historical leaded automobile emissions: Soil Pb monitoring data from locations similar to the case study area but not imp acted by significant ongoing Pb emissions will be used to represent Pb concentrations from older (primarily auto emissions) deposition.

• Deposition modeling and applicatio n of a soil reservoir model to estimate soil Pb for current/ongoing emissions sources: The contribution of ongoing local point source and

mobile emissions to soil concentrations w ill be modeled by using the deposition rates from ongoing (current) air emissions, to predict loading to soil and translating those to soil concentrations with a simple reservoir model.

The combination of these two estimates is intend ed to produce an estimate of baseline soil Pb

concentrations that reflects both older (e.g., primarily automob ile-related deposition) and more current loading related to local sources of interest (e.g., secondar y smelters). As with ambient air, future soil concentrations will be projected us ing modeling to reflect any changes in soil Pb concentrations resulting from deposition of Pb under alternate NAAQS scenarios.

We are considering estimating indoor dust concentr ations attributable to outdoor air and soil/dust based on empirical relationships of Pb concentrations in outdoor soil and ambient air to indoor

dust concentrations. For reside nces impacted by Pb paint, this approach would estimate total indoor dust concentrations by comb ining these modeled concentrati ons with existing estimates ofDRAFT – May 31, 2006 16 Pb concentrations in indoor dust impacted by Pb-b ased paint, taking into account the variability

in indoor dust concentrations base d on the presence of Pb paint.

Characterization of baseline conditio ns for locations with sufficient soil monitoring data will be simpler than the modeled approach described above, since these monitor data reflect the aggregate impact of older and ongoing (current) deposition, and can be used directly. For projection of future soil concentrations for th ese locations, however, we will still rely on modeling to project changes in so il concentrations associated with reductions in ambient air concentrations and deposition of Pb.

Estimating Blood Pb Levels

Exposures to media containing Pb for the populati ons of interest will be quantified using blood Pb concentration as the exposure metric. For example, we plan to use the IEUBK model to estimate blood levels for children less than 7 year s of age. Other models being considered are described in Section 4.3. Exposur e to air, outdoor soil, and in door dust will be estimated from

the concentrations described above

. These exposures, along with es timates of exposures from other pathways (e.g., drinking water, diet), will be provided as inputs to the blood Pb models. Any and all models used will be subjected to perf ormance evaluation to ensure that: (1) they are being applied appropriately; (2) assumptions related to Pb inta ke and uptake (where they are calculated) are consistent across models; and (3 ) the models, as applied, produce estimated blood Pb levels that are reasonably consistent with their underlying ba sis and previous evaluations, and with available data sets (e.g., NHANES).

In modeling exposure for children, we are planni ng to use IEUBK as typically applied in the regulatory context, including the use of a GSD to provide coverage for inter-individual variability in both exposure and bi okinetics. However, we are al so considering an alternative approach of using probabilistic simulation to model inter-individual variability in exposure outside of IEUBK and then running those simula ted individuals through IEUBK in batch-mode. Inter-individual variability in biokinetics might then be addres sed using a purpose-defined GSD. A similar approach is being considered for the adult age group, should ri sk for that group be

modeled.

Estimating Health Effects Incidence (Risk Characterization)

From the estimated blood Pb levels, we will esti mate distributions of health effects for the populations of interest using con centration-response functions for the chosen endpoints. Risk estimates will be generated by applying the blood-P b adverse effect relationships to the blood Pb

distributions derived as described above. For the Pilot anal ysis, we are planning to model IQ loss for children and are considering the option of modeling of blood pressu re changes and renal effects for adults. In the case of children (IQ loss), we are planning to use concentrationresponse functions derived from the Lanphear et al. (2005) study, incl uding nonlinear models that predict higher slopes at lower blood Pb le vels. While Lanphear ( 2005) represents our preferred study, we are also cons idering including a range of st udies to provide coverage for uncertainty in the modeling of this endpoint. For adults, we are considering the use of the Nawrot et al. (2002) meta-analy sis as the basis for a concentr ation-response function for blood-DRAFT – May 31, 2006 17 pressure effects, while for renal effects we have not identified a si ngle preferred study and instead, are considering a range of studies.

The focus of this analysis is on characteri zing population-level risk for modeled study populations (i.e., characterization the distribution of risk leve ls across a particular study population). Once these distributions have been developed, we then plan to generate several types of population-level risk metrics from these di stributions including: (a) the degree of risk (e.g., IQ loss) experienced by individuals at vari ous percentiles on the ri sk distribution and (b)

the number of simulated individu als falling within specific risk ranges (e.g., number of children predicted to have 1-2 IQ point lo ss). We are planning to generate risk metrics that will specify the degree of risk attributable to NAAQS-relevant emissions sources at a particular case study location, relative to risk associated with back ground exposures. As noted earlier, for the Pilot analysis, we are also planning to generate ri sk metrics for both current conditions and any

reductions in air concentrations associated with attainment of the current NAAQS standard.

Estimation of risk reductions asso ciated with attaining alternativ e standards would be reserved for the Full Scale risk analysis.

3.3 Spatial Scale of Analysis

To capture the spatial variabili ty in exposure media concentr ations, a “GIS-based spatial template” will be created for each case study lo cation that defines the outer boundary of the study area and determines the level of refinement to be incorporated in subdividing the area into discrete spatial units.

Defining the Study Area Boundary

The overall size of the area to be modeled will depend on the specific attributes of each case study (see Section 2.0). For case studies wher e transport of Pb through the air beyond the immediate area surrounding the faci lity of interest, the study ar ea size may extend out to 50 km from the primary source. For a case study that includes multiple contribu ting sources, the study area size may extend up to 50 km from the source with the highest emissions. A maximum radius of 50 km is being considered because ap plication of Gaussian plume dispersion models (e.g., AERMOD, ISC) are not recommended beyond this distance. This radius is expected to capture the area over which the impact of th e source is distinguishable from background concentrations. A smaller radius may be select ed for case studies with lower emissions levels where estimated media concentrations are close to background.

Refinement within the Study Area

The level of spatial refinement within the mode ling region will depend on the available data and modeling methods that are employed. As appropr iate for the case study type (see Section 2.0), media concentrations will be estimated for spatial units that adequately re present the observed or expected variability in media concentrations. C onsequently, the level of spatial refinement may vary by media.DRAFT – May 31, 2006 18 For case studies employing air dispersion mode ling, modeled air concentration and deposition estimates will be generated at grid points designe d to capture variability in media concentrations across the study area (taking into ac count the locations of any monito ring stations present in the case study area). The resolution of the grid may vary depending on the case study and within a case study (e.g., dense near the source and less dens e with greater distance from the source). The grid for each case study will also be designed taking into account locations of measured air

concentrations and soil concentra tions in order to optimize comparisons with and utilization of monitoring data. At a minimum, the grid will be designed to allow for the calculation of media

concentrations at the resolution of the demographic data being used.

To select the appropriate resolution for demogr aphic data, the staff considered the expected spatial variability in media concen trations and the availability of demographic data at different

resolutions from the U.S. Census. Based on thes e considerations, U.S. Census block groups are being considered as the demographic spatia l units because they provide the required demographic information and sufficient resoluti on to capture spatial variability in media concentrations (including “hot spots”). In ma ny locations, Census tracts would be too coarse to capture spatial variability, and Census blocks, while providing even more resolution than block groups, offer only limited demographic data and would require increased computational resources (see Section 4.2 for add itional detail on study populations).

3.4 Uncertainty and Variability Characterization

For the Pilot analysis, we are considering the us e of an integrated approach for addressing both uncertainty and variability, which will comb ine probabilistic simulation (for addressing exposure-related variability) with sensitivity analysis techniques (for addressing parameter and model uncertainty). At this stage, it is not possible to address uncertainty using probabilistic simulation due to data limitations, necessitating th e need for a sensitivity-based approach that characterizes the range of risk results refl ecting specific sources of model and parameter uncertainty.

The integrated approach will use a modeling options "tree" to represent model uncertainty, with each branch on the tree representing a distinct co mbination of modeling options for the analysis (a number of the modeling steps in the analysis are subject to model uncertainty, leading to multiple modeling options at those nodes). For th e Pilot analysis, we are considering the option of identifying the high-bound (M ax), low-bound (Min) and central tendency modeling branches, these reflecting, respectively, the combination of model options that produce the highest risk, lowest risk and central-tendency risk for a given age-group/endpoint combination. We would then examine those three modeling branches (Ma x, Min and central tendency) in greater detail, by considering (a) parameter uncer tainty through the application of various sensitivity analysis techniques and (b) exposure parameter-related variability using pr obabilistic simulation.

This integrated approach, will generate risk metrics that reflect the distribution of risk across modeled age group/endpoint combinations result ing from exposure-related variability. With regard to uncertainty, this approach will show th e range of potential risk results resulting from uncertainty in key model steps and input parame ters. However, as noted above, we will not beDRAFT – May 31, 2006 19 producing confidence intervals fo r population risk distributions since uncertainty is being addressed with sensitivity analysis technique s and not through probabi listic simulation.

An expanded discussion of methods used in the Pilot Analysis to address both variability and uncertainty is includ ed in Section 7.0.DRAFT – May 31, 2006 20 4.0 EXPOSURE ASSESSMENT

4.1 Estimating Media Concentrations

This section describes the approaches we inte nd to use and others we are considering for estimating Pb concentrations in the exposure media of interest for this an alysis. In order to model the exposure pathways outlined in Figure 3- 1, we need to estimate Pb concentrations in the following media:

• Ambient air;

• Indoor air;

• Soil/dust – outdoors; and

• Soil/dust – indoors.

Our needs for characterizing media concentrations for this analysis can be divided broadly into (a) estimating baseline conditions (for all relevant media) a nd (b) estimating future conditions (for all media) reflecting a partic ular NAAQS scenario. The extent to which empirical data will be used relative to modeling in establishing baseline conditions for a particular case study location will depend on the degree of coverage pr ovided by available monitoring data. In some instances, it may be possible to establish base line conditions largely using measured data for ambient air, soil and even indoor dust, while ot her case study locations may require modeling to characterize baseline conditions, with measured data being used largely for performance assessment of those modeled concentrations. And there may be locations for which a combination is employed. By contrast, future co nditions for ambient air will be estimated either by (a) adjusting air concentration surfaces to reduce air concentrations in areas exceeding a particular NAAQS standard or (b) conducting m odeling with reduced emissions estimates in

order to achieve a modeled surface without excee dances of a particular standard. Future conditions for other media besides ambient air (e .g., outdoor soil, indoor dus t) will be estimated through modeling.

The remainder of this section begins with a brief discussion of the general approach for characterizing spatial variability in demographi cs and media concentrations using a GIS-based spatial template, followed by descriptions of the approaches being c onsidered for estimating

media concentrations in each medium of interest.

4.1.1 Ambient Air

As described in Section 3.2, the a pproach used to characterize am bient air Pb concentrations for a particular case study will depend on the sufficien cy of the monitoring da ta available. For current conditions, we will rely upon air monitoring data, as feasible, to estimate ambient air concentrations. As described in the draft AQC D (EPA, 2006b) there are a number of monitoring networks that may be useful for this purpose, an d we will also investigat e the availability of additional data in particular locations (e.g., near sources of interest). We will augment this approach with modeling as needed for current and for future conditions.DRAFT – May 31, 2006 21 We have identified a number of modeling t ools which may be combined with ambient monitoring data in order to characterize air c oncentrations resulting fr om different emissions sources (i.e., current dire ct releases and reentrainment). These tools include:

• Air quality modeling : We intend to rely on air quality models (e.g., AERMOD, ISC, and CALPUFF) 6 for modeling ambient air concentrations associated with sources of new Pb emissions (e.g., ongoing stationary and mobile sources), as well as re-emission of historically deposited Pb (i.e., re-entrainme nt of Pb associated with soil/dust).

