# Lead: Human Exposure and Health Risk Assessments for Selected Case Studies

Volume I. Human Exposure and Health Risk Assessments - Full-scaleEPA-452/R-07-014a October 2007



## Lead: Human Exposure and Health Risk Assessments for Selected Case Studies

Volume I. Human Exposure and Health Risk Assessments - Full-scale

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Research Triangle Park, North CarolinaDISCLAIMER

This document has been reviewed by the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency (EPA), and approved for publication. This document has been prepared by staff from the Office of Ai r Quality Planning and Standards, U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or recommendations are those of the authors and do not necessarily reflec t the views of the EPA Mention of trade names or commercial products is not intended to const itute endorsement or recommendation for use. Any questions or comments concerning this docu ment should be addressed to Zachary Pekar, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06, Research Triangle Park, North Carolina 27711 (email: pekar.zachary@epa.gov).i 

### TABLE OF CONTENTS

List of Tables ................................................................................................................

...... iv List of Figures...............................................................................................................

....... v

1 INTRODUCTION.................................................................................................. 1-1

1.1 MULTIMEDIA ASPECT OF THE RISK ASSESSMENT ............................ 1-1

1.2 RISK ASSESSMENT FROM LAST REVIEW..............................................1-4

1.3 PILOT PHASE ASSESSMENT FO R THE CURRENT REVIEW ................1-5

1.4 CASAC ADVICE ............................................................................................ 1-6

1.5 ORGANIZATION OF THE DOCUMENT..................................................... 1-8

REFERENCES .......................................................................................................... 1-9

2 DESIGN OF EXPOSURE AND RISK ASSESSMENTS................................... 2-1

2.1 BACKGROUND INFORM ATION ON LEAD EXPOSURE AND RISK .... 2-1 2.1.1 Sources, Pathways and Routes...............................................................2-3 2.1.2 At-risk Populations ................................................................................ 2-4 2.1.3 Internal Disposition................................................................................ 2-6 2.1.4 Health Endpoints.................................................................................... 2-7 2.1.4.1 Developing Nervous System.................................................. 2-8 2.1.4.2 Adult Nervous System .........................................................2-10 2.1.4.3 Cardiovascular System......................................................... 2-11 2.1.4.4 Renal System ....................................................................... 2-12 2.1.4.5 Heme Synthesis.................................................................... 2-12 2.1.4.6 Immune System ................................................................... 2-13 2.1.5 Metric and Model for Risk Quantitation..............................................2-14
2.2 USE OF CASE STUDIES AN D LOCATION SELECTIONS ..................... 2-19 2.2.1 General Urban Case Study................................................................... 2-20 2.2.2 Point Source Case Studies ...................................................................2-20 2.2.2.1 Primary Pb Smelter Case Study........................................... 2-21 2.2.2.2 Secondary Pb Smelter Case Study....................................... 2-24
2.3 ASSESSMENT SCENARIOS....................................................................... 2-25

2.3.1 Air Concentrations............................................................................... 2-25 2.3.2 Policy-relevant Background................................................................. 2-28 2.3.3 Outdoor Soil/Dust ................................................................................ 2-29
2.4 ANALYTICAL APPROACH ....................................................................... 2-31 2.4.1 Temporal Aspects ................................................................................ 2-31 2.4.2 Spatial Scale and Resolution................................................................2-31ii 2.4.3 Categorization of Policy-relevant Exposure Pathways........................2-32 2.4.4 Overview of Analytical Steps .............................................................. 2-34 2.4.4.1 Exposure Assessment........................................................... 2-37 2.4.4.2 Risk Characterization........................................................... 2-39 2.4.5 Variability Characterization................................................................. 2-39 2.4.6 Uncertainty Characterization and Sensitivity Analysis ....................... 2-40 2.4.6.1 Performance Evaluations ..................................................... 2-41 2.4.6.2 Generating Multiple Sets of Results .................................... 2-41 2.4.6.3 Sensitivity Analysis ............................................................. 2-42 2.4.6.4 Qualitative Discussion of Sources of Uncertainty...............2-43
REFERENCES ........................................................................................................ 2-44

3 EXPOSURE ASSESSMENT ................................................................................ 3-1

3.1 METHODS FOR ESTIMATING ME DIA CONCENTRATIONS................. 3-1

3.1.1 Ambient Air Concentrations.................................................................. 3-3 3.1.1.1 General Urban Case Study..................................................... 3-3 3.1.1.2 Primary Pb Smelter Case Study............................................. 3-4 3.1.1.3 Secondary Pb Smelter Case Study......................................... 3-5 3.1.2 Inhalation Exposure Concentrations...................................................... 3-5 3.1.3 Outdoor Surface Soil/Dust Concentrations............................................ 3-6 3.1.3.1 General Urban Case Study..................................................... 3-6 3.1.3.2 Primary Pb Smelter Case Study............................................. 3-6 3.1.3.3 Secondary Pb Smelter Case Study......................................... 3-7 3.1.4 Indoor Dust Concentrations ................................................................... 3-8 3.1.4.1 General Urban Case Study................................................... 3-10 3.1.4.2 Primary Pb Smelter Case Study........................................... 3-12 3.1.4.3 Secondary Pb Smelter Case Study....................................... 3-14
3.2 METHODS FOR ESTIMATI NG BLOOD PB LEVELS ............................. 3-15 3.2.1 Blood Pb Modeling..............................................................................3-15 3.2.1.1 Primary Analysis.................................................................. 3-16 3.2.1.2 Sensitivity Analysis ............................................................. 3-17 3.2.2 Exposure Pathway Apportionment and Probabilistic Population Modeling........................................................................................................3-18 3.2.2.1 General Urban Case Study................................................... 3-20 3.2.2.2 Point Source Case Studies ...................................................3-20 3.2.3 GSD for Population Blood Pb Modeling Procedure............................3-23 3.2.3.1 General Urban Case Study................................................... 3-25 3.2.3.2 Point Source Case Studies ...................................................3-26
3.3 ESTIMATED MEDIA CONCENTRATIONS.............................................. 3-26

3.4 ESTIMATED BLOOD PB LEVELS ............................................................ 3-29

3.5 UNCERTAINTY CHARACTERI ZATION AND SENSITIVITY

ANALYSIS................................................................................................... 3-33iii 3.5.1 Performance Evaluation Related to Exposure Media Modeling..........3-33 3.5.1.1 Evaluation of Modeled Ambien t Air Pb Concentrations.....3-33 3.5.1.2 Evaluation of Modeled Outdoor Soil/Dust Pb Concentrations .............................................................................. 3-34 3.5.1.3 Evaluation of Modeled Indoor Dust Pb Concentrations...... 3-35 3.5.2 Performance Evaluation Relate d to Blood Pb Modeling.....................3-38 3.5.2.1 Evaluation of Candidate Blood Pb Models..........................3-38 3.5.2.2 Evaluation of model-derived outdoor air Pb-to-blood Pb ratios .............................................................................................. 3-39 3.5.2.3 Comparison of modeled blood Pb levels to nationally representative data ..............................................................................3-42
REFERENCES ........................................................................................................ 3-46

4 RISK ASSESSMENT ............................................................................................ 4-1

4.1 METHODS FOR DERIVING RISK ESTIMATES........................................ 4-1 4.1.1 Concentration-Response Functions ....................................................... 4-1 4.1.1.1 Log-Linear Function with Cutpoint....................................... 4-3 4.1.1.2 Log-Linear Function with Lo w-Exposure Linearization.......4-3 4.1.1.3 Two-piece Linear Function.................................................... 4-4 4.1.2 Projection of Population Risk ................................................................ 4-5
4.2 RISK ESTIMATES ......................................................................................... 4-6

4.3 UNCERTAINTY CHARACTERI ZATION AND SENSITIVITY

ANALYSIS.................................................................................................... 4-13 4.3.1 Qualitative Discussion of Key Sources of Uncertainty ....................... 4-13 4.3.2 Sensitivity Analysis ............................................................................. 4-17 4.3.3 Performance Analyses ......................................................................... 4-19 4.3.4 Uncertainty in Modeling Approach es – Multiple Sets of Results .......4-21
REFERENCES ........................................................................................................ 4-23

5 ADDITIONAL ANALYSES ............................................................................................. 5-1

5.1 DESIGN OF EXPOSURE AND RISK ASSESSMENTS .............................. 5-1

5.1.1 Assessment Scenarios ............................................................................ 5-1 5.1.2 Analytical Approach .............................................................................. 5-1 5.1.3 Location-Specific Urban Case Studies .................................................. 5-2
5.2 EXPOSURE ASSESSMENT .......................................................................... 5-4

5.2.1 Methods for General Urban and Prim ary Pb Smelter Case Studies ......5-4 5.2.2 Methods for Location-specifi c Urban Case Studies .............................. 5-5 5.2.2.1 Ambient Air and Inhalation Exposure Concentrations.......... 5-5 5.2.2.2 Other media Concentrations .................................................. 5-6 5.2.2.3 Blood Pb levels ......................................................................5-7 5.2.3 Media concentrations............................................................................. 5-7 5.2.3.1 Location-specific Urba n Case Studies................................... 5-7iv 5.2.3.2 General Urban and Primary Pb Smelter Case Studies........... 5-8 5.2.4 Blood Pb levels .................................................................................... 5-13 5.2.5 Uncertainty Characterization ............................................................... 5-16 5.2.5.1 Performance Evaluation of M odeled MediaConcentrations 5-16 5.2.5.2 Performance Evaluation of Modeled Blood Pb Levels........5-17
5.3 RISK ASSESSMENT.................................................................................... 5-20 5.3.1 Methods for Deriving Risk Estimates.................................................. 5-20 5.3.1.1 Concentration-response Functions....................................... 5-20 5.3.1.2 Projection of Population Risk .............................................. 5-24 5.3.2 Risk Estimates...................................................................................... 5-24 5.3.2.1 Population Risk Distri bution Estimates............................... 5-25 5.3.2.2 IQ Loss Incidence Estimates................................................5-28 5.3.3 Uncertainty Characterization and Sensitivity Analysis ....................... 5-31 5.3.3.1 Qualitative Discussion of Key Sources of Uncertainty .......5-31 5.3.3.2 Performance Analyses ......................................................... 5-33 5.3.3.3 Uncertainty in Modeling A pproaches - Multiples Sets of Results .............................................................................................5-34 5.3.3.4 Sensitivity Analysis – Indoor Dust Pb Modeling ................ 5-34
REFERENCES ..................................................................................................................... ...5-37



### List of Tables
Table 2-1. Key aspects of prim ary Pb smelter case study. ....................................................... 2-23 Table 2-2. Key aspects of sec ondary Pb smelter case study..................................................... 2-24 Table 2-3. Air quality scenarios assesse d for the general urban case study. ............................ 2-28

Table 3-1. Case study approaches for es timating media Pb concentrations............................... 3-2 Table 3-2. Hybrid model for indoor dust Pb in general urban case study. ............................... 3-12 Table 3-3. Estimated annual ambient air concentrations.......................................................... 3-27 Table 3-4. Estimated inhalation exposure concentrations. ....................................................... 3-28 Table 3-5. Estimated outdoor so il/dust concentrations.............................................................3-28 Table 3-6. Estimated indoor dust concentrations...................................................................... 3-29 Table 3-7. Summary of blood Pb estimates for medians in total-exposure blood Pb distributions. ............................................................................................................ 3-31 Table 3-8. Summary of blood Pb estimates for 95 th percentiles in total-exposure blood Pb distributions. ............................................................................................................ 3-32 Table 3-9. Evaluation of model- predicted indoor dust Pb levels against empirical data obtained from the literature.................................................................................................... 3-37 Table 3-10. Air-to-blood Pb ratios for “recent ai r” contribution to concurrent blood Pb level. 3-40 Table 3-11. Blood Pb levels for 7 year olds in the U.S. (interpolated from NHANES IV, 1999-2002). ............................................................................................................. 3-44

Table 4-1. Comparison of total and incremental IQ loss estimates below 10 μg/dL for the three concentration-response functions..............................................................................4-2 Table 4-2. Summary of risk estimates for medi ans of total-exposure risk distributions. ......... 4-11v Table 4-3. Summary of risk estimates for 95 th percentiles of total exposure risk distributions. ............................................................................................................ 4-12 Table 4-4. Impact of multiple sources of uncertainty on risk results. ......................................4-22 Table 5-1. Estimated annual average ambient air concentrations. ............................................. 5-9 Table 5-2. Estimated inhalation exposure concentrations. ....................................................... 5-10 Table 5-3. Estimated outdoor so il/dust concentrations.............................................................5-11 Table 5-4. Estimated indoor dust concentrations...................................................................... 5-12 Table 5-5. Summary of blood Pb level es timates for median total blood Pb. .......................... 5-14 Table 5-6. Summary of blood Pb level estim ates for high-end total blood Pb.........................5-15 Table 5-7. Air-to-blood ratios derived by comparing air quality scenario air and blood Pb estimates. ................................................................................................................. 5-19 Table 5-8. Comparison of total and incremental IQ loss estimates below 10 μg/dL for the four concentration-response functions............................................................................5-23 Table 5-9. Summary of risk estimates for medi ans of total-exposure risk distributions. ......... 5-26 Table 5-10. Summary of risk estimates for 95 th percentiles of total exposure risk distributions. ............................................................................................................ 5-27 Table 5-11. Incidence of children with >1 point IQ loss........................................................... 5-29 Table 5-12. Incidence of childre n with >7 points IQ loss. ........................................................5-30 Table 5-13. Comparison of hybrid indoor dust model with a modified form of the model. ..... 5-36



### List of Figures
Figure 1-1. Principal pathways of human and ecological exposure to Pb. . ...............................1-3 Figure 2-1. Conceptual model for Pb human health risk assessment. ......................................... 2-2 Figure 2-2. Overview of analysis approach. ..............................................................................2-36 Figure 2-3. Modeling approaches for case study analyses presente d in chapters 3 and 4. ........2-42 Figure 3-1. Procedure for generating population blood Pb distributions for point source case studies......................................................................................................................3- 22 Figure 3-2. Comparison of NH ANES IV blood Pb levels with modeled estimates. ................. 3-44 Figure 4-1. Comparison of three concentra tion-response functions for concurrent blood Pb levels < 10 μg/dL. ...................................................................................... 4-2 Figure 5-1. Core modeling approach for each case study............................................................ 5-2 Figure 5-2. Comparison of four concentration-response functi ons for concurrent blood Pb levels < 10 μg/dL....................................................................5-231-1 

### 1 INTRODUCTION

This document is the first volume of the report Lead: Human Exposure and Health Risk Assessments for Selected Areas . This volume describes the qu antitative human exposure and
health risk assessments 1 conducted to inform the U.S. Environmental Protection Agency’s (EPA’s) current review of the National Ambient Ai r Quality Standards (NAA QS) for lead (Pb). As with the last review of the Pb NAAQS (see Section 1.2), the human exposure and health risk assessments (the risk assessment) 2 for this review reflect multimedia exposure pathways, and their influence on blood Pb levels as an internal index of exposure or dose (Section 1.1). The assessment for this review, as with that for the last review, utilizes a case study approach wherein a set of sp ecific locations or case studies associated with policy-relevant Pb exposures are evaluated in detail. The cas e studies have been selected to provide a perspective on the nature and magnit ude of air source Pb exposures a nd risk in the United States. There are two phases to the risk assessment fo r the current review: pilot and full-scale. The first phase (the pilot assessm ent, described in Section 1.3) wa s presented in the first draft Staff Paper and accompanying technical report (U SEPA, 2006a; ICF, 2006), and was the subject of a review by the Clean Air Scientific Advisory Committee (CASAC) on February 6 and 7, 2007 described in Section 1.4 (Henderson, 2007a). Th e initial full-s cale analyses were presented in the July 2007 draft report (USEPA, 2007b) and were the subj ect of a CASAC review at a public meeting on August 28 and 29, 2007. In res ponse to CASAC recommendations described in Section 1.4 (Henderson, 2007b), additional analyses, using a core modeling approach were conducted to complete the full-scale assessment. All analyses for the full-scale assessment are described in this document, with the initial analyses being the focus in Chapters 3 and 4 and the additional analyses in Chapter 5. Further, given the significant time constraints of this revi ew, risk results are provided in this document without substantial interp retation. Rather, interpretative discu ssion of these results is provided in the Staff Paper.

1 As described in the Preface to this document, the ecol ogical risk analysis performed for this review, which will be considered in the policy assessment for the secondary standard, is presented in the draft technical report of the pilot phase risk assessments (ICF, 2006) and described in the Staff Paper (USEPA, 2007a).

2 Throughout the remainder of this document, the term "risk assessment" will be used to refer to both the human exposure and health risk assess ments collectively, unless specific refere nce to either the human exposure or health risk assessment is required.1-2 1.1 MULTIMEDIA ASPECT OF THE RISK ASSESSMENT The focus for this Pb NAAQS risk assessment is on Pb derived from those sources emitting Pb to ambient air. In designing and implementing this assessment, we have been faced with significant limitations and complexity that go far beyond the situation for similar assessments typically performed for other criteria po llutants. In addition to the constraints of the timeframe allowed for this review, we are also co nstrained by significant limitations with regard
to data and tools needed for the assessment. Th e multimedia and persistent nature of Pb and the role of multiple exposure pathways contribute significant additional complexity to the assessment as compared to other assessments that focus only on the inhalation pathway. First, exposures to Pb emitted into the air o ccur via multiple pathways. As described in
the Air Quality Criteria for Lead (USEPA, 2006b; henceforth refe rred to as the CD), “The multimedia aspects of Pb exposure can be seen in that Pb emissions to the air contribute to Pb concentrations in water, soil and dusts; Pb in so il and dust also can make important contributions
to Pb concentrations in am bient air” (CD, p. 3-1). Inhalation exposures can result from Pb emitte d to the ambient air recently or from Pb emitted in the past that has deposited from air to soil or dust and then become resuspended in the ambient air. Further, Pb emitted into the am bient air can contribute to ingestion exposures (associated with indoor dust, outdoor soil/dus t, agricultural products and surface water) of recently deposited Pb and of Pb that was deposited in the past. Consequently, this is a multipathway risk assessment in which we are cons idering both airborne Pb, as it contributes to human exposures through direct inhalation of particles containi ng Pb, and also Pb that has deposited from air to dusts, soil and other en vironmental media and that contributes to human exposures through ingestion. Fu rther, we are considering that Pb, once deposited, may be resuspended in the air, contributing to huma n inhalation exposures or, upon redeposition, to human ingestion exposures. Thus, as illustrated in Figure 1-1, pathways that are directly relevant to a review of the NAAQS include both newly emitted Pb from currently operating sources, and Pb emitted in the past, either from currently operating sources or historic sources, which are collectively referred to as “ policy-relevant sources”.1-3 Newly emitted Pb Historically emitted Pb

Air Outdoor Soil, Dusts

Indoor Dust Paint

Surface Waters & Sediments Policy-relevant Background* Policy-relevant Sources

Non-air Pb releases Policy-relevant Background*

Human Exposures -Inhalation & ingestion Diet**

Ecological Exposures Drinking water

* Policy-relevant background sources and pathways are indicated by dashed lines.

** Dietary exposure should not be considered to be limited to policy-relevant background, as it reflects a combination of Pb introduced into food items during processing (policy-relevant background), as well as Pb associated with atmospheric deposition (policy-relevant sources).

Figure 1-1. Principal pathways of human and eco logical exposure to Pb. Among the policy-relevant pathways, heavy arrows indicate the predominant human
exposures.
Due to limited data, models, and time availabl e, however, we are not able to fully and completely characterize in our risk assessment all of the various comp lexities associated with Pb. Consequently, in our efforts to focus on and char acterize risk associated with the ambient airrelated 3 sources and exposures, we have made a number of simplifying assumptions in a number of areas. For example, Figure 1-1 illustrates that people are also exposed to Pb that originates from nonair sources, including leaded paint or dr inking water distribution systems. For purposes of this assessment, the Pb from these nonair sources is collectively referred to as “policy-relevant background” 4 . Although Pb in diet and drinking water sources may derive from Pb emitted into

3 Ambient air related sources are those emitting Pb into the ambient air (including resuspension of previously emitted Pb), and ambient air related exposures in clude inhalation of ambient air Pb as well as ingestion of Pb deposited out of the air (e.g., onto outdoor soil/dust or indoor dust).

4 This categorization of policy-relevant sources and ba ckground exposures is not intended to convey any particular policy decision at this stage regarding the Pb st andard. Rather, it is simply intended to convey an area of
interest to this review.1-4 the ambient air, the contribution from air pathways to these exposure pathwa ys is not explicitly recognized, such that these exposures ar e treated as policy-relevant background.

1.2 RISK ASSESSMENT FROM LAST REVIEW In the risk assessment conducted in support of the last review, air qua lity scenarios were compared in terms of their impact on the per centage of modeled populat ions that exceeded specific blood Pb levels chosen with consideration of the health effects evidence at that time (USEPA, 1986a, 1986b, 1990a). The 1990 analysis fo cused on both children (birth through 7 years of age) and middle-aged men residing in th ree case study locations, two near secondary Pb smelters and one near a primary Pb smelter (U SEPA, 1990b). The analysis also introduced the use of pharmacokinetic blood Pb modeling for children, although it used empirically derived slope models for adult men to relate cha nges in air Pb to ch anges in blood Pb. In the 1990 Staff Paper, staff concluded that at levels of 10-15 micrograms per deciliter ( μ g/dL) of blood Pb, there appeared to be “a convergence of evidence of lead-induced
interference with diverse set of physiological functions and pr ocesses, particularly evident in several independent studies s howing impaired neurobehavioral function and development” (USEPA, 1990). 5 Accordingly, the staff used blood Pb levels of 10 and 15 μ g/dL to evaluate effects of alternate NAAQS on children in the 19 90 analysis (USEPA, 1990). These values were
chosen with consideration of the full body of health effects evidence at that time. Staff then used dispersion modeling (the Indus trial Source Complex (ISC) m odel) combined with source characterization data to generate Pb air concentrations for each case study area. Statistically derived relationships based on data from other industrial locations , including Pb smelters, that
linked concentrations of Pb in ai r to Pb in indoor dust and outdoor soil were then used to predict Pb in these media for the three case study locations , based on the modeled air Pb concentrations. An uptake/biokinetic model was also developed to predict child bl ood Pb levels. This model was used in place of a statistical re gression slope model to allow c onsideration of the dynamic nature of Pb exposure in children. EPA combined model- derived central tendency blood Pb levels with an estimated geometric standard deviation (GSD ) reflecting interindividu al variability in blood Pb levels, to generate population distributions of blood Pb leve ls. These distributions were then used to estimate the percentage of children at each case study location that exceeded the blood
Pb levels 10 and of 15 μ g/dL, respectively. For adult men, the 1990 assessment used blood Pb levels of 10 and 12 μ g/dL to compare
relative effects of alternat e NAAQS (USEPA, 1990). The same approach was used for

5 As a result of a parallel activity, the U.S. Centers for Disease Control and Prevention in 1991 reduced the children’s blood Pb level warranting individual intervention to 15 μ g/dL and identified a level of 10 μ g/dL for implementing community-wide prevention activities (CDC, 1991; CDC, 2005).1-5 generating media concentrations fo r the adult analysis as was used for the child assessment. However, rather than using a biokinetic m odel, as for the children’s assessment, the 1990 analysis for adults used statistically derived slope models to relate air Pb to blood Pb levels with two versions of the slope mode ls being employed: (a) the aggregate model which predicts blood Pb in adults based solely on air Pb levels (h ere a single slope factor captures both the direct inhalation pathway as well as the more comple x pathway of Pb deposition to soil and dust followed by incidental ingestion) and (b) the disaggregate model which uses media-specific slopes to predict blood Pb based on Pb concentrati ons in soil, dust and air. Since the projected blood Pb levels were mean population levels, a GSD term was included to develop populationlevel blood Pb distributions. The GSD estimates for adults and children were derived from
information on observed blood Pb levels in these subgroups. These population-level distributions were then queried to identify the percentage of adu lt men at each case study location with modeled blood Pb levels exceeding the levels of interest for adults (10 and 12

μ g/dL). The primary difference between the risk asse ssment approach used in the current pilot analysis and the assessment completed in 1990 invol ves the risk metric employed, which reflects the quantitative and qualita tive health effects evidence availabl e today that was not available in 1990 (CD). Rather than estimating the percenta ge of study populations with exposures above
blood Pb levels of interest as was don e in the last review (i.e., 10, 12 and 15 μ g/dL), the current pilot analysis estimates the degree of health decrement in study populations exposed to Pb. Specifically, the pilot analysis es timates the distribution of IQ lo ss associated with Pb exposure for child populations at each of the case study lo cations with that IQ lo ss further differentiated between background Pb exposure an d policy-relevant exposures.

1.3 PILOT PHASE ASSESSMENT FO R THE CURRENT REVIEW The pilot phase of the risk assessment for the current review is described in the first draft Staff Paper and accompanying technical report (U SEPA 2006a; ICF 2006). The pilot assessment was intended primarily as a demonstration of th e risk assessment methodology being developed for the current review. Consequently, exposure a nd risk results from the pilot assessment are considered preliminary. The pilot assessment included three case st udies: (a) a primary Pb smelter (in Herculaneum, Missouri), (b) a secondary Pb sme lter (in Troy, Alabama), and, (c) a near roadway (urban) location along a short ro ad segment in Houston, Texas. 6 The case studies modeled for

6 This case study was intended to provide perspective on the near roadway exposure scenario but was not intended to estimate total population risk for a full urban or metropolitan area.1-6 the pilot were selected to provide a preliminar y perspective on the nature and magnitude of air sourced Pb exposures and risk. In addition, th ey provided a range of exposure scenarios in which to test the risk assessment methodology de veloped for the current review. Because of differences in the exposure scenarios and availabl e data at each of the case study locations, the approach used for modeling exposure and risk di ffered among the case studies. Results from the pilot assessment, as well as comments received from the public and CASAC (see Section 1.4) have informed decisions on the design for the fu ll-scale assessment, including the types of case
studies included.

1.4 CASAC ADVICE

The staff consulted with the CASAC on the dr aft analysis plan for the risk assessment (USEPA, 2006c) in June 2006 (Henderson, 2006), and subsequently developed the pilot assessment, summarized in Section 1.3, and de scribed in the first draft Staff Paper and accompanying technical report (USEPA, 2006a; IC F 2006). On February 6-7, 2007, the CASAC Pb panel met to discuss these documents and CASAC’s written comments and recommendations were provided in March 2007 (Henderson, 2007a).
Consistent with their mandate under the Cl ean Air Act, CASAC provided comments on both scientific aspects of the risk assessment a nd aspects related to the standards themselves (Henderson, 2007a). 7 With regard to the risk assessment, they recommended that the case study approach implemented for the p ilot risk assessment be supplemented with a “population-based” analysis, and, in discussion at th e public meeting, the panel ra ised the general occurrence of lower Pb levels in urban areas beyond more point source impacted areas as being an important focus for the risk assessment. As described below, consideration of comments in this area led to a significant difference in the design of the fu ll-scale assessment as compared to the pilot

assessment. CASAC also recommended th at uncertainty be characte rized with regard to the relationship between a change in the NAAQS and the distribution of population blood Pb concentrations, and with regard to the relationship between blood Pb concentrations and the risk
of adverse health effects (Henderson, 2007a). With re spect to alternate NAAQS for consideration by EPA, CASAC recommended consid eration of levels less than or about 0.2 μg/m 3 (micrograms per cubic meter) and of a monthly averaging time. Additionally, they indicated that they consider a population loss of 1-2 points in intelligence quotient (IQ) to be highly significant from a public health perspective and that the primary Pb standard should be set

7 Consistent with the focus of this document on th e human exposure and health risk assessment, CASAC comments regarding the ecological risk assessment and secondary standard considerations are not discussed here.1-7 to protect 99.5 percent of the population from exceeding that loss (Henderson, 2007a, p. 6). 8 CASAC also recommended conducting future Pb monitoring with samplers for particulate matter less than ten microns in size (PM 10 ) rather than with samplers for total suspended particulate matter (TSP) (Henderson, 2007a, p. 7).
In consideration of CASAC’s comments on the pilot phase as sessment (Henderson, 2007a), we considered a number of alternate a pproaches for the full-scale assessment. As a result, several additions and m odifications to the assessment de sign were implemented for the initial analyses of the full-s cale assessment presented in the July 2007 draft risk assessment report (USEPA, 2007a) and chapters 1 through 4 of this document. The most significant of these modifications was the replacement of the near roadway case study with a general urban case study. This case study was designed to provide estimat es of risk in urban areas associated with broad population level exposures to different ambient air levels of Pb. The general urban case study was assessed in addition to the two point sour ce case studies, and differs from those case studies in basing the estimate of air quality on monitoring data (rather than on results from air quality modeling). The alternate NAAQS levels were selected to overlap with the range of levels suggested by CASAC. The target populati on and endpoint for the assessment remains young children and risk of IQ decrements associated with Pb exposure. To address CASAC comments on the cutpoint employed in the pilot assessmen t (Henderson, 2007a), the blood Pb concentration response function was re-examined, and three alte rnatives were included in the assessment. Additionally, in consider ation of CASAC recommendations re garding the geometric standard
deviation used in the blood Pb modeling, two values were included in the initial analyses for the general urban case study. In their review of the July draft risk asse ssment report, in addition to reiterating their previous recommendations on aspect s of the standards and concurri ng with several aspects of the draft assessment, the CASAC Pb Panel made se veral recommendations for additional exposure and health risk analyses (Henderson, 2007b), of wh ich several notable ones are summarized here. First, they recommended that the general urban case study be augmented by the inclusion of risk analyses in specific urban areas of the U.S. Fu rther, they recommended use of the hybrid dust Pb model in preference to the alternate that had ad ditionally been employed in the initial analyses for the general urban case study. The Panel also recommended limiting presentation of population risk results to the 5 th through 95 th percentile of the distribution. 9 In response to

8 CASAC’s reference to 99.5 percent of the population in this statement is consistent with their recognition earlier in the same letter of the 99.5 percent figure being the EPA’s risk management choice when they established the Pb NAAQS in 1978 (Henderson, 2007a, pp. 5 and 6).