• Source apportionment modeling : We are considering the use of source apportionment models for two purposes: 1) to identify contributions of diffe rent source types to measured air concentrations (e.g., to appor tion ambient monitor Pb concentrations between specific industrial point sources and reentrainment), and 2) to evaluate model performance and guide refinement of ai r quality modeling (i.e., compare modeled surfaces in the vicinity of a monitor to the relevant source-apportioned component of that monitor to determine whether modeling seems representative and if not, then reexamine elements of modeling including emissions profiles). Specific source apportionment methods being considered include: (a) Chem ical Mass Balance (CMB ) and (b) Positive Matrix Factorization (PMF). In addition, we will cons ider using source-apportioned results conducted for PM (from the liter ature) and then applying soil/dust Pb

concentration values to convert those PM -related sourced-apportioned data into Pbspecific estimates.

• Reentrainment modeling: To estimate re-emissions of hist orically deposited Pb (for use in the air quality modeling), we are consid ering the following tools: (a) particulate emission factors (PEFs) as described in the Superfund Soil Screening Guidance (EPA, 1996a) and EPA's AP-42 emission factor documentation and (b) Wind Erosion Prediction System (WEPS) which is a dus t resuspension model developed by the USDA Agricultural Research Service.

• Use of National Air Toxics Assessment- national scale assessment (NATA-nsa): Another approach for developing estimates of re-emissi ons of historically deposited Pb that we are considering involves comparison of the ambi ent concentrations predicted in the most

recent NATA-nsa 7 to ambient monitoring data. As the NATA-nsa estimates are developed from dispersion m odeling of emissions from the National Emissions Inventory (NEI), which does not include reentr ainment of road and soil dust, the delta, which may result form several factors in cluding reentrainment, may provide a perspective on the contribution of that sour ce type to ambient air concentrations.

6 AERMOD and ISC (Industrial Source Complex) are Gaussian plume dispersion models, with AERMOD being the more current model. CalPuff, another refined model, is a puff dispersion model. http://www.epa.gov/scram001/di spersion_prefrec.htm#aermod

7 EPA’s NATA-nsa is a national scale assessment of ai r toxics emissions, ambient concentrations, inhalation exposures, and human health risk performed on a triennial basis. http://www.epa.gov/ttn/atw/nata1999/ The most recently available assessment is based on the 1999 NEI.DRAFT – May 31, 2006 22 • Literature-based re-entrainment estimates: We intend to consider, as appropriate, information in the literature in developing estimates of emissions of historically deposited Pb , e.g., that described an d cited in the draft AQCD (EPA 2006b).

Figure 3-3 illustrates an applica tion of tools described above to characterize baseline ambient air concentrations (e.g., a "fully modeled approach "). In this example, both point/mobile source emissions and re-entrainment of historically de posited Pb are modeled explicitly. As feasible, performance evaluation of this t ype of approach could involve source-apportioned monitor data. Specifically, modeled air concentrations resultin g from point and mobile sources, as well as reentrainment could be compared with the re levant source-apportioned signal from ambient monitors within the study area.

4.1.2. Indoor Air

To estimate indoor inhalation exposures indoo rs associated with ambient (outdoor) air concentrations, we intend to use either ambien t-to-exposure concentratio n ratios or inhalation exposure modeling. The inhalation exposure m odeling approach, described in Section 4.1.5, Modeling

Step Input Dataset

Output KEY

Baseline Ambient Air

Concentrations Soil Measurement Data From Different Location Representing Older Auto-Related Emissions (location similar to case study but not impacted by local point sources)

Re-entrainment

modeling (WEPS/PEF)

Ambient Air Emissions

(reentrainment of

historical auto-sourced soil/dust lead) Point/Mobile

Source Emissions

Data

AERMOD

Baseline Ambient Air

Concentrations (reentrainment of historical

auto-sourced lead) AERMOD

Wet/Dry Deposition

Simple Soil

Reservoir

Model

Baseline Pb Soil
Concentrations

Attributable to
Point/Mobile

Sources Re-entrainment

modeling (WEPS/PEF)

“Current”

Reentrainment

Emissions

AERMOD

Baseline

Ambient Air
Concentrations

(Current Emissions) Baseline Ambient Air
Concentrations (Reentrainment of Current emissions) Baseline Pb Soil Concentrations Attributable Historic (auto) Impacts Figure 3-3 Overview of Modeling Approach for Ambient Air (where measurement data are available and sufficient, they will be used in preference to, or in combination with modeling steps shown here to ch aracterize baseline conditions)

Modeling Step Input Dataset

Output KEY

Baseline Ambient Air

Concentrations Soil Measurement Data From Different Location Representing Older Auto-Related Emissions (location similar to case study but not impacted by local point sources)

Re-entrainment

modeling (WEPS/PEF)

Ambient Air Emissions

(reentrainment of

historical auto-sourced soil/dust lead) Point/Mobile

Source Emissions

Data

AERMOD

Baseline Ambient Air

Concentrations (reentrainment of historical

auto-sourced lead) AERMOD

Wet/Dry Deposition

Simple Soil

Reservoir

Model

Baseline Pb Soil
Concentrations

Attributable to
Point/Mobile

Sources Re-entrainment

modeling (WEPS/PEF)

“Current”

Reentrainment

Emissions

AERMOD

Baseline

Ambient Air
Concentrations

(Current Emissions) Baseline Ambient Air
Concentrations (Reentrainment of Current emissions) Baseline Pb Soil Concentrations Attributable Historic (auto) Impacts Figure 3-3 Overview of Modeling Approach for Ambient Air (where measurement data are available and sufficient, they will be used in preference to, or in combination with modeling steps shown here to ch aracterize baseline conditions)DRAFT – May 31, 2006 23 takes into account differences in indoor and am bient air concentrations, time spent indoors vs. outdoors and other activity patterns, and variability in concentrations across a variety of different microenvironments (e.g., car s, offices, outside).

4.1.3 Outdoor Soil/Dust

The approach used to characterize outdoor soil/d ust concentrations for a particular study area will depend on the amount of site-specific soil measurement data available. However, it is anticipated that for all case study locations, potentia l future changes in soil Pb concentrations under different NAAQS scenarios will need to be modeled to reflect the potential impact of decreases in deposition of Pb across the study ar ea, reflecting decreases in ambient air-related

Pb.

To predict future soil Pb concentrations (or ba seline conditions for t hose case studies lacking sufficient monitoring data), we are considering using deposition predicted from the air quality modeling combined with a simple soil reservoi r model, such as EPA's Multipathway Exposure (MPE) methodology (EPA, 1998a). MPE is a set of algorithms developed to estimate cumulative soil concentration as a function of dry and we t particle deposition, soil mixing depth and bulk density, and a soil loss constants fo r processes of interest that im pact concentrations in surface soil. As an alternative to MPE, we are c onsidering the option of using EPA's Total Risk Integrated Methodology Fate, Transport and Ec ological Exposure model (TRIM.FaTE) which employs a fully coupled mass balanced approach to simulating distribut ion of pollutants of interest among media of the simulated ecosystem (http://www.epa.gov/ttn/fera/trim_fate.html). TRIM.FaTE includes the capability for dynamic as well as steady-state modeling and has sensitivity analysis and MonteCarlo features.

4.1.4 Indoor Soil/Dust

The approach used to characterize indoor dust concentrations for a pa rticular study area will depend on the amount of site-specific measurement data available. For example at the primary smelter case study location, some site-specific resident ial dust data are availa ble. For other case study locations, we may consider the use of lite rature-based concentrations for comparable residences or a modeling approach (e.g., below). Additionally, for all locations under future air quality scenarios , we intend to use a modeling approach.

We are considering the mode ling approach employed by the IEUBK model (EPA 2005a, EPA 2006b, EPA 1994) which presumes a linear regre ssion relationship between air Pb (PbA), outdoor soil Pb (PbS) and indoor dust Pb (PbD ), and accommodates user-specified inputs.

PbD = β (other sources) + β S PbS + β A PbA

The IEUBK recommended default values for β S and β A , respectively, are 0.70 and 100 ug/g per ug/m 3 , with the former being based on site-specifi c data where soil was a major contribution to household dust. Prior to adoption, we intend to evaluate the application of this equation and these defaults in light of alternates currently available in the literatur e, including information discussed in the draft AQCD (EPA, 2006b). Note, that β (other sources) covers background exposureDRAFT – May 31, 2006 24 sources, including Pb paint in older houses (the characterization of background exposures including Pb paint levels in indoor dust is discussed in Section 4.1.6). We also plan to evaluate the performance of the mode ling approach in light of the current literature.

4.1.5 Estimating Inhalation Exposure Concentrations

Estimates of inhalation exposure concentrations for the population subgroups of interest will be derived in the Pilot assessment using ambient air concentration estimat es (see Section 4.1.1). A potentially important factor to c onsider in assessing i nhalation exposure is an individual's daily activity patterns including their commuting activity (e.g., home to wo rk, home to school). In order to incorporate cons ideration for daily mobility into our modeling of i nhalation exposure, we are considering the use of data generated as part of the recent NATA- nsa. Specifically, we are considering also using ratios of modeled exposure Pb concentrations to ambient air Pb

concentrations (the former reflecting daily mobility and generated at the US Census tract-level) to adjust our ambient air concentration values to reflect consideration for daily mobility. For the NATA-nsa, annual average ambient air concentrati ons were estimated for all U.S. census tracts and 30 replicates of annual average exposure con centrations were estima ted for five different population subgroups (i.e., five age groups) in each census tract re flecting daily ac tivity patterns including commuting trips. We may use these data to develop 30 replicate exposure-to-ambient concentration ratios per census tract and populati on group in a study area and these ratios in turn, used to derive 30 replicate estimates of annual average inhalation exposu re concentrations for each population subgroup in each geographic unit (block group) in the study area. The 30 replicate estimates of annual average inhalation co uld then be used to establish central tendency

inhalation estimates at the US Ce nsus block group-level and the fu ll distribution could be used in Monte Carlo simulation of exposure-re lated variability (see Section 4.3.4).

If the Pilot analysis suggests that inhalation expo sure to Pb is a significant contributor to overall Pb exposure, then we may consider a further refi nement in our consideration of daily mobility by running HAPEM or APEX for each study area and population subgroup of interest rather than relying on the ratios generated from the NATA-nsa.

4.1.6 Background (Non-Air-Related) Exposure

In addition to estimating media concentrations for air-related exposure pathways, we will also estimate Pb exposures for media pathways that are not directly linked to ai r concentrations (i.e., background exposure levels). Consideration of th ese background exposures allows us to model total Pb exposure for our study populations. Poten tial sources of data and a brief overview of how media concentrations will be estimated are presented here by medium:

• Indoor and Outdoor Soil/Dust Associated with Lead Paint

. EPA, HUD, FDA, and other Agencies have developed exte nsive monitoring programs to address non-air related Pb exposure concentrations. For ch ildren living in olde r housing or in urban areas, Pb levels in outdoor soils and house dust are likely to cont ribute the bulk of total Pb intake. For the portion of these exposures not due to re-entrainment, we ar e considering a number of surveys of urban soil and house dust Pb levels (Lanphear et al. 1998; EPA 1998b, 2000, Jacobson et al., 2002) as potenti al sources of exposure con centration values spanning theDRAFT – May 31, 2006 25 range of housing stock and urban settings. The National Research Council (2005) has also reviewed a number of soil Pb data sets for communities near Pb mining and smelting operations.

• Dietary Exposures

. Similar to air Pb levels, Pb con centrations in food have also been decreasing over the last two decades. In a recent project for the Office of Water (OW), Agency contractors reviewed available data so urces related to dietar y Pb concentrations (ICF 2005) and evaluated the trends in Pb c oncentrations in major food groups reported in FDA’s Total Diet Survey data from 1992-2002 (FDA 2004). These and similar data are being considered, al ong with information related to f ood intake patterns, to estimate Pb intake from foods. We do not expect to identify any site-spe cific food consumption data for the case study sites.

• Drinking Water

. We plan to gather data on the di stribution of Pb concentrations in drinking water to monitor compliance with the “Lead and Copper Rule” (EPA 2004b).

These data document decreases in water con centrations since the promulgation of that rule in 1991. However, the summarized data pr ovided in this data se t are not suitable for detailed exposure estimation because (1) the sampling program is targeted rather than random and (2) data are reported as 90 th percentile values rather than means, percentiles, or individual values. As part of ongoing wo rk for OW, we will pursue additional data, either individual sample data from EPA’s SDWIS system and/or data from a small number of individual systems in an effort to characterize exposure concentrations related to drinking water.

4.2 Study Population(s) and Potential Stratification Based on

Socioeconomic Factors

The selection of subpopulations to be included in this assessment focused on those for which there are endpoints associ ated with the range of exposures expected to most closely reflect current levels and for which there is adequate information to support quantitative risk estimates. We are currently planning on modeling risk metric s related to IQ loss fo r children under seven years of age and are considering various options for modeling blood pressu re and renal endpoints for adults. Each of these demographic groups is discussed below, including the degree to which they can be differentiated in modeling based on gender and race. In add ition, several additional subpopulations of potential interest are disc ussed (e.g., pregnant women, post-menopausal women), although we do not expect to qua ntitatively assess risks to them.

• Children from birth through age 6: We will consider the option of differentiating blood Pb level modeling for different socioeconomic subpopulations to the extent that there are data that (a) specify different behavior translating into diffe rential exposure levels for key media (e.g., dietary ingestion rates), or (b) sp ecify different Pb concentration levels (e.g., paint-related dust contamination based on age of housing). Consideration of socioeconomic-related differences in exposur e may be treated as part of sensitivity analyses rather than explicitly in population-level exposure modeling.DRAFT – May 31, 2006 26 • Adults: We are considering modeling both worki ng-age (18-64 year olds) and retired (65 to 80 year olds) adult populations. In addition, we may consider modeling adult subpopulations known to have high occupati onal exposures to Pb, or subpopulations expected to experience high Pb exposure due to a confluence of Pb exposure-related factors (e.g., housing age, dietary exposure, pr oximity to ambient air sources). As with the child study population, we are consider ing the option of differentiating blood Pb modeling for adults based on socioeconomic st atus (e.g., differences in behavior that translates into increased expos ure levels, differences in Pb concentrations in key media such as indoor dust related to housing age).

• Pregnant women : While this subgroup will not be quantitatively assessed here (see EPA 2006b for summary of currently available studies), fetal exposure from the mother will be include in estimating blood Pb levels for children.

• African Americans (and blood pressure modeling): Racial and ethnic differences in blood

Pb concentrations are well-demonstrated and age-specific average blood pressure is known to be substantially higher in African Americans than in other groups (Pirkle et al. 1998, Vupputuri et al. 2003). Howeve r, we are not aware of an approach to differentiate exposure-response relationships for the African American sub-population in particular. However, should we undertake an assessment of the potential shift in population-level blood pressure distributions for the case studie s as part of the Pilot, we may consider African Americans separately through the use of socioec onomically-differentiated blood pressure distributions and the impact that Pb-related exposure has on those distributions.

4.3 Estimating Blood Lead

Once exposure levels in the form of either m odeled intake rates (e.g., for dietary items and indoor dust) or exposure concen trations (e.g., for ambient air) have been generated for study populations of concern, the next st ep, as outlined in Figure 3-2, is to model blood Pb levels for those populations. The concentration of Pb in whole blood is the most commonly used measure, or “biomarker,” primarily because it is most c onvenient and easily measured, but also because blood Pb tends to be a good indicator of r ecent exposures. Pb in long-term body stores (primarily bone) may also contribute to blood Pb concentrations and to the risk of adverse effects. Thus, most approaches for estimating ad verse effects take into account the “biokinetics” (i.e., uptake, deposition, mobilization, and excret ion) of Pb in the body. Simplified forms of biokinetic models (e.g. “slope fact or models”) involve a linear pr ojection of blood Pb from either intake or uptake estimates developed outside the model. “Empirical ” approaches bypass the explicit modeling of biokinetics a nd predict blood Pb levels direc tly based on concentrations in exposure media. This section discusses the approaches we are considering for modeling blood Pb levels in children and adults, which include all three types of models.

Although the various models involve differe nt ways of accepting “inputs” (e.g., media concentration with ingestion rate vs intake rate), we intend to en sure that we provide the models used with the same inputs. For example, th e Model B input for intake rate will match the combination of Model A’s inputs for media concentr ation and ingestion rate . This will ensure that differences in model outputs will be reflec tive of differences in th e models themselves.DRAFT – May 31, 2006 27 The section begins by discussing the models selected for children, followed by options identified for modeling adults. We note that in our use of any model, all inputs will be considered, with

values chosen based on the best available inform ation as replacement for defaults as supported.

The section then discusses blood Pb metrics unde r consideration for bot h children and adults (e.g., concurrent or lifetime-averaged values). The section then discusses a probabilistic Monte Carlo-based approach being considered for expos ure variables, with s ubsequent "batch mode" modeling of blood Pb levels through the various m odels (Note, this probabilistic simulation of exposure is an option that applies both to children and adults and c onsequently is discussed in its own subsection). The section concludes with a discussion of model prep aration and evaluation.

4.3.1 Children

We intend to use the IEUBK model (EPA, 1994, 2002a, 2002b) for modeling blood Pb levels in children. We are also considering employing an additional biokinetic model (Leggett et al., 1993) and an empirical model (Lan phear et al., 1998) in order to consider model uncertainty in this key step in the risk assessment. As disc ussed below, the Leggett model is being considered for predicting both children’s and adult (if incl uded) blood Pb levels. Chapter 4 of the draft AQCD (2006b) discusses these models.

We are also considering the pharmacokinetic model of O’Flaherty et al. (1993) as another option for use in this analysis. While its approach to blood Pb modeling is also quite sophisticated, the O’Flaherty model has been subject to consider ably less independent va lidation against human population data and other models than the Leggett model. Also, the precise model structure and parameter values are less well-documented than those of the other models.

As noted earlier, we are also considering an “empirical” model (the Lanphear model) for estimating blood Pb levels in children, however, its use poses some challenges. The model includes no component for inhalation exposures, so it is necessary to assume that the main blood Pb impact of changes in ambient air levels oc curs through changes in soil and indoor dust Pb levels. In addition, the full Lanphear et al. m odel includes parameter va lues for many variables for which data may not be available for some or all of the study populations. In addition, the study identifier variable (specifying the different populations combined in the analysis) is highly significant and has a substantial im pact on estimated blood Pb levels. This variable presumably captures unidentified site-specific covariates that strongly effect blood Pb levels, that may or may not apply to a specific case study location.

One way to apply the model that is being consid ered involves adapting th e Lanphear et al. (1998) output tables as “response surfaces” for evaluati ng changes in exposure concentrations. The tables give predicted geometric mean blood Pb levels for various combinations of soil Pb

concentration and house dust Pb lo ading, with all other covariates held at mean or median values. Changes in ambient and indoor air conc entrations associated with the different case studies/exposure scenarios would enter into the model through their effect on soil concentrations and indoor dust Pb loading, estimated through deposition modeling. We recognize, however, that this approach would ignore th e significant role of covariates in predicting blood Pb levels.DRAFT – May 31, 2006 28 4.3.2 Adults

For adults, we are considering the use of both a biokinetic model (Leggett 1993) and a “slope factor” model (the Adult Lead Methodology, AL M, EPA, 1996b, Maddaloni 2005 or Bowers et al 1994). The Leggett model was discussed in th e previous section and will not be covered further here. However, it is important to note that if employed, the Leggett model will provide a “bridge” between the children’s and adult’s blood estimates. The remainder of this section will focus on the ALM, derived from the Bowers et al (1994) model.

The ALM was originally developed by EPA (1996b) for the purpose of estimating changes in long-term (months or years) adult blood Pb levels arising from non-occupational (i.e., residential) exposures to contam inated soil near “Superfund sites. ” The model has two parts. The first, which is implicit, is the calculation of a long-term average Pb uptake from soil Pb concentration data. The parameters in the upt ake calculation are the so il Pb concentration, a combined ingestion rate of soil, including bot h outdoor soil and indoor soil-derived dust, an absolute gastrointestinal absorption fraction (for soil and dust from soil), and exposure frequency and averaging times.

The estimated Pb uptake is multiplied by a “biokinetic slope factor”, derived from data from human exposures (Pocock et al 1983, Sherlock et al 1982, 1984), which is intended to capture the relationship between long-term Pb intake an d the estimated increment in blood Pb levels. The increment is added to a population-specific “b ackground” central tendenc y blood Pb level to generate an estimate of the cen tral tendency blood Pb concen tration associated with soil exposures at the specified concentration. In hazardous waste site appl ications, this background

blood level has been derived from a na tional databases such as the NHANES.

The output of the ALM is a central estimate of th e “quasi-steady state blood Pb concentration for the population exposed to the specified Pb concen tration in soil. Ma ddaloni et al (2005) presented an approach to estimating the distri bution of estimated blood Pb levels based on the assumption of lognormality, again using data from NHANES. The ALM model can be expressed as a set of simple equations, facilitating adapting of the model for Monte Carlo analysis, should that be undertaken.

4.3.3 Blood Lead Metrics

The “raw” outputs from the blood Pb models will be time profiles of blood Pb levels across the ages of interest, which will be converted to metric s that can serve as inputs to the adverse effects

models discussed in the followi ng section. Metrics under consider ation for children and adults

include:

• Child blood lead metrics: The metrics will be those particular to the adverse effects models chosen to quantify IQ risk, such as “concurrent” (i.e ., blood level at a particular age typically associated with testing for the e ndpoint of interest su ch as IQ) or lifetime average blood Pb levels (birth through particular age). Th e models being considered are presented in Table 5-1.DRAFT – May 31, 2006 29 • Adult blood lead metrics : If adults are included, estimat ed adult blood Pb concentrations

will be used with the adverse effects models chosen to qua ntify blood pressure or renal

effects. Blood Pb metrics for adults will focus on the average over the adult-aged exposure period (e.g., the averag e modeled blood Pb levels for ages 18-64 for the "working aged" adult population).

4.3.4 Probabilistic Analysis of Exposure Parameters

As described in Section 3.2, the IEUBK model is typically applied with the use of a GSD to

estimate the distribution of blood Pb concentrations reflecting inter-i ndividual variability in both exposure and biokinetics. In their recent evalua tion of a risk assessment conducted for soil Pb contamination at large Superfund Sites, the NRC (2005) suggested that fu ture use of the IEUBK and similar blood Pb models, in that contex t, should employ proba bilistic simulation of exposure-related variables. We are considering employing such an approach 8 in this assessment for all of the selected models. This approach would involve implementa tion of external Monte Carlo-based modeling of populati on exposure, followed by “batch-mode” blood Pb modeling for the set of Monte Carlo generated exposure simulations.

If this is pursued, the probabilistic simula tion of exposure will be conducted using a onedimensional Monte Carlo model which uses probab ility distributions for exposure concentrations and intake/uptake factors in the case of IEUB K, Leggett and ALM models. The probabilistic exposure model used to feed the Lanphear m odel would include distri butions of estimated changes in soil Pb and house dust concentrations , since that model does not include any exposure

factor values.