9 This recommendation was in recognition by Panel members of uncertainties in quantitative estimates from the assessment for this point on the distribution (Henderson, 2007b, Appendix D).1-8 discussion by the CASAC Pb Panel associated w ith these recommendations at the August public meeting, staff has developed addi tional analyses that include three location-sp ecific urban case studies, and focus on a core mode ling approach (with regard to dust Pb model, blood metric and concentration-response func tions). With regard to the blood Pb concentration-response function,
CASAC recommended use of a two- piece or dual linear function that recognizes a change in slope (to a notably higher value) at blood Pb levels of 7.5 μ g/dL and indicated less favor for the two-piece linear func tion with hinge at 10.82 μ g/dL derived for the init ial analyses of the fullscale assessment (see Section 4.1.1.3) (Henderso n, 2007b). Accordingly, a different set of concentration-response functions was employed in the additiona l analyses (see Section 5.3.1.1), and risk estimates for the two-pie ce linear function w ith hinge at 10.82 μ g/dL are not included in the range of risk estimates presented for the initial analyses in Chapter 4.

1.5 ORGANIZATION OF THE DOCUMENT The remainder of this document is organized as follows. Chapter 2 describes the design of the exposure and risk assessments, covering su ch topics as the conceptual model used in designing the analysis (Section 2.1 ), the case studies included in the assessment (Section 2.2), the air quality scenarios simulated in the assessment (S ection 2.3), and an overvi ew of the analytical approach (Section 2.4). Chapter 3 describe s the methods and results for the exposure assessment, as well as the performance evaluatio n. Chapter 4 describes the methods for deriving risk estimates, the resultant estimates, sensitivit y analyses and a characte rization of uncertainty. Chapter 5 presents additional analyses using a co re modeling approach completed in response to the August 2007 CASAC review of in itial full-scale analyses.1-9 REFERENCES
Centers for Disease Control (1991) Pr eventing lead poisoning in young children: a statement by the Centres for Disease Control. Atlanta, GA: U.S. Department of health and Human Services, Public Health Service; October 1. http://wonder.cdc.gov/wonder/prevguid/p0000029/p0000029.asp

Centers for Disease Control and Prevention (2005) Preventing lead poisoning in young children: a statement by the Centers for Disease Control and Prevention. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. August.

Henderson, R. (2006) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee (CASAC) Lead Review Panel’s Consultation on EPA’s draft Analysis Plan for Human Health and Ecological Risk Assessment for the Review of the Lead National Ambient Air Quality Standards . July 26, 2006.

Henderson, R. (2007a) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of the 1 st Draft Lead Staff Paper and Draft Lead Expo sure and Risk Assessments. March 27, 2007.

Henderson, R. (2007b) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of the 2nd Draft Lead Huma n Exposure and Health Risk Assessments Document. September 27, 2007.

ICF International. (2006) Lead Human Exposure and Hea lth Risk Assessments and Ec ological Risk Assessment for Selected Areas. Pilot Phase. Draft Technical Report. Prepared for the U.S. EPA's Office of Air Quality Planning and Standards, Research Triangle Park, NC. December.

U.S. Environmental Protection Agency. (1986a) Air Qu ality Criteria for Lead. Environmental Criteria and Assessment Office, Office of Research and Developmen t, Research Triangle Park, NC. EPA/600/8-83-028 a-d. June 1986.

U.S. Environmental Protection Agency. (1986b) Lead effe cts on cardiovascular functi on, early development, and stature: an addendum to U.S. EPA Air Quality Criteria for Lead (1986) Environmental Criteria and Assessment Office, Office of Research and Developmen t, Research Triangle Park, NC. September, 1986.

U.S. Environmental Protection Agency. (1990a) Air Quality Criteria for Lead: Supplement to the 1986 Addendum. Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office; report no. EPA/600/8-89/049F. Available from: NTIS, Springfield, VA; PB91-138420.

U.S. Environmental Protection Agency. (1990b) Review of the National Ambient Air Quality Standards for Lead: Assessment of Scientific and Tech nical Information: OAQ PS Staff Paper. Research Triangle Park, NC: Office of Air Quality Planning an d Standards; report no. EPA-450/2-89/022. Available from: NTIS, Springfield, VA; PB91-206185. Available on the web: http://www.epa.gov/ttn/naaqs/standards/pb/data/rnaaqsl_asti.pdf

U.S. Environmental Protection Agency. (2006a). Review of the National Ambient Air Quality Standards for Lead: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper – First Draft. Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/P-06-002. Available online at:

U.S. Environmental Protection Agency. (2006b) Air Quality Criteria for Lead. Washington, DC, EPA/600/R5/144aF. Available online at: www.epa.gov/ncea/

U.S. Environmental Protection Agency. (2006c) Analysis Plan for Human Health and Ecological Risk Assessment for the Review of the Lead National Ambient Air Quality Standards. Office of Air Quality Planning and1-10 Standards, Research Triangle Park, NC. Available online at: http://www.epa.gov/ttn/naaqs/standards/pb/s_pb_cr_pd.html

U.S. Environmental Protection Agency. (2007a). Review of the National Ambient Air Quality Standards for Lead: Policy Assessment of Scientific an d Technical Information, OAQPS St aff Paper. Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-07-013. Available online at: http://www.epa.gov/ttn/naaqs/standards/pb/s_pb_cr_sp.html

U.S. Environmental Protection Agency. (2007b). Lead: Hu man Exposure and Health Risk Assessments for Selected Case Studies (Draft Report). Volume I. Human Expo sure and Health Risk Asse ssments – Full-scale. and Volume II. Appendices. Office of Air Quality Planni ng and Standards, Resear ch Triangle Park, NC. EPA452/D-07-001a. and EPA-452/D-07-001b. Available online at: http://www.epa.gov/ttn/naaqs/standards/pb/s_pb_cr_td.html2-1 

### 2 DESIGN OF EXPOSURE AND RISK ASSESSMENTS

The risk assessment design relies on the use of case studies. The types of case studies included, as well as the analytical aspects of th e assessment of each, reflect consideration of the evidence presented in the CD, air quality analyses , and findings of the pilot assessment (Section 1.3), as well as comments received from CASAC (Section 1.4 ) and the public. Drawing primarily from the CD, Secti on 2.1 provides background for the risk assessment, with regard to key elements of Pb exposure and effects. The assessment scenarios
evaluated in the assessment ar e described in Section 2.2. Background information on the three
case studies is described in Sect ion 2.3. Section 2.4 describes the analytical approach, with attention to key analytical st eps, and discussion of temporal and spatial aspects of the assessment, as well as the categorization of policy-relevant expos ure pathways, and the uncertainty characterization.

2.1 BACKGROUND INFORMATION ON LEAD EXPOSURE AND RISK

As recognized in Section 1.1, there are a vari ety of complexities associated with the assessment of air-related Pb exposure and risk. In this risk assessment, we have attempted to focus effort on those aspects that are most importa nt and feasible to address within our scope and given the constraints of time, pertinent data, m odels, etc. With regard to some aspects, simplifying assumptions have been implemented. The following subsections describe elements of Pb exposure and effects pertinent to evaluatin g public health risks associated with Pb from ambient air, and specify those that are explicitly addressed in this quantita tive risk assessment. This is summarized in Figure 2-1, with boxes outlin ed in bold indicating items included in the quantitative risk assessment and sources and pathways for which ambient air has played a role
identified in bold text.2-2 Figure 2-1. Conceptual model for Pb human health risk assessment.

Note: Boxes outlined in bold are included in the quantitative risk assessment. Sources and pathways for which ambient air has played a role are in bold text.2-3 2.1.1 Sources, Pathways and Routes

As described in Section 1.1, for the purposes of this assessment, policy-relevant sources (in Figure 2-1 in bold type) include both s ources of new Pb emissions (e.g., from active stationary and mobile sources) and re-emission or resuspension of historically deposited Pb (e.g., near roadways or associated with now inactive, or now lower emitting stationary sources, as discussed in Appendix A, Section A.1.1.3). There are more than 13,000 individual sources in the U.S. for which we have estimated Pb emissions to the air (Appendix A, Section A.1.2). Cumulatively, those sources, in addition to
mobile sources and other sources not individually quan tified, emit some 1600 tons per year (tpy) of Pb in the U.S. (Appendix A). The larges t categories (in terms of aggregate national emissions) include mobile sources (specifically combustion of leaded general aviation fuel), boilers and process heaters, and metals processes, such as primary and secondary Pb smelting. Of these, metals processing industries are among th e largest emitters of Pb, in terms of emissions from individual facilities (Appe ndix A, Section A.1). Another potentially large category of Pb emissions, for which we do not have quantitative estimates in our national emissions inventory, is resuspension of recent and historically deposited Pb (Appendix A, Section A.1.1.3; CD, Section 2.3.3). Studies of emissi ons in southern California suggest that Pb in resuspended road dust may represent up to 40%-90% of Pb emi ssions in some areas (Appendix A, Section A.1.1.3). Resuspension is represented to differing degrees in the three case studies included in the risk assessment (Sections 2.2.1 and 2.2.2) Lead in outdoor dust and soil may be derive d from a range of s ources including current and historical air emissions sources, as well as miscellaneous nonair so urces (e.g., land disposal of wastes and subsequent weathering). Outdoor dust and soil may play a substantial role in human exposures, particularly for children (CD, Section 3.2). Additionally, Pb in house dust, which may be derived from Pb in outdoor dust and soil as well as from ambient air Pb (including previously deposited Pb resuspended into ambien t air), is another source of children’s exposure (CD, Sections 3.2 and 4.4). For example, blood Pb levels in children have been shown to be particularly influenced by exposures to Pb in dust (e.g., Lanphear and Roghmann 1997; Lanphear et al., 1998). Such findings and “other studies of populati ons near active sources of air emissions (e.g., smelters), substantiate the effect of airborne Pb and resuspended soil Pb on interior dust and blood Pb” (CD, p. 8-22). In addition to airborne emissions (recent or those in the past), sources of Pb to the environment or to human exposure included old lead ed paint, Pb in drinking water and Pb in the diet (Figure 1-1). As menti oned in Section 1.1, Pb in diet and drinking water may have air pathway related (i.e., policy-releva nt) contributions as well as cont ributions from policy-relevant2-4 background (e.g., Pb solder on water distribution pipes and Pb in materials used in food processing). Limitations in our data and modelin g tools have handicapped our ability to separate
these contributions in the risk assessment, such that we have labeled diet and drinking water policy-relevant background. Cons equently, these sources of Pb exposure are depicted in Figure 2-1 as policy-relevant background (in nonbold type), although this is not intended to convey any particular policy decision at this stage regarding the Pb standa rd. Policy-relevant pathways (bold text in Figure 2-1) include i nhalation of newly or previously emitted Pb, ingestion of outdoor soil/dust containing previously deposited Pb, a nd ingestion of indoor dust containing newly or previously emitted Pb. Human exposure to environmental Pb occurs predominantly via ingestion and inhalation routes, with ingestion (i ncluding incidental inge stion of dust and soil) recognized as generally playing a larger role for the general human popula tion (CD, Section 4.5). The dermal route is relatively less well characterized bu t is not considered to play a large role in total Pb exposure (CD, Section 4.5), and is not included in this assessment (Figure 2-1).

2.1.2 At-risk Populations

In considering populations for inclusion in th e risk assessment, we considered evidence regarding those with increased su sceptibility (i.e., physiological f actors contributing to a greater response for the same exposure), and those with increased exposure (including that resulting from behavior leading to increased contact with contaminated media). A behavioral factor of great impact on Pb exposure is the incidence of hand-to-mouth activity that is prevalent in very young children (CD, Section 4.4.3). Physiological factors include both conditions contributing to a subgroup’s increased risk of effects at a gi ven blood Pb level, and those that contribute to blood Pb levels higher than those otherwise asso ciated with a given Pb exposure (CD, Section 8.5.3). An additional population characterizat ion for which evidence was considered was vulnerability to pollution-related effects which ad ditionally encompasses situations of elevated exposure, such as residing in old housing with Pb-containing paint or near sources of ambient Pb, as well as socioeconomic factors, such as reduced access to health care or low socioeconomic status (SES) (USEPA, 2003, 2005) th at can contribute to increased risk of
adverse health effects from Pb. Three particular physiological factors contribut ing to increased risk of Pb effects at a given blood Pb level are recognized in the CD (e .g., CD, Section 8.5.3): age, health status, and genetic composition (or genotype). With regard to age, the susceptibility of young children to the neurodevelopmental effects of Pb is well recognized (e.g., CD, Sections 5.3, 6.2, 8.4, 8.5, 8.6.2), although the specific ages of vulnerability have not been established (CD, pp 6-60 to 6- 64). Early childhood may also be a time of increa sed susceptibility for Pb immunotoxicity (CD,2-5 Sections 5.9.10, 6.8.3 and 8.4.6), and childhood exposures have been associated with increased
risk of cardiovascular and neurode generative effects in adulthood (C D, p. 8-74). Health status is another physiological factor in that subpopulations with pre-exis ting health conditions may be more susceptible (as compared to the general po pulation) for particular Pb-associated effects, with this being most clear fo r renal and cardiovascular outcomes. For example, African Americans as a group, have a higher frequency of hypertension than the general population or other ethnic groups (NCHS, 2005), and as a result may face a greater risk of adverse health impact from Pb-associated cardiovascular effects. A third physiological f actor relates to genetic polymorphisms. That is, subpopulations defi ned by particular genetic polymorphisms (e.g., presence of the δ -aminolevulinic acid dehydratase-2 [ALAD- 2] allele) have al so been recognized as sensitive to Pb toxicity, which may be due to increased susceptibility to the same internal dose
and/or to increased internal dose associated with same exposure (CD, p. 8-71, Sections 6.3.5, 6.4.7.3 and 6.3.6). Several physiological factors pertain to sus ceptibility by contributi ng to increased blood Pb levels (i.e., increased internal dose levels) over those otherwise associated with a given Pb exposure (CD, Section 8.5.3). These include nutritional status, which plays a role in Pb absorption from the GI tract (CD, Section 5.10.2. 5); polymorphism for the vitamin D receptor, which studies suggest may contribute to increased Pb absorption from the GI tract (CD, Section 8.4.2.7); presence of the ALAD-2 allele, which studi es suggest contribute to increased blood Pb levels (Section 8.5.3); and bone de mineralization, such as occu rs during pregnancy, lactation, and aging, which appears to influence Pb release from bone into the blood (CD, Section 4.3.2). In summary, there are a vari ety of ways in which Pb exposed populations might be characterized and stratified for the pu rposes of health risk assessment. Age or lifestage is used to distinguish potential groups on which to focus th e risk assessment (see Fi gure 2-1) in recognition of its influence on exposure and su sceptibility. In consideration of the health effects evidence regarding endpoints of greatest public health concern and a recogniti on of effects on the developing nervous system as a sentinel endpoint for public health impacts of Pb (see Section 2.1.4), young children have been selected as the prio rity population for this risk assessment (see Figure 2-1). We recognize, how ever, other population subgroups as described above may also
be at risk of Pb-related health e ffects of public health concern at similar or higher exposures. As currently available data do not generally support quantitative m odeling that differentiates blood
Pb levels and associated health risk within a particular population gr oup such as young children on the basis of enhanced or reduced susceptibi lity to Pb effects (e .g., concentration response functions for IQ loss that differentiate between populations that are calcium deficient and those that are not), the assessment does not develop separate risk esti mates for such subpopulations of2-6 young children. In the risk assessment, however, interindividual variability in blood Pb is quantitatively consid ered (Section 3.2.3).

2.1.3 Internal Disposition

Once inhaled or ingested and absorbed into the blood stream, Pb is distributed throughout the body via the blood, with bone being the predomin ant site of Pb accumulation and storage in the body (CD, Sections 4.2 and 4.3). Additionally, the epidemiologic evidence indicates that Pb freely crosses the placenta resulting in continue d fetal exposure throughout pregnancy, and that exposure increases during the later ha lf of pregnancy (CD, Section 6.6.2). During childhood development, bone represen ts approximately 70% of a child’s body burden of Pb, and this accumulation continues through adulthood, when more than 90% of the total Pb body burden is stored in the bone (CD, S ection 4.2.2). Accordingly, levels of Pb in bone are indicative of a person’s long- term, cumulative exposure to Pb. In contrast, blood Pb levels are usually indicative of recen t exposures. Depending on exposure dynamics, however, blood Pb may – through its interaction with bone - be indicative of past exposure or of cumulative body burden (CD, Section 4.3.1.5). Throughout life, Pb in the body is excha nged between blood and bone, and between blood and soft tissues (CD, Section 4.3.2), with va riation in these exchan ges reflecting “duration and intensity of the exposure, age and various physiological variables” (CD, p. 4-1). For example, resorption of bone (e.g., in pregna nt or nursing women, a nd associated with osteoporosis in postmenopausal women or, to a lesser magnitude, in older men) results in a
mobilization of Pb from bone in to circulation (CD, Sections 4.3.2.4 and 4.3.2.5). Past exposures that contribute Pb to the bone, consequently, may influence current levels of Pb in blood. Where past exposures were elevated in comparison to recent exposures, this influence may complicate interpretations with regard to recent exposure (CD, Sections 4.3.1.4 to 4.3.1.6). That is, higher blood Pb concentrations may be indicative of higher cumulative exposures or of a recent elevation in exposure (C D, pp. 4-34 and 4-133). Bone measurements, as a result of the ge nerally slower Pb turnover in bone, are recognized as providing a better measure of cumu lative Pb exposure (CD, Section 8.3.2). The bone pool of Pb in children, however, is thought to be much more labile than that in adults due to the more rapid turnover of bone mineral as a resu lt of growth (CD, p. 4-27). As a result, changes
in blood Pb concentration in children more cl osely parallel changes in total body burden (CD, pp. 4-20 and 4-27). This is in contrast to ad ults, whose bone has accumulated decades of Pb exposures (with past exposures of ten greater than current ones), and for whom the bone may be a significant source long af ter exposure has ended (CD, Section 4.3.2.5).2-7 Given the association with exposure, particular ly recent exposure, and the relative ease of collection, blood Pb levels are exte nsively used as an index or biomarker of exposure by national and international health agencies (CD, Sec tion 4.3.1.5). Although recent methods are making
bone Pb measurements easier to collect (CD, Section 4.3.2.2) and consequently, their use more widespread, epidemiological and toxicological studies of Pb h ealth effects and dose-response relationships tend to be dominated by blood Pb as the exposure metric (CD, Sections 4.3.1.3, 8.3.2 and Chapter 5). Accordingly, blood Pb level is the index of exposure or exposure metric in this risk assessment. The use of concentration-response f unctions that rely on blood Pb (e.g., rather than ambient Pb concentration) as the exposure metric reduces uncertainty in the causality aspects of Pb risk estimates. The relationship between specific sources and pathways of exposure and blood Pb level is needed, however, in order to iden tify the specific risk co ntributions associated with those sources and pathways of greatest interest to this asse ssment (i.e., those related to Pb emitted into the air). For example, the blood Pb-response relationships developed in epidemiological studies of Pb-e xposed populations do not distingui sh among different sources or pathways of Pb exposure (e.g., inhalation, ingesti on of indoor dust, ingestion of dust containing leaded paint). In the exposure assessment for th is review, models that estimate blood Pb levels associated with Pb exposure (e.g., CD, Section 4.4) are used to inform estimates of contributions to blood Pb arising from ambient air related Pb versus contributions from other sources.

2.1.4 Health Endpoints

Lead has been demonstrated to exert “a broad array of deleterious effects on multiple organ systems via widely diverse mechanisms of action” (CD, p. 8-24 and Section 8.4.1). This array of health effects, the evidence for which is comprehensively described in the CD, includes

• Heme biosynthesis and related functions;

• Neurological development and function;

• Reproduction and physical development;

• Kidney function;

• Cardiovascular function; and,

• Immune function. There is also some evidence of Pb carcinogenic ity, primarily from animal studies, with limited human evidence of suggestive associa tions (CD, Sections 5.6.2, 6.7, and 8.4.10). 1

1 Lead has been classified as a probable human ca rcinogen by the International Agency for Research on Cancer, based mainly on sufficient anim al evidence, and as reasonably antici pated to be a human carcinogen by the2-8 This review is focused on those effects most pertinent to ambient exposures. Given the reductions in ambient Pb levels over the past 30 years, these effects are generally those associated with the lowest Pb levels of expos ure. These are neurological, hematological and immune effects for children, and neurological, hematological, cardiovascu lar and renal effects for adults (CD, Tables 8-5 and 8-6), with neurol ogical effects in children and cardiovascular effects in adults appearing to be of greatest public health concern (CD, p. 8-60). The toxicological and epidemiological information available since the time of the last review
“includes assessment of new evidence substantiati ng risks of deleterious e ffects on certain health endpoints being induced by distinct ly lower than previously dem onstrated Pb exposures indexed by blood Pb levels extending well below 10 μ g/dL in children and/or ad ults” (CD, p. 8-25). The
CD indicates some health effects associated w ith blood Pb levels that extend below 5 μg/dL, with some studies observing these effects at the lowest blood levels considered (i.e., threshold
levels for these effects cannot be discerned from the currently available studies). The endpoints identified above are important considerations for this review and are described briefly in sections belo w, with detailed discussion of th e evidence presented in the CD. Of these health endpoints, the fo cus of the quantitative health risk assessment is developmental neurotoxicity in children, with IQ decrement as th e risk metric (Figure 2-1), described in Section
2.1.5

2.1.4.1 Developing Nervous System The nervous system has long been recognized as a target of Pb toxicity, with the developing nervous system affected at lower exposures than the mature system (CD, Sections 5.3, 6.2.1, 6.2.2, and 8.4). While blood Pb levels in U. S. children ages one to five years have
decreased notable since the late 1970s, newer studies have investig ated and reported associations of effects on the neurodevelopment of children with these more recent blood Pb levels (CD, Chapter 6). Functional manifestations of Pb neurotoxicity during childhood include sensory, motor, cognitive and behavioral impacts. Nu merous epidemiological studies have reported neurocognitive, neurobehavioral, sensory, and motor function eff ects in children at blood Pb
levels below 10 μ g/dL (CD, Section 6.2). Further, “exte nsive experimental laboratory animal evidence has been generated that (a) substantiates well the plausibility of the epidemiologic findings observed in human children and adults and (b) expands our understanding of likely mechanisms underlying the neurotoxic eff ects” (CD, p. 8-25; Section 5.3).

U.S. National Toxicology Program (CD, Section 6.7.2). U.S. EPA classified it in the past as a probable carcinogen ( http://www.epa.gov/iris/subst/0277.htm ).2-9 Cognitive effects associated with Pb e xposures that have been observed in epidemiological studies have included decrements in intelligence test results, such as the widely used IQ score, and in academic achievement as assessed by various standard ized tests as well as by class ranking and graduation rates (CD, Sect ion 6.2.16 and pp 8-29 to 8-30). As noted in the CD with regard to the latter, “Associations between Pb exposure and academic achievement
observed in the above-noted studie s were significant even after ad justing for IQ, suggesting that Pb-sensitive neuropsychological processing a nd learning factors not reflected by global intelligence indices might contribute to reduced performance on academic tasks” (CD, pp 8-29 to 8-30). Other cognitive effects observed in studies of children have include d effects on attention, executive functions, language, memory, learni ng and visuospatial processing (CD, Sections 5.3.5, 6.2.5 and 8.4.2.1), with attention and executive function effects associated with Pb exposures indexed by blood Pb levels below 10 μ g/dL (CD, Section 6.2.5 and pp. 8-30 to 8-31).
The evidence for the role of Pb in this suite of effects includes experimental animal findings (discussed in CD, Section 8.4.2.1; p. 8-31), which provide strong biological plausibility of Pb effects on learning abilit y, memory and attention (CD, Sec tion 5.3.5), as well as associated mechanistic findings. Further, Pb-induced defi cits observed in animal and epidemiological studies, for the most part, have been found to be persistent in the absence of markedly reduced environmental exposures (CD, Sections 5.3.5, 6.2.11, and 8.5.2). It is additionally important to note that there may be long-term consequences of such deficits over a lifetime. Poor academic skills and achievement can have "enduring and important effects on objective parameters of success” later in life, as well as increased risk of antisocia l and delinquent behavior (CD, Section 6.2.16). Other neurological effects asso ciated with Pb exposures in dexed by blood Pb levels near or below 10 μg/dL include behavioral effects, su ch as delinquent behavior (CD, Sections 6.2.6 and 8.4.2.2), sensory effects, such as those rela ted to hearing and vision (CD, Sections 6.2.7, 7.4.2.3 and 8.4.2.3), and deficits in neur omotor function (CD, p. 8-36). Neurocognitive impact, specifically decrement in IQ in young children, is a focus of the quantitative risk assessment due to the strength of ev idence for association with blood Pb levels
below 10 μ g/dL, and the strength of the dose-response information at these exposure levels. As discussed in the CD (Section 8.4.2) a nd by Rice (1996), while there is no direct animal test parallel to human IQ tests, “in anim als a wide variety of tests that assess attention, learning, and memory suggest that Pb exposure {of animals} re sults in a global deficit in functioning, just as it is indicatedby decrements in IQ scores in children” (CD, p. 8-27). The animal and epidemiological evidence for this en dpoint are consistent and complementary (CD, p. 8-44). As stated in the CD (p. 8-44):2-10 Findings from numerous experimental studies of rats and of nonhuman primates, as discussed in Chapter 5, parallel the obse rved human neurocognitive deficits and the processes responsible for them. Learni ng and other higher order cognitive processes show the greatest similarities in Pb-indu ced deficits between humans and experimental animals. Deficits in cognition are due to the combined and overlapping effects of Pbinduced perseveration, inability to inhib it responding, inability to adapt to changing behavioral requirements, aversion to dela ys, and distractibilit y. Higher level neurocognitive functions are affected in both animals and humans at very low exposure levels (< 10 μ g/dL), more so than simple cognitive functions. Further, “epidemiologic studies of Pb and ch ild development have demonstrated inverse
associations between blood Pb concentrati ons and children’s IQ and other outcomes at successively lower Pb exposure levels” over the pa st 30 years (CD, p. 6-64). This is supported by multiple studies performed over the past 15 y ears (see CD, Section 6.2.13), with particularly compelling evidence for decrements in IQ at blood Pb levels below 10 μ g/dL provided by a recent international pooled analys is of seven prospective studies (Lanphear et al., 2005; CD, Section 6.2.13). For example, this pooled analys is estimated a decline of 6.2 points (with a 95% confidence interval bounded by 3.8 and 8.6) in full scale IQ occurring between approximately 1
and 10 μ g/dL blood Pb level, measured concurrent with the IQ test (CD, p. 6-76). This analysis (Lanphear et al., 2005) is relied upon in the qua ntitative risk assessment for this endpoint.