While the focus here is on sources of variability (with sources of uncertainty reserved for the sensitivity analysis - see Section 7.0), as is of ten the case with stochas ticity in exposure, some sources mix variability and uncertainty. For exam ple, distributions of exposure concentrations estimated for specific portions of a study area us ing a particular modeling approach can reflect both spatial variability in Pb levels across that area, as well as uncertainty in capturing that variability. In this case, it is di fficult to separate these two sources of variability in Pb levels and it is likely that Monte Carlo simulation of expos ure variation will reflect both variability and uncertainty in the case of this factor.

Exposure factor distributions us ed in probabilistic modeling (refl ecting variations in receptor behavior) will be developed based on available literature data. For some variables (drinking water and food consumption), substantial data ar e available from surveys and other studies to support the derivation of probab ility distributions reflecting the expected wide range of variability across the exposed populations (EPA 1997, 2002a, 2002b) Similarly, the databases used in the inhalation exposure models include probabilistic representations of time activity patterns. Children’s soil and house dust ingestio n have been subject to extensive study, while for other exposure factors (adult so il ingestion, for example) ther e are far fewer data to support estimation of probability distributions (Maddaloni et al. 2005). Probabilis tic representations ofDRAFT – May 31, 2006 30 gastrointestinal absorption fraction will be chosen to reflect the literature related to GI absorption from various Pb species (EPA 2005b, Maddaloni et al. 2005).

An initial assumption used in developing the pr obabilistic simulation would be that the input distributions of the exposure concentrations and exposure factors are independent (not correlated) except to the extent that they co-vary in space and ar e age-specific. This assumption could be examined more closely in an effort to evaluate uncertainty relate d to correlation for the Full Scale analysis, should probabi listic simulation be undertaken.

Evaluation of this modeling analysis will in clude comparison of the resultant modeled

distributions of blood Pb levels with empirical measures of variability in blood Pb levels obtained either from site-speci fic data or NHANES. Such a comparison may also inform our understanding of the contribution to population bl ood Pb variability (e.g., as characterized by GSD) from biokinetic variabil ity for modeled populations.

If this option is pursued, the blood Pb distri butions generated will be carried through the

assessment of adverse health effects in order to generate distributions of IQ loss estimates and blood pressure effects fo r modeled populations.

4.3.5 Preparation of Models and Inputs, and Evaluation of Performance

Briefly, the preparation of mode ls for application and their ev aluation will involve the following
steps.

• Develop and document consistent sets of in take/uptake assumptions (exposure factor values, absorption fractions, etc.) for the three biokinetic models 9 derived from the outputs of the case study expos ure concentration models.

• Run the IEUBK, and Leggett, and Lanphear et al. models (assuming these other models are employed) for a small number of childre n’s exposure scenarios covering a credible range of exposure conditions and parameter values. If adults are included, run Leggett and the ALM for a small number of adult exposure scenarios.

• Compare results of models to age-specific population blood Pb data from the most recent NHANES, and data from Superfund sites and/or HUD housing studies.

• Compare model results to each other within age ranges.

• Conduct sensitivity analysis to identify parame ters/assumptions contributing the most to differences/uncertainties in results. The first step in this analysis will be the comparison of model performance in the selected test scenarios; additional model runs will be conducted if necessary.

• Fully document the process.

• Evaluate overall performance of the models for each age group.

In these steps, we will draw extensively on the previous published modeling comparisons and studies, and may confer with the model author s and other experts during the process. The

9 A preliminary review of the available literature sugges ts that previous comparisons did not always involve consistent exposure, intake, and uptake models and parameter values across the models being compared. Once consistent intake/uptake assumptions have been defined, the results from the various models can be compared.DRAFT – May 31, 2006 31 sensitivity of the models to changes in specifi c parameters will be evaluated. Systematic differences among the model predictions will also be noted; that is, where one or more model predictions differ consistently relative to anot her under specified sets of exposure conditions. Analysis of the patterns of differences will info rm consideration of specific model intake/uptake parameters. In evaluating the reasonableness of model predicti ons, greater weight would be given to deviations from observa tional data (when available) than to differences among models.DRAFT – May 31, 2006 32 5.0 EFFECTS ASSESSMENT

Lead exposure has been shown to affect a wi de range of organ systems and physiological processes at differing doses in adults and childre n. The most recent epidemiological studies have been reviewed by EPA in the latest draft AQCD (EPA 2006b). Because extensive de novo biostatistical analyses are beyond the scope of the assessment, we intend to draw from the

evaluation and identification of adverse effect s models contained in the draft AQCD (EPA, 2006b). The staff's basic approach will be to (1) identify a small number of the most appropriate studies and associated models and (2) apply them to each endpoint of concern to derive a range of model-dependent risk estimates.

5.1 Endpoints to be Evaluated

For the Pilot analysis, we are planning to model IQ effects in children and will consider options for modeling blood pressure and renal effects using creatinine cleara nce (a commonly used measure of glomerular filtration rate (GFR), an index of kidney function) in adults. As feasible, the full assessment may include all three endpoints. As described in the current draft of the draft AQCD (EPA 2006b), there are a number of high-qual ity studies reporting these effects that are

available and a consensus that these effects are “adverse” and occur in the range of exposure anticipated to be relevant to this analysis and the case studies incl uded. The draft AQCD (EPA, 2006b) presents findings regarding susceptible s ubpopulations (e.g., renal effects in hypertensive subpopulations) that will be considered in char acterizing risk associat ed with the endpoints

included in the assessment.

5.2 Models for Estimating Adverse Effects

5.2.1 IQ Reduction in Children

We intend to focus on the Lanphear et al ( 2005) study as our primar y source of exposureresponse functions for use in quantifying the re lationship between children’s blood Pb and IQ decrements. In our use of Lanphear et al (2005) in this assessment, we intend to rely on a nonlinear model, such as the log-linear or piece -wise linear fits depicted in Figures 3 and 4, respectively, of that study, to estimate IQ decrement associated with estimated blood Pb concentrations, and to capture the higher slope indicated by this anal ysis at lower blood Pb levels (note, the current approach does not call for conducting adjustments for covariates for individual case study populations).

We are also considering the other studies id entified in the draft AQCD (Table 6-2.2 of EPA 2006b) reporting quantitative relationships of IQ and blood Pb for populations with blood Pb levels less than 10 μg/dL. 10 These studies are listed in Tabl e 5-1. Although included in this set are several studies on non-U.S. populations, the re sponses of these populati ons do not appear to differ substantially from the re sponses in the U.S. populations. For example, the estimated slopes for blood Pb under 10 μg/dL provided in Ta ble 6-2.2 of the draft AQCD (EPA 2006b) for the two U.S. studies range from -0.8 to -1.6 IQ points per μg/dL blood Pb, while the other

10 We have not included Al-Saleh et al (2001) which studied children of notably older ages (6-12 years) than the other studies being considered.DRAFT – May 31, 2006 33 studies, which include non-US populations, rang e from -0.8 to -1.1 IQ points per μg/dL blood Pb. In selecting a set of studies for use in char acterizing the range of uncer tainty associated with modeling the IQ endpoint, preference will be gi ven to studies conducted in the US reflecting concerns over study population a nd exposure condition relevance.

Table 5-1. Studies with Quantitative Relation ships of IQ and Blood Lead for Blood Lead Levels Less than 10 μg/dL (drawn fr om draft AQCD Table 6-2.2, EPA, 2006b).

Reference Study Location Exposure/Dose Metric having strongest association with IQ

Tellez-Rojo et al. (2006) Mexico City, Mexico Pb-B @ 24 months

Bellinger et al. (1992) Boston, Massachusetts Pb-B @ 24 months

Kordas et al. (2006) Torreón, Mexico Concurrent Pb-B , 6-8 yr

Canfield et al. (2003) Rochester, New York Concurrent @ 5yrs

Lanphear et al. (2005) International Pooled Analysis Concurrent Pb-B

4.8 – 10 yrs

5.2.2 Blood Pressure Effects in Adults

For quantifying blood pressure changes associated w ith blood Pb levels in this assessment, if included, we are focusing on the Nawrot et al (2002) meta-analysis a nd its log-linear slope estimate as our primary blood Pb-blood pressure model for adult populations. The study also provides confidence limits that can be considered for use as an index of the uncertainty in the assessment associated with sample sizes c onsiderations, and for sensitivity analyses.

We are also considering the other studies id entified in the draft AQCD (Table 6-5.1 of EPA 2006b) reports quantitative relationships between systolic blood pressure and blood Pb for mean blood Pb levels below 10 μg/dl. These studies (listed in Table 5-2) analyzed blood pressure effects in particular populations differentiated by gender and race. If this endpoint is included, these studies may be relied on in the full assessm ent to provide risk estimates based on gender and race specific response func tions. Additionally, the curren t draft of the AQCD includes a meta analysis inclusive of studies published sin ce the Nawrot et al (2002) analysis (EPA 2006b). This analysis also is being consider ed for use in quantifying this effect.DRAFT – May 31, 2006 34 Table 5-2. Studies under Consideration for Quantification of Blood Pressure Change Associated with Blood Lead Levels (dra wn from draft AQCD Table 6-5.1, EPA, 2006b).

Reference Study Location – and gender

Vupputuri et al. (2003) NHANES III* - White Males

NHANES III - White females

NHANES III - Black Males

NHANES III - Black females

Den Hond et al. (2002) NHANES III 1988-94 - White males

NHANES III 1988-94 - White females

NHANES III 1988-94 - Black males

NHANES III 1988-94 - Black females

Nash et al. (2003) NHANES II - Females

Cheng et al. (2001) Boston normat ive aging- Males (about 97% white)

Procter et al. (1996) US Boston Normative aging study -Males

Nawrot et al. (2002) 31 US and European Studies (includes occupationally exposed)

* NHANES – United States population sample.

5.2.3 Renal Effects in Adults

For quantifying renal effects associ ated with blood Pb levels in this assessment, if included, we intend to use changes in urinar y creatinine clearance as the specific endpoint. Creatinine clearance is a commonly used meas ure of glomerular filtration ra te (GFR), an index of kidney function. The draft AQCD describes multiple studies in which a relationship of this effect with blood Pb (and bone Pb) has been observed for both the general population and specific subgroups such as hypertensives (EPA 2006b). To qua ntify this endpoint in the risk assessment, we are considering the studies for which es timates of slope for creatinine clearance 11 per blood Pb are presented in Figure 6-4.1 of the draft AQCD (EPA 2006b). These studies, which were for general populations and generally had mean blood Pb levels less than 10 μg/dL, are listed in

Table 5-3.

11 For studies where creatinine clearance was not measur ed, it was estimated using the relationship based on creatinine levels, age and weight from Cockcroft and Gault (1976).DRAFT – May 31, 2006 35 Table 5-3. Studies under Consideration for Quantifica tion of Creatinine Clearance Change Associated with Blood Lead Levels (drawn from draft AQCD Figure 6-4.1 and Table 6-4.1, EPA, 2006b).

Reference Study location - Study population

Payton et al. (1994) Boston, MA - Normative Aging Study, 1988-1991

Kim et al. (1996) Boston, MA - Normative Aging Study, 1979-1994

Tsaih et al. (2004) Boston, MA- Normative Aging Study- 1991-~2001

Staessen et al. (1992) Belgium - Cadmibel Study

Akesson et al. (2005) Women’s Health in the Lund Area Study, Sweden - 1999-2000

We are also considering potential use of Muntner et al (2003), which included a focused analysis on creatinine clearance changes in hypertensives.DRAFT – May 31, 2006 36 6.0 RISK ASSESSMENT

Once blood Pb metrics have been generated for the study populations of interest (for each case study location), the next step, as outlined in Fi gure 3-2, is to combine these exposure estimates with concentration responses func tions developed as part of Eff ects Assessment to generate risk estimates. This analysis is primarily focuse d on generating population risk metrics (i.e., distributions of risk levels across modeled popul ations). Note, however, that we may consider generating more hypothetical individual or small- group risk estimates in order to cover higherlevel exposures which may not have been captu red in the population-level risk estimates generated for the analysis. We are planning curren tly, to generate risk estimates for IQ loss for children and are considering the option of generating ri sk estimates covering blood pressure and

renal function effects for adults. This section begins by describing options for defining modeled populations from the stand point of risk ch aracterization, including options for assigning exposure concentrations (e.g., mode ling a cohort as beginning exposur e all at the same age, or modeling a cohort with distribut ed ages that experiences varying exposure windows). The

section then describes risk me trics being considered for both the child and adult age groups. Note, that risk metrics generated as part of th e integrated strategy for addressing uncertainty and variability described in Section 7.0 are di scussed separately in Section 7.2.