2.1.4.2 Adult Nervous System

The nervous system has long been recognized as a target of Pb toxicity (CD Sections 5.3.1, 8.4.2). For example, those chronically expose d in the workplace are at risk for various neurological effects including pe ripheral sensory nerve impair ment, visuomotor and memory impairment, and postural sway abnormalities, with a blood Pb concentration >14 μ g/dL being a possible threshold (CD, p. 6-87). Past occupa tional exposure also increases the risk of developing amyotrophic lateral sclerosis and mo tor neuron disease (CD, Section 6.3.5 and p. 6- 87). Essential tremor is also associated with Pb exposures, particularly for those with genetic susceptibility (CD, Sections 6.3.5 and 6.3.6 and p. 6-86). In elderly populations, significant associati ons have been reported between bone Pb levels and impaired cognitive performance or dysfunction (CD, Section 6.3.3 and 6.3.3.1), but not with blood Pb levels, perhap s indicating a role of cumulative and/or past Pb exposures (CD, p. 6-83). During demineralization of bone in the el derly, Pb may be releas ed into the blood, thus augmenting blood Pb associated with current ambient exposures (CD, Section 4.3.2.4). An increased susceptibility among the elderly to Pb effects on cognitive function is supported by animal evidence (Section 5.3.7). Wi th lifetime exposure, senescent animals have exhibited an2-11 increased susceptibility to Pb, due to the increased exposure from bone resorption, and an apparently greater sensitivity to the biochemical effects of Pb (CD, Section 5.3.7). Laboratory animal research in rats and monkeys also indi cates a potential for cogn itive function effects in the elderly to be related to physio logical effects (regula tion of protein thought to play a role in Alzheimer’s disease) of Pb exposures in early childhood (CD, p. 5-67; Basha et al., 2005). Thus, early life exposure to Pb may c ontribute to neurocognitive eff ects later in lif e due to the redistribution of Pb body burden from bone to brain and by enhanced susceptibility caused by age-related degenerative changes in various organs, including brain (CD, p. 8-40).

2.1.4.3 Cardiovascular System

Epidemiologic and experimental toxicol ogy studies provide strong support for the relationship between Pb exposure and increase d adverse cardiovascular outcomes, including increased blood pressure, increased incidence of hypertension, and cardiova scular morbidity and mortality (CD, Sections 5.5, 6.5 and 8.4.3). The cardi ovascular effect most frequently examined in epidemiological studies is increased systol ic blood pressure in adults, which has been repeatedly associated with Pb exposur e (CD, Sections 8.4.3, 8.6.3, 6.5.2.3, and 6.5.7). The
association has been observed with Pb levels in bone and also, in some cohorts, with Pb in blood (including blood Pb levels below 10 μ g/dL). A recent meta-analysis by Nawrot and others (2005), that included a range of blood Pb levels from 2.3 to 63.8 μ g/dL, reported an association of increased systolic blood pre ssure and decreased diastolic pr essure with increased blood Pb level, including levels below 10 μ g/dL. The magnitude of cha nge observed has considerable significance at the population leve l (CD, p. 8-45, Section 8.6.3). The epidemiological evidence is supported by evidence in numerous animal st udies of arterial hypertension with low Pb exposures, an effect that persists in animals long after cessation of exposure (CD, Sections 5.5 and 8.4.3). Multiple studies reporting positive associations of blood pressure and hypertension with bone Pb levels highlight the important role of cumulative past Pb exposure in development of cardiovascular health effects (Sections 6.5.2.3 a nd 6.5.7). A study of young adults who lived as children in an area of high Pb exposures also indicates the potential role of childhood exposure. In this study, higher bone Pb leve ls were associated with high er systolic and diastolic blood pressure (CD, p. 6-138), while current blood Pb levels (mean of 2.2 μ g/dL) were not (CD, p. 6- 124). Systolic blood pressure exer ts a strong influence on more serious cardiovascular events by its role in hypertension and its adverse ca rdiovascular sequelae (CD, p. 8-83). Several analyses of National Health and Nutrition Examination Survey (NHANES) cohorts, including some recently released, have collectively suggested a “significant effect of Pb on cardiovascular2-12 mortality in the general U.S. population” (CD, p. 8-88, Sections 6.5.3.2 and 8.6.3). For example recent analyses of NHANES blood Pb data from 1976 to 1980 and 1988 to 1994 provide supportive evidence for an increased risk of cardi ovascular mortality, consistent with projected likely increases in serious cardiov ascular events (stroke, heart a ttack) resulting from Pb-induced increases in blood pressu re (CD, Section 8.6.3).

2.1.4.4 Renal System

Lead nephrotoxicity is mediated by alterations in the glomerular filtration rate (CD, Sections 5.7.3 and 8.4.5). The interaction of Pb with the kidney, including occurrences and mechanisms of Pb uptake by and accumulation in the kidney, and associated cellular alterations, is well described in animal research (CD, S ection 5.7). A set of sc reening tests involving markers of nephrotoxic effects have been establ ished for screening individuals exposed to Pb occupationally or environmentally (CD, Secti on 5.7.1). In the epidemiological literature,
associations between blood Pb and indicators of renal function impairment (e.g., measures of glomerular integrity, such as creatinine levels in urine) have been found at blood Pb levels extending below 10 μ g/dL, to as low as ~2 to 4 μ g/dL (CD, Sections 6.4.4.1.5 and 8.4.5).
Associations are also observed with cumulative Pb dose, assesse d via bone Pb, and longitudinal renal function decline (CD, p. 6-94), indicating the potential role of earlier exposures. The findings for non-occupationa l populations since the la st review provide “strong
evidence that renal effects occur at much lowe r blood Pb levels than previously recognized” (CD, p. 6-113). These findings of lower Pb renal effects thresholds in environmental compared to occupational research may be a result of potentially larg er proportions of susceptible individuals in the general popula tion as compared to occupationa l cohorts (CD, p. 6-107). The
data available are not sufficient to determine wh ether these effects are related more to current blood-Pb levels, higher levels from pa st exposure, or both (CD, p. 8-49). The findings regarding Pb exposures and rena l effects are of particular concern with regard to certain susceptible subpopulations. At levels of exposure in the general U.S. population overall, Pb combined with other risk factors, such as diabetes, hypertension, or chronic renal insufficiency from causes unrelated to Pb, can result in clinical ly relevant effects. Notably, the size of such suscepti ble populations is increasing in th e United States due to obesity (CD, p. 6-113). That is, Pb is r ecognized as acting cumulatively w ith other renal risk factors to cause early onset of renal insufficiency and/or a steeper rate of re nal function decline in individuals already at risk for renal disease (CD, p. 6-107).

2.1.4.5 Heme Synthesis

It has long been recognized that Pb exposure is associat ed with disruption of heme synthesis in both children and adults. The evidence regarding effects on heme synthesis and2-13 other hematological parameters in animal and humans is strong, and includes documented quantitative relationships between exposure and effects in children and adults. Interference with heme synthesis was identified as one of the target s of low-level Pb toxicity in children during the time of the last NAAQS review (USEPA, 1990), and was the primary focus for the initial setting of the Pb NAAQS in 1978 (USEPA, 1978).
Mechanisms associated with Pb interference with heme synthesis include inhi bition of the enzymes ALAD and ferrochelatase (Tab le 3-1; CD Sections 5.2.1, 6.9.1, 6.9.2; USEPA 1986). Inhibition of ALAD has been associated w ith increased blood Pb concentrations at and
somewhat below 10 μ g/dL, in children and adults (Tables 3-1 and 3-2; CD, Table 6-7). Blood Pb concentrations at and above approximately 15 μ g/dL, in children, and 15-30 μ g/dL, in adults,
are associated with elevation of erythrocyte proto porphyrin (EP), and notable reductions in hemoglobin synthesis (Tables 3-1 and 3-1; CD, p. 8-47; USEPA, 1986). In the setting of the Pb NAAQS in 1978, the Agency concluded that “the st ate of elevated EP must be regarded as potentially adverse to the health of young child ren” (USEPA, 1978). Blood Pb concentrations at and above 40 ug/dL are associated with frank anem ia, a clinical sign of severe Pb poisoning (CD, p. 8-47). The evidence regarding Pb disruption of heme synthesis and associated mechanisms is presented in detail in past CDs (USEPA 1986, 1977), with more recent findings, including the role of genetic polymorphisms, discussed in the current CD (Sections 8.4.4, 5.2.1, 6.9.1 and 6.9.2).

2.1.4.6 Immune System

Since the time of the last review, ther e has been substantial research on the immunotoxicity of Pb. As summarized in th e CD, “studies across humans and a variety of animal models are in general agreement con cerning both the nature of the immunotoxicity induced by Pb as well as the exposure conditions that are required to produce immunomodulation” (CD, p. 5-244, Section 5.9 ). Lead is distinguished from other immunotoxicants, however, by the fact that the most sensitive biom arkers of its immunotoxicity are associated with specific func tional capacities that influence ri sk of disease, as opposed to being associated with changes in immune cell numbers or pat hological changes of lymphatic system organs (CD, Section 5.9.1). The main immune system targets of Pb are macrophages and T lymphocytes, leading to a potential fo r increased tissue inflammation, reduced cellmediated immunity, and increased risk of au toimmunity (See CD, Figure 5-18, Section 5.9.11). Additionally, Pb exposures in bot h animal and human studies ar e associated with increased production of IgE, an immunoglobulin involved in allergic respons es and asthma (CD, Section 5.9.3.2). These effects have been reported in ep idemiologic studies of children, and supported2-14 by evidence in neonatal and juvenile animal s, at blood Pb levels extending below 10 μ g/dL (CD, p. 6-197 and Sections 5.9.10 and 8.4.6).

2.1.5 Metric and Model for Risk Quantitation

The health endpoint on which we focused in the quantitative health risk assessment for this review is developmental neurotoxicity in children (Section 2.1.4.1), with IQ decrement as the risk metric. Among the wide variety of hea lth endpoints associated wi th Pb exposures, there is general consensus that the developing nervous system in young children is the most sensitive
and that neurobehavioral effect s (specifically neurocognitive defi cits), including IQ decrements, appear to occur at lower blood levels than previously believed (i.e., at levels <10 μ g/dL). For example, the overall weight of the available evidence, described in the CD, provides clear substantiation of neurocognitive decrements bei ng associated in young children with blood Pb levels in the range of 5 to 10 μ g/dL, and some analyses indicate Pb effects on intellectual attainment of young children ranging from 2 to 8 μ g/dL (CD, Sections 6.2, 8.4.2 and 8.4.2.6). That is, while blood Pb levels in U.S. children ages one to five years have decreased notably since the late 1970s, newer studies have investigated and reported associations of effects on the neurodevelopment of children with these more recent blood Pb levels (CD, Chapter 6).
The evidence for neurotoxic effects in children is a robust combination of epidemiological and toxicological eviden ce (CD, Sections 5.3, 6.2 and 8.5). The epidemiological evidence is strongly supported by animal studies that substantiate the biological plausibility of the associations , and provides an understanding of mechanisms of action for the effects (CD, Section 8.4.2). The selection of children’s IQ for the quantitative risk assessment
reflects consideration of the ev idence presented in the CD as well as advice received from CASAC (Henderson, 2006, 2007a). The epidemiological studies that have i nvestigated blood Pb effects on IQ (see CD, Section 6.2.3) have considered a variety of specific blood Pb metrics, including: 1) blood
concentration “concurrent” with the response assessment (e.g., at the time of IQ testing), 2) average blood concentration over the “lifetime” of the child at the time of response assessment (e.g., average of measurements taken over child’s fi rst 6 or 7 years), 3) peak blood concentration during a particular age range and 4) early childhood blood concentra tion (e.g., the mean of measurements between 6 and 24 months age). All four specific blood Pb metrics have been correlated with IQ (see CD, p. 6- 62; Lanphear et al., 2005). In th e international pooled analysis by Lanphear and others (2005), however, the conc urrent and lifetime averaged measurements were considered “stronger predictors of lead-associated inte llectual deficits than was maximal measured (peak) or early chil dhood blood lead concentrations,” with the concurrent blood Pb level exhibiting the strongest relationship (CD, p. 6-29) . It is not clear in this case or for similar2-15 findings in other studies, whether the cognitive deficits observed were due to Pb exposure that occurred during early childhood or were a functi on of concurrent exposure. Nevertheless, concurrent blood Pb levels likely reflected bo th ongoing exposure and preexisting body burden (CD, p. 6-32). Using concurrent blood Pb level as the exposur e metric and IQ as the response from the pooled dataset of seven international studi es, Lanphear and others (2005) employed mathematical models of various forms, includi ng linear, cubic spline, log-linear, and piece-wise linear, in their investigation of the blood Pb concentration-response relationship (CD, p. 6-29; Lanphear et al., 2005). They obser ved that the shape of the con centration-response relationship is nonlinear and the log-linear model provides a better fit over the full range of blood Pb measurements than a linear one (CD, p. 6-29 a nd pp. 6-67 to 6-70; Lanphear et al., 2005). In addition, they found that no indi vidual study among the seven was responsible for the estimated nonlinear relationship between Pb a nd deficits in IQ (CD p. 6-30). Others have also analyzed the same dataset and similarly concluded that, acro ss the range of the dataset’s blood Pb levels, a log-linear relationship was a sign ificantly better f it than the linear rela tionship (p=0.009) with
little evidence of resi dual confounding from included mode l variables (CD, Section 6.2.13; Rothenberg and Rothenberg, 2005). A nonlinear blood Pb concentration-response re lationship is also suggested by several
other studies that have observed that each μ g/dL increase in blood Pb ma y have a greater effect on IQ at blood Pb levels below 10 μ g/dL than at higher levels (CD, pp. 8-63 to 8-64). While this may at first seem at odds with certain fundament al toxicological concepts, a number of examples of non- or supralinear dose-response relationships exist in toxicology (CD, pp. 6-76 and 8-83 to 8-89). 2 With regard to the effects of Pb on ne urodevelopmental outcome such as IQ, the CD suggests that initial neurodevelopmental effects at lower Pb levels may be disrupting very different biological mechanisms (e.g., early deve lopmental processes in the central nervous system) than more severe effect s of high exposures that result in symptomatic Pb poisoning and frank mental retardation (CD, p. 6-76). In comparing across the individual studies and the pooled analysis, it is observed that at higher bloo d Pb levels, the slopes derived for log-linear and linear models are almost identical, and for studies with lower blood Pb levels, the slopes appear to be steeper than those observed in studies involving higher blood Pb levels (CD, p. 8-78, Figure 8-7).
Given the evidence summarized here and descri bed in detail in the CD (Chapters 6 and 8), and in consideration of CASAC recomm endations (Henderson, 2006, 2007a, 2007b), the risk

2 Similarly, a nonlinear concentration-response relationship was observed for the relationship between blood Pb levels and blood pressure in adults (CD, pp. 8-83 to 8-89).2-16 assessment for this review relies on the functions presented by Lanphear and others (2005) that relate absolute IQ as a function of concurrent blood Pb or of th e log of concurrent blood Pb, and lifetime average blood Pb, respectively. As disc ussed above, the slope of the concentrationresponse relationship described by these functions is greater at the lower blood Pb levels (e.g.,
less than 10 μ g/dL). The impact of the nonlinear slope is illustrated by the estimates of IQ decrements associated with increases in blood IQ for different ranges of blood Pb level reported for the log-linear model (Lanphear et al., 2005). These estimates were IQ decrements of 3.9 (with 95% confidence interval, CI, of 2.4-5.3), 1.9 (95% CI, 1.2-2.6) a nd 1.1 (95% CI, 0.7-1.5),
for increases in concurrent blood Pb from 2.4 to 10 μ g/dL, 10 to 20 μ g/dL, and 20 to 30 μ g/dL, respectively (Lanphear et al., 2005). For an increase in concurrent blood Pb levels from <1 to 10 ug/dL, the log-linear model estimates a decline of 6.2 points in full scale IQ which is comparable to the 7.4 point decrement in IQ for an increase in lifetime mean blood Pb levels up to 10 ug/dL observed in the Rochester study (CD, pp 6-30 to 6-31).
Several studies have examined the relationshi p of IQ decrement with blood Pb, quantified by a variety of metrics, at lower blood Pb levels. On a change in IQ per μ g/dL basis, estimates of IQ decrement associated with blood Pb levels (concurrent, 24-month, peak, lifetime average or lifetime cumulative) below 10 μ g/dL range from -0.4 to -1.8 (CD, Table 8.7). At the upper end of this range are the slopes derived for the subsets of children in the Rochester and Boston cohorts for which peak bl ood Pb levels were <10 μ g/dL; these slopes are -1.8 (for concurrent blood Pb influence on IQ) and -1.6 (for 24-month bl ood Pb influence on IQ), respectively. The
number of children in these low blood Pb subs ets of the Rochester and Boston cohorts are 101 and 48, respectively. A similar st ratification of the pooled datase t by Lanphear and others (2005) yielded a slope for the linear function of IQ change associated with concurrent blood Pb of -0.8 for the subset of the children in the pooled da ta set for which maximal or peak blood Pb levels
were below 10 μ g/dL. Of the 1333 children in the full pool ed dataset, there were 244 in this
subset. When the full dataset was restricted to a still smaller subset of 103 children for which peak blood Pb levels were below 7.5 μ g/dL the slope of concurrent blood Pb and IQ was -2.94 (Lanphear et al., 2005). The analysis of this latter subset supporte d the authors’ conclusions that “for a given increase in blood l ead, the lead-associated intellectual decrement for children with a
maximal blood lead level <7.5 μ g/dL was significantly greater than that observed for those with
a maximal blood lead level > 7.5 μ g/dL (p-0.015)” and that “envi ronmental lead exposure in
children who have maximal blood lead levels < 7.5 μ g/dL is associated with intellectual deficits”. This subset was composed primar ily of children from the Rochester cohort (69 children), with smaller numbers of children from fi ve of the other seven cohorts (Lanphear et al., 2005). The Rochester data included IQ test and concurrent blood Pb measurements taken at age six (Lanphear et al., 2005). The linear slope observed for this subset of the pooled dataset,2-17 however, was notably greater than that previously reported for the low blood Pb subset of the Rochester cohort at age five desc ribed above, and greater than thos e slopes from other studies for
blood Pb < 10 μ g/dL summarized in the CD (e.g., CD, Ta ble 8-7), providing some uncertainty with regard to the precise magnitude of slope for the full range of blood Pb below 7.5 μ g/dL. As discussed in the CD, threshold blood Pb le vels for these effects cannot be discerned from the currently available epidemiological studies, and the evidence in the animal Pb neurotoxicity literature does not define a threshol d for any of the toxic mechanisms of Pb (CD, Sections 5.3.7 and 6.2). In applying relationships observed with the poo led analysis (Lanphear et al., 2005) to the risk assessment, which includes blood Pb leve ls below the range repr esented by the pooled analysis, several alternative blood Pb concentr ation-response models were considered in recognition of a reduced confiden ce in our ability to character ize the quantita tive blood Pb concentration-response relationship at the lowest blood Pb levels repres ented in the recent epidemiological studies. The functions considered and employed in the initial risk analyses for this review include the following.

• Log-linear function with low-exposure linear ization, for both concurrent and lifetime average blood metrics, a pplies the nonlinear relationship down to the blood Pb concentration representing the lower bound of bl ood Pb levels for that blood metric in the pooled analysis and applies the slope of the tangent at that point to blood Pb concentrations estimated in the risk assessment to fall below that level.

• Log-linear function with cutpoint, for bot h concurrent and lifetime average blood metrics, also applies the non linear relationship at blood Pb concentrations above the lower bound of blood Pb concentrations in th e pooled analysis dataset for that blood metric, but then applies zero ri sk to all lower blood Pb concentrations estimated in the risk assessment.

• Two-piece linear function, for both concurrent and lifetime average blood metrics, applies a two-piece linear model derived fr om the log-linear func tion to all blood Pb concentrations estimated in the risk assessment. In the additional risk analyses performed subsequent to the A ugust 2007 CASAC public meeting (Section 1.4) using the co re modeling approach, the first two functions listed above and the following two functions were employed (see Section 5.3.1 of the Risk Assessment Report for
details on the forms of these functions as applied in this risk assessment).

• Population stratified dua l linear function for concurrent blood Pb, derived from the pooled dataset stratified at p eak blood Pb of 10 μg/dL and

• Population stratified dua l linear function for concurrent blood Pb, derived from the pooled dataset stratified at 7.5 μg/dL peak blood Pb.2-18 In interpreting risk estimates derived using th e various functions, cons ideration should be given to the uncertainties with re gard to the precision of the coeffi cients used for each analysis. The coefficients for the log-linear model from Lanphear et al. (2005) had undergone a careful development process, including sensitivity analyses, using all available data from 1,333 children. The shape of the exposure-response relationship was first assessed through tests of linearity, then by evaluating the restricted cubi c spline model. After determ ining that the log-linear model provided a good fit to the data, covariates to adjust for potential c onfounding were included in the log-linear model with careful consideration of the stability of the parameter estimates. After the multiple regression models were develope d, regression diagnostics were employed to
ascertain whether the lead coefficients were aff ected by collinearity or influential observations. To further investigate the stability of the mode l, a random-effects model (with sites random) was applied to evaluate the results and also the eff ect of omitting one of the seven cohorts on the lead
coefficient was assessed. In the various sensitivity analyses perf ormed, the coefficient from the log-linear model was found to be robust and st able. The log-linear model, however, is not biologically plausible at very low blood Pb concentrations as they approach zero; therefore, in the first two functions the log-linear model is ap plied down to a cutpoint, selected based on the low end of the blood Pb levels in the pooled data set, followed by a linearization or an assumption
of zero risk at leve ls below that point. In contrast, the coefficients from the two analyses using the population stratified dual linear function with cutpoints at 7.5 μ g/dL and 10 μ g/dL, peak blood Pb, have not undergone such careful development. These analyses were primarily done to compare the lead-associated
decrement at lower blood lead concentrations an d higher blood lead concentrations. For these analyses, the study population was st ratified at the specified cutpoi nt and separate linear models were fitted to the data above and below the cutpoint. The fit of the model or sensitivity analyses were not conducted (or reported) on these coefficients. While thes e analyses are quite suitable for the purpose of investigating whether the sl ope at lower concentra tion levels are greater compared to higher concentration levels, use of su ch coefficients in a risk analysis to assess public health impact may be inappropriate. Further, only 103 children had maximal blood lead
levels less than 7.5 μ g/dL and 244 children had maximal blood lead levels less than 10 μ g/dL. While these children may better represent current blood lead levels, not fitting a single model using all available data may lead to bias. Slob et al. (2005) noted that th e usual argument for not considering data from the high dose range is th at different biological mechanisms may play a role at higher doses compared to lower doses. Ho wever, this does not mean a single curve across the entire exposure range cannot describe the rela tionship. The fitted curve merely assumes that the underlying dose-response follows a smooth curv e over the whole dose range. If biological mechanisms change when going from lower to higher doses, this change will result in a2-19 gradually changing slope of the dose-response. The major strength of th e Lanphear et al. (2005) study was the large sample size and the pooled analys is of data from seven different cohorts. In
the case of the 7.5 μ g/dL cutpoint, less than 10% of the ava ilable data is used in the analysis, with more than half of the data coming from one cohort (Rochester) and the six other cohorts contributing zero to 13 children to the analysis. Su ch an analysis dissipates the strength of the Lanphear et al. study. In consideration of the preceding discussion, we place greater confidence in the log-linear model form compared to the dual-linear stra tified models for our purposes in this risk assessment. Further, in considering risk estim ates derived from the four core functions (loglinear function with low-exposure linearization, log-linear function with cut point, dual linear function, stratified at 7.5 μg/dL peak blood Pb, and dual linear function, stratified at 10 μg/dL peak blood Pb), we have assigned greatest confid ence to risk estimates derived using the loglinear function with low-exposure li nearization since this function (a) is a nonlinear function that describes greater response per unit blood Pb at lower blood Pb levels consistent with multiple
studies identified in the discus sion above, (b) is based on fitting a function to the entire pooled dataset (and hence uses all of th e data in describing response ac ross the range of exposures), (c) is supported by sensitivity analyses showing the model coefficients to be robust, and (d) provides an approach for predicting IQ loss at the lo west exposures simulated in the assessment (consistent with the lack of evidence for a th reshold). Note, however , that risk estimates generated using the other three concentration-response functions are also presented to provide perspective on the impact of uncerta inty in this key modeling step.

2.2 USE OF CASE STUDIES AND LOCATION SELECTIONS

Consistent with the risk assessment perfor med during the prior Pb NAAQS review and for the pilot phase assessment, the full-scale risk assessment for the current review relies on a case study approach. This approa ch is intended to provide a framework for considering the nature and magnitude of Pb expos ures associated with air sources and associated risk to human health, and for comparing the impact of alte rnate NAAQS on those expos ures and risks.
With consideration of CASAC comments on the pilot phase assessment (see Section 1.4), the design for the full-scale assessment expanded from the pilot assessment by replacing the urban near roadway case study with a general, non-location-specific, case study focused on population exposures in urban ar eas (Section 2.2.1), while retain ing the two stationary source case studies (Sections 2.2.2.1 and 2.2.2.2). The point source case studies were intended to illustrate risks associated with Pb near point sources, with the primary Pb smelter case study representing one of significant impact on an indi vidual basis, while the general urban case study is intended to assess more widespread Pb exposures , due to the large populat ions in urban areas.2-20 Background information for all three case studies is presented in appendices A and B, and briefly summarized in Sections 2.2.1 and 2.2.2, below.
Consideration of CASAC comments on the draf t full-scale assessment (see Section 1.4) led to the development of additional case studies which are presented in Chapter 5.

2.2.1 General Urban Case Study

In consideration of CASAC comments on the pilot assessment (Henderson, 2007a), a general urban case study is incl uded in the assessment. This case study is designed to provide
estimates of risk associated with the current and alternate NAAQS in urban areas, as well as with current conditions. This case study differs from th e point source case studies in several ways. First it is not based on a specific location. Rather, it is designed to generally represent large
urban areas in the U.S. 3 Second, as discussed further in s ubsequent sections (e.g., Sections 2.4.2 and 3.1.1), the media concentrations are assumed to be spatially uniform throughout the case study area (i.e., spatial variation wi thin the area is not considered). Third, the ambient air quality for this case study is spec ified, based on analyses of Pb levels in large urban areas of the U.S. (Appendix A and Section 2.3.1), rather than derived from air quality modeling of particular air
Pb sources. As a result of this third differe nce, this case study incl udes different types of uncertainties as compared to the case studi es employing air quality models, and it does not distinguish among the different air Pb sources influencing the air con centrations, be they currently active stationary or mobile sources, or resuspension of prev iously deposited Pb. Fourth, the case study does not rely on any speci fic demographic values; that is, a theoretical population of unspecified size is as sumed to be uniformly distributed across the study area. All
of these distinctions of this case study from the others have produced a platform that is a simplified representation of urban areas intende d to inform our assessment of the impact of changes in ambient Pb concentrations on risk.