6.1 Defining Modeled Populations in (Assigning Exposure Concentrations)

A key issue in generating population risk estimat es is whether study populations are defined as cohorts which begin exposure all at the same age and then are tracked forward in terms of exposure and risk, or as cohorts with a distribution of ages experiencing a range of exposure windows across the simulation period. An additiona l factor to consider in modeling populationlevel risk, is how to treat the period before the exposure of interest begins (e.g., assign all

modeled individuals baseline e xposure levels prior to implementation of the alternate NAAQS standard). There are several options under c onsideration for defini ng study populations for purposes of this analysis. As th is issue has policy-related aspe cts, we do not expect to be selecting a specific approach, and instead, may r un multiple options in order to characterize the sensitivity of risk results to this factor. Sp ecific options available for defining the modeled populations in terms of assigning ex posure concentrations include:

• Uniform age cohort : This option defines the population as a group of individuals at the age defining the lower end of the age range being modeled (e.g., fo r children, beginning all modeled individuals at age 0 yrs). Therefor e, exposure to the levels of interest (e.g., those associated with a partic ular alternate NAAQS standard), would begin when they are at the lower-bound age. In the case of adults (and the mothers of children for purposes of predicting prenatal Pb contribu tions where appropriate), we would assume that exposures prior to the start age is at background levels.

• Distributed (demographically representative) cohort : This option defines the population as distributed across the age ra nge of interest, as reflected in available demographic data (e.g., 10% of children at 0-1 yrs, 15% at 1- 2 yrs and so on). With this option, modeled individuals begin the exposure of interest (e.g., an alternativ e NAAQS level) at differentDRAFT – May 31, 2006 37 ages across the age range. As with the uniform age cohort option, the assumption would be made that exposures at ages before in itiation of the NAAQS level of interest, would occur at background levels.

In modeling adult blood Pb levels, depending on the approach used, it may be necessary to track blood Pb levels across consecutive age ranges. For the childhood period, biokinetic and/or empirical models could be used to estimate cen tral tendency blood Pb pr ofiles as a function of age for the adult population. For adults of working age, it is likely adult average blood Pb levels would be used across the ages of 18 to 64 to estimate health im pacts. For retired adults, the working age exposure scenarios would be r un, followed by the exposures scenarios for retirement, and time-average blood levels for ages 65 to 80 would be used to estimate health impacts.

6.2 Risk Metrics for Children

In previous analyses of Pb risks, EPA has a dopted a number of metrics for expressing population risks from Pb exposure in children. These have included average changes in blood Pb levels, changes in the numbers of children with blood Pb levels above 10 μg/dL, and population average and aggregate changes in children’s IQ.

For the Pilot, we are planning to generate risk metrics characterizing IQ changes in children, including degrees of IQ reductions for speci fic percentiles of th e population as well as population-level estimates of the number of kids experiencing specific ranges of IQ reductions. In addition to population-level risk metrics, we may consider changes in population-level blood

Pb distributions.

We are also planning to differe ntiate all risk metrics as to background versus NAAQS-relevant exposures (e.g., Pb exposure-related IQ loss for a pa rticular percentile of the children at a case study location would be further differentiated as to magnitude of IQ loss from exposures to "NAAQS-relevant exposures" versus exposures to "background".

It is important to note that the degree to whic h we can generate refined population-level risk metrics that capture risks at the tails of the distribution (i.e., fo r more highly exposed individuals), or identify the number of individua ls with blood Pb levels exceeding science-policy thresholds of interest, will depend critically on the degree of uncertainty associated with our characterization of population vari ability in blood Pb levels. Th is, in turn, will depend on the overall degree of uncertainty associated with the two methods described above for modeling population variability in blood Pb levels (i.e., application of the GSD approach and use of probabilistic simulation to cover ex posure-related variability).

It is also important to make clear that the population-level risk metric planned for the Pilot assessment is the distribution of IQ loss across the case study populat ion, not the change in absolute IQ levels in the exposed populations. The pro cess of modeling shifts in absolute IQ for a population requires that (a) the underlying IQ distribution for that population be established and (b) that a reasonable understa nding of the correlation between absolute IQ a nd the level of Pb-related IQ loss exist (e.g., is greater IQ loss correlated with lower IQ for a particularDRAFT – May 31, 2006 38 population or are they uncorrelated? ). The process of establishing the absolute IQ distributions for specific populations associated with a given case study location would be challenging and subject to considerable uncertainty, which in tur n, would mean that an analysis of a shift in absolute IQ resulting from Pb-related exposure would be subject to as much, if not more uncertainty (since the potential correlation betw een Pb-related IQ loss a nd absolute IQ could have an significant impact on this risk metric). Because of the significant uncertainty associated with projecting reductions in abso lute IQ distributions for specif ic populations (uncertainty that can not be reduced without significa nt research to identify repres entative data for a particular population), we are not planning to project shifts in absolute IQ as part of the Pilot analysis.

6.3 Risk Metrics for Adults

As was the case for children, if undertaken, risk ca lculations for adults will be based on estimates of blood Pb changes for central tendency adul ts derived from the Leggett or ALM models. Corresponding log-normal distributions of blood Pb levels will be derived using GSD estimates derived from large populati on data (NHANES) for groups w ith similar demographic and socioeconomic characteristics as the exposed populations or for the general population where population-specific data are not ava ilable. Average increases in ce ntral tendency blood Pb levels will serve as simple summary values for comparing the general magnitude of effects of different exposure scenarios. Percentile values and propor tions of the exposed populations with estimated blood Pb levels in specified ranges will also be provided.

As with children, the staff will also consider using a Monte Carlo-based simulation of blood Pb levels for adults, reflecting exposur e factor variability. This proba bilistic analysis will generate population-level blood Pb results paralleling those generated usi ng the GSD approach described above, with the exception that they will not provide coverage for biokinetic variab ility (we will look into options for developing a GSD just to c over this specific fact or). The primary outputs from the adult risk assessment will be tabular summaries of the changes in the distribution of blood pressure in the exposed populations.

In the Pilot assessment, we will explore the estim ation of incidence of clinical hypertension by adding the estimated increase in blood pressure to national gender- and ag e-specific absolute blood pressure distributions relevant to the case study locations.

As noted earlier, there will be no attempt to pred ict the changes in incidence of cardiovascular disease resulting from Pb exposur e in the risk assessment.DRAFT – May 31, 2006 39 7.0 UNCERTAINTY AND VARIABILITY ASSESSMENT

Modeling of Pb-related exposure and risk is subjec t to a variety of sour ces of variability (e.g., residential location, daily activity patterns, dietar y ingestion rates, Pb uptake rates) as well as sources of uncertainty (e.g., different blood Pb m odels, different health endpoint concentrationresponse functions). A comprehensive analysis of uncertainty and variab ility typically involves two-dimensional probabilistic simulation with on e dimension designed to represent variability and the other uncertainty. Probabilistic simula tion of uncertainty requires development of confidence distributions for uncertain input variables (parameter uncertainty) and the establishing of degrees of confidence for multiple competing models identified for the same modeling step (model uncertainty). Because of data limitations and constraints, it is not feasible to develop confidence di stributions for many of the sources of parameter and model uncertainty identified for this risk analysis. Therefore, for the Pilot analysis, the staff will use sensitivity analysis techniques to examine the impact of sources of uncertainty on exposure and risk results. These techniques involve estab lishing plausible ranges for input parameters and identifying multiple modeling options reflecting model uncertainty for specific modeling steps. Then each uncertainty parameter (or possibly pairings of pa rameters expected to exhibit correlation) is varied across its plausible range as the remaini ng parameters are held at their central tendency (or expected) values. The impact of each i nput parameter on model outputs can then be ascertained. The impact of indivi dual parameters can then be compared to identify those having the greatest impact on model result s and the general degree of that impact can be evaluated to gain perspectives on the magnitude of potential un certainties. Similarly with model uncertainty,

alternate models can be substituted one at a time into the analytical framework to determine the consequent impact on model results. As with sensitivity analysis results for parameter uncertainty, these results can be evaluated to co mpare the impact from different sources of model uncertainty and the overall degree of the impact fr om model uncertainty on model results can be

determined.

Regarding variability, as noted ea rlier in Section 7.2, we are consid ering the use of probabilistic (Monte Carlo-based) simulation to characterize the impact of exposure variability on blood Pb levels and ultimately, risk metrics. It is al so important to note that the modeling framework developed for this analysis reflec ts coverage for a range of othe r sources of variability besides the exposure related factors covered in the proba bilistic analysis. For example, the GIS-based spatial modeling framework explicitly reflects va riability resulting from the demographic spatial profiles associated w ith modeled populations within a given study area, as well as the intersection of those populations with the Pb medi a concentration fields of interest (e.g., ambient air, outdoor soil/dust, indoor dust). Spatial and tem poral variability in media concentration fields

will also be reflected in both th e air monitor data and dispersion modeling used to establish those

fields.

Table 7-1 presents examples of: (a) competi ng models for specific steps in the modeling framework (representing model unc ertainty), (b) sources of input parameter uncertainty and (c) specific sources of exposure-related variability a ssociated with the risk modeling framework. It is these sources of model and parameter uncertainty as well as exposure-re lated variability that are addressed in the integrated approach discussed below.DRAFT – May 31, 2006 40 Table 7-1 Sources of Model/Parameter Uncertainty and Exposure-Related Variability Potentially Impacting the Risk Assessment

Model Uncertainty Parameter Uncertai nty Exposure-Related Variability

• Source apportionment models (PMF, CMB, NATA-based)

• Blood Pb modeling (IEUBK, Leggett, ALM, Lanphear)

• Health effects (concentration-response model) for IQ (Canfield, Lanphear)

• OTHERS • Pb Emissions Estimation

• Blood Pb Metrics

• Pb Uptake Factors

• Soil/House Dust Pb

• Blood Pb GSD

• Background blood Pb

levels

• OTHERS • Exposure/Intake Factors

• Background Exposure Levels (e.g., diet, paint)

• NAAQS-Relevant Exposure Levels

7.1 Integrated Approach for Considering Uncertainty and Variability

We are considering the use of an integrated approach that combines probabilistic modeling of variability with sensitivity analysis technique s intended to examine both parameter and model uncertainty. This approach, which is depict ed in Figure 7-1, involves development of a

Blood Lead Modeli ng (children) CM PMF NAT

IEUB Leggett

Canfield Lanphear

Conduct Sensitivity Analysis for Input

Parameters for this Modeling Branch Conduct ProbabilitySimulation of ExposureRelated Variability for this Modeling Branch Source Apportionment Option

Concentration-res ponse Function

Evaluate impact of key parameter

values on variability-based

risk distribution Modeling Tree Supporting Sensitivity Analysis of Model Uncertainty

(NOTE: hypothetical “MIN” modeling branch

identified as CMB-IEUBKLanphear combo) Figure 7-1 Integrated Approach for Considering Model Uncertainty, Parameter Uncertainty and Exposure-Related VariabilityDRAFT – May 31, 2006 41 modeling options tree where each branch represents a uni que combination of models identified for the analysis (i.e., a selection of specific models for each modeling step subject to model uncertainty). This means that the risk estimat es generated by a specific combination of modeling options would be represented as a distinct set of risk results located at the end of a particular branch of the modeling options tree. Therefore, looking across the range of risk results generated at the ends of the branches, provides perspective on the overall impact of model uncertainty on risk results. Note, however, that because confiden ce levels have not been assigned to any of the modeling options, it is not possible to develop a confidence distribution across the branches. All that can be said is that the spread in risk esti mates across the branches re flects the range of model uncertainty impact on risk estimates.