2.2.2 Point Source Case Studies

Based on the Analysis Plan (USEPA, 2006a) an d conclusions from the pilot phase risk assessment (USEPA, 2006b; ICF, 2006), the poin t source case studies for this full-scale assessment include a study area near a primary Pb smelter, and a second near another, smaller, stationary Pb source. The locations for these tw o case studies were chosen with consideration of

3 While the air monitoring data used to characterize the current conditions scenario for this case study area are from large urban areas, other empirical datasets used in developing this case study, such as those for outdoor residential soil Pb and indoor dust Pb, are generally repr esentative of U.S. residential properties (see Sections 3.1.1.1, 3.1. 3.1 and 3.1.4.1) . As most U.S. residential properties are in large urban areas because that is where a significant share of the U.S. population resides, these da tasets will include greater representation by urban areas, particularly large ones, than non-urban areas. These datasets, however, are not limited to urban locations.2-21 factors described in the Analysis Plan: (a) availability of site-specific monitoring data for ambient air Pb, (b) availability of measuremen t data for other environmental media (soil and indoor dust) and for Pb exposure (i.e., blood Pb levels), and (c ) demographic and socioeconomic factors related to Pb exposure and risk (USEPA 2006a). Both of these case studies rely on air quality modeling to estimate air Pb concentrations used in the risk assessment and as a result they include different type s of uncertainties as compared to those associated w ith the air concentrations for the general urban case study. One of these is related to the representation of resusp ension of previously deposited Pb as a source of
Pb to ambient air. To the extent that emissions estimates are available for this source, this is represented in these case studies. However, as recognized in Section 2.1.1 and Appendix A, Section A.1.1.3, such emissions estimates are often uncertain or unavailable. Some resuspension sources are included in the primary Pb smelter ca se study, however no such sources are explicitly modeled in the secondary Pb smelter case study. Background information for these case studies is briefly summarized below with regard to: (a) population characteristics, (b) reported emissions, (c) ambient air Pb levels, and (d) the availability of site-specific data characterizing levels of Pb in key media and Pb exposures (e.g., soil, blood Pb level data).

2.2.2.1 Primary Pb Smelter Case Study

The primary Pb smelter case study is focused on the only currently ope rating facility in the U.S., which is located in Herculaneum, Miss ouri. At primary Pb smelters, Pb-bearing ore concentrates are smelted to produce Pb metal. This smelter is one of the largest individual
sources of Pb metal in the U.S ., has been active for over a century and there exist a large amount of site-specific data in the surrounding area characterizing both media concentrations (soil, indoor dust, outdoor air) and popul ation blood Pb levels (Appendix B, Section B.1). The facility’s century of operation has cont ributed to Pb contamination of the area surrounding the facility 4 , which has triggered various remedi ation activities (e .g., removal of Pbcontaminated residential soil) as well as enlargem ent of the facility property to encompass many of the most heavily impacted private properties. The remediation activity introduces a complication to the risk mode ling, especially aspects invol ving characterization of the relationship of ambient air Pb a nd residential soil Pb to indoor dust Pb (see Section 3.1.4).

4 Portions of this study area comprise an active Superfund site and are subject to ongoing evaluation under the Superfund program administered by the Office of Solid Waste and Emergency Response. Methods used in conducting the human health exposure and risk assessment for the pilot analysis have been selected to address policy questions relevant to the Pb NAAQS review and consequently may differ from those used by the Superfund

program.2-22 Some key aspects of the background inform ation for this case study (Appendix B) are summarized briefly in Table 2-1.2-23 Table 2-1. Key aspects of primary Pb smelter case study.

Population As of the 2000 U.S. Census, approx imately 38,000 people lived within 10 kilometers (km) of the facility, 10% of which were ch ildren less than 8 years of age. Since 2000, actions associated with reducing facilit y-related Pb exposu res have reduced the population size within 2 km of the facility such that six previously occupied census blocks near the facility were unoccupied in 2004. With those counts subtracted from the 2000 counts, the numbers of children (less than 8 years old) residing within 2 km, between 2 and 5 km and between 5 and 10 km were 171, 1,545 and 2,164, respectively (Appendix B). Emissions Lead is emitted from a wide variety of activities associated with the primary Pb smelter, including the transport of materials into and within the facility. The facility is estimated to be the largest Pb emitter in the U.S. (Appendix B, Section B.1 and Appendix A, Section A.1). Air Quality In 2005, annual average concentrations of Pb in total suspended particulate matter (Pb-TSP) at the nine monitors in this town, for which data are reported in the U.S. Air Quality System (AQS), ranged from 0.046 to1.56 μ g/m 3 . All of these nine monitors, fall within the top 30% of the 2005 annual average levels at AQS monitors nationally, with four of the nine monitors falling in the top 10% (Appendix B). Maximum quarterly average Pb-TSP concentrations at one of these monitors exceeded the current NAAQS in 2005, 2006 and 2007 (Appendix A; AQS).

The area within the city limits of Herculaneum is designated nonattainment for the Pb NAAQS and the existing State Implementation Plan (SIP) was approved in 2002 (67 FR 18497). EPA determined the existing SIP to be inadequate to attain and maintain the current NAAQS in 2006 (71 FR 19432), and consequently the state of Mi ssouri developed a revised SIP for the area. U.S. EPA, Region 7 received Missouri’s proposed SIP revision on May 31, 2007 (MDNR, 2007). The air dispersion modeling performed for the risk assessment described in this document built on the information and modeling developed for the revised SIP. 5 The significant amount of site-specific data available for Herculaneum, paired with air dispersion modeling for the facili ty conducted in support of SIP development for Pb, provides a
substantial data set for this st udy area which enhances the modeling of exposure and risk. For example, the Herculaneum facility has more site -specific monitoring data available to support risk assessment than the second point source case study location, including residential yard soil,
indoor dust and road dust Pb measurements co llected in areas potentially impacted by the facility. In addition, the Agen cy for Toxic Substances and Disease Registry (ATSDR) has
conducted a number of health consultations that involve d the collection of blood Pb measurements for children (Appendix B, Section B.1.3). Extensive air Pb monitoring data are also available and were considered in the pe rformance evaluation of the modeling setup. The Herculaneum case study location also has a number of attributes that add complexity to the modeling of Pb exposure and risk including (a) complex terrain and meteorology which

5 The 2007 draft SIP revision (MDNR, 2007), including the modeling, is currently under review by EPA.2-24 complicate the modeling of Pb transport in ambien t air, (b) a large and complex facility with a long history of operation and si gnificant opportunity for fugitive emissions, making source characterization challenging, and (c ) a history of remediation activ ities which has contributed to widely varying residential soil Pb concentrations across the town.

2.2.2.2 Secondary Pb Smelter Case Study

The secondary Pb smelter case study, in Troy, Alabama, involves a smaller point source than the primary Pb smelter case study, with re latively less site-specifi c data characterizing media concentrations and exposure levels. Seco ndary Pb smelters produce Pb from scrap and provide the primary means for recycling Pb aci d automotive batteries, an d are among the larger source categories of Pb emitters (see Appendix A, Section A.1). The Troy facility was one of 15 secondary Pb smelters operating within the U.S. in 2002 (see Appendix B, Section B.2). Some key aspects of the background information for this case study (Appendix B) are summarized briefly in Table 2-2.

Table 2-2. Key aspects of secondary Pb smelter case study.

Population As of the 2000 U.S. Census, approximatel y 18,000 people lived within 10 km of the facility, 10 percent of which were children less than 8 years of age. Specifically, 187 children of that age group lived within 2 km of the fa cility, 896 lived between 2 and 5 km and 589 lived are between 5 and 10 km from the facility (Appendix B). Emissions Lead is emitted from the facility operati ons, from materials storage and handling, and from facility roadway dust. Similar to most secondar y Pb smelters, emissions from this facility are estimated to fall between 1 and 5 tpy (see Appendix B, Section B.2.2 and Appendix A, Attachment A-1). Air Quality Annual average concentrations of Pb-TSP for 2005 at the two monitors located within 1 km of the Troy facility (300 and 800 m from the fac ility), for which data are reported in the U.S. Air Quality System, are approximately 0.4 and 0.1 μ g/m 3 , respectively. These values fall within the top 15% of Pb-TSP annual average values for 2005 (see Appendix B, Section B.2.5.1). Maximum quarterly average Pb-TSP concentrations at one of the monitors exceeded the current NAAQS in 2003 (Appendix A).

In contrast to the Herculaneum facility, we have not identified so il or indoor dust Pb measurements for this case study locati on. Additionally, although there are blood Pb measurements in children available at the county level, they are not avai lable at a more refined scale that might relate more directly to this case study. The relative sparseness of site-specific Pb measurements means that the exposure asse ssment conducted for the secondary Pb smelter case study is more dependent on model projections, and consideration of measurements available for similar locations, and that th ere is less opportunity for rigorous performance evaluation of the modeling steps. The available air Pb monitori ng data, however, are used in the performance evaluation of the air quality modeling.2-25 2.3 ASSESSMENT SCENARIOS

The design of the scenarios a ssessed for each case study includes aspects particular to air concentrations of Pb (Section 2.3.1), surface soil /dust concentrations of Pb (Section 2.3.3) and background (Section 2.3.2). As the scenarios are primarily distingui shed by the differences in air concentrations, we generally refer to the different assessment scenar ios as air quality scenarios in this document (including the appendices). The different air quality scenarios include current conditions, meeting the current NAAQS of 1.5 μg/m 3 (maximum quarterly average) and meeting several alternate, lower NAAQS.

2.3.1 Air Concentrations

The air concentrations assessed in the diffe rent air quality scenarios include current conditions, meeting the current NAAQS of 1.5 μg/m 3 (maximum quarterly average) and meeting several alternate, lower NAAQS. In consideration of the range of levels suggested by CASAC (Henderson, 2007a), the alternat e NAAQS scenarios included in the assessment are: 0.5 μg/m 3

, 0.2 μg/m 3 and 0.05 μg/m 3 as maximum monthly averages, and 0.2 μg/m 3 as a maximum quarterly average. In response to discussion at the August 2007 meeting of the CASAC Pb Panel to extend lower the range of a lternate levels consid ered, an alternate NAAQS scenario of 0.02 μg/m 3 , as a maximum monthly average, is also incl uded in the additional analyses presented in Chapter 5. While the current and alternate NAAQS scenarios are characterized by quarterly or monthly averaging times, it is the associated annu al average ambient air c oncentrations that are
then used in the risk assessment 6

. The current conditions scenario, performed for the general urban and secondary Pb smelter case studies, is intended to generally refl ect recent conditions for these case studies based
on data available for the characterization. For exam ple, for the urban case study, air Pb levels for
current conditions are based on 2003-2005 air quality data (Appendix A). For the secondary Pb smelter case study, for which we used air quality modeling, air Pb levels for current conditions
are based on emissions characterizations drawn from currently available emissions information and recent meteorological data (see Appendix E). The current NAAQS attainment air quality s cenario was performed for the primary Pb smelter case study, for which current monitoring data indicate exceedance of the current Pb

6 Use of the annual average concentra tion is consistent with the temporal period of this input to the primary blood Pb model (Appendix H) and also the generally longer term resolution of the blood Pb metrics associated with the concentration-response functions (“concurrent ” and “lifetime average”) (Lanphear et al., 2005).2-26 NAAQS (Appendix B, Section B.1.5.1). 7 Additionally, although we consider it extremely unlikely that air concentrations in urban areas across the U.S. that are well below the current standard would increase to just meeting the st andard, we recognize the potential for air Pb concentrations in some areas currently well below th e standard to increase to just meet standard by way of, for example, expansion of existing sources (e.g., facilities operating as secondary smelters exercising previously used capabilities as primary smelters) or by the congregation of multiple Pb sources in adjacent locations. Accordi ngly, we have simulated this type of scenario (increased Pb concentrations to just meet the current standard) in the general urban case study and in the additional analyses for three location-specific urban case studies described in Chapter
5. In developing the reduced ai r concentrations for the alternate NAAQS scenarios for the point source case studies (for which air quality models are em ployed), the maximum monthly or quarterly average (depending on averaging time for the altern ate NAAQS) for each modeled receptor point is compared to the NAAQS leve l to identify the factor by which the highest average exceeds the NAAQS level. All monthly or quarterly averages are then reduced by this factor (i.e., a proportional roll back is implemented) and the associated annual average recalculated for each receptor point.
Two different current conditions scenarios ar e assessed for the gene ral urban case study
based on air Pb concentrations for the period 2003-2005 at monitors in U.S. urban areas with population greater than one million (Appendix A). 8 One of these two s cenarios is based on the mean maximum calendar quarter average for thes e monitors, and the second is based on the 95 th percentile of maximum calendar qua rter averages for these mon itors. Additionally, although the
mean and 95 th percentile maximum quarterly average of the large urban area monitors nationally do not exceed the current NAAQS level, an increase d air concentration scenar io (i.e., to the level of the current standard) has been included for this case study.
As the air Pb concentrations for this case st udy do not vary spatially (see Section 3.1.1.1), the air Pb concentration is simply set to the level specified for each scenario as either a monthly or quarterly average. The annual average air concentrati on (the metric used in the dust and blood

7 Given the status of this area with regard to nona ttainment and SIP revision, as well as the use of the modeling set-up developed for the SIP attainment demonstration, a current conditions scenario was not developed for the primary Pb smelter case study.

8 In designing the general urban case study, we recognized CASAC’s interest in our development of risk estimates that were more informative to the larger nationa l population and exposures related to ambient air reflecting the aggregate contributions from multiple sources of Pb than might be discrete populations near particular point sources of Pb (Henderson, 2007a). Urban areas of greater than one million population were identified as informative for large populations in established cities (as compared to more newly-developed urban areas).2-27 Pb modeling) is derived from the current condi tions or NAAQS level (and averaging time) using relationships based on current Pb -TSP monitoring data for monitors in large U.S. urban areas (Appendix A). Lead in TSP monitoring data fo r the time period 2003-2005 from monitors in U.S. urban areas of population size greater than on e million were analyzed to derive estimates of maximum quarterly average, maximum monthly av erage and annual average for each monitor. From these estimates, the ratios of the maximu m quarterly and maximum monthly averages to the annual average were derived for each monitor and the arithmetic mean and 95 th percentile value of the monitor-speci fic ratios were derived. To derive the annual average air concentration used in the dust and blood Pb modeling for each scenario, one of these ratios was applied to the air quality scenario level. For example, the alternate NAAQS level of 0.5 (for a maximum monthly averaging time) was divided by the mean monitor ratio of maximum monthly average to annual average to derive the annual average air concentration estimate for that alternate NAAQS scenario. The air quality values associated w ith the different scenarios assessed for the urban case study are summarized in Table 2-3.2-28 Table 2-3. Air quality scenarios assessed for the general urban case study. 1

Air Quality Scenario Level (μg/m 3 ) Averaging Time (Form) Ratio Associated Annual Average Concentration (μg/m3) 95 th Percentile Current Conditions 0.87 calendar quarter (maximum) 7.6 a 0.11

Mean Current Conditions 0.14 calendar quarter (maximum) 2.5 b 0.056

Alternate NAAQS 0.5 Month (maximum) 4.0 c 0.13

Alternate NAAQS 0.2 Month (maximum) 4.0 c 0.05

Alternate NAAQS 0.2 calendar quarter (maximum) 2.5 b 0.08

Alternate NAAQS 0.05 Month (maximum) 4.0 c 0.013

Current NAAQS 1.5 calendar quarter (maximum) 2.5 b 0.60

a This is the 95 th percentile of the ratios of maximum quarterly aver age to annual average for monitors at sites in urban areas with population of one million or more people.
b This is the mean of the ratios of maximum quarterly av erage to annual average for monitors at sites in urban areas with population of one million or more people.

c This is the mean of the ratios of ma ximum monthly average to annual average for monitors at sites in urban areas with population of one million or more people. 2

2.3.2 Policy-relevant Background 3 Given the multimedia, multipathway nature of Pb, levels of Pb in all exposure media 4 (including those other than air) ar e essential aspects of the scenar ios assessed for each case study. 5 As discussed in Section 1.1, some of the Pb in other media may be derived from policy-relevant 6 sources, while, for our purposes here, we have categorized others as po licy-relevant background. 7
Some amount of Pb in the air also derives from background sources , such as volcanoes, sea salt, 8 and windborne soil particles from areas free of anthropogenic activity (CD, Section 2.2.1). The 9 impact of these sources on current air concentrati ons is expected to be quite low and has been 10 estimated to fall within the range from 0.00002 μg/m 3 and 0.00007 μg/m 3 based on mass balance 11 calculations (CD, Section 3.1 and USEPA 1986, S ection 7.2.1.1.3). The midpoint in this range, 12 0.00005 μg/m 3 , has been used in the past to represen t the contribution of naturally occurring air 13 Pb to total human exposure (USEPA 1986, Section 7.2.1.1.3). It is noted that the data available 14
to derive such an estimate are limited and th at such a value might be expected to vary 15 geographically with the natural distribution of Pb. Compari ng this to reported air Pb 16 measurements is complicated by limitations of the common analytical methods and by 17 inconsistent reporting practices. This value is one half the lowest reported nonzero value in 18 AQS. For the purposes of this asse ssment, however, the value of 0.00005 μg/m 3 was selected as 19 representative of policy-relevant background Pb in air. Unlike fo r other criteria pollutants, the 202-29 role of this value for Pb is limited. In cons idering risk contributions from policy-relevant 1 background, the contributions from exposures to nona ir media are such that any credible estimate 2 of policy-relevant background in air is likely in significant in comparison. In developing the air 3
Pb concentrations associated with the alte rnate NAAQS scenarios, the estimate of policy4 relevant background in air was the floor below which concentrations were not lowered. 5

2.3.3 Outdoor Soil/Dust 6 With regard to surface soil 9 Pb concentrations for the al ternate NAAQS scenarios, the 7 presence of historically deposited Pb, associated with past periods of higher air concentrations 8 and associated atmospheric deposition, affects the soil Pb dynamics. Under the alternate 9 NAAQS scenarios, atmospheric deposition of Pb w ill continue to occur, albeit it will be reduced 10
from the current rate which is reduced from histor ic rates. At any location, the type of response 11
of the surface soil concentrations to the changed deposition rate will depend on the relationship 12
of current surface soil concentrations to the surf ace soil concentrations associated with a steady 13 state condition (i.e., when the rate of Pb addition to the surface soil equals the rate of Pb loss 14 from the surface soil) at that lo cation. If current surface soil concentrations are below their 15 steady state levels for the current conditions (i.e., the rate of Pb addition is greater than the rate 16 of Pb loss) and air concentrations are reduced (i.e., the rate of Pb addition via deposition is 17 reduced), surface soil concentrations might be ex pected to continue an increasing trend, although 18
at a reduced rate from the current rate. Altern atively, if current surface soil concentrations are 19 above their steady state levels (i.e., the rate of Pb loss is greater than the rate of Pb addition) and 20 air concentrations and associated deposition ar e reduced, surface soil concentrations might be 21 expected to continue a decrea sing trend and do so at a greater rate than the current one. 22 Information regarding the current dynamics of Pb concentrations in surface soils is 23 limited with the predominant focus for such stud ies being somewhat remote forested areas (e.g., 24 CD, Section 3.2 and 3.2.2 and pp. AX7-33 to AX7-34). Findings to date indicate that systems 25
with little influence from local point sources are still respondi ng to reduced Pb deposition rates 26 associated with reduced atmospheric emissions ofPb, including those associated with the phase27 out of leaded gasoline. For example, studies of forest soils have concluded that surface 28 concentrations of Pb are decreasing in respons e to the reduced Pb de position rates since the 29 phase-out of leaded gasoline (M iller and Friedland, 1994; Kaste a nd Friedland, 2003). Studies in 30

9 In the risk assessment, outdoor surface soil or dust is an important exposure pathway. Use of the term
surface soil here is intended to incl ude the terms outdoor soil and outdoor dust, with there being some overlap
between those two terms in that the su rface layer of outdoor soil might be refe rred to as outdoor dust. Specifically, the phrase “outdoor dust” refers to particles deposited on any outdoor surface, including, for example, soil, sidewalks, roadways, etc.2-30 urban areas of southern California, where Pb has accumulated from past sources, suggest an 1 environment in which Pb may remain at the soil surface (and other surfaces ), contributing to air 2 concentrations via resuspension in the near term (CD, pp. 2-65 to 2-67 and 3-18 to 3-19). 3 Accordingly, the temporal trend in surface soil c oncentrations in this environment is considered 4 to be influenced by the rate of resuspension, such that little to no reduction in soil Pb 5 concentration in southern California is expect ed over the next few hundred years (CD, pp. 2-65 6 to 2-67 and 3-18 to 3-20; Harris and Davidson, 2005) . Temporal trends in surface soils near 7 established point sources are not well characterized. Available information for a few areas 8 surrounding smelters after implementation of pollution controls shows a decline in Pb 9
concentrations in outdoor dustfal l, street dust and indoor dustf all, but has not indicated a 10
noticeable decline in soil Pb concen trations (CD, pp. 3-23 to 3-24). 11 The above discussion suggests that a reduced air concentration in the three case studies 12 would not be expected to yield a changed surface soil concentration over the near term, yet may 13 yield a reduced surface concentration over a much longer term. An exception to this may be 14 some areas of the primary Pb smelter case study where contaminated soil has been removed and 15 replaced with “clean” soil. Measurements taken of Pb concentrations in such “clean” soil placed 16 within 3⁄4 mile of the facility exhibit small in creasing temporal trends over a period of a few 17

year 10 (USEPA, 2006c). In lieu of additional data or a multimedia modeling analysis, however, 18
the surface soil concentrations fo r the current and alternate NAAQS scenarios in all case studies 19 have been set equal to those used for the current conditions scenario s. This is generally believed 20 to be a reasonable representation of soil Pb resp onse to alternate NAAQS for at least six years, 21 and likely much longer, after a new standard might be implemented. 11 A potential exception is 22 the area of the primary Pb smelter case study within 3⁄4 mile of the facility, where it may be that 23
surface Pb concentrations in remediated soil ma y increase to higher levels under the current and 24 some of the alternate NAAQS. This remains an area of uncertainty with potential implications 25
for areas in which a Pb source or combinati on of Pb sources may locate where ones of 26 comparable size had no t been previously. 27 Additionally, we recognize that implementati on of some alternate NAAQS could in some 28 areas (e.g., areas of substantial past atmospheri c deposition) involve c ontrol of surface soil/dust 29
to reduce surface soil/dust Pb leve ls. That is, in places where su rface soil/dust Pb concentrations 30 contribute substantially to air concentrations, co ntrols implemented to attain various alternate 31 lower NAAQS might include reducing soil Pb concentr ations. Such specific control actions have 32

10 An increasing trend was not seen with soil at the two locations just beyond 3⁄4 miles away.

11 This was also the approach used in the risk assessment performe d in the last Pb NAAQS review (USEPA, 1990).2-31 not been addressed in this assessment, and as st ated above, outdoor soil/dust concentrations in all 1 air quality scenarios have been set equal to th e values for the current conditions scenarios. 2

2.4 ANALYTICAL APPROACH 3 This section provides an overview of the an alytical approach, de scribing key elements 4 including: (a) temporal aspects, (b) spatial scale of the analysis and the type of spatial template 5 used in modeling, (c) overview of the analytical steps of pr edicting media concentrations, 6 modeling exposure, and modeling ri sk, (d) performance evaluation completed in support of the 7 analysis and (e) the approach used to characte rize uncertainty and eval uate model sensitivity. 8 The approach described here pertains to the initial analyses of the full-scale assessment 9 for which results are described in Chapters 3 a nd 4, and to some aspects of the core modeling 10 approach employed for the additional analyses described in Chapter 5. 11

2.4.1 Temporal Aspects 12 The risk assessment conducted for each case study uses a simulated child population for 13 which exposure begins at birth and continues for 7 years. That is, the study population is 14 assumed to be a single group, for which exposure begins at birth and continues until the group 15 reaches 7 years of age. 12 Furthermore, it is assumed that no migration or immi gration of these 16 children occurs during this simula tion period; that is, none of the children move out of the study 17
area and no children move in. 18 For the point source case studies , the use of modeling (with a constant emissions rate and 19 temporally varying meteorology) provides temporal ly varying air concentrations. However, the 20 primary blood Pb model for this assessment is lim ited in the temporal resolution of its inputs (see 21 Section 3.2), because the finest temporal resolu tion of inputs to the blood Pb model is a year. 22 Consequently, in characterizing exposure media concentrations, annual averages are used. 23 With regard to temporal variation acro ss the seven-year exposure period, several 24 exposure factors and physiological parameters are varied on an a nnual basis within the blood Pb 25 modeling step (see Section 3.2). Once set for th e air quality scenari o, however, the media 26
concentrations of Pb are held constant thr oughout the seven year peri od (see Section 3.1). 27

2.4.2 Spatial Scale and Resolution 28 The size and resolution of the study area diffe red among the three case studies. The 29 general urban case study is not set in a specific location, a nd involves non-spatially-varying 30 media concentrations and population density. Th ere is no spatially di fferentiated template per se 31

12 Modeling of blood Pb levels for the child population includes contributions representative of prenatal Pb

exposure.2-32 and instead, a single generic urban study area is assumed with uniform population density and 1 exposure concentrations. For th e exposure modeling for the point source case studies, however, 2 spatial templates were developed. The temp lates subdivide the study area into subunits, 3 composed of U.S. Census blocks or block groups, across which media concentrations differ. 13 4 Media concentration estimates (e .g., for outdoor air, soil and indoor dust) are developed for each 5 block or block group, and from these a central te ndency estimate is developed of concurrent and 6 lifetime average blood Pb levels for the resident children. Interindividual variability of blood Pb 7 levels for children within a block or block group is considered through the use of a statistically 8 derived GSD. The specific spatial templates used for each of the point s ource case studies are 9 presented in Appendices D (Secti on D.1) and E (Section E.1). 10 For the two point source case studies, population risk estimat es were derived separately 11 for a portion of the full study area in addition to th e estimates derived for the full study area. The 12 subareas extended approximately 1.5 km out from the point source locations (see Appendix P). 13

2.4.3 Categorization of Policy-relevant Exposure Pathways 14 To inform policy aspects of the Pb NAAQS review, we have attempted to parse the 15 assessment estimates for indoor dust Pb, blood Pb a nd IQ loss into the fraction associated with 16 policy-relevant background (e.g., diet and drinki ng water) versus that associated with policy17 relevant pathways, which include inhalation, outdoor soil/dust ingestion and indoor dust 18 ingestion (Section 2.1.1). We have further categorized the policy-relevant pa thways into one of 19 two categories, “recent air” or “past air”. C onceptually, the recent air category includes those 20 pathways involving Pb that is or has recently been in the air, whether or not it was also in the air 21 in the past, and the past air ca tegory includes those pathways invol ving Pb that was in the air in 22 the past and was not in the air recently. As disc ussed below, this conceptual distinction could 23 not be entirely reflected in the risk assessment due to technical limitations. Thus, risk estimates 24 associated with “past air” reflect some Pb that was recently in the air. Similarly, diet and 25 drinking water are treated entirely as policy -relevant background, de spite also reflecting 26 contributions from policy-relevant path ways such as atmospheric deposition. 27 Conceptually, recent air refers to exposure contributions associated with inhalation of 28 ambient air Pb and ingestion of the fraction of in door dust Pb derived from recent ambient air Pb. 29
To the extent that ambient air Pb includes co ntributions from resuspension of previously 30

13 US Census block groups vary in size from several c ity blocks in densely populated urban areas to many square miles in less populated rural areas. Their population count varies from 600 to 3000 people per block group with the typical block group in the U.S. containing 1,500 people. US Census blocks are more refined than block groups and typically contain several hundred people or less. Their size can vary from a single city block in urban areas to multiple square miles in less populated rural locations.2-33 deposited Pb, that source is represented in the recent air category. Thus, a “recent air” exposure 1 may involve previously deposited Pb that is (1 ) resuspended into the air and inhaled or (2) 2 resuspended into the air, transported into a building, deposited into the dust, contacted and 3 ingested. 4 Conceptually, past air includes exposure cont ributions from ingesti on of outdoor soil/dust 5 that is contacted on surfaces outdoors, and ingesti on of indoor dust Pb that is derived from past 6 air sources. Although Pb that is currently in outd oor soil/dust may have be en in the air recently 7 or some time ago, we have assigned ingestion of outdoor soil/dust Pb contacted outdoors to the 8 past air category in recogn ition of our inability to maintain a dynamically changing 9 categorization of recent versus past air. 14 The past air category al so includes the ingestion of 10
indoor dust Pb that was in the ambient air in th e past but not recently. These sources to indoor 11 dust Pb include any residual legacy of historical air Pb in the indoor dust of older homes, as well 12 as Pb occurring in indoor dust that is derived from outdoor soil/dust Pb that was not transported 13 indoors by an air pathway. This latter pathway in cludes outdoor soil/dust Pb that is carried 14 indoors by human contact (e.g., “tracking in”). 15 To implement this categorization in the assessm ent, we developed estimates of the recent 16 air portion of indoor dust Pb (i.e., contributions asso ciated with recent ambient air Pb levels), and 17 assigned the remainder of indoor dust Pb to other sources, which include those relevant to past 18 air. That is, the “other sources ” component of indoor dust Pb re fers to contribu tions from indoor 19 paint, outdoor soil/dust and a dditional sources. Among the a dditional sources is any residual 20 legacy of historical Pb in the indoor dust of older homes. 21
The indoor dust Pb subdivision reflects and is limited by the models and inputs used to 22
estimate indoor dust Pb levels for the different scenarios. All of them predict dust Pb 23 concentration as a function of, among other factor s, ambient air Pb concentration, and all of the 24 models include a constant (e.g., the intercept in the regression -based models) that captures 25 “other” sources, as well as uncertainty associat ed with the relationship with ambient air Pb. 15 26 One of the models (used in the secondary Pb sm elter case study and in part of the primary Pb 27 smelter case study) also includes a dependency on outdoor soil/dust Pb concentration. This 28 difference among the models leads to an inconsiste ncy across the case studi es in the ability to 29 separate the contribution to indoor dust Pb fr om outdoor soil/dust. Consequently, we have 30

14 In concept, this assignment appears to inherently contribute to an underestimate of the recent air category. However, the reality of much higher air emissions in the past that have contributed to Pb concentrations in other media that are higher than those that would be associated with more recent lower emissions, complicates consideration of this assumption (see Section 2.3.3).

15 The extent to which this inter cept captures uncertainty about the relationship with ambient air will indicate presence of a recent air pa thway in the past air category.2-34 limited to two categories the subdivision of indoor dust contributions, with that from outdoor 1 soil/dust included in “other” whether it is estimated by a model that includes a soil concentration 2 coefficient or is accommodated by the constant or intercept term. Additionally, we have not 3 subdivided indoor dust Pb estimates for the primary Pb smelter due to uncer tainty regarding this 4 categorization with the model used for that case study (Section 3.1.4.2). In presenting risk 5 estimates associated with policy-re levant pathways (Chapter 4), we have included risk associated 6 with the “other” component of indoor dust in the past air category. We recognize that the 7 potential for this “other” component to include am bient Pb unrelated to ai r emissions contributes 8 to a potential for the risk estimates associated with past air to be overest imates. Additionally, as 9 the recent air portion of indoor dust Pb, which derive s from transport of airborne Pb into a house, 10 depends on the estimate of ambient air Pb concen tration, selection of an ambient air Pb estimate 11 that is not appropriate to this relationship (e.g., one that does not represent ambient air Pb levels 12 immediately outside the house) ma y contribute to an under- or overe stimate of recent air indoor 13
dust Pb. 14 There is inherent uncertainty associated with the approaches used to divide indoor dust15 related Pb exposures and risk into contributi ons from “recent ambient air” and from “other” 16 sources. Further, the uncertainty may differ am ong the three case studie s due to the different 17 approaches used in modeling indoor dust Pb. Fo r example, uncertainty associated with the 18 hybrid mechanistic-empirical model used in th e general urban case st udy includes that which 19 arises from model inputs and model performan ce, while the empirica l, regression-based, 20 statistical models used in the poi nt source case studies entail uncer tainty regarding similarity of 21
the conditions from which the model was derived to those to which it is applied, as well as 22 uncertainty regarding variables th at may be correlated with thos e explicitly represented in the 23 model. Thus, given the various limitations of our modeling tool s, blood Pb levels associated 24 with air-related exposure pathways and current levels of Pb emitted to the air (including via 25 resuspension) are likely to fall between the estimat es derived for “recent ai r” pathways and those 26 for “recent” plus “past air” pathways. 27

2.4.4 Overview of Analytical Steps 28 As illustrated in Figure 2-2, the risk assessmen t completed for the two point source case 29 studies generally includes four anal ytical steps: (a) fate and trans port of Pb released into outdoor 30 air, including the disper sion of Pb away from the point of release and the deposition of Pb onto 31 surfaces, (b) prediction of the resulting concentr ation of Pb in media of concern including 32 outdoor air, outdoor surface soil/dust and indoor dust, (c) use of thes e Pb concentrations together 33 with estimates of Pb in background exposure path ways, including diet, to estimate associated 34 blood Pb levels in children using biokinetic modeling and (d) use of concentration-response 352-35 functions derived from epidemiology studies to estimate IQ loss associated with the estimated 1 blood Pb levels. The modeling approach for the general urban case study is somewhat simpler, 2
since it does not involve fate a nd transport modeling for air con centration estimates and instead, 3
uses ambient monitor levels combined with an assumption of uniform ambient air Pb levels 4 across the study area. Subsequent steps in the ge neral urban case study analysis are fairly similar 5 to what is described above for the point source case studies, with the generation of population 6 blood Pb levels being somewhat simplified fo r the general urban case study. Figure 2-2 7 identifies the key input data se ts, modeling steps and intermediate model output in each of the 8 four analytical steps. The first three steps are employed in the exposure assessment (Section 9 2.4.4.1), while the fourth is the ri sk assessment step (Section 2.4.4.2). 10 Prior to focusing on a single modeling appr oach (see Chapter 5), and because of the 11 quantitative influence of certain analytical steps on the results, we employed multiple approaches 12 to perform some of the analytical steps. The mul tiple sets of results generated in this way for a 13 case study and air quality scenario are intended to span a rang e indicative of the model and 14 parameter uncertainty associated with those analy tical steps of the risk analysis (see Section 15 2.4.6). 162-36 Figure 2-2. Overview of analysis approach. 1

Characterizing ambient air levels and deposition to soil

Air dispersion modeling ( primary, secondary Pb smelters ) Ambient monitoring data (mean and high-end values) ( urban) Stack and fugitive emissions data

Ambient air concentrations for study areas Lead deposition rates across study area

Characterizing soil and indoor dust concentrations

Outdoor residential soil concentrations Multi-media fate & transport model-derived soil concentrations scaled using measured data from surrogate location (secondary Pb smelter) Site-specific soil monitoring data combined with

statistical extrapolation (primary Pb

smelter)Nationallyrepresentative residential soil Pb value selected from literature (urban) Combination of (a) statistical (regression) and/or (b) mechanistic (compartmental) indoor dust prediction models ( all 3 case studies )

Indoor residential dust concentrations

Biokinetic blood Pb modeling ( all 3 case studies ) Background Pb exposure levels • diet • drinking water • indoor paint (actually reflected in dust modeling)

Probabilistic modeling of blood Pb levels for children within each case study location ( all 3 case studies ) GSD reflecting interindividual variability in behavior related to

Pb exposure and

biokinetics Characterizing blood Pb levels

Concentration-response function • log-normal distribution (Lanphear pooled analysis) • breakpoint reflecting confidence in CR function • effects estimation based on concurrent and lifetime average blood Pb metric Demographics (distribution of

children within study areas) ( two point source case studies)

Distribution of IQ loss for study populations(partitioned between policyrelevant exposures and policy-relevant background) Characterizing risk (IQ loss) Ambient air concentrations

(see above)

Risk estimation - IQ change estimation ( all 3 case studies )EXPOSURE ASSESSMENTRISK CHARACTERIZATION

22-37 1

2.4.4.1 Exposure Assessment 2
Concentrations of Pb are estimated in ambient media and indoor dust using a 3 combination of empirical data and modeling projectio ns. The use of empirica l data brings with it 4 uncertainty related to the potenti al inclusion of bac kground source signals in these measurements 5 (e.g., house paint contributions to indoor dust and outdoor soil Pb). Conversely, the use of 6 modeling tools introduces other unce rtainties (e.g., model and parame ter uncertainties). Both of 7 these uncertainties are recognized in Section 4.3. Specific appro aches used at the three case 8 study locations are briefly described below. 9
Characterization of Pb in ambient air relies on (a) dispersion modeling of facility-related 10 (including fugitive) Pb emissions for the primary and secondary Pb smelter case studies and (b) 11 the use of ambient monitor data for the gene ral urban case study. For the general urban case 12 study, monitoring data for U.S. urban areas of more than a million in population were used to 13 identify two current conditions scenarios, one “typical”, and one highe r end, and to relate 14 alternate NAAQS (of other forms) to annual aver age levels needed for blood Pb modeling. A 15 key aspect of the general urban case study is that ambient air lead levels do not vary spatially 16 within the study area. The appro ach is monitor-based rather than source-based as compared to 17 two point source case studies. This means that we did not explicitly model specific source 18 contributions for the general urban case study (e.g., resuspension of ro adside dust, “fresh” 19 industrial emissions) and instead, relied on empirical da ta to define ambient air Pb levels for this 20 general case study, with these leve ls reflecting contributions from all contributing sources, be 21 they currently active stationary or mobile sources , resuspension of previously deposited Pb or 22
other. 23
Characterization of Pb concentrations in outdoor surface soil/dust, resulting from 24 deposition of airborne Pb is based on the use of (a) existing site-specific measurements (primary 25 Pb smelter case study), (b) nationally representa tive residential soil measurements obtained from 26 the literature (general urban case )study and (c) fate and tr ansport modeling (secondary Pb 27 smelter case study). In the case of the primary Pb smelter case study, soil Pb concentration data 28
were available for a zone close to the facility and statistical extrapolation from the available 29 empirical data was used to pred ict soil levels for portions of the study area beyond this zone. 30 To predict concentrations of ambient Pb in indoor dust, we have relied on a combination 31 of (a) regression-based models th at relate indoor dust to outdoor air Pb a nd/or outdoor soil Pb 32 and (b) mechanistic models that predict indoor dust Pb based on key mechanisms (e.g., exchange 33 of outdoor air with indoor air, deposition rates of Pb to indoor surfaces, house cleaning rates). 34 For both point source case studies, a combination of regression-based models obtained from the 352-38 literature and developed based on site-specific data were used, and a customized hybrid 1 empirical-mechanistic model was developed for th e general urban case study. This reflected the 2 fact that available regression- based models had been develope d largely based on residential 3 exposures near large point source s and were not considered repres entative of more general urban 4 exposures. Consequently, a mechanistic model, augmented with empirical data, was developed 5 for the general urban case study. Additional deta il on methods used to characterize media Pb 6 concentrations for each case study can be found in Section 3.1. 7 Blood Pb levels are predicted from estimates of Pb contained in various media (e.g., 8 ambient air, diet, water, indoor dust) using the Integrated Exposure and Uptake Biokinetic 9 (IEUBK) model (Section 3.2.1.1). A second biokinetic model, the International Commission for 10 Radiation Protection model (hereafter referred to as the “Leggett model”), is included in the 11 sensitivity analysis (Section 3.2.1.2). 16 The same fundamental approach was used to estimate 12 population distributions of blood Pb levels for each of the two point source case studies, and a 13 somewhat simpler approach was used for the ge neral urban case study. The approach used for 14 the two point source case studies involved two main steps: 15 1) Use biokinetic model to predict central tend ency blood Pb levels for children within 16 each exposure zone: The model outputs are then aggregated into the “concurrent” 17 and “lifetime average” blood Pb metrics used in the concentration- response functions. 18 2) Implement probabilistic exposure model to generate a population distribution of 19
blood Pb levels for children in each case study location: Th e probabilistic model 20 generates a distribution of simulated blood Pb levels for the children in each study 21 area based on consideration of three key f actors: (a) the central tendency blood Pb 22 levels generated for each exposure zone in the preceding step, (b) demographic data 23 (distribution of children 0-7 years of ag e) across the zones comprising a given study 24 area and (c) use of a GSD char acterizing interindividual va riability in blood Pb (e.g., 25 reflecting differences in behavior and biokinetics rela ted to Pb). 26 The step involving modeling population-level expo sures for the general urban case study is 27 somewhat simpler than that used for the two poi nt source case studies in that demographic data 28 for a specific location is not considered. As di scussed in Section 3.2.2, this avoids the need for 29 implementing a population-weighted pr obabilistic sampling procedure. 30

16 The Leggett model was included along with IEUBK in the pilot analyses. Findings for the model in the pilot analyses and in subsequent perfor mance analyses (Appendix J and Section 3.5) contributed to the decision to use the IEUBK model as the primary model in this full-s cale assessment (Section 3.2), and the Leggett model in the sensitivity analysis (Section 3.5.2).2-39 2.4.4.2 Risk Characterization 1 The risk characterization step involves gene rating a distribu tion of IQ loss estimates for 2 the set of children simulated in the exposure asse ssment. Specifically, estimated blood Pb levels 3 (for the two blood Pb metrics) are combined with three blood Pb c oncentration-response 4 functions for IQ loss (see Section 4.1). Th ree differing concentra tion-response functions 5 (described in Section 4.1.1) have been selected to provide thr ee different characterizations of 6 behavior at low exposures. The decision to use three different f unctions is in recognition of 7 uncertainty related to modeling this endpoint, pa rticularly at lower e xposure levels (e.g., blood 8 Pb levels < 5 μg/dL). These three functions are all based on the lognormal concentration9 response function described in the Lanphear et al, (2005) pooled analysis of epidemiology 10 studies focusing on IQ loss in children. As thes e three functions were developed for each of the 11 blood Pb metrics included in the analysis, concurrent and lifetime average, six separate functions 12 were used in the analysis. 13 For each of the two point source case studies, we have produced two categories of risk 14
metrics: 15

• Population risk percentiles: The IQ loss associated with policy-relevant exposure 16 pathways for specific percentiles of the child population (e.g., the 50 th , 90 th , and 95 th 17 percentile modeled child ). This category of metric provides perspective on the 18 distribution of IQ loss resulting from po licy-relevant exposure pathways, ranging 19 from the typical or average child (50 th percentile, mean) to children experiencing 20 higher exposures (90 th , 95 th percentiles). 21

• Child frequency counts associated with specif ic risk percentiles: Number of children 22 associated with each of the population pe rcentiles (e.g., the nu mber of children 23 predicted to have risk le vels at or above the 95 th percentile). This risk metrics 24 provides a perspective on the number of childre n associated with various levels of IQ 25 loss for a particular case study. 26 For the general urban case study, because it is not location-specific, onl y the first type of 27 risk metric, population risk percentiles, was de veloped because this case study is not location28 specific. Child frequency count s are not applicable, since a specific locati on with associated 29 demographic data was not modeled. 30
Additional detail on the risk characterization is presen ted in Sections 4.1 and 4.2. 31

2.4.5 Variability Characterization 32 There are a variety of sources of variability associated with the results of this assessment 33 which are presented in terms of risk estim ates for specific population percentiles: 342-40 • variability in the concentrati on of lead in key media (e.g., diet, drinking water, ambient 1 air, indoor dust), 2

• variability in behaviors which effect Pb exposure (e.g., dust ingestion, soil ingestion), 3

• variability in physiological response to Pb exposure leading to variations in blood Pb 4 levels, and 5

• variability in the toxic response to Pb, resu lting in differing degrees of IQ loss for the 6 same degree of Pb exposure. 7

8 A variety of methods have been used to in corporate, to a limited extent, the above 9 sources of variability in the risk assessment such th at they are reflected in the results for the three 10
case studies. 11

• For the two point source case studies, us e of spatial templates developed using a 12 geographic information system (GIS) to re flect the distribution of children across a 13 study area (in relation to ambient air Pb and related media concentrations) in 14 projecting population exposures. 15

• For the general urban case study, inclusion of two current conditions scenarios (mean 16
and 95 th percentile air Pb scenarios) which t ogether, provide a degr ee of coverage for 17
variation in ambient air Pb levels seen across urban areas in the U.S. 18

• Use of empirically derived GSDs reflecti ng interindividual vari ability in blood Pb 19 levels, to provide coverage for multiple so urces of variability associated with Pb 20 exposure and biokinetics. Note, that the application of these GSDs provides the 21 primary means of reflecting interindividual va riability in blood Pb levels in this 22 analysis. These GSDs also reflect uncerta inty associated with measuring blood Pb 23 levels and characterizing population-le vel distributions of those levels. 24
25 There is significant uncertain ty associated with reflecting the sources of variability 26 identified above in population-level exposure a nd risk. For example there is uncertainty 27 associated with the GSD selected to reflect interindividual variability in blood Pb levels for a 28 particular case study. This is considered in the uncertainty characteri zation (Section 2.4.6). 29

2.4.6 Uncertainty Characterization and Sensitivity Analysis 30
Several methods have been used to examine uncertainty in our modeling approach and its 31 potential impact on exposure and risk estimates (Section 4.3). These includ e: (a) development of 32 multiple sets of exposure and risk estimates fo r each case study and air quality scenario that 33 illustrate the combined impact of different key models and parameters on risk results and the 34 associated uncertainty, (b) evaluation of mode l performance (e.g., by comparison with empirical 352-41 data) to provide confidence in individual mode ling steps and (c) qualita tive discussion of key 1 sources of uncertainty and their potential impact on exposure and risk estimates. Each of these 2 elements of the uncertainty characterization is briefly summarized below. 3 In addition, we have completed a sensitivity analysis, intended to characterize the 4 potential impact of individual modeling el ements on risk results (see Section 2.4.6.3). 5

2.4.6.1 Performance Evaluations 6 Performance evaluation for the exposure a ssessment (Section 3.5) focused on evaluation 7 of estimates of Pb in ambient air, outdoor so il, and indoor dust (discusse d in Section 3.5.1) and 8 estimates of Pb in blood (covered in Section 3.5. 2). This evaluation focused on those estimates 9 based on modeling. 10

2.4.6.2 Generating Multiple Sets of Results 11 There are multiple models or inputs that could be implemented for each of the analytical 12 steps of the assessment. For those more highly influential analytical steps for which it is not 13 clear which model or input would generate “best estimate” results, we have implemented 14 multiple modeling approaches in the initial analyses presented in Chapters 3 and 4. Risk results 15 considered across these multiple modeling approaches provide perspective on the range of 16 potential risk, given key sources of uncertain ty in the analysis. The multiple modeling 17 approaches for each case study were devel oped by the following stepwise strategy: 18

• Identification of those modeling elements believed to contribute significant 19 uncertainty to risk results, 20

• Identification of a set of pl ausible options for each of these key modeling elements 21 (e.g., alternative models or input parameters), and 22

• Development of alternative modeling appr oaches by combining individual options 23 from the previous step. 24 Identification of the modeling elements believed to contribute significan t uncertainty (step 1) 25
involved consideration of a number of factors including the results of the sensitivity analysis 26 completed for the pilot analysis, and comments provided by CASAC and the public on the pilot 27 analysis and analysis plan. 28 Because each of the case studies uses different modeling approaches for some of the 29 analytical steps (e.g., different indoor dust models are used for each case study, and these are 30 associated with differing uncertainty), the identify and size of the areas of uncertainty associated 31 with each case study differs. The specific modeling approaches for each case study and their 32 elements are presented in Figure 2-3. For th e general urban case study, two different dust 33 models and two GSDs were used, compared to on e model and GSD for these analytical steps in 342-42 the two point source case studies. However, the same number of blood Pb metrics and IQ loss 1
functions are used for all three case studies. 2

Figure 2-3. Modeling approaches for case study analys es presented in chapters 3 and 4. 3

Elements of modeling approaches

Case Study Indoor dust modeling Blood Pb metric GSD Concentrationresponse function Number of sets of results

General Urban Case Study 2 models: (a) hybridmechanisticempirical (b) statistical (regression) 2 sets of GSDs, representing: (a) smaller scale (b) larger, regional scale 2 * 2 * 2 * 3 = 24

Each Point Source Case Study 1 model: statistical (regression) approach b 2 metrics: (a) concurrent (b) lifetime average

1 set of GSDs 3 functions: (a) log-linear with linearization, (b) log-linear with cutpoint, and (c) two-piece linear

1 * 2 * 1 * 3 =6

b Different models used for each point source case study. 4 The set of exposure and risk results generate d for each case study and air quality scenario 5 using the alternative modeling approaches indicate s to some extent the magnitude of uncertainty 6 surrounding the risk results. However, as discusse d in Section 4.3, these se ts of risk results do 7 not represent an uncertainty distribution, sinc e confidence levels are not specified for each 8 modeling approach. In presenting the multiple sets of results generated for each case study, we 9 have selected the highest and lowest sets of risk results from those generated, and used them to 10 represent, respectively, upper and lower bounds on risk. The degree to which these actually 11 represent upper and lower bounds depends on th e whether the various modeling approaches 12 evaluated in this analysis capture the largest sour ces of uncertainty. For ex ample, if an important 13 source of uncertainty was excluded in designing th e alternative modeling approaches for a given 14 case study, than the bounds represen ted by the set of risk results generated for that case study 15 might not be wide enough. 16

2.4.6.3 Sensitivity Analysis 17 Sensitivity analysis techniques were used to examine the uncertainty for individual 18 modeling elements and its impact on exposure and risk estimates. We used a "one element at a 19 time elasticity analysis" approach, running the full risk model with one of the selected modeling 20 elements adjusted to reflect an alternate input value or modeling c hoice. The results of that run 21 with the modified modeling element would then be compared to those for the "baseline risk" run 22 to determine the magnitude of the impact on risk results of selections for that one modeling 23
element. 242-43 While the sensitivity analysis for the pilot was based on the primary Pb smelter case 1 study, for the full-scale analysis we have focused the sensitivity analysis on the general urban 2 case study. This reflects a desire to more fully understand the sensi tivity of the modeling 3 approach used for the general urban case study to key sources of uncertainty, recognizing that the 4 results of the pilot sensitivity analysis are info rmative with regard to the point source case 5 studies. The sensitivity analysis completed for the full-scale analysis focused on those modeling 6 elements (including input datasets and modeling steps) believed to have a significant potential 7 for impacting exposure and risk results. Those modeling elements include oral uptake factor, 8
interindividual blood Pb variabil ity GSD, biokinetic model, con centration-response function for 9 IQ loss. This type of sensitivity analysis indicates which of the modeling elements included in 10 the sensitivity analysis has the greatest impact on risk results, and can be used to guide future 11
efforts to refine the overall risk model. 12

2.4.6.4 Qualitative Discussion of Sources of Uncertainty 13 In addition to the quantita tive analyses described above , we have also included a 14 qualitative discussion of key sour ces of uncertainty in the analys is. This includes sources not 15 explicitly included in an y of the above quantitative analyses due to a lack of necessary data 16 (Section 4.3.1). This discussion also attempts to describe the nature of the impact of these 17 sources of uncertainty on risk results, e.g., woul d a particular source of uncertainty likely result 18
in an over- or underestimation of risk. To the ex tent possible, the likely magnitude in qualitative 19 terms of a particular source of uncertainty is also discussed. 202-44 REFERENCES 1 2
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### 3 EXPOSURE ASSESSMENT

This chapter describes the methods and results of the exposure assessment and performance evaluation for the initial analyses of the full-scale assessment. Additional analyses are described in Chapter 5.

3.1 METHODS FOR ESTIMATING MEDIA CONCENTRATIONS

To estimate media Pb concentrations for the three case studies, we used a combination of empirical data and fate and transport modeling, reflecting the different availability of Pb measurements for the two point source case studies and the non-location-sp ecific nature of the general urban case study (Table 3-1). For all three case studies, media concentrations were estimated for multiple air quality scenarios incl uding a range of altern ative NAAQS (see Section 2.3). However, outdoor dust/soil concentrations for each of the three case studies were established for current conditions or the current NAAQS scenarios and those values were used for the current NAAQS and each of the alternate NAAQS scenarios evaluated (see Sections 2.3.3 and 3.1.3). Further, as describe d in Section 2.4.1, media concentr ations, once defined, are held constant for the full exposure period simulated with the blood Pb modeling. For ambient air, outdoor soil/dust and indoor dust, estimates of a nnual average concentration were used for this purpose for all three case studies. Additionally, from the ambient air concentrations, annual average inhalation exposure concen trations were estimated with consideration for daily activity patterns by children and differences in outdoor (amb ient) versus indoor air Pb levels (see Section 3.1.2).3-2 Table 3-1. Case study approaches for estimating media Pb concentrations.

Modeling Step General Urban Case Study Primary Pb Smelter Case Study Secondary Pb Smelter Case Study

Spatial template Single generic study area (with spatially uniform media concentrations and population density) Combination of U.S. Census blocks and block groups out to a 10 km radius around the facility (with media concentrations and demographics uniform within blocks/block groups) U.S. Census blocks out to a 10km radius around the facility (with media concentrations and demographics uniform within blocks) Ambient air concentrations for current conditions and/or current NAAQS Monitor data (mean and highend urban monitors selected to represent two current conditions scenarios) Dispersion modeling

Performance evaluation [Monitoring data used as basis characterizing ambient air levels] Comparison to Pb-TSP monitor data from study area

Inhalation
exposure concentrations Estimates for all three case studies are based on ambient air concentrations and reflect the application of location-specific adjustment fact ors that account for (a) the time spent by children at different locations and at variou s activity levels and (b) differences between indoor and outdoor ambient air Pb levels

Outdoor soil concentrations Nationally representative residential soil value selected from literature (same value used for entire study area and for all air quality scenarios) Near facility (remediation zone) relied on sampling data, and regression model used for outer portions of study area Multiple Pathways of Exposure (MPE) model used to predict spatial distribution of surface soil Pb levels, then scaled using empirical data from surrogate location

Performance evaluation [Empirical data used in characterizing soil levels] [Estimates based on surrogate data] Modeled estimates compared to surrogate data for other industrial (point source) locations

Indoor dust concentrations Two approaches used: (a) hybrid mechanistic (compartmental) model augmented with empirical data developed specifically for thisanalysis and (b) empirical (aironly regression) model obtained form the literature Near facility (remediation zone) relied on site-specific regression model (based on air) and pooled analysis regression model (based on air plus soil) for remainder of study area Statistical (air-only regression) model obtained from the literature

Performance evaluation Subcomponents of hybrid model evaluated, and case study estimates compared to literature estimates and national-scale
survey Site-specific sampling data used in deriving regression model for remediation zone; case study estimates compared to literature estimates and national-scale
survey Case study estimates compared to literature estimates and national-scale survey3-3 3.1.1 Ambient Air Concentrations

Different methods were used for estimating a nnual average ambient air concentrations for the three case studies. For the primary and s econdary Pb smelter case studies, air dispersion modeling of Pb emissions was performed, while Pb-TSP measurement data from the years 2003- 2005 for urban areas with greater than one million re sidents were used for the general urban case study.