We may then integrate consideration of parameter uncertainty into the modeling options tree by taking a particular modeling branch and conducting parameter sensitivity analysis on that model combination. Specifically, a given input parameter th at is subject to uncer tainty can be varied from its central tendency (or expected) value acr oss its plausible range and the impact on risk estimates at the end of that modeling branch r ecorded. This process can be repeated for other uncertain parameters (and for pairings of input likely to exhibit correla tion) to determine the potential impact of each uncertain parameter on ri sk estimates. This procedure when completed provides a parameter sensitivity analysis for that pa rticular branch. This procedure could then be repeated for each modeling branch in the tree, prov iding a set of parameter sensitivity results for each modeling option. This then would reflect a combination of sensitivity analysis techniques considering both model uncertainty and parameter uncertainty.

To facilitate comparison of sensitivity analysis re sults both within a given modeling branch and across branches, the parameter sensitivity analys is results for each modeling branch can be standardized by dividing th e results derived using the high-end values by the all-central tendency estimate. This will provide a rough estimate of the degree of uncertainty (roughly equivalent to the proportion of variance) contributed to the risk estimate by each step in the analysis.

Finally, we may integrate consideration of expos ure-related variability into this integrated modeling options tree by conducti ng Monte Carlo simulation of e xposure parameter variability (as described in Section 4.3.4) fo r each modeling branch. This would produce a distribution of risk estimates at the end of each modeling branch which represent inter-individual variability in key exposure-related factors. In addition, we may use the parameter uncertainty sensitivity analysis approach described above to derive mu ltiple variability distri butions for each branch, each reflecting the impact of a specific uncertain input parameter. By looking across the risk distributions at the end of the modeling bran ches, the impact of model uncertainty on the distribution of population risk can be considered. For example, comparison of the 95th% risk estimates at the end of the modeling branches may reflect the impact of model uncertainty on this risk metric. Furthermore, the set of 95th% risk estimates at the end of a particular modeling branch can be compared to provide an indicati on of the impact of sp ecific uncertain input parameters on that particular risk metric (f or that particular m odeling combination).

Rather than generating full probabilistic risk me trics for the full spread of modeling branches (i.e., running the probabilistic variability simula tion for each combination of modeling options), we would likely focus the analysis on those key br anches of greatest utility to decision makers.DRAFT – May 31, 2006 42 These include (a) the central tendency modeling br anch (i.e., the combination of model options generating risk estimates falling in the middle of the full range of estimates generated) and (b) the upper- and lower-bound modeling branches (i .e., the modeling options producing the highest

and lowest risk distributions of all the branch es). Central tendency and upper- and lower-bound modeling branches could be identified by first running each modeling branch without consideration for either probabilistic variability analysis or parameter uncertainty. The point estimate risk results generated for each modeling branch will then be compared to identify

branches with the lowest (MIN), the highest (MAX) and the central tendency risk estimates.

The staff could then conduct full comprehensive a pplication of the integrated approach to these three key modeling branches. This approach will allow identification of a clear central tendency modeling approach (which can be presented as th e best estimate, in the absence of additional confidence information allowing a more exp licit and quantitative treatment of model uncertainty). Risk results for the max and min modeling branches can then be evaluated to provide perspective on the range or spread of model uncertainty impact on modeled results.

7.2 Risk and Exposure Metrics Genera ted with the Integrated Approach

As mentioned above, we are considering impl ementing the integrated approach for model uncertainty, parameter uncertainty and exposure-re lated variability for the max, min and central tendency modeling branches. This approach wi ll generate three sets of risk and exposure metrics, each reflecting a partic ular combination of modeling op tions. In addition, each of these

three sets of results will invol ve consideration for input para meter uncertainty as described above. Therefore, it would be possible, for example, to estimate at the numb er of children with IQ loss from air pathway-related Pb sources in the range of 1-2 IQ points for the max, min and central tendency modeling options. It would also be possible to see how this population risk-bin metric varies given parameter uncertainty for each modeling option (and to compare the impact of that parameter uncertainty ac ross the three modeling branches). This hypothetical example is presented in Figure 7-2.DRAFT – May 31, 2006 43 Blood Lead Modeling (children) CMB PMF NATA

IEUBK Leggett

Canfield Lanphear

Sensitivity Analysis for Input Parameters and impact on population risk metric generated using Monte

Carlo simulation Source Apportionment Option used in fate/transport modeling

Concentrationresponse Function Modeling Tree Supporting Sensitivity Analysis of Model Uncertainty Figure 7-2 Example Risk Metrics Generated Using the Integrated Approach

IEUBK

Canfield

MIN

Branch CENTRAL

TENDENCY

Branch MAX

Branch

Monte Carlo-based (exposure-related) estimate of number of children with IQ loss in the 1-2 point range ( 22 ) ( 34 ) ( 43 )

Pb Emissions Estimation (20 – 44)

Blood Lead Metric (25 – 37)

Soil/House Dust Lead (31 – 36)

Baseline blood lead levels (15 – 48)DRAFT – May 31, 2006 44 

## ECOLOGICAL RISK ASSESSMENT

8.0 OVERVIEW OF ANALYSIS PLAN

8.1 Conceptual Model for Lead Ecological Risk Assessment

Figure 8-1 represents a conceptual model of the elements pertin ent to assessing ecological risks associated with environmental Pb exposures. Th e freshwater and soil pathways are the primary pathways of interest to this analysis and will be addressed quantitatively in this assessment.

Sources: The focus of the NAAQS review is on sour ces of Pb to ambient air (e.g., stationary and mobile emissions sources and resuspension of anthropogenic Pb). Other non-air sources of environmental Pb (including mining activities, contaminated landfills, etc.) may contribute to Pb

concentrations in environmental media. This analysis deals pr imarily with present and past emissions to air and the resulting deposition.

Pathways: The ecologically significant pathways of exposure are through ambient air and the accumulation of Pb in media (soil, water, sedime nt). Direct inhalation of Pb, while a source of exposure, is probably the less signi ficant one. This analysis will deal primarily with deposition and resulting concentrations in environmental media.

Organisms: Those organisms in direct contact wi th Pb contaminated media whether directly or by prey selection are most likely to be the most highly exposed organisms in the environment. There is limited evidence for biomagnification of Pb in food chains, but sensitivities to lead do vary widely within and among groups of organisms with similar exposures.

Endpoints: Exposure to Pb at significantly high levels can ca use effects to individuals and populations thereby altering proces ses and interdependencies of eco systems. Known effects of high Pb exposure include stunted growth, decrea sed fecundity, and increased mortality rates among some organisms.

Risk Metrics: Metrics could be developed to look at individual, population, and ecosystem effects from led exposures. Indivi dual-level toxicity data that ar e likely to represent thresholds for population level effects in sensitive species we re used to develop the screening methods that are be considered for this analysis.DRAFT – May 31, 2006 45 Figure 8-1. Conceptual Mode l of Lead in EcosystemsDRAFT – May 31, 2006 46 8.2 General Overview of Analysis

The primary route of exposure to Pb for ecologica l receptors is very diffe rent than the typical inhalation exposure path of most other criteria air polluta nts. In general, exposure to Pb comes through contact with or ingestion of media that contains Pb by way of air deposition, runoff, dispersion of man made material s containing Pb, and exposed mi ning waste. In addition, Pb

accumulates in the environment over time and does not dissipate readily. These two facts make a multimedia approach to Pb analysis necessary to assess the long term effects of deposition

from ambient air.

This assessment will be performed in two steps or tiers. The first tier will use case study locations developed in conjunction with the primary standard assessment to determine predicted

concentrations of Pb in soils and will consider water and sediment based on availability of data. These media concentrations will then be compared to environmental screening levels developed for ecological receptors to focus the analysis on those receptors most likely to be affected by

ambient Pb concentrations. The second tier of th e analysis will then use available modeling to determine what the intake rate is likely to be fo r sensitive receptors and compare these rates with

available data on concentration effects.

The sections that follow describe in more de tail what is envisione d for analyzing each component as illustrated in Figure 8-1. Section 9 .1 describes how estimates of current and future media concentrations will be generated usi ng empirical data and modeling to provide a comparison with several screeni ng tools available for predicting the likelihood of adverse effect to organisms from specific concentrations of Pb in soil, freshwater, and sediment. These screening risk results can be used to focus furt her analysis on those rece ptors thought to be most at risk based on the outcome of the screening st ep. Section 9.2 is a discussion of the proposed detailed analysis of those receptors found to be at risk in the tier 1 analys is. A discussion of the model and method for determining intake rates or body/tissue concentrations for the susceptible receptor(s) is also found in this section of the plan. Figure 8-2 gives an overview of the proposed analysis.DRAFT – May 31, 2006 47 Figure 8-2. Overview of Planned AnalysisDRAFT – May 31, 2006 48 8.3 Uncertainty and Variability

Modeling of Pb related exposure and risk to ecolo gical receptors is subjec t to a wide array of sources of both variability and uncertainty. Va riability is associated with geographic location, habitat types, physical and chemical characteri stics of soils and wate r that influence Pb bioavailability, terrestrial and aquatic community composition, Pb uptake rates by invertebrates, fish, and plants by species and season. For wild life, variability also is associated with food ingestion rates by species and season, prey sel ection, and locations of home ranges for foraging

relative to the Pb contaminati on levels. Sources of uncertainty include modeling choices for future media concentrations and assumptions used to derive the ecotoxicity screening benchmarks. Uncertainty and variability will be di scussed to the extent possible for each step of the analysis, and where feasible, comparisons w ill be made between model outputs and empirical

data.

As discussed in Section 7 for the pilot human hea lth risk analysis, we are considering the use of an integrated approach for addressing both un certainty and variability, which will combine probabilistic simulation (for addressing exposure-re lated variability) with sensitivity analysis techniques (for addressing both parameter and mode l uncertainty). The integrated approach would include a "modeling tree" to represent mode l uncertainty with three branches representing the high-bound (Max), low-bound (Min), and central tendency modeling risks for a given receptor/medium combination. Because of data lim itations and constraints, it is not feasible to develop confidence distributions for many of th e sources of parameter and model uncertainty identified for this risk analysis. Thus, the st aff will use sensitivity analysis techniques to examine the impact of sources of uncerta inty on exposure and risk results.

The sensitivity analysis technique s involve establishing plausible ra nges for input parameters and identifying multiple modeling options reflecting model uncertainty for specific modeling steps, as described in Section 7.1. For the screening- level ecological risk assessment, the key sources of modeling uncertainty should be th ose related to future predictions of concentrations of Pb in environmental media and the assumptions or models used to develop media-specific ecotoxicity benchmarks (e.g., EPA’s sediment criteria based on the equilibrium partitioning approach). Exposure parameters for which uncertainty is likely to be key include the emissions input parameters and various exposure parameter values that are built into the ecotoxicity benchmarks (e.g., for Eco-SSLs for birds and mammals, food ingestion rates, diet selection; for EPA equilibrium-based sediment benchmarks, values for SAV, FOC, and TOC) and the applicability of the species represented in the ecotoxicity benchmarks to a given case study site location. Values for these parameters are both variable (if they were known with accuracy) and uncertain (given that we don’t know the tr ue distribution of values or ev en have good estimates of mean values for a parameter for given species and locati ons). Data from a variety of sources can be used to bound the possible parameter values in general or for a specif ic case study location.

At a screening level, the impact of using more stringent compared to le ss stringent toxicity reference values could be evaluated to develop both minimum and maximum estimates of risk. For soils, these could be based on NOAEL to L OAEL-based toxicity reference values; for sediments these could be based on TECs to PECs; and for surface waters, these could be based on AWQC calculated for a water hardness of 50 to 200 mg/L CaCO 3 . Also, for soils, the impactDRAFT – May 31, 2006 49 of different parameters used to calculate the Eco-SSL (e.g., for birds and mammals, soil ingestion rate, food ingestion rate, and compos ition of diet) on the risk results could be examined. Possible variability in Pb concentr ations for each environmental medium could be assessed on the basis of available data (e.g., ar e there enough soil or surface water/sediment sampling sites for the case study location to character ize spatial and/or tempor al variability in Pb concentrations in these media) or discussed qualitatively.

Additional sources of uncertainty that would require qualitative discussion include uncertainty about the presence or absence of most susceptible or se nsitive species that are the basis of the ecotoxicity benchmarks at a particular site, prop ortion of a population of plants or animals that might be exposed at or above different Pb c oncentrations, and whethe r local pH levels and organic content are much different than those associated with the experiments on which the ecotoxicity benchmarks are based.DRAFT – May 31, 2006 50 9.0 ECOLOGICAL RISK ANALYSIS PLAN

9.1 Tier 1: Screening Level Analysis

9.1.1 Overview

To establish to potential for adve rse effects to ecosystems from ambient Pb, it is necessary to

first characterize current levels of Pb in environmental media (air , soil, freshwater, and sediment). A broader analysis would include terrestrial systems, aquatic systems, marine systems, and estuarine/wetland systems. Given th e available data and th e potential for effects from Pb, this analysis focuses on current and future concentrations of Pb in terrestrial soils as

well as concentrations in ambient air. We are cu rrently evaluating the availability of data and the feasibility of models to charac terize aquatic exposures using fre shwater and sediment. Evidence presented in the CD suggests that , in highly impacted areas at l east, soil concentrations do not change over small time intervals even if sources of Pb are removed or emissions decreased. Given advances in modeling and larger data sets, this analysis attempts to test and expand this assumption by applying more recent data, existing sc reening tools, and current models to look at areas with high media concentratio ns of Pb as well as more typi cal areas in which Pb emissions may be very low.

Current media concentrations for specific cas e study locations can then be compared to established ecotoxicity screeni ng benchmarks for soils (e.g., Ecol ogical Soil Screening Levels or Eco-SSLs), freshwater (e.g., ambient water quality criteria for the protecti on of aquatic life, or AWQC), and sediments (e.g., sediment quality criteria for the protection of aquatic life) to select locations for analysis in which there appears to be the potential for adverse effects from Pb and to identify which media and receptor combinations , if any, are likely to be adversely impacted by current and future Pb concentrations given cu rrent ambient air conditions. Once current Pb concentrations are known for a specific location, fu ture concentrations may be predicted based on various air quality scenarios to determine what happens to Pb concentrations in media over time given changes to Pb concentrations in ambient air.

9.1.2 Data Sources for Determining Media Concentrations

Empirical data sources for current ambient levels of Pb in air, soil, freshwater, and sediment will

be needed to establish current media conditions. Modeling will be used to estimate future media concentrations given various NAAQS-relevant scenarios. The CASAC review of the 1989 exposure and risk assessment recommended that a constant soil concentration should be used in predicting future scenarios. The plan detail ed here considers using current methods for estimating the relationship between soil Pb and air concentrations for future scenarios.

Air: Sources of data for air emissions and monitoring data for selected locations will be used to

establish current air conditions as discussed in Section 4.1.1. As described in the AQCD, there are a number of monitoring networks that may be useful for this purpose, and we will also investigate the availability of additional data in particular locations (e.g., near sources of interest).DRAFT – May 31, 2006 51 Soils:
Relevant literature will be sca nned to determine the most reasona ble source of data on current soil concentrations for a given study location. Depending on the study location there are several options for soil data (e.g., regi onal and local entities, soil mon itoring protocols for primary and secondary sources, and urban areas monitoring pr ograms). Data sources might be needed for “background” Pb concentrations in areas remove d from historical and current anthropogenic Pb

sources as well.

In developing the Eco-SSLs, EPA compiled data on metals concentrations in soils across the United States (EPA 2005a; Appendix A of Attachment 1-4) in areas that might be considered “background”. The data sources for Pb in Appe ndix A include a variety of county- and statewide assessments conducted using a variety of different analytic t echniques and sampling strategies. National-level assessments have been conducted by the US Geological Survey (USGS).

The USGS data primarily were collected in th e 1970’s and therefore were taken before the removal of Pb in gasoline; however, collection s ites were far from urban areas and other point

sources of Pb and also were at least 100 m from any road. Preliminary review of this data set indicates that the higher soil Pb concentrations in the US (i.e., 25 to 30 ppm) tend to be localized in certain areas, including the western mineral mi ning areas, but high Pb concentrations also are present in areas of the northeast. Given that Pb is very stable in soil, these data are probably still the best available for larg e areas of the country.

For screening-level ecological risk assessments for areas in the vicinity of selected case study sites, the county- and state-wi de soil assessments presented in the Eco-SSL documentation (Appendix A of Attachment 4-1) may provide additional information, depending on the location.

Staff currently is assessing the degree to wh ich the USGS soil sampling data, from the 1970s, may or may not be representative of current concentrations. For this data set, only 14 percent of samples fell below the detection limit of 10 ppm, wh ich is very close to the Eco-SSL for birds of 11 ppm based on the woodcock (see Section 9.1.5.1) . More than 99 percent of these background

soil concentrations for Pb are less than the next highest Eco-SSL for birds of 46 ppm for the dove (see Section 9.1.5.1).

Freshwater Surface Waters Ambient water concentrations of Pb may be found in several national datasets which are currently being evaluated for this analysis , including both EPA and USGS datasets.

The EPA STORET database from the EPA Office of Water includes the water quality sampling data developed by states to determine compliance with water quality standards and reported to EPA annually, sampling data from Superfund sites, and data from other sources (e.g., U.S. Army Corps of Engineers). EPA’s Envi roMapper can be used to focus on localized geographic areas to identify sampling locations and to download requ ested water quality data for those locations.

Limitations of STORET data incl ude differences in sampling dens ity and detection limits across states. For Pb, a limitation is that many stat es do not attempt to measure dissolved PbDRAFT – May 31, 2006 52 concentrations, only total Pb concentrations, beca use most of the Pb in most surface waters is sorbed to particulate matter in the water column or is in the sediments. (The AWQC for Pb is based on dissolved (bioavailable) Pb, not total Pb). For the states that do sample for dissolved Pb, the analytic techniques used do not detect an y dissolved Pb in most samples (exceptions near urban areas and mining sites). Moreover, the det ection limit for dissolved Pb varies by state and some states use analysis methods with detection limits at or above the chronic AWQC for Pb in freshwater (2.5 ug/L at a wate r hardness of 100 mg/L as CaCO 3 ). Preliminary review of data in STORET indicate exceedances of the AWQC for a quatic life at only a fe w locations, principally in the midwest near mining sites and at some urban or industrial locations.

The USGS maintains water quality data in two separate data sets: the National Water Information System (NWIS) Network and th e National Water Quality Assessment (NAWQA) Data Warehouse. The NWIS system was original ly developed for water flow data, and includes data from 1.5 million sites across all 50 states, the District of Columbia, and Puerto Rico. Chemical quality data have been added to it in recent years. The data have been compiled from a variety of projects ranging from national level studies to small watershed projects up through 2004; therefore, the sampling methods, density of sampling sites, and detection limits are

variable across the data set. L ead has been an analyte at some of the sites; however, how well different geographic areas are repr esented for Pb is not known at this time and might be difficult

to determine.

The NAWQA Data Warehouse includes the sa mpling data from the NAWQA program that started systematically collecting chemical, biolog ical, and physical wate r quality data from 42 study unit basins across the nation in 1991 (dat a compiled through 9/30/2004 at this time). Basins from all regions of the United States are included; however , only approximately 50 percent of the land base is covered by these basins . Lead is one of the analytes in the program,

and measurements of dissolved and total Pb are av ailable from most locations. An analysis of the data in NAWQ by EPA, the current draft AWQCD found a total of 3,445 measurements of dissolved Pb in surface waters, for which 86 percen t were non-detects. When looking at a subset

of those data determined to be “natural” or b ackground areas, of 430 samples for dissolved Pb in surface waters, 88 percent were nondetects. The mean and upper 95 th percentile concentrations of dissolved Pb in the total sample were 0.66 and 1.10 ug/L, respectively, both of which are less than the most stringent chronic AWQC of 1.2 ug/L (at a water hardness of 50 mg/L CaCO 3 ). The mean and upper 95 th percentile concentratio ns of dissolved Pb in the natural samples were slightly lower, 0.52 and 0.50 ug/L, respectivel y, indicating a skewed distribution of Pb concentrations, as is expected. Thus, risks for aquatic biota in the water column, as assessed by exceedance of the AWQC for Pb (see Section 9.1.5.2) , are expected at less than 5 percent of the NAWQA sampling stations nationwid e. Maximum dissolved Pb concentrations for all sites and

for the subset of natural sites were 29.8 and 8.4 ug/L, both of which exceed the AWQC. With the latitude and longitude of sa mpling locations known for both da tabases, it is possible to use existing GIS data layers to identify the local la nd uses and other relevant river basin/watershed data in the vicinity of the sampling lo cations that show exceedances of AWQC.

Of the three available surface water quality data bases, STORET and NWIS include samples from

more locations in the US than does the NAW QA data set, but the sampling techniques and sampling methods represented in STORET and NWIS are inconsistent from site to site. TheDRAFT – May 31, 2006 53 NAWQA data set provides representative, althou gh not complete, coverage of the US and a consistent approach to sampling and analysis of the elements assessed. Thus, the NAWQA data set may be more appropriate for a national-leve l ecological risk screen ing assessment than the

other two data sets. For the a ssessment of ecological risks at selected ca se study locations, the availability of data from NA WQA, STORET, and NWIS could be assessed, in that order.

Sediments:
Sediment data are available from the USGS NAWQA program as well as from the EPA STORET database. The USGS program database includes Pb concentrations in sediments measured using consistent sampling techni ques from 42 river basin areas across the

conterminous United States. The STORET sediment Pb data, while providing more complete coverage of the United States, again vary from st ate to state with respec t to density of sampling locations, sampling methods, and de tection limits. With the latit ude and longitude of sampling locations known for both databases, it is possible to use existing GIS data layers to identify the

local land uses and other relevant river basin/watershed data.

An analysis of the data in the NAWQA Data Warehouse by EPA, the AW QCD found a total of 1,466 measurements of Pb in bulk sediments with a gr ain size less than 63 um. Of the total, only 0.48 percent of all samples were non-detect. Looking at a subset of 258 of those data determined to be from “natural” areas, 1.2 percent were nondetects. The mean and upper 95 th percentile

concentrations of Pb in bulk sediments for all samples were 120 and 162 ug/g dry wt, respectively. The mean and upper 95 th percentile concentrations of Pb in the natural samples were slightly lower, 109 and 162 ug/L, respectiv ely. Using the “Consensus-based” sediment quality guidelines for Pb of 36 mg/kg dry weight as a concentration that is unlikely to cause adverse effects in benthic organisms (threshold- effect concentrations or TECs) and 130 mg/kg dry weight as a concentration that is likely to cause adverse effects on sediment-dwelling organisms (probable-effect concentrations or PECs) (MacDonald et al. 2003) (see Section 9.1.5.3), it is apparent that a much higher proporti on of the samples of Pb concentrations in sediments in the NAWQA data set exceed one or both of the sediment quality guidelines for the protection of benthic life than do the surf ace water concentrations exceed AWQC. The consensus-based guidelines were developed by the USGS in cooperation with EPA and other federal, state, and local agenci es, as discussed in Section 9.1.5.3.