3.1.1.1 General Urban Case Study

The general urban case study is not site-specific and has been designed to represent general Pb exposures experienced by children residing in urban ar eas within the United States. The case study involves spatially uniform Pb con centrations in ambient air, outdoor soil/dust and indoor dust with a matching uniformly distributed ch ild resident population. This is in contrast to the point source case studies which each have a relatively large number of exposure zones to track potentially significant spatial gradients in their concentrations of Pb in environmental media and the spatial distribution of children.
Two current conditions scenario s are included in the general urban case study based on Pb-TSP monitoring data from urban areas in the United States. Specifically, these two scenarios include: (a) a central tendency current conditions scenario based on the mean maximum quarterly average Pb measurement (0.14 μg/m 3 ) seen, in 2003-05 time period, at Pb-TSP monitors in urban areas with greater than a million people, and (b) a high end current conditions scenario based on the 95th percentile maximum quarterly averag e Pb measurement (0.87 μg/m 3 )
obtained from the same urban Pb-TSP dataset. The data analysis associated with these values is described in Appendix A, Section A.2.2.2.
In addition to these current conditions s cenarios, the current NAAQS scenario (1.5 μg/m 3 , as a maximum quarterly average) and four alternate NAAQS scenarios have also been evaluated (see Section 2.3 for a dditional details on the NAAQS scen arios). In each of these instances, the specific NAAQS of interest ha s been evaluated by assuming that air Pb concentrations in the entire study area are reduced to meet that sp ecific ambient air Pb level and
form. Because the blood Pb modeling (Section 3.2) is based on annual average media concentrations, the maximum quarterly and maxi mum monthly average valu es used in defining the air quality scenarios were translated into equivalent annua l average ambient air
concentrations for the blood Pb modeling (Section 2.4.1). This is accomplished using ratios obtained from the 2003-2005 Pb-TSP monitoring da taset that relates maximum quarterly or maximum monthly averages to associated annual av erage values for each monitor located in an urban area of population more than a million (Appendi x A, Section A.2.2). Ratios were selected3-4 for each of the air quality scenarios according to the averaging time (calendar quarter versus month) and the percentile represented by the air quality scenario (e.g., mean versus high end for current conditions). Consequently, the annual av erage Pb concentration for the central tendency
current conditions scenario was derived from th e mean of monitor-specific maximum quarterly average concentrations using the mean of the monitor-specific maximum quarterly-to-annual

average ratios, while the high end current conditi ons scenario was derived from the 95 th percentile of monitor-specifi c maximum quarterly average concentrations using the 95 th percentile of the monitor-specific maximum quarterly-to-annual average ratios. The mean of the maximum quarterly-to-annual average ratios was also used to derive the annual average
concentration estimates for th e alternative NAAQS ai r quality scenario with a quarterly averaging time. The mean of the maximum monthly-to-annual average ratios was used to derive the annual average concentration estimates for the alternative NAAQS scenarios with monthly averaging times. The derivation of these ratios is described in Appendix A, Section A.2.2.

3.1.1.2 Primary Pb Smelter Case Study

The air quality scenarios incl uded for the primary Pb smelte r case study were the current NAAQS and four alternate NAAQS (see Section 2.3.1). As the study area of the primary Pb smelter is currently in nona ttainment for the current NAAQS and emissions profiles from the facility are being modified as additional controls are put in place, ambient air Pb concentrations for the current NAAQS scenario were estimated using the model, emissions and source parameters used in developing the 2007 proposed revision to the State Implementation Plan for the area (MDNR, 2007a, 2007b). Annual average Pb concentration estimates for the current NAAQS scenario were derived for each census block or block group from model outputs. Additionally, for the purposes of developing the alternative NAAQS scenarios, hourly estimates from the model were used to generate quarterly average and monthly aver age concentrations for each census block or block group. A proportional roll back procedure was then used to adjust the set of maximum monthly or quarterly averag es to represent meeting a particular NAAQS scenario. That is, the block or block group with the greatest exceedance was reduced to meet the particular NAAQS and all locations were then reduced by that same fraction. After the proportional rollback procedure had been applied to the set of location-specific monthly or quarterly averages to meet a particular NAAQS, these adjusted quarterly or monthly average
values were then used to deri ve annual averages which, in tu rn, were used in the exposure analysis. The development of air Pb concentration estim ates for this case study is described more fully in Appendix D, Section D.2.3-5 3.1.1.3 Secondary Pb Smelter Case Study

Outdoor air concentration and deposition rate s for Pb were estimated for the secondary Pb smelter case study current conditions s cenario using the AERMOD dispersion model 1 and
source characterization and emissions information for the facility (See Appendix E, Section E.2 for details). The Pb emissions modeled reflecte d processes at the facil ity (e.g., stack emissions) and fugitive dust emissions from materials storage and handling and roadway dust. Annual average Pb concentrati ons for the current conditions scenario were derived for each census block from model outputs. As with the primary Pb smelter case study, alternative NAAQS scenarios were modeled using the propor tional rollback procedure (see Section 3.1.1.2). The development of air Pb concentration estim ates for this case study is described more fully in Appendix E, Section E.2.

3.1.2 Inhalation Exposure Concentrations

Inhalation exposure concentrations for Pb were estimated for young children, the population of interest, from the estimated ambien t air concentrations using age group- and location-specific relationships for Pb devel oped using the exposure modeling component of
EPA’s 1999 national-scale air t oxics assessment (USEPA 2006a), one of the U.S. EPA’s National Air Toxics Assessment (NATA) activi ties. These relationships account for air
concentration differences indoors and outdoors and mobility or time spent in different locations (e.g., outdoors at home, inside at home etc.) for the population of interest. The exposure modeling component of the NATA national-scale assessment generated inhalation exposure concentrations for sets of modeled children for each U.S. Census tract (USEPA, 2006a). For the two point source case studies, we used the ratio of these NATA
national-scale assessment inhalati on exposure concentrations to that assessment’s corresponding
estimates of ambient air Pb concentration matche d by U.S. Census tract to develop adjustment
factors that could be used to derive inhalation exposure concen trations from our estimates of ambient air Pb concentration (see Attachment D, Section D.2.3, and Attachment E, Section E.2.3 for additional detail on this procedure). Althoug h analyses of the ambient air concentrations predicted in the NATA national-scale assessment indicate potential underp redictions, and there are particular uncertainties in the assessment pred ictions at small scales, the relationship between
ambient air concentrations and e xposure concentrations (i.e., th e comparison used here) is not expected to be affected by thes e factors. For the general urba n case study, we used the median adjustment factor from the full NATA national-s cale assessment. The 0-4 years old age group is

1 AERMOD is the current preferred Gaussian plume dispersion model for assessing stationary sources under the Clean Air Act (70FR(216): 68217-68261).3-6 the closest age group to the age group of intere st for this assessment for which outputs are available. For this age group, the adjustment factors or ratios between NATA national-scale assessment Pb inhalation exposure concentrations and ambient air concentrations ranged from
0.37 to 0.46 for the Census tracts within the two point source case study areas. A value of 0.43 was used for the general urban case study (see Appe ndix C, Section C.1.2). Use of these ratios for the 0 to 4 year old age group to represent the 0 to 7 year old age group modeled in this assessment contributes some uncertainty in the estimate of inhalation exposure concentrations.

3.1.3 Outdoor Surface Soil/Dust Concentrations

Pb concentrations in outdoor surface soil/dust were char acterized for the current conditions or current NAAQS scenarios for the three case studies us ing a combination of modeling and empirical data. These estimates were also used for the alternate NAAQS scenarios. That is, it was assumed that reductions in ambient air concentrations associated with the alternate NAAQS scenarios did not have a significant impact on soil concentrations 2

.

3.1.3.1 General Urban Case Study

The outdoor surface soil concentration for the general urban case study was derived after considering empirical data coll ected both at urban and other re sidential areas across the United States (Appendix C, Section C.2) . A single outdoor soil Pb leve l representative of general residential yards in the U.S. was then selected for use with this case study. Specifically, a nationally representative arithme tic mean of soil Pb levels in residential yards (198 μg/g) was obtained from the National Study of Lead and Allergen in Housing (NSLAH) (USEPA, 2000). The NSLAH survey, which was conducted by the Department of Housing and Urban Development (HUD) between 1998 and 1999, was intended to generate a nationally representative sample of residential housi ng, including both private and public residences constructed between 1940 and 1998 (but excl uding institutional and group housing).

3.1.3.2 Primary Pb Smelter Case Study

In the primary Pb smelter case study, a differe nt approach was used to estimate outdoor
surface soil/dust Pb concentrations near the facilit y than that used for more distant locations. This difference is in recognition of the remediat ion activities that have included the removal of contaminated soil at many of the residential yard s closest to the facilit y, and replacement with "clean" soil. Consequently, soil Pb concentr ations are estimated using a combination of

2 As mentioned in Section 2.3.3, this also presumes that implementation methods for any of the alternate NAAQS do not involve taking action to separately change soil Pb concentrations.3-7 measurement data for blocks within the remedi ation zone and statisti cally based predictions beyond the remediation zone (see Appendix D, Section D.3 for more detail).
Surface soil/dust Pb levels near the facility (within the reme diation zone) are based on the most recent postremediation measurements avai lable for a given block (obtained between 2001 and 2004 - see Appendix B Section, B.1.5.2). Prer emediation soil levels are not used in estimating soil levels within the remediation zone . Although analyses of sequential (over time) postremediation measurements have indicated re contamination of remediated yards (USEPA, 2006b), the postremediation measurement based es timates were used for the current NAAQS scenario, and for the alternate NAAQS scenarios. It is recogni zed that this approach may produce an underestimate of Pb exposure for the ingestion of surface soil/dust for the current NAAQS scenario, and perhaps some alternate NAAQS scenarios. However, an analysis of the impact of a reduced NAAQS on the remediation z one temporal trend in surface soil/dust Pb
levels was not available to inform an alternate approach. Further, the impact of such a potential bias is limited to the surface soil/dust pathwa y and does not affect the indoor dust pathway
because the indoor dust Pb concentrations for th is region of the primary Pb smelter case study were derived using an approach that did not rely on outdoor soil/dust Pb concentrations.
Characterization of soil levels for blocks and block groups beyond the remediation zone are based on a regression model predicting soil Pb as a function of distance from the facility, which was fitted to preremediation soil measurement data (available closer to the facility). The use of preremediation soil data in deriving the re gression equation reflects the fact that little
remediation has occurred in these more distant locations and conse quently, spatial trends seen in the preremediation soil levels are more likely to be representative for these outer portions of the study area. The regression model us ed in these estimates has an r 2 of 0.92 which suggests a good
fit and increases overall confidence in these stat istical estimates. However, it should be noted
that this increased confidence holds for areas of interpolation (areas with sampling data used to
fit the model – out to about 2.3 km from the facili ty) more than for areas of extrapolation (areas without sampling data – beyond 2.3 km from the facility). The development of surface soil/dust Pb con centration estimates for this case study is described more fully in Appendix D, Section D.3.

3.1.3.3 Secondary Pb Smelter Case Study

As noted in Section 2.2.2.2, soil sampling data for Pb were not identified for this case study. Consequently, a hybrid mechanistic-em pirical modeling approach was used to characterize soil Pb levels for this case study, with fate and transport modeling employed to
derive a soil Pb concentration surface for th e study area and sampling data obtained from a surrogate secondary Pb smelter study area employe d to adjust (calibrate ) that surface.3-8 In lieu of historical estimates of emi ssions, the fate and transport modeling was performed using current emissions estimates ove r a period consistent w ith the operating period of the facility. As the emissions estimates used did not reflect levels of historical emissions, generally believed to have been much higher than current estimates, the resultant Pb
concentrations did not reflect current conditions for the locati on, and, as expected, were lower than concentrations reported for areas near other secondary smelters (see comparisons in Appendix E, Section E.3). Accordingly, the so il concentration surface was scaled up based on a set of factors (differing with di stance from the facility) deri ved from empirical data for a surrogate secondary Pb smelter location. Spec ifically, Pb concentra tions across the entire modeled surface were increased by a factor ranging from1 to 13 (depending on distance from the facility) to obtain surface soil/dust Pb concen trations consistent with those reported in the literature for areas near other secondary Pb sme lters. These estimates for the current conditions scenario were also used for the alternate NAAQS scenarios. That is, it was assumed that
reductions in ambient air concentrations associ ated with the alternat e NAAQS scenarios did not have a significant impact on soil concentrations. The development of surface soil/dust Pb con centration estimates for this case study is described more fully in Appendix E, Section E.3.

3.1.4 Indoor Dust Concentrations

Pb in indoor dust can originate from a variet y of sources including (a) outdoor soil which
is tracked into the hous e, (b) Pb in outdoor soil which is resuspended into the air and subsequently transported indoors (c) Pb rel eased directly into outdoor air through ongoing anthropogenic activity (e.g., industrial point emi ssions) which is trans ported indoors and (d) interior sources of Pb (e.g., pa int, hobbies) (Adgate et al., 1998, Von Lindern, 2003). In the exposure assessment conducted for the 1990 Staff Pa per, indoor dust Pb concentrations were predicted based on Pb concentrations in outdoor soil and ambient air (USEPA, 1989). This is also the case for the default approach in the exposure component of the IEUBK model (USEPA, 1994a). The importance of outdoor soil re lative to outdoor air in influe ncing indoor dust Pb levels appears to depend on the nature of the Pb sources involved. Investigations in urban areas and
near contaminated waste sites with elevated soil Pb levels without a currently active industrial point source emitter of Pb have indicated a greater association of measurements of dust Pb
concentration with measurements of soil Pb conc entration than with measurements of ambient air concentration (e.g., Adgate, 1998 and Von Lindern, 2003). By contrast, investigations in areas with currently operating larg e point sources of Pb (e.g., active Pb smelters) have implicated ambient air Pb as an important source of Pb to indoor dust (Hilts, 2003). Contributions of3-9 ambient air Pb to indoor dust Pb levels have also been illustrated by a deposition study conducted in New York City (Car avanos et al., 2006). Carava nos and others described Pb deposition indoors resulting primaril y from exterior environmental sources and not from interior
Pb sources. For additional discussion of the relationship between indoor dust Pb, outdoor
ambient air Pb and other related factor s, refer to Appendix G, Section G.2 The prediction of indoor dust Pb based on Pb concentrations in out door ambient air and other media (e.g., outdoor soil/dust Pb, indoor pain t) can be conducted using empirical models, mechanistic models, or by a combination of both techniques. Empiri cal models (typically implemented as regression models) have the adva ntage of being able to specify a relationship between indoor dust Pb and predictor variable s (e.g., outdoor soil/dust, outdoor air) even when these relationships are complex and uncertai n. However, statistic al models have the disadvantage of requiring a signi ficant amount of site-specific da ta for their derivation and not being well suited to extrapolation to scenario s with conditions different from those underlying their development. Mechanistic models, by co ntrast, can be develope d in the absence of extensive site-specific data and are not as limited in the types of scenarios to which they can be applied as long as those scenario s are conceptually consistent w ith the scenario underlying their development. However, the development of me chanistic models can be quite challenging and subject to uncertainty if the system or pro cess being predicted is complex and uncertain. To date, efforts to predict indoor dust Pb have focused primarily on the development of empirically-based regression models (EPA, 1989) . Furthermore, most of these regression
models have been based on data associated with large industr ial point sources (smelters) and their impacts on surrounding residential populati ons. Little progress has been made in developing comparable models for areas where ambi ent Pb levels are not so greatly influenced by a large industrial point source, such as in mo re general urban residential areas, including either regression models specific to these more general urban scenarios, or mechanistic models which could be applied more r eadily to different exposure scen arios including urban residential populations. In this assessment, a combination of (a) sta tistical (regression) models obtained from the literature, (b) statistical regres sion model developed specifically for individual case studies and (c) mechanistic models (developed specifically for the general urban case study) was used in predicting indoor dust Pb. This re flects the fact that varying am ounts of site-specific data were
available across the three case studies for characte rizing indoor dust Pb and related factors. In addition, the absence of statisti cal regression models in the lit erature specifically focused on
urban residential locations n ecessitated the development of a hybrid mechanistic-empirical model for the general urban case study (see below). The approach es used to predict indoor dust3-10 Pb for each of the case studies are presented be low and described in greater detail in Appendix
G.

3.1.4.1 General Urban Case Study

Two models are used to estimate indoor dust Pb in the general urban case study: a hybrid mechanistic-empirical model (the hybrid model), and a regression-based m odel. Application of
these models with the ambient air Pb concentr ations for this case study produced two sets of
indoor dust Pb estimates for each air quality scenario (see Appendix C, Section C.3). The hybrid model uses a mechanistic model to predict indoor dust Pb resulting from the
infiltration of outdoor air containi ng Pb into indoor residential ai r with subsequent contribution to indoor dust Pb. This portion of indoor dust Pb derived with this model is subsequently
referred to in this document as the recent ai r or recent ambient air related component or
contribution to indoor dust Pb. To the extent that outdoor air Pb incl udes contributions from resuspension of historically deposited Pb, it is represented here. Other contributions to indoor dust Pb (e.g., tracking of outdoor soil/dust indoors, indoor paint flaking, et c.) are addressed using an empirically based estimate derived from a na tional-scale dataset charac terizing indoor dust Pb loadings in U.S. residences (the U.S. Hous ing and Urban Development [HUD] National Survey
of Lead-based Paint in Housi ng - USEPA, 1995). When combin ed, the recent air component (derived using the mechanistic model) and the ot her contributions component (derived using the HUD dataset) provide an estimate of total i ndoor dust Pb for the general urban case study (Appendix C, Exhibit C-7). Note that while th e indoor dust Pb concentration contributed by the hybrid model's recent air Pb component will var y, depending on the ambient air Pb level, the
concentration contributed from other sources will remain constant across the different air quality scenarios. The two components of the hybrid model (the mechanistic and empirical) are described in greater detail below. The mechanistic model linking ambient air Pb to indoor dust Pb was developed by obtaining the steady state solution to a dynamic ma ss balance equation that predicts Pb in both
indoor air and indoor floor dust as a function of outdoor air Pb. This dynamic mass balance equation was developed specifically for this assessment. In recognition of the complexity of the larger task of simulating contributions to indoor dust Pb from all sources, the mechanistic model development activity was limited to the area consid ered most essential to the needs of this assessment, i.e., the contribution to indoor dust Pb from recent ambient air. See Appendix G,
Section G.3.2 for additional detail on the derivation of the mechanistic ambient air-to-indoor dust
Pb model. As noted above, contributions of other sources to Pb in indoor dust we re addressed in the hybrid model using empirical data (rather than trying to model them mechanistically). The HUD3-11 dataset, described above, that ch aracterized the national distributi on of residential indoor dust Pb (USEPA, 1995) was selected as the basis for th is other sources component of indoor dust Pb. Specifically, a median value from the HUD datase t characterizing residential indoor dust Pb loadings (USEPA, 1995) was identified, and the ambient air related component of this indoor
dust value estimated and subtracted from the HU D median value, leaving only the other sources component. This provided a cen tral tendency estimate of th e component of indoor dust Pb loadings associated with sources other than rece nt air Pb, for typical re sidences in the United States (see Appendix G, Section G.3.3 for additional detail). As noted earlier, this single
estimate of other sources indoor dust Pb is used for modeling all air qual ity scenarios, with no change associated with the ambient air Pb level being evaluated. The hybrid model generates estimates of indoor dust Pb in terms of loading, rather than concentration, while concentrati on is the form required by the bl ood Pb models used in this analysis. Conversion from loading to concentrat ion was accomplished using a log-log regression equation derived from the HUD dataset described above. This dataset has matched data for sampled residences for both indoor dust load ings from vacuum samples, and indoor dust
concentrations. Use of the HUD dataset based lo ading to concentration conversion required an
additional conversion between load ing estimates based on wipe samples (the form used by the hybrid model) and those based on vacuum sample s (the form of the HUD data). This was accomplished using an equation developed by EPA (USEPA, 1997). Details on both conversions are provided in Appe ndix G, Sections G.3.4 and G.3.4.1. The individual elements of the hybrid model, including both the mechanistic and empirical components as well as the loading-to-concentration conve rsion equations are presented in Table 3-2. The final hybrid equation (i ncluding all components) is presented last.3-12 Table 3-2. Hybrid model for indoor dust Pb in general urban case study.

Model component Formula

Hybrid mechanistic equation for “recent” air FLOOR LOADING (PbWipe) = 104.2 * PbAIR Where, PbAIR is Pb in outdoor ambient air, and FLOOR LOADING is in terms of wipe sample loading.

Hybrid empirical component for other sources 1.15 μg/m 2

Converting wipe loadings to vacuum loadings PbVAC = 0.185 * PbWIPE 0.921

Converting vacuum loadings to concentrat ion Ln(PbCONC) = 4.92 + 0.52 * ln(PbVAC)

Final combined hybrid equation PbDust = EXP [4.92 + 0.52 * Ln (0.185 * (104.2 * PbAir + 1.15) 0.931 )]

Because the hybrid model has not been subj ect to extensive revi ew and application outside of this analysis and gi ven the influence of indoor dust Pb on Pb exposure and risk, dust Pb concentrations for the genera l urban case study are also estimat ed using an additional dust Pb model (see Appendix G, Section G. 3.5). The use of two models is intended to inform our characterization of model uncertainty in this key portion of the analysis. Specifically, we have included the air-only regression model (EPA, 1989) as a second, parallel a pproach in predicting
indoor dust Pb. That model estimates indoor du st Pb based on (a) outdoor ambient air Pb (multiplied by an air-related factor) and (b) an in tercept which captures other impacts besides air (e.g., indoor paint). The air factor used in this equation is expected to capture longer-term impacts of outdoor air Pb on indoor dust, incl uding the indirect effect of air Pb on outdoor soil/dust Pb with subsequent impacts of that out door soil/dust Pb on indoor dust Pb through other mechanisms (EPA, 1989). The air-only re gression model is presented below: PbDUST(mg/kg or ppm) = 60 + 844 * PbAIR( μ g/m 3 )

3.1.4.2 Primary Pb Smelter Case Study

We used different regression models for pred icting Pb concentrati ons in indoor dust in areas near the primary Pb smelter facility where soil has been remediated and more distant areas.
For the remediation zone near the facility, a regression equati on was developed using dust Pb
measurements which had been collected from some of the houses within this area. These data, while adequate for development of a site-speci fic regression model, di d not have sufficient spatial coverage to be used alone to represent indoor dust Pb levels for that portion of the study
area. For the remainder of th e study area, we employed a regre ssion equation developed for the3-13 last review (USEPA, 1989). Becau se of the presumed impact of the remediation activity on dust Pb, the site-specific dust Pb model developed for the remediation zone was not considered appropriate for use in areas beyond that area. The dataset used to develop the model for the remediation zone was based on indoor dust samples collected in 17 houses within the remedi ation zone. Independent variables included in the analysis were: (a) estimated annual average Pb concentrations in air at census block centroids located within 200 meters of each of the 17 houses, (b) road dust Pb measurements for locations within 300 meters of each house and (c) postremedia tion residential soil Pb measurements for the yard of each house. Preremediation soil Pb concen trations were not included in the regression analysis since they were not expected to repr esent current conditions at the site. Multiple samples for each medium associated with a sp ecific house within the dataset (e.g., reflecting multiple samples collected over time) were averaged to produce a "temporally averaged" value. A number of regression models were evaluated, (see Appendix G, Section G.4), and the “H5” model was ultimately selected based on goodness of fit and other considerations. This model relates the natural log of indoor house dust to the natural log of ambient air Pb (r 2 =0.625): ln(house dust, mg/kg or ppm) = 7.7892 + 0.7200*ln(air Pb, μ g/m 3 ) In applying the H5 model to the remediation zone portion of the study area, a constraint was applied in recognition of the our application of this regre ssions to scenarios with much lower
air Pb than that for the dataset on which it is based, and findings for similar towns of residual
levels of Pb in house dust that reflect histor ical contributions (e.g., von Lindern et al., 2003; Hilts, 2003). A floor value for the predicted dust Pb for alternativ e NAAQS scenarios was assigned to any model predictions falling belo w it. This value was 60 ppm, which is the intercept for the air only regressi on model derived at the time of the last review, see previous section. While this value may be an underestimat e of the historical component of Pb in house dust of existing houses or houses existing prior to remediation, its use is intended to balance the need for a floor for such houses and the potential for the population to also have newer houses. The use of and need for this floor recognizes th e uncertainty associated with this model. Additionally, the regression model used here does not lend itself to partitioning the recent air related Pb from other contributions (discussed in Section 2.4.3); accordingly, this partitioning is not done for the primary Pb smelter case study. Several points regarding th e other variables considered for the remediation zone regression are noted here. For example, road dust Pb concentration was not found to have significant predictive power for indoor dust Pb. This may reflect th e fact that the road dust Pb dataset does not provide significan t coverage for homes located n ear to the truck haul routes. Additionally, yard soil Pb concentration was found to be slightly, and statistically significantly, negatively correlated with indoor dust Pb levels. Th is counterintuitive finding may be a result of3-14 the existence within the remedia tion zone of a patchwork of reme diated yards, such that the remediation activity may have interfered with any correlation between yard soil Pb levels,
ambient air Pb levels and indoor dus t Pb levels that might have existed previously. The resulting slight negative correlation of dust Pb levels with soil Pb levels led us to exclude soil Pb in predicting indoor dust Pb, leadi ng to the selection of the H5 model, which only considers ambient air Pb in predicting indoor dust Pb. The y-intercept for the select ed model may reflect a
number of factors not correlated with ambient air or distance from the facility, such as a general
level of soil Pb contamination in the area and indoor Pb paint. For areas beyond the remediation zone, we us ed a regression equation developed during
the last review from data collected at a number of operational primary Pb smelters, including this case study location (USEPA, 1989, Appendix B). This model, the "AGG" or "aggregate" model, predicts indoor dust Pb concentr ation from both outdoor soil and am bient air Pb concentrations.
We have selected the AGG model for the nonremed iation portion of the primary Pb smelter case study area since this area has not been subjected to extensive remediation and is therefore likely to resemble the locations included in the pooled da taset used in deriving this model in terms of relationships among air, outdoor surface soil/ dust and indoor dust. The AGG air and soil regression model (USEPA, 1989), selected for areas beyond the remediation zone is the following: House dust (mg/kg or ppm) = 31.3 + 638*air Pb ( μ g/m 3 ) + 0.364*soil Pb (mg/kg)

3.1.4.3 Secondary Pb Smelter Case Study

A version of the empirical regression mode l used for the primary Pb smelter (USEPA, 1989) was also used for the secondary Pb sme lter case study. In this case study, an air-only version of the model (USEPA, 1989) was employed reflecting the reduced overall confidence
associated with soil characterization at this case study (see Section 3.1.3.3). The AGG model for estimating indoor dust (USEPA, 1989) was derive d in two forms including an air-only model
that based indoor dust concentra tions on outdoor ambient air Pb (w ithout explicitly considering outdoor soil Pb levels) and an air+soil mode l which based estimates on both outdoor soil and ambient air Pb data. It is important to note, however, that the air-only model implicitly reflects
some consideration for the air-to -soil-to-indoor dust mechanism in the air signal. Specifically, the larger air factor for the air-only model relative to the air+plus dust model's air factor, reflects contribution of air Pb both directly to dus t through penetration indoors and subsequent deposition to surfaces and indirectly to dust through deposition to outdoor soil which impacts indoor dust (USEPA, 1989). This air-only regression model (U SEPA, 1989) is as follows: House dust (mg/kg or ppm) = 60 + 844*air Pb ( μ g/m 3 )3-15 The model used for this case study was base d on a number of studies focusing mainly on primary Pb smelters (a number of primary Pb sm elters were operational at the time of model development). This may introduce uncertainty into indoor dust Pb predictions generated for this case study to the extent that factors related to the dependency of indoor dust Pb on ambient air Pb, such as particle size profile s and the nature of the airborne Pb compounds, differ for primary versus secondary Pb smelters.

3.2 METHODS FOR ESTIMATING BLOOD PB LEVELS

This section presents the methodology used to estimate blood Pb levels in the child study populations. The section begins with an overview of the primary biokinetic model used in this full-scale risk analysis, the Integrated Expos ure and Uptake Biokineti c model (USEPA, 1994a). Findings associated with the use of both the IEUB K and Leggett models in the pilot analysis, in addition to subsequent performance evaluation analyses (see Appendix J) contributed to the decision to use IEUBK as the primary blood Pb model and reserve the Leggett model for use in the sensitivity analyses. In a ddition, we have considered an em pirical slope model (the Lanphear model) in conducting our performance ev aluation (Appendix J, Section J.2). Following the overview of the IEUBK biokineti c model, the probabilistic approach used to generate population-level distributions of blood Pb levels for each study population is
described. The section ends with a discussion of the GSDs used within each case study to reflect interindividual variability in behavior related to Pb exposur e and Pb biokinetics (a key component in modeling population-level blood Pb).

3.2.1 Blood Pb Modeling

Blood Pb models are used in the assessment in order to (a) apportion exposure, the metric for which is blood Pb, between policy-relevant Pb exposures and policy-relevant background, and (b) estimate potential changes in blood Pb leve l distributions that woul d result from alternate
ambient air Pb levels. As discussed in Section 4.4.1 of the CD, th ere are two broad categories of blood Pb models available to support exposure and risk assessment:

• Statistical (regression) mode ls, which attempt to apportion variance in measured blood Pb levels for a study population to a range of dete rminants or control variables (e.g., surface dust Pb concentrations, air Pb concentrations). The development of these models requires paired predictor-outcome data whic h restricts these empirical models to the domain of their observations (i.e., to app lications involving the study population(s) and exposure scenarios used in their derivation or at least to scenarios very similar to the original study conditions).3-16 • Mechanistic models, which attempt to model the process of transf er of Pb from the environment to human tissues. While these models are considerably more complex compared with the regression models (in term s of both the number of variables and their computational structure), by incorporating variab les that vary temporally and spatially, or across individuals or populati ons, mechanistic models can be extrapolated to a wide range of scenarios, including those outsid e of the original pop ulations and exposure scenarios used to develop/ parameterize the models.