9.1.3 Case Study Selection

The challenge in selecting locations for analys is are that the ideal location would be 1) ecologically relevant (contain significant ecological resources such as forests, water bodies, and known populations of specific receptors used in the screening levels); 2) contain complete empirical data sets from air monitoring, media sampling, and la nd use data; and 3) contain a known air source with good data on em issions. It is also important to analyze a location that is highly impacted by Pb emissions (exceeds ecotoxici ty screening values) to be certain that the long term effects at this atypically high level of deposition be capture d as well as a site(s) that are more typical. However, it is possible that at su ch sites, existing Pb concentrations in soils and sediments are sufficiently high as to be relativel y insensitive to alternative NAAQS scenarios. Therefore, locations in areas where current Pb cont amination is close to levels that might reflectDRAFT – May 31, 2006 54 a threshold for adverse ecological effects might be more informative in analyzing alternative NAAQS. To conserve time and resources, locations with higher exposures to Pb, as described in section 2.0, will be parameterized as much as possible to include ecologically relevant criteria and thereby used in the welfare assessment. In addition, 1-2 locati ons that are more typical of low level exposures or alternate ecological scen arios might be modeled in tier 2 as well.

9.1.4 TRIM.FaTE Model

In addition to the MPE model discussed in se ction 4.1.3, TRIM-FaTE is being considered to

model both future media concentrations and intake rates for susceptible receptors. TRIM is a time series modeling system with multimedia ca pabilities for assessing human health and ecological risks from hazardous and criteria air pollutants developed by the US EPA. The Environmental Fate, Transport, and Ecologi cal Exposure module, TRIM.FaTE, accounts for movement of a chemical through a comprehensive system of discrete compartments (e.g., media and biota) that represent possi ble locations of the chemical in the physical and biological environments of the modeled ecosystem and prov ides an inventory, over time, of a chemical throughout the entire system. TRIM.FaTE is a mass-balanced based multimedia model, with broad flexibility in spatial, temporal and simulation design complexity.

In the tier 1 analysis, th is model (or MPE) may be used to load various media over time under

current ambient air conditions to estimate concentra tions of Pb in soil, freshwater, and sediment for case study locations. The model will need to be parameterized for Pb and it requires inputs

for current media concentrations and air emission s/deposition rates for each location. Data for emissions and deposition rates will be similar to those discussed in ea rlier sections of this plan. The contents of the biota compartments may be simplified to provide only media concentrations

not biotic concentrations for this tier of the analysis.

9.1.5 Determination of Potential Adverse Effects

9.1.5.1 Ecological Soil Screening Levels

Ecological Soil Screening Levels (Eco-SSLs) were developed by the EPA (2005a,b) as

concentrations of contaminants in soil that ar e protective of ecological receptors that commonly

come into contact with soil or i ngest biota that live in or on soil (Table 9-1). They are derived separately for four groups of ecological receptors : plants, soil invertebrates, birds and mammals and as such, are presumed to pr ovide protection of te rrestrial ecosystems. Several species in each receptor category were assessed to develop th e Eco-SSL for that category. In general, the same toxicity reference value (TRV) applies (e.g., the reference dose for the most sensitive of the adverse ecological effects on birds) to all species in each receptor categor y; however, differences in chemical intake associated with foraging tech niques and diet generally result in different EcoSSL values, expressed as a soil concentration, for the different receptors (e.g., bird species) in each receptor category. Scenarios were developed for each of the representative species, and the most sensitive organism for which the Eco-SSL was lowest was chosen in each category to establish the Eco-SSL. They are conservative va lues and are intended to be applied at the screening stage of an ecological risk assessment.DRAFT – May 31, 2006 55 Table 9-1. Ecological Soil Screening Levels for Lead for Various Receptors (EPA 2005b) Receptor Soil Concentration (mg Pb/kg soil dry weight)

Plants 120

Soil Invertebrates 1,700

Birds 11

Mammals 56

In this analysis, Eco-SSLs are intended to be us ed as screening values to focus further analyses on receptors that are likely to be exposed to soils that exceed the relative Eco-SSL for that receptor, thereby indicating the potential for adverse effects to that receptor. It should be noted that the value given for birds (11 mg/kg dry weight in soil) is likely to be exceeded currently by most soils in the United States. Therefore, this value is not useful in focusing the analysis or looking at the impact of current ambient air on this receptor. As the avian value is too

conservative to reflect conditions in the real worl d, there are three options for the purpose of this analysis: 1) ignore birds in the analysis and use other receptors as necessary, 2) use another bird value from the original Eco-SSLs for Pb to deve lop the Eco-SSL for birds (e.g., the next highest avian Eco-SSL of 46 mg/kg soil dr y weight for a dove), or 3) r ecalculate value for the current bird species, the woodcock, based on more realistic diet composition (not 100 percent earthworms), food intake rates a nd incidental soil intake rates for woodcock (both are somewhat higher in the calculation of the Eco-SSL for woodcock than provided in EPA’s 1993 Wildlife Exposure Factors Handbook; EPA 1993a,b), and a best estimate of earthworm absorption of Pb, rather than an upper 95 th percentile estimate of earthworm absorption of Pb.. The second option may be most useful for this analysis as it would rely on peer-reviewed Eco-SSLs and woodcock are found only in the eastern half of the United States and are not f ound in all habitat types there. Recalculation of the woodcock Eco-SSL using more realistic exposure assumptions for the

woodcock would also be considered for an ecolo gical risk assessment for areas in the eastern

half of the United States for which the conser vative Eco-SSL value for woodcock is exceeded.

9.1.5.2 Ambient Water Quality Criteria

Ambient Water Quality Criteria (AWQC) were de veloped by the EPA to provide guidance to states and tribes to use in adopting water qualit y standards. AWQC values for Pb are given for chronic and acute exposures as well as for freshwater and ma rine environments (EPA 1985). The freshwater criteria depend on water hardne ss. The values provi ded in Table 9-2 for freshwater are based on a water hardness of 100 mg /L as CaCO3; while the values for saltwater are not dependent on any water characteristics. The current equations/values may be reissued based on pH at the conclusion of the current revi sion scheduled to be completed in 2007. These are values based on toxicity te sting in aquatic organisms.

Table 9-2. Recommended Ambient Water Quality Criteria Freshwater (μg Pb/L) Saltwater (μg Pb/L)

Acute Chronic Acute Chronic

65 2.5 210 8.1DRAFT – May 31, 2006 56 In this analysis AWQC will be used to evaluate the potential for adverse effect to freshwater organisms (exposed via the water column) ove r time under the current ambient air levels.

9.1.5.3 Sediment Criteria

Sediment screening-level (and ot her) criteria or benchmarks have been established by EPA regions and at the state level fo r Pb. Some work also has been done at the national level (EPA 2005c). For the EPA national sediment quality benchmarks, equations based on an equilibrium partitioning approach have been established to rela te the Pb concentration benchmark to pH (acid volatile sulfide or AVS), fraction organic car bon (FOC), and total organic carbon (TOC). These

benchmarks also assess three levels of effect on benthic invertebrates: no -effect, threshold-effect, and probable-effect concentrations. The depe ndence of these benchmarks on values for AVS, FOC, and TOC mean that appropriate data for th eir calculation might or might not be available for a given site. That dependence also means that the values are not read ily useful for screening national-level data to identify locations wher e sediment concentrations might exceed the threshold- or probable-effect levels.

Another source of national-level sediment quality benchmarks is from the cooperative Freshwater Sediment Qua lity Assessment Initiative that began in 2000 an d that included the Florida Department of Environmental Protec tion (FDEP), the EPA, and the USGS, with participants from the US Fish and Wildli fe Service (USFWS), National Oceanic and Atmospheric Administration (NOA A), consultants, academics, county governments, and water management districts (MacDonald et al. 2003) . The Initiative recommended use of a “Consensus approach” to developing threshold- and probable-effects concentrations (TEC and PEC values, respectively) of metals in sedime nts. The approach basically consisted of calculating a geometric mean sediment concentra tion from, in the case of Pb, sediment values from five different approaches to estimating both the TEC and PEC. These values can be used in the absence of data on AVS, FOC, and TOC; ho wever, it is likely that they will be less “accurate” for sediments for which the AVS, FOC, and TOC values are far from typical values.

Note that the screening-level se diment quality benchmarks repr esenting a threshold for effects (not a probable-effect level) for EPA Regi ons 4, 5, and 6, Environment Canada, and EPA’s Assessment and Remediation of Contaminated Sediments (ARCS) Program are all between 30 and 36 mg Pb/kg dry weight. 12 Given that mean sediment concentrations measured in the NAWQA program are well above these levels, at pr obable-effect level may be more appropriate for the screening ecological risk assessment. As an initial screen for a large number of sites, the Consensus-based benchmarks may be easier to use because additional data on AVS, FOC, and TOC are not needed. For specific case study locations, once selecte d, EPA’s equilibrium partitioning approach-based values, based on site -specific values for AVS, FOC, and TOC, may be more appropriate for comparison with current and future predicted concentrations of Pb in

sediments.

12 Available from the Risk Assessmen t Information System (RAIS) (see http://risk.lsd.ornl.gov/homepage/benchmark.shtml).DRAFT – May 31, 2006 57 9.2 Tier 2: Analysis of Sensitive Receptors

9.2.1 Overview

The second tier analysis consists of more de tailed modeling of intake rates or body/tissue concentrations for receptor(s) identified for poten tial adverse effects in the tier 1 analysis and may include additional case study locations based on empirical data. This step will provide less conservative estimates of the potential for eff ect based on published concentration effects for specific receptors, if such data is available, and based on more realistic exposure factors (e.g., dietary composition, food ingestion rates) and possibly a less conser vative threshold for effects. For example, the current Eco-SSL toxicity refe rence values (TRVs) for birds and mammals are based on a geometric mean of no-observed-adve rse-effect levels (NOAELs) for the most sensitive of the effects on repr oduction or growth. It may be that use of a lowest-observedadverse-effect level (LOAEL) for such effects ma y be more representative of a threshold for population-level effects in the most su sceptible bird or mammal populations.

9.2.2 Estimation of Exposure for Sensitive Receptor(s)

9.2.2.1Detailed Modeling of Intake or Body/Tissue Concentration

TRIM.FaTE may be used to provide a more detail ed picture of actual intake rates or body tissue concentrations expected in se nsitive receptor(s) given a spec ific soil (or surface water or sediment) concentration. This step may be undertaken for those loca tions in the tier one screening analysis where there is the potential for adverse effect to a specific receptor (screening values are exceeded either currently or in the futu re). The model will be more fully developed to include more biotic compartments and tailored to produce either intake rates or body/tissue concentrations depending on the metric used by pub lished concentrations effects studies for that
receptor.

9.2.2.2 Comparison to Known Concentration Effects

Reference toxicity values expressed as a daily dose (daily intake via ingestion) have been developed for the Eco-SSLs for birds and mammal s. Those values are based on NOAELs, rather than LOAELs, for reproduction and growth. The corresponding LOAEL data are provided, however, and could be analyzed for use in this context.

If other receptor groups turn out to be more at risk than birds and mammal s, in support of a Tier 2 analysis, we will identify appropriate toxicity benchmarks from other sources or perform a review of scientific literature to identify con centration effects studies relevant to these other sensitive receptor(s) chosen in the tier one anal ysis. Specific known physiol ogical effects, if any, will be discussed based on likely intake rates or body/tissue concentrations resulting from the future media concentrations. This step is depend ent on the availability of data on specific effects to specific receptors (or classes of receptors).DRAFT – May 31, 2006 58 10.0 REFERENCES

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