Given concerns over applying regression mode ls to populations and exposure scenarios
other than those used in thei r derivation, and consistent with recommendations from CASAC (see Section 1.4), we have relied primarily on mechanistic models in conducting the exposure analysis for this assessment. Additionally, a regression model developed by Lanphear et al. (1998) and described in Appendix H, Secti on H.2.3.was included in the blood Pb modeling performance evaluation (Appendix J, Section J .3.2). The CD (Secti on 4.4) describes three mechanistic (biokinetic) models developed ove r the past several decad es including IEUBK for modeling child Pb exposure (CD, Section 4.4.5 a nd Appendix H, Section H.2.1) and two models designed to simulate Pb bioki netics and blood Pb levels from birth through adulthood, the Leggett model (CD, Section 4.4.6) and the model de veloped by O’Flaherty (CD, Sections 4.4.7). The Leggett and O’Flaherty models simulate sm aller time steps and consequently demonstrate blood Pb responses on much shorter time scales than the IEUBK model, the outputs for which
are described as indicative of quasi steady-state conditions (CD, Sections 4.4.5-4.4.7). All three models have the potential for a pplication in Pb risk assessment and have been evaluated to varying degrees using empirical datasets (CD, Section 8.3.4). Based on results of the pilot analysis, subsequent performance evaluations, an d advice from CASAC, we selected the IEUBK model as the primary blood Pb model for the fu ll-scale analysis. The Leggett model has been included as part of the sensitivit y analysis intended to assess the potential impact of uncertainty in blood Pb modeling on the ove rall analysis (See Section 4. 3.2). A brief overview of the IEUBK and Leggett models is presented below.

3.2.1.1 Primary Analysis

The IEUBK model was selected for use in generating the primary set of exposure and risk results. IEUBK is a multicompartment pharmacokinetics model for children 0 through 84 months of age (the first 7 years of life), whic h predicts average quasi-steady state blood Pb concentrations corresponding to daily average ex posures, averaged over periods of a year of more. The exposure module provides average daily intakes of Pb (averaged over a 1 year time increment) for inhalation (air, including consid eration for both outdoor and indoor) and ingestion (soil, indoor dust, diet and wa ter) (Section 4.4.5.1 of the CD). The model is intended to be applied to groups of children expe riencing similar levels of Pb e xposure and to generate a central3-17 tendency blood Pb estimate for that group (USE PA, 1994a). In applications of IEUBK, interindividual variability in bioki netics and behavior (e.g., varying rates of dietary Pb ingestion) of the study population is typica lly characterized through the in corporation of a GSD which, together with the IEUBK-generated blood Pb level, provides a distribution of blood Pb levels for a group of modeled children. For each exposure zone in each case study air quality scenario, estimates of the concurrent and lifetime average blood Pb level are derived from the outputs of the IEUBK model as described in Appendix H. Brie fly, the concurrent metric is de rived as the average over ages 73 to 84 months (approximately 6 to 7 years of age) 3 , and the lifetime average metric is the average of blood Pb levels between ages 6 and 84 months.
Additional detail on the IEUBK model is desc ribed in Appendix H (Section H.2.1) and in Section 4.4.5 of the CD. Appli cation of the IEUBK model is de scribed in Appendix H, Section H.3.1, and input parameter values, and their basi s, are described in Appendix H, Section H.4.

3.2.1.2 Sensitivity Analysis

The Leggett model has been included as part of the sensitivity analys is (Appendix L) and in model performance evaluations (Appendix J), but has not been used in generating primary exposure and risk results for this assessment. Originally developed from a model designed to simulate radiation doses for bone-seeking radi onuclides, the Leggett biokinetic model has a temporal resolution of one day and can model exposure from infancy through adulthood (CD, Section 4.4.6). The daily resolution in Leggett allows more comprehensive treatment of the temporal pattern of exposure and its shorte r-term impact on blood Pb levels than IEUBK, although for this assessment, which focuses on longer-term trends in Pb exposure, this functionality is less relevant. The Leggett model does not include a detailed pathway-level exposure module as does IEUBK. Rather the Le ggett model takes total in gestion and inhalation exposure estimates as inputs. However, it is possible to link the Le ggett model to a more detailed pathway-level exposure model, thereb y allowing a more detailed treatment of Pb exposure pathways and their impact on blood Pb. The use of this type of external exposure model including pathway-specific modeling of e xposure levels was implemented for the pilot analysis. As with IEUBK, Leggett can be used to derive central tende ncy blood Pb levels for groups of similarly exposed children. The same GSD used for IEUBK is then used to produce

3 As described in Appendix I (Section I.2.1), the concurrent metric is calculated by averaging blood Pb estimates generated for ages 75 and 81 months with these estimates representing the first and second halves of the 7th year of life, respectively. Use of th e 7th year of life is consistent with or similar to the average age of the IQ testing in the Lanphear et al. (2005) study on which the concentration-response function is based (Sections 2.1.5 and 4.1).3-18 estimates of the distribution of blood Pb levels within study populations. For additional details on the Leggett model see Section 4.4.6 of the CD and Appendix H.

3.2.2 Exposure Pathway Apportionment and Probabilistic Population Modeling

This section describes the method used to es timate contributions to blood Pb and IQ from exposure pathways of interest (see Secti on 2.4.3), and the method used for probabilistic population modeling. Both are app lied to the central tendency estimates of blood Pb developed for the two blood Pb metrics (concurrent and lif etime average) from the IEUBK model outputs. To the extent feasible with the modeling tools and assessment design, estimates of the contribution to blood Pb (centr al tendency estimate) are developed for policy-relevant background versus policy-relevant exposures (Sec tion 2.4.3). This is done by considering blood Pb model estimates for an exposure zone derive d using only the pathways of interest (Appendix I, Section I.1). As discussed in Section 2.4.3, there are limitations on the resolution to which policy-relevant exposures can be distinguished which results in some simplifying assumptions. We developed estimates of contribution to blood Pb estimates (and IQ estimates) for the following pathways (or pathway combinations):

• Inhalation of ambient air Pb (i.e., “recent air” Pb): This is deri ved using the blood Pb estimate resulting from Pb exposure limited to the inhalation pathway.

• Ingestion of “recent air” indoor dust Pb: Th is is derived using the blood Pb estimate resulting from Pb exposure limited to inge stion of the Pb in indoor dust that is predicted to be associated with ambien t air concentrations (i.e., via the air concentration coefficient in the regression- based dust models or via the mechanistic component of the hybrid blood Pb model (S ection 3.1.4). For the primary Pb smelter case study, estimates for this pathway are not separated from estimates for the pathway described in the subsequent bullet due to unc ertainty regarding this categorization with the model used for this case study (Section 3.1.4.2).

• Ingestion of “other” indoor dust Pb: This is derived using the blood Pb estimate resulting from Pb exposure limited to ingest ion of the Pb in indoor dust that is not predicted to be associated with ambient ai r concentrations (i.e., that predicted by the intercept in the dust models plus that predicted by the outdoo r soil concentration coefficient, for models that include one (Section 3.1.4)). This is interpreted to represent indoor paint, outdoor soil/dust, and additional sources of Pb to indoor dust including historical air (see Section 2.4.3). As the intercep t in regression dust models will be inclusive of error associated with the model coefficients, this category also includes some representation of dust Pb associated with current ambient air concentrations (described in previous bu llet). For the primary Pb smelter case study, estimates for this pathway are not separate d from estimates for the pathway described above due to uncertainty regard ing this categorization with the model used for this case study (Section 3.1.4.2).3-19 • Ingestion of outdoor soil/dust Pb: This is derived using the blood Pb estimate resulting from Pb exposure limited to ingestion of outdoor soil/dust Pb.

• Ingestion of drinking water Pb: This is de rived using the blood Pb estimate resulting from Pb exposure limited to ingestion of drinking water Pb.

• Ingestion of dietary Pb: This is derived us ing the blood Pb estimate resulting from Pb exposure limited to ingestion of dietary Pb.

The goal of this probabilistic exposure modeling is to generate population-level distributions of blood Pb levels that allow (a) specific percentiles of exposure (e.g., 50th, 90th, and 95th) within a study population to be iden tified. These are pr esented along with the differentiation by exposure pathway (e.g., policy -relevant background versus policy-relevant exposures, with the latter further differentiated as to ambient air inhalatio n, indoor dust ingestion and outdoor surface soil/dust inges tion, as indicated above). Therefore, for example, we may have an estimate of exposure for the 95 th percentile child in the pr imary Pb smelter case study,
with that blood Pb level differentiated as to th e fraction coming from (a) diet and drinking water, (b) recent air Pb, including ambien t air inhalation and ingestion of the recent air component of indoor dust Pb, with this fraction potentially including resuspended, previously deposited Pb (see Section 2.4.3), and (c) other sources including outdoor soil/dust and historical air Pb, as well as indoor paint. The effort to differentiate the r ecent air and other sources of Pb to indoor dust is subject to different degrees of uncertainty for the various case studies, reflecting the different approaches used in modeling indoor dust (see Sections 2.4.3 and 3.1.4). Furthermore, it is noted that given the various limitations of our modeling tools (Sections 2.4.3), blood Pb levels associated with air-related exposure pathways and current levels of Pb emitted to the air (including via resuspension) are likely to fall betw een the estimates for “recent air” and those for “recent” plus “past air”. Probabilistic exposure modeling differs for the two point source case studies and the general urban case study. Because the two point source case studies are location-specific, demographic data for each study area are used to evaluate the inte raction between child populations in those study areas and the distribution of Pb concentrations in each media (e.g., outdoor ambient air, outdoor soil/dust and indoor dust). By contrast, because the general urban case study is not location-specifi c, probabilistic exposure mode ling does not involve locationspecific demographic data and instead, is based on the assumption of a uniformly distributed child receptor population contacting medi a with uniformly distributed Pb.3-20 3.2.2.1 General Urban Case Study

The approach used to generate exposure dist ributions for the genera l urban case study is simpler than that used for the two point source case studies (Sec tion 3.2.2.2), due to the use of a uniformly distributed population and spatially unif orm media concentrations of Pb in the design of this case study. This design negates the need for population-we ighted sampling in generating the exposure distribution. Inst ead, the central tendency blood Pb level generated for a specific air quality scenario using IEUBK, is combined deterministically with the GSD reflecting interindividual variability in Pb exposure and biokinetics to produce a population-level
distribution of blood Pb levels. 4 Specific percentile exposure levels are then identified from that distribution, with these levels in terpreted as representing modele d individual children for the general urban case study (i.e., estim ates conceptually equivalent to those generated for the two point source case studies). However, because th e analysis is not location-specific, population incidence estimates are not generated. The study area for the general urban case study can be considered a single exposure zone with a single IEUBK-deri ved central tendency blood Pb level th at is combined with the GSD to produce the population-level exposur e distribution for that study area. As is the case with individual exposure zones modeled for the point source case studies, the entire child population in the single exposure zone modeled here is give n the same pathway apportionment. As with the point source case studies, this approach introduces uncertainty into the an alysis, especially for
simulated individuals with hi gh-end blood Pb levels, since pa thway apportionment would likely
not be the same for all in dividuals in an exposure zone, even if all were exposed to the same Pb
concentrations in each medium.

3.2.2.2 Point Source Case Studies

Probabilistic exposure modeling for the two point source case studies relied on
information in three areas as summarized below:

• Central tendency blood Pb levels for each e xposure zone: Outputs from the biokinetic blood Pb modeling (considered to be central tendency estimates) are used to produce central tendency estimates of concurrent and lifetime average blood Pb levels for each exposure zone (i.e., census block or block group) in each case study area.

4 Note that as discussed in Section 3.2.3.1, the range of GSDs used in the general urban case study to reflect interindividual variability in lead exposure and biokinetics has been selected to include some degree of coverage for small-scale variation in media concentrations. Therefore, while the overall scenario modeled in the general urban case study is based on the assumption of generally uniform media concentrations (e.g., an ambient air concentration level which is generally constant across the study area), the use of larger GSDs provides some coverage for
residence-to-residence variation in these media concentrations.3-21 • Demographics (child distribution within st udy areas): The distri bution of children (<7 yrs old) within each case study area is usedto insure that the generation of populationlevel blood Pb level distributions for each cas e study reflects where ch ildren are located.

• GSD reflecting interindividual variability in bl ood Pb levels: A GSD is used to reflect interindividual variability for blood Pb levels in groups of similarly exposed children (i.e., within each exposure zone of a case st udy area). The GSD is combined with the central tendency blood Pb level estimates to ge nerate a distribution of blood Pb levels for the group of children located in each exposure zone.

The stepwise procedure used to generate popu lation-level blood Pb distributions for each of the point source case studies is illustrated in Figure 3-1, w ith the information described in the bullets above recognized as input data. Several points related to implementation of th is procedure are noted. For this assessment, 50,000 simulated individuals were ge nerated for each point source ca se study in order to insure that the population-level blood Pb distributions generated met target stability goals (see Appendix M, Section M.2.2). 5 This simulation count represents a higher total child count than actually is associated with either study area. Using a higher number of simulated individuals was necessary, however, to generate blood Pb di stributions with "sta ble" higher-end exposure estimates. If simulations matching the actual po pulation count at each case study had been conducted, the distributions that would have resu lted would have been "unstable" at higher percentiles. It is important to note, however, that child population count estimates for individual percentiles (Section 3.4) have been scaled to reflect the actual ch ild count associated with each study area.

5 The analysis of simulation stability focused on the ge neral urban case study and demonstrated that even the highest population percentiles generated for this analysis (95 th percentile) were relatively stable with coefficients of variation of less than 1% for total IQ loss estimates (see Appendix M, Section M.2.2).3-22 Figure 3-1. Procedure for generating population blood Pb distributions for point source case studies.

Step 1

– select a central-tendency blood Pb level for a specific exposure zone based on population-weighted random sampling : The central-tendency blood Pb level dataset is combined with the demographic dataset to conduct population-weighted sampling of a central-tendency blood Pb level for a specific exposure zone (i.e., sampling proportional to child count within each exposure zone is used to select a specific zone and the central-tendency blood Pb level for that zone is chosen as the output form Step 1).

Step 2 – select an adjustment factor reflecting inter-individual variability in behavior and biokinetics related to Pb exposure for a single simulated child : A value is sampled randomly from the adjustment factor distribution described above. This single value represents the behavior and biokinetics (related to Pb exposure) for a single simulated child.

Step 3

– generate a blood Pb level for a single simulated individual : The central-tendency blood Pb level selected in Step 1 is multiplied by the adjustment factor in Step 2 to produce an “adjusted” blood Pb level for a single simulated child within the exposure zone selected in Step 1 (this value reflects both the central-tendency PbB for that exposure zone as well as consideration for inter-individual variability in PbB levels). Repeat procedure 50,000 times to generate set of 50,000 simulated individuals (Note, this set can be weighted down to reflect the actual population of children (0-7 years) within each study area in generating population-count related risk metrics). Input distributions and datasets used in the Monte Carlo-based population-exposure modeling :

• Central-tendency blood Pb level dataset : Blood Pb modeling (described in Section 3.2.1) generates a central-tendency blood Pb level for each exposure zone (e.g., block, or blockgroup) within each study area (results dimensioned on blood metric and blood Pb model).

• Demographic dataset: The child count (children <7 yrs of age) for each exposure zone is used to support population-weighted sampling of exposure zones.

• Adjustment factor distribution representing inter-individual variability in behavior and biokinetics related to Pb exposure : These adjustment factor distributions are derived by centering the GSDs (representing inter-individual variability in PbB levels) on a median value of 1.0. This results in distributions that can be used to obtain adjustment factors reflecting the behavior and biokinetics (related to Pb exposure) for a single simulated child.

Step 4

– place simulated child blood Pb level (output of Step 3) in pool of modeled blood Pb levels for that study area: This pool of simulated child blood Pb levels represents the distribution of blood Pb levels across the study area and reflects (a) the demographic distribution of children across that study area (and their relation to Pb levels in contact media) and (b) inter-individual variability in behavior and biokinetics related to Pb exposure. Monte Carlo-Based Population Blood Pb Level Modeling Procedure:

Step 1

– select a central-tendency blood Pb level for a specific exposure zone based on population-weighted random sampling : The central-tendency blood Pb level dataset is combined with the demographic dataset to conduct population-weighted sampling of a central-tendency blood Pb level for a specific exposure zone (i.e., sampling proportional to child count within each exposure zone is used to select a specific zone and the central-tendency blood Pb level for that zone is chosen as the output form Step 1).

Step 2 – select an adjustment factor reflecting inter-individual variability in behavior and biokinetics related to Pb exposure for a single simulated child : A value is sampled randomly from the adjustment factor distribution described above. This single value represents the behavior and biokinetics (related to Pb exposure) for a single simulated child.

Step 3

– generate a blood Pb level for a single simulated individual : The central-tendency blood Pb level selected in Step 1 is multiplied by the adjustment factor in Step 2 to produce an “adjusted” blood Pb level for a single simulated child within the exposure zone selected in Step 1 (this value reflects both the central-tendency PbB for that exposure zone as well as consideration for inter-individual variability in PbB levels). Repeat procedure 50,000 times to generate set of 50,000 simulated individuals (Note, this set can be weighted down to reflect the actual population of children (0-7 years) within each study area in generating population-count related risk metrics). Input distributions and datasets used in the Monte Carlo-based population-exposure modeling :

• Central-tendency blood Pb level dataset : Blood Pb modeling (described in Section 3.2.1) generates a central-tendency blood Pb level for each exposure zone (e.g., block, or blockgroup) within each study area (results dimensioned on blood metric and blood Pb model).

• Demographic dataset: The child count (children <7 yrs of age) for each exposure zone is used to support population-weighted sampling of exposure zones.

• Adjustment factor distribution representing inter-individual variability in behavior and biokinetics related to Pb exposure : These adjustment factor distributions are derived by centering the GSDs (representing inter-individual variability in PbB levels) on a median value of 1.0. This results in distributions that can be used to obtain adjustment factors reflecting the behavior and biokinetics (related to Pb exposure) for a single simulated child.

Step 4

– place simulated child blood Pb level (output of Step 3) in pool of modeled blood Pb levels for that study area: This pool of simulated child blood Pb levels represents the distribution of blood Pb levels across the study area and reflects (a) the demographic distribution of children across that study area (and their relation to Pb levels in contact media) and (b) inter-individual variability in behavior and biokinetics related to Pb exposure. Monte Carlo-Based Population Blood Pb Level Modeling Procedure:3-23 An additional point of clarification relates to the process used in differentiating specific percentile total blood Pb levels into pathway-sp ecific fractions for the two point source case
studies. All simulated individuals associated with a given exposur e zone (i.e., U.S. Census block or block group), were assigned the same path way-specific apportionment, which was derived from the blood Pb modeling performed for the expos ure zone. The set of simulated individuals with a range of total blood Pb levels, produced subsequently vi a application of the GSD, are assumed to all have the same pathway-specific apportionment of those blood Pb levels (i.e., the same apportionment as that generated for the cen tral tendency blood Pb level modeled for that exposure zone). In reality, it is likely that pathway apportionment w ould vary across children
with different blood Pb levels located in the sa me exposure zone (e.g., th e contribution of indoor dust exposure to total blood Pb might differ fo r kids living near each other who demonstrate different total blood Pb levels). The mode ling approach used, however, does not provide pathway apportionment within an exposure zone , only across exposure zone s (i.e., each exposure zone has a different pattern of pathway apportionment for its simulated children). The modeling approach presented in Figur e 3-1 and described above, generates a population-level distribution of to tal blood Pb levels with pathwa y apportionment as described
above. These distributions are used to generate several type s of exposure metrics including:

• Population-weighted exposure percentiles : total blood Pb levels (with pathway apportionment) for simulated individuals repr esenting specific point s along the population blood Pb distribution (e.g., 50, 90, and 95 th percentile).

• Incidence counts: number of children within a given study area projec ted to experience a specific degree of Pb exposure (total blood Pb level).

3.2.3 GSD for Population Blood Pb Modeling Procedure

A key aspect of the population-level blood Pb modeling for all three case studies is the application of the GSD reflecti ng interindividual vari ability in blood Pb levels. This GSD reflects a number of factors which operate together to produce interindividual variability in blood Pb levels, including: (a) biokinetic variability (differences in the uptake, distribut ion or clearance of Pb), (b) differences in behavior related to Pb exposure (e.g., varying hand-to-mouth activity, tap water ingestion rates, and time spent playing indoors) and (c ) differences in environmental Pb exposure concentrations (e.g., spatial gradient s in ambient Pb levels of a resolution beyond that simulated in each case study, differences in cleaning/vacuuming rates and air exchange rates). GSDs will tend to be larger for more diverse populations and/or larger study areas, reflecting the potential for grea ter variability in the factors listed above. Specifically, more diverse populations will tend to demonstrate greate r diversity in behavioral and biokinetic factors3-24 related to Pb exposure, thereby producing greate r variation in blood Pb levels. Larger study area will tend to produce greater GSDs due both to more diverse populations, with their greater
behavioral and biokinetic variabil ity, as well as greater variation in Pb media concentrations across the study area.
The dramatic reduction in blood Pb levels am ong children in the United States that was observed between the NHANES surveys of the late 1970s and those of th e 1980s and later was accompanied by an increase in GSD (see Appendix H, Exhibit H-7). Possible reasons for this include the likelihood that, as bl ood Pb levels decrease, a wider variety of exposure pathways begin to play a role in determining overall blood Pb levels (at higher bloo d Pb levels, it likely that one, or a few related pathwa ys dominate exposure). As more pathways come into play, the potential for interindividual vari ability in behavior and biokine tics related to these pathways increase, thereby producing great er variability in blood Pb le vels (see Section 4.2.2 of EPA, 1994b). Another possible explanation for the increas e in GSDs is that, while overall Pb exposure levels have decreased, some frac tion of children nationwide continue to be exposed to Pb paint and Pb in drinking water (associated with Pb solder used in older plumbing). These higher nonair-related exposures can produ ce elevated blood Pb level, es pecially when compared to average blood Pb levels in the current general population. Theref ore, while the geometric mean blood Pb level may have decreased, the tail of th e distribution may have remained anchored (for these paint and drinking water exposed ch ildren) resulting in a larger GSD.
A number of studies have been conducted over the past three decades which provide insights into interindividual variability in Pb levels under various exposure conditions. Many of
the studies from the 1970’s and 1980’s focused on populations living near smelters with fairly elevated blood Pb levels compared with levels modeled for our three case studies. For example, as seen in Appendix H, Exhibit H-7, geometric mean blood Pb levels for populations near active smelters in the past tended to exceed 8 μg/dL with GSDs on the order of 1.7. Note, that these
earlier studies do not have read ily available summaries of blood Pb levels for the metrics of interest in this analysis (conc urrent and lifetime average metr ics) and instead, provide more generalized summaries based on the i ndividual measurements collected. Beginning in the 1980’s and extending through the 1990’s, a number of studies were conducted focusing on urban populations in specific U.S. cities (e.g., Lanphear et al 2005). These studies, which were intended to examine the link between Pb exposure and neurological effects in children, have readily available blood Pb summary data for both c oncurrent and lifetime average blood metrics. As can be seen in Appendix H, Exhibit H-9, geometric mean blood Pb levels vary significantly across these studies, with concurrent values ranging from 5.5 μg/dL to 14.5 μg/dL and lifetime average values ranging fro m 4 μg/dL to 14.2 μg/dL. GSDs also vary3-25 across the studies with GSDs of 1.4 to 1.7 reporte d for the lifetime average metric and 1.5 to 1.9
for the concurrent metric.
In addition to these smelter-re lated and city-specific studie s, the CDC has also conducted
several iterations of its NHANE S national-scale survey over the past three decades which track changes in the national distribut ion of blood Pb levels among ch ildren in the U.S. (see CD, Section 4.3.1.3 and Appendix H). As mentione d above, between the earliest NHANES surveys and the later (post 1980) surveys average blood Pb levels in children in the U.S. decreased dramatically (following initiativ es to remove Pb from gasoline and other products in the late 1970’s and 1980’s), and the GSD increased sign ificantly. This is most pronounced in the geometric mean blood Pb levels and associat ed GSDs reported for the first NHANES survey (1967-1980) (CDC, 2005) and the second NHAN ES survey (1988-1991) (CDC, 2005) with blood Pb levels decreasing from 14.9 to 3.6 a nd GSDs increasing from 1.4 to 2.1. GSDs were selected for each case study ba sed on consideration for the study data
summarized above. The selection of these GSDs reflected consider ation for a number of factors including: (a) the type of study area and underlying populatio n involved (e.g., point source versus more general urban area), (b) the fact that all three st udy areas use exposure zones with fairly uniform media concentrations and that the GSD selected will be used to represent blood Pb variability within each of those zones (c) age of the underlying survey population (the goal was to match the survey population to our study populati on to the extent possible) , and (d) date of the survey (generally there is a desire, when possible, to use studies that are more contemporary to capture any underlying downward trends in blood Pb levels which have occurred). The GSDs for each of the case studies, as well as the rationa le for their selection, is presented below.

3.2.3.1 General Urban Case Study

For this case study, two sets of population blood Pb estimates are developed for each blood Pb metric. This was done using GSDs inte nded to reflect: (a) a more uniform population living in a smaller urban area (represented by a smaller GSD for each metric) and (b) a more diverse urban population living in a larger ur ban area (represented by a larger GSD for each metric). Together, these two sets of estimates pr ovide a range of results for each blood Pb metric intended to reflect uncertainty associated with this key component of the analysis (i.e., interindividual variability in blood Pb levels). The lower-bound GSDs (representing the more uniform, smaller urban population) were obtaine d from the Boston study (Bellinger, 1992) (i.e.,
concurrent GSD of 1.7 and a lifet ime average value of 1.6). This study represents one of the more contemporary of the urban studies focusi ng on a smaller population. By contrast, the upper-bound GSDs (representing the larger, more diverse population) were obtained using NHANES IV (CDC, 2005). Here, the GSD from NH ANES IV for 1-5 yr olds (2.1) is used for3-26 the concurrent metric, and the GSD for the lifetime average metr ic (2.0) was derived by scaling the value for the concurrent metric using the rela tionship between concurrent and lifetime seen in
the smaller urban-scale Bost on study (Bellinger, 1992).

3.2.3.2 Point Source Case Studies

A critical consideration in iden tifying the GSD for use in the point source case studies, is the fact that each is modeled using a spatial te mplate that divides the study area into exposure zones with relatively uniform media concentratio ns. As noted earlier, the GSDs are used to
reflect interindividual va riability in blood Pb levels for th e group of modeled children located
within each of those zones. Th erefore, the GSD does not need to provide full coverage for media concentration variability and its impact on exposure, since this is covered to some extent by the spatial template. Therefore, a larger GSD such as that suggested by NHANES would likely overstate variability and a smalle r GSD, possibly in line with valu es from smaller-scale studies such as the smelter or city-specific studies woul d seem to be more appropriate. Following this logic, a GSD of 1.7 was selected for the concur rent blood metric, based on consideration for the range of values from these smaller-scale studies . A matching lifetime average metric GSD value of 1.6 was selected, also based on the city-specific studies.

3.3 ESTIMATED MEDIA CONCENTRATIONS

This section summarizes the media concentrati on estimates for all air quality scenarios at all three case studies (Tables 3-3 to 3-6). The co mplete set of media concentration estimates for each air quality scenario is presented in Appendix C for th e general urban case study, in Appendix D (Attachments D-7 through D-11) fo r the primary Pb smelter case study, and in Appendix E (Attachments E-3 through E-7) for th e secondary Pb smelter case study. Estimates presented in this section are pres ented to three (for air) or zero (for dust and soil) decimal places, which results in various numbers of implied sign ificant figures. This is not intended to convey greater precision for some estimates than others; it is simply an expedient and initial result of the
software used for the calculation. For each air quality scenario for the two point source case studies, a range of percentile estimates derived from a population-weighted di stribution of these media concentrations are presented for each exposure medium. For the general urban case study, however, only a single value is presented for each exposure medium. This reflects the fact that, while the point source case studies are modeled using spatial template s that include a large number of US Census blocks and/or block groups (all owing percentile media concentra tions to be identified), the general urban case study is modeled usi ng a single study area with uniform media3-27 concentrations. Consequently, there is only one value presented for the general urban case study for each medium in each air quality scenario. As discussed in Section 2.3.3, Pb concentrati on in outdoor soil/dust is not changed with the alternate air quality scenarios. Rather, outdoo r soil/dust concentration is held constant at the current conditions or current NAAQS level. This reflects the judgement that in most cases, a
reduced air concentration would not yield a changed soil concentr ation over the near term (e.g., years to decades). In the case of an area such as the remediation zone of the primary Pb smelter case study, however, where soil dynamics have been changed by the substitution of contaminated soil with clean soil, or in areas where local sources may pose a more significant
source to outdoor soil/dust than historic sources – and where there may be a currently increasing
trend in surface Pb concentrati on - this may underestimate soil concentrations under some alternate NAAQS. As expected, the highest media concentrations for each case study are associated with the current NAAQS scenario. The relatively lower media concentrations for the alternate NAAQS scenarios vary in the following orde r of decreasing estimates: 1) the 0.5 μ g/m 3 maximum monthly average, 2) the 0.2 μ g/m 3 maximum quarterly, 3) 0.2 μ g/m 3 maximum monthly and 4)
the 0.05 μ g/m 3 maximum monthly average. In the case of the general urban case study, both of
the current conditions scenarios (mean and high-end) generate media concentrations below the current NAAQS, since both monitor-based va lues are below the current NAAQS.

Table 3-3. Estimated annual ambient air concentrations.

Average Annual Air Pb Concentration ( μ g/m 3 ) Alternative NAAQS

Statistic Current Conditions Current NAAQS 0.2 μ g/m 3

, max quarterly 0.5 μ g/m 3 max monthly 0.2 μ g/m 3 max monthly 0.05 μ g/m 3 max monthly General urban case study NA - single study area High-end: 0.11 Mean: 0.056 0.600 0.080 0. 130 0.050 0.013 Primary Pb smelter case study Maximum 0.740 0.161 0.326 0.130 0.033 95 th percentile 0.153 0. 033 0.067 0.027 0.007 Median 0.042 0.009 0.019 0.007 0.002 5 th percentile 0.015 0.003 0.007 0.003 0.001 Minimum NA

0.006 0.001 0.003 0.001 < 0.001 Secondary Pb smelter case study Maximum 0.126 0.034 0.071 0.028 0.007 95 th percentile 0.015 0. 004 0.008 0.003 0.001 Median 0.003 0.001 0.002 0.001 < 0.001 5 th percentile 0.001 < 0. 001 < 0.001 < 0.001 < 0.001 Minimum < 0.001 NA a

< 0.001 < 0.001 < 0.001 < 0.001

a The current conditions scenario for secondary Pb smelter case study met the current NAAQS.3-28 Table 3-4. Estimated inhalation exposure concentrations.

Average Annual Inhalation Exposure Concentration of Pb ( μ g/m 3 ) Alternative NAAQS

Statistic Current Conditions Current NAAQS 0.2 μ g/m 3 max quarterly 0.5 μ g/m 3 max monthly 0.2 μ g/m 3 max monthly 0.05 μ g/m 3 max monthly General urban case study NA - single study area High-end: 0.049 Mean: 0.024 0.026 0.034 0. 054 0.021 0.005 Primary Pb smelter case study Maximum 0.310 0.067 0.136 0.055 0.014 95 th percentile 0.064 0. 014 0.028 0.011 0.003 Median 0.017 0.004 0.007 0.003 0.001 5 th percentile 0.006 0. 001 0.003 0.001 < 0.001 Minimum NA

0.002 < 0.001 0.001 < 0.001 < 0.001 Secondary Pb smelter case study Maximum 0.056 0.015 0.031 0.013 0.003 95 th percentile 0.007 0. 002 0.004 0.002 < 0.001 Median 0.001 < 0.001 0.001 < 0.001 < 0.001 5 th percentile < 0.001 < 0. 001 < 0.001 < 0.001 < 0.001 Minimum < 0.001 NA a

< 0.001 < 0.001 < 0.001 < 0.001

a The current conditions scenario for secondary Pb smelter case study met the current NAAQS.

Table 3-5. Estimated outdoor soil/dust concentrations.

Statistic Projected Average Outdoor Soil/Dust Pb Concentration (mg/kg) (Same for all air quality scenarios) a General urban case study NA - single study area 198 Primary Pb smelter case study Maximum 958 95 th percentile 245 Median 85 5 th percentile 30 Minimum 17 Secondary Pb smelter case study Maximum 315 95 th percentile 66 Median 12 5 th percentile 1 Minimum <1

a Estimates developed for current conditions (or current NAAQS) scenario were used for all alternate NAAQS scenarios.3-29 Table 3-6. Estimated indoor dust concentrations.

Projected Average Indoor Dust Pb Concentration (mg/kg or ppm) Alternative NAAQS

Statistic Current Conditions Current NAAQS 0.2 ( μ g/m 3 ) max quarterly 0.5 ( μ g/m 3 ) max monthly 0.2 ( μ g/m 3 ) max monthly 0.05 ( μ g/m 3 ) max monthly General urban case study NA - single study area High-end: 157-198 Mean: 107-146 426-566 128-169 166-206 102-140 71-88 Primary Pb smelter case study Maximum 1,944 648 1,077 557 383 95 th percentile 219 152 172 149 138 Median 84 68 73 67 63 5 th percentile 53 45 47 44 43 Minimum NA

41 38 39 38 38 Secondary Pb smelter case study Maximum 166 89 120 84 66 95 th percentile 73 63 67 63 61 Median 63 61 61 61 60 5 th percentile 60 60 60 60 60 Minimum 60 NA a

60 60 60 60

a The current conditions scenario for secondar y Pb smelter case study met the current NAAQS.

3.4 ESTIMATED BLOOD PB LEVELS

Estimates of concurrent and average lifetim e blood Pb level derived from outputs of the IEUBK model (Section 3.2) have been developed for each air quality scen ario in each case study (see Appendix I). Further, multiple sets of blood Pb estimates were generated for each air quality scenario of each case study, reflecting an e ffort to consider key sources of uncertainty (e.g., indoor dust model, blood metric, GSD) and their impact on blood Pb estimates (see Section 2.4.6.2). That is, eight separate blood Pb distributions were generated for each air quality scenario of the general urban case study (four for each of the two blood Pb metrics) and two distributions were generated for each air quality s cenario of the point source case studies (one for each of the two blood Pb metrics) (see Table 2-3) . The greater number of blood Pb distributions for the general urban case study re flects the larger number of m odeling approaches implemented for this more conceptual case study, which differ by indoor dust model and GSD. Because general trends in blood Pb levels across both population percentiles and air quality scenarios (for a given cas e study) are similar for both the c oncurrent and lifetime average blood metrics, we have only presented estimates for the concurrent metric here. Full results are presented in Appendix I. Conc urrent blood Pb estimates reflec ting all pathways (total), the “recent air” pathways (see Section 3.2.2), and the r ecent plus past air pathways are presented in Table 3-7. The total estimates presented in Table 3-7 are those for the median in the distribution for each case study, and the estimates for the othe r two categories are the values for those3-30 categories associated with the median for th e total blood Pb estimate. The corresponding
estimates for the 95 th percentile in the distribution of to tal blood Pb estimates for each case study are presented in Table 3-8. In these tables, all values are rounded to one decimal place, and we have presented all values below 0.05 as <0.1. It is noted that given the various limitations of our m odeling tools (Sections 2.4.3 and 3.2.2), blood Pb levels associated with air-related exposure pathwa ys and current levels of Pb emitted to the air (including via resuspension) are likely to fall between the estimates for “recent air” and those for “recent” plus “past air”. Additionally, with regard to the urban case study “recent air” and “recent” plus “pas t air” categories, an artifact of the hybrid dust Pb model tends to mask trends in the two components of dust Pb (recent air and other), which contribute to “recent air” and “past air” estimates, resp ectively (see Section 4.3.1 discussion of this uncertainty). For the primary Pb smelter case study, uncertainty in parsing out the "recent air" and "other" components of indoor dust (specifically for the site-specific regression model used in the remediation zone) have lead us to conclude that only "recent plus pa st air" exposures should be presented and "recent air" s hould not be separately presented, as is done for the other case studies (see Section 3.1.4.2). With regard to total blood Pb, estimates for the general urban and primary Pb smelter case studies indicate higher valu es for the current NAAQS scenario compared to any of the other scenarios. The difference is 1-2 μg/dL for the me dian estimates and up to 6 μg/dL difference for
the 95 th percentile estimates. A lthough no difference in median total blood Pb estimates (rounded to whole numbers) is observed between current conditions or alternative NAAQS scenarios for the general urban or primary Pb smelter case studies, differences among alternate NAAQS scenarios are observed for the primary Pb smelter case study subarea (Appendix P). Additionally, a 1 μg/dL difference in total blood Pb is observed at the 95 th percentile between
current conditions and lower alternative NAAQS scenarios fo r the general urban case study.3-31 Table 3-7. Summary of blood Pb estimates for medians in total-exposure blood Pb distributions.

Concurrent Blood Pb Level a Case Study and Air Quality Scenario Recent air b Recent plus past air b Total Pb exposure General urban case study Low High Low High Low High Current NAAQS (1.5 μg/m 3 , max quarterly) 1.9 2.2 2.6 3.2 3.1 3.7 Current conditions - 95 th percentile (0.87 μg/m 3 , max quarterly) 0.4 0.8 1.4 1.6 2.0 2.1 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.5 0.9 1.5 1.6 2.0 2.2 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.3 0.7 1.3 1.5 1.9 2.0 Current conditions - mean (0.14 μg/m 3 , max quarterly) 0.2 0.6 1.2 1.4 1.8 1.9 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.2 0.5 1.2 1.3 1.7 1.9 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.1 0.2 1.1 1.1 1.6 1.7 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 0.8 1.5 Alternative NAAQS (0.5 μg/m 3 , max monthly) 1.1 1.4 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.7 1.4 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.5 1.4 Alternative NAAQS (0.05 μg/m 3 , max monthly) NA c

0.7 1.4 Primary Pb smelter - 1.5 km subarea Current NAAQS (1.5 μg/m 3 , max quarterly) 4.0 4.6 Alternative NAAQS (0.5 μg/m 3 , max monthly) 2.6 3.2 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 1.8 2.5 Alternative NAAQS (0.2 μg/m 3 , max monthly) 1.8 2.3 Alternative NAAQS (0.05 μg/m 3 , max monthly) NA c

1.1 1.7 Secondary Pb smelter - full study area Current conditions <0.1 0.4 1.0 Alternative NAAQS (0.5 μg/m 3 , max monthly) <0.1 0.1 1.0 Alternative NAAQS (0.2 μg/m 3 , max quarterly) <0.1 0.4 1.0 Alternative NAAQS (0.2 μg/m 3 , max monthly) <0.1 0.4 1.0 Alternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 0.4 1.0 Secondary Pb smelter - 1.5 km subarea Current conditions 0.1 0.7 1.3 Alternative NAAQS (0.5 μg/m 3 , max monthly) <0.1 0.7 1.3 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.1 0.8 1.3 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.1 0.8 1.3 Alternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 0.6 1.2 a - Estimates are rounded to one decimal place. b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from inhalation of ambient air Pb or ingestion of indoor dust Pb predict ed to be associated with outdoor ambient air Pb levels, with outdoor ambient air also potentially including resuspen ded, previously deposited Pb (see Section 2.4.3). c – “Recent air” estimates were not developed for the primary Pb smelter case st udy (see Section 3.1.4.2).3-32 Table 3-8. Summary of blood Pb estimates for 95 th percentiles in total-exposure blood Pb distributions.

Concurrent Blood Pb Level a

Air Quality Scenario

(and case study) Recent air b Recent plus past air b Total Pb exposure General urban case study Low High Low High Low High Current NAAQS (1.5 μg/m 3 , max quarterly) 4.6 7.5 6.3 9.9 7.6 11.5 Current conditions - 95th% (0.87 μg/m 3 , max quarterly) 1.1 2.8 3.4 5.4 5.1 7.2 Alternative NAAQS (0.5 μg/m 3 , max monthly) 1.2 2.6 3.5 5.1 4.8 6.7 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.8 2.3 3.1 5.1 4.4 6.9 Current conditions - mean (0.14 μg/m 3 , max quarterly) 0.5 1.9 2.9 4.7 4.2 6.5 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.5 1.8 2.9 4.6 4.2 6.4 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.1 0.7 2.6 3.9 3.9 5.7 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 2.8 4.6 Alternative NAAQS (0.5 μg/m 3 , max monthly) 3.1 4.2 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 2.4 4.0 Alternative NAAQS (0.2 μg/m 3 , max monthly) 2.5 4.0 Alternative NAAQS (0.05 μg/m 3 , max monthly) NA c

1.9 3.8 Primary Pb smelter - 1.5 km subarea Current NAAQS (1.5 μg/m 3 , max quarterly) 10.3 12.3 Alternative NAAQS (0.5 μg/m 3 , max monthly) 7.6 8.5 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 5.9 6.6 Alternative NAAQS (0.2 μg/m 3 , max monthly) 4.9 6.1 Alternative NAAQS (0.05 μg/m 3 , max monthly) NA c

3.5 4.5 Secondary Pb smelter - full study area Current conditions <0.1 0.8 2.4 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.1 1.1 2.4 Alternative NAAQS (0.2 μg/m 3 , max quarterly) <0.1 0.8 2.3 Alternative NAAQS (0.2 μg/m 3 , max monthly) <0.1 1.1 2.4 Alternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 0.8 2.4 Secondary Pb smelter - 1.5 km subarea Current conditions 0.5 2.2 3.3 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.4 2.5 3.3 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.2 2.3 3.1 Alternative NAAQS (0.2 μg/m 3 , max monthly) <0.1 1.4 3.1 Alternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 1.6 3.1 a - Estimates are rounded to one decimal place. b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air contribution to indoor dust, and outdoor soil/dust pathway s, while “recent air” refers to contributions from inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3). c – “Recent air” estimates were not developed for the primary Pb smelter case st udy (see Section 3.1.4.2).3-33 3.5 UNCERTAINTY CHARACTERIZATION AND SENSITIVITY ANALYSIS

The characterization of uncertainty asso ciated with exposure assessment included performance evaluation which is discussed in this section. In addition, uncertainty in exposure assessment is also considered as part of charact erizing uncertainty in the risk estimates and is reflected in inclusions of multiple modeling ap proaches and the qualitative discussion of key sources of uncertainty which are discussed in Se ction 4.3. Elements of exposure assessment are also included in the sensitivity an alysis described in Section 4.3.2. Performance evaluation for the exposure asse ssment focused on evalua tion of projections of Pb in exposure media (i.e., ambient air, out door soil, and indoor dust) (discussed in Section 3.5.1) and projections of Pb in blood (covered in Section 3.5.2). Those case studies (or case study elements) for which media con centrations were estimated usi ng empirical data as the basis are not considered here; only those estimates based directly on modeling were included. Comparing model output to monitoring data is often considered the most desirable form of performance evaluation. In addition to mon itoring data, or in the absence of such data, outputs from other models and expert opinion about how outputs should look can be used as comparison benchmarks in performance evalua tion. The performance evaluation activities reported here have focused on the comparison of model outputs to measurements. Favorable comparisons, including a lack of systematic trends in either over- or underestimation of modeled results relative to empirical data, prov ide confidence in the modeling estimates.

3.5.1 Performance Evaluation Related to Exposure Media Modeling

This section discusses pe rformance evaluation conducted for modeling of Pb concentrations in exposure media including am bient air, outdoor soil and indoor dust.

3.5.1.1 Evaluation of Modeled Ambient Air Pb Concentrations

Performance evaluation for ambient air Pb pr edictions focused on the two point source case studies, in which air di spersion modeling was used. For the primary Pb smelter case study cu rrent NAAQS scenario, the ISCST3-Prime model was used with input file s (e.g., source character ization, meteorologi cal data) used in developing the 2007 proposed revision to the SIP for that location (Appendix D). The submission to EPA for the propos ed SIP revision included a model performance evaluation, focused on the “actual value” modeling scenario (MDNR 2007a). The actual value modeling included three separate comparisons based on relating model predictions to measured Pb
concentrations at five monitor sites in th e study area. These co mparisons included:3-34 • Day-to-day evaluation of modeling output co mpared to monitor values: The review of the model performance evaluation conduc ted by the state of Missouri concluded that all sites demonstrated a pattern of overall accuracy for directional prediction (i.e., high modeled days being high monitored days and low modeled days being low monitored days), suggesting that the model wa s performing well in relating wind direction to Pb transport (MDNR, 2007a).

• Comparison of source contribu tion analysis using chemical mass balance (CMB) with dispersion model predicted re lative contributions: Source contribution analysis using CMB of monitor filter residue to identify significant sour ces of Pb for each monitor (e.g., roads inside facility boundaries and ya rd dust, blast furnace) were compared with relative contributions predicted by the dispersion m odel for individual modeled sources. The review of the model perfor mance evaluation concluded that there was generally good agreement between the CMB re sults and the air dispersion results in terms of major sources contributing Pb at each monitor (MDNR, 2007a).

• Comparison of overall average modeled results with monitored levels: This performance evaluation involved comparing modeled results (for 247 days simulated for 2005) at six monitor locations with actual measured values for that same period at those locations. Results of this evaluati on suggested a slight overprediction bias (<10%) for those sites likely to have the greatest impacts from the facility.

This evaluation of model performance for th e actual value modeling scenario increases confidence in estimates developed for the current NAAQS scenario with this modeling setup and inputs particular to the 2007 proposed SIP revision. Performance evaluation of air dispersion m odeling for the secondary Pb smelter is discussed in Appendix E (Section E.2.4), and involved comparing modeled ambient air Pb levels to measured levels at two monitoring locations wi thin the study area. Results of that evaluation suggest that the model might be slightly underpredict ing levels at the closest monitor, and slightly more underpredictive of levels at the mo re distant monitors. Th e use of meteorological data that is not site-specific and may not be fully representati ve of actual wind patterns in the area may be contributing to this. When modeled concentrations at distances matching those of the monitors, and for all directi onal points around the facility, ar e compared with monitor values,
modeled values are identified which ma tch or exceed the measured values.

3.5.1.2 Evaluation of Modeled Outdoor Soil/Dust Pb Concentrations

Modeling was used in estimating the sp atial pattern of outdoor soil/dust Pb concentrations for the secondary Pb smelter cas e study (see Section 3.1.3.3). As the modeling
did not use estimates of historic al Pb emissions (presumed to be higher than current emissions), absolute values of the model predictions were not expected to be representative of current Pb concentrations in soil/dust in this case study. Although measurements were not available for the3-35 case study location, comparison of model predicte d soil Pb levels with Pb concentrations reported for a surrogate secondary Pb smelter location confirmed this expectation (see Appendix E, Section E.3). Model predictions were - on average - a factor of 3 lower than Pb concentrations at the surroga te location. Accordingly, we used a hybrid approach for characterizing outdoor soil Pb levels for this cas e study, scaling the model estimates of soil Pb concentration across the study area by factors derived from studies at other secondary Pb smelter locations (see Appendix E, Section E.3).

3.5.1.3 Evaluation of Modeled Indoor Dust Pb Concentrations

Performance evaluation completed in support of indoor dust Pb pr ediction involved two components. First, the hybrid model develope d for the general urban case study was evaluated using available data from the literature (see Appendix G, Section G.3.6). Results of this evaluation help us assess the overall reasonabl eness of this model in supporting indoor dust prediction for the general urban case study in part icular. Additionally, predicted indoor dust Pb levels for all three case studies were compared with data identified in the literature, including measurements from individual studies focusing on smaller areas and data from national-scale surveys. The general urban cas e study model and model projectio ns are discussed here first The hybrid indoor dust model developed for the general urban cas e study (Section 3.1.4.1 and Appendix G) is a combination of mechanisti c model (to relate outdoor ambient air Pb to indoor dust Pb) and empirical data (to characteri ze the nonair related fraction of indoor dust Pb
in the residential urban setti ng). Components of the mechanis tic portion of the model were subjected to a range of evaluations based on avai lable data in the literature. In addition, the conversion of model-generated indoor dust Pb load ings to concentrations (a key step required prior to blood Pb modeling) was evaluated. Fina lly, model predictions of indoor dust Pb levels were evaluated using data from se veral studies for specific cities. Evaluation of the mechanistic component of the hybrid dust model focused on (a) predicted deposition fluxes of Pb to indoor surf aces and (b) prediction of relationship between indoor air Pb and ambient outdoor air Pb (Appe ndix G, Section G.3.6). Generally, modeled indoor deposition fluxes for Pb appear to be in line with values reported for a location in Manhattan with closed windows (C aravanos et al., 2006). The m odel predicted ratio of indoor air Pb to outdoor ambient air Pb are lower (~ 50%) than ratios based on data collected from residences in the Midwest (Roy et al., 2003) but similar to ratios generated by a different hybrid model which combines empirical data with a mass balance modeling approach in predicting
indoor ambient air concentrations (Riley et al., 2002). The unde restimate of this ratio, when compared with the empirical data from Roy et al . (2003), may result from the fact that the hybrid model does not consider resuspension of indoor dus t Pb. Generally, these findings with regard to3-36 substeps of the mechanistic (and air-focused) component of the hybrid model (Appendix G, Section G.3.6) suggest a potenti al to underpredict the influenc e of outdoor ambient air Pb on indoor dust Pb. The conversion for predicted ind oor dust loadings to co ncentrations, however, may overpredict concentrations based on a comp arison data collected in residences in the Midwest (Roy et al., 2003; Appendix G, Sectio n G.3.6). This finding may counterbalance any potential for the mechanistic component of the model to underestimate th e influence of ambient
air Pb on indoor dust Pb. Indoor dust Pb concentrations (in terms of mass per mass) were identified from a few studies in the literature (e.g., CD, Table 3-8; Tang et al., 200 4), as well as the HUD National Survey of Lead-based Paint in Housing (U SEPA, 1995) described in Section 3.1.4.1. These
studies are identified in Tabl e 3-9. Although the HUD survey was used for the empirical component of the hybrid model, it is consider ed here with regard to the total dust Pb concentrations predicted by both the hybrid and air-only models used in the general urban case study (use of these models is described in Section 3.1.4.1). 6 Comparison of the model-predicted
indoor dust concentrations for th e current conditions scenarios (146 and 198 ppm for the hybrid model and 107-157 ppm for the air-only model) to em pirical data collected in Jersey City, NJ, and Ottawa Canada (Table 3-9) suggest that the m odel may be underpredicting indoor dust Pb, although the housing stock in these studies was much older than housing generally in U.S., which may mean that the measurements are impact ed to a greater extent by indoor Pb paint than would be the general case in th e U.S. Thus, it would be expect ed that those reported values would be higher than the model predictions. Additionally, the model-predicted values fall between the medians for the youngest and oldest houses sampled in the HUD national survey (Table 3-9). Given that indoor dust Pb modeli ng completed for the general urban case study was aimed at capturing central tendency indoor dust Pb le vels (and is not expected to, for example, capture variability related to cleaning rates or indoor paint Pb levels), this finding provides
confidence in the estimates.

6 The use of HUD indoor dust Pb data for performance evaluation is considered reasonable, even in light of its use in deriving the air-related portion of the hybrid model, since total indoor dust Pb levels generated by the hybrid model (reflecting both the air-related and non air-rela ted components of the model) are being examined in the performance evaluation. In this context, nationally representative indoor dust Pb concentrations (obtained from the HUD dataset) are considered a useful empirical dataset for the evaluation.3-37 Table 3-9. Evaluation of model-predicted indoor dust Pb levels against empirical data obtained from the literature.

Modeled indoor dust Pb levels (ppm)

Case study Air quality scenario Median 5 th to 95 th Percentile (min-max) Indoor Dust Pb Observations reported in the literature

General urban case study Current conditions

(air-only regression and hybrid) Mean current conditions: (107 to 146) 95th percentile current conditions: (157-198) a
Primary Pb smelter case study Current NAAQS 84 53 - 219 (41 - 1,944)

Secondary Pb smelter case study Current conditions 63 60 – 73 (60 – 166) - Residences near smelters: 1283-4140 ppm (CD, Table 3-8)

- Jersey City, NJ housing (floor): 857 ppm (CD, Table 3-8)

- Residences in the Midwest (windowsill): 954ppm (CD, Table 3-8)

- Ottawa Canada housi ng (floor): 222 ppm (median), 406 ppm (mean) (Tang et al., 2004)

- HUD survey of US housing: 87 ppm (median for newest houses, built 1960-1979), 406 ppm (median for oldest housing, build <1940) (USEPA, 1995).

a - because a single study area is used for the general urban ca se study, a single dust estimate was generated for each combination of current conditions air qua lity scenario and indoor dust model (i.e., four separate estimates were generated for the general urban case study).

For the two point source case studies, the model-predicted indoor dust Pb concentrations were compared to observations from the literature (see Table 3-9). An important factor to keep in mind when reviewing the modeled results pres ented in Table 3-10 is that the current NAAQS scenario modeled for the primary Pb smelter invo lved regions of the study area with ambient air Pb levels significantly higher than either of the other case studies. In consideration of indoor dust Pb levels pr edicted for the primary Pb smelter, we have focused more on the central tendency values. This reflects the fact th at the higher-end modelgenerated values for this case study likely reflect significant am bient air impacts which are not captured in any of the empirical data identified in the literature, thereby reducing the utility of performance evaluations for these higher-end predictions. The median indoor dust Pb concentration generated for the primary Pb sme lter case study (84 ppm) fa lls near the lower end of the range in the HUD dataset (i.e., near the median of 87 pp m reported for newer housing in that study). This seems reasonable since a significant fraction of th e study area for this case study has ambient air Pb levels not significantly differe nt from ambient air Pb levels seen across the U.S. (see Table 3-4). Regarding the secondary Pb smelter case study, the median value for this case study (63 ppm) is below the range of values reported in the past for smelters (CD, Table 3-8), and also just3-38 below median value reported for the youngest housing in the HUD national data set (USEPA, 1995), suggesting that indoor dust Pb levels ma y be underpredicted fo r this case study.

3.5.2 Performance Evaluation Related to Blood Pb Modeling

Performance evaluation completed in support of blood Pb modeling i nvolved three steps: (a) evaluation of candi date blood Pb models for the anal ysis, (b) comparison of ambient outdoor
air Pb-to-blood Pb ratios generated for the three case studies against ratios obtained from the literature and (c) comparison of modeled blood Pb levels for these three case studies against NHANES IV data. Each of these evaluation steps is discussed below.

3.5.2.1 Evaluation of Candidate Blood Pb Models

Evaluation of candidate blood Pb models (I EUBK and Leggett) involved three separate stages: (a) application of the candidate models to three hypothetical individual child exposure scenarios used previously by EPA and others in evaluations of blood Pb models, (b) comparison of candidate model predictions for a general U. S. childhood exposure scenario (using typical Pb exposures for key pathways) to NHANES IV empi rical data, and (c) ev aluation of candidate model performance in replicating measurements of urban child blood Pb levels obtained in
Rochester. Detailed results of the evaluation of candidate blood Pb models are presented in Appendix J (and summarized in Section J.4). The first stage (focusing on reproducing resu lts of previous performance evaluations) demonstrated that we were applying the candidate models correctly. Tests of the models against specific individual exposure scenar ios (Section J.1.3) reproduced, to a high degree, the results of previous model comparison. The second stage of the model evaluation (f ocusing on reproducing general US child blood Pb levels presented in NHANES IV) de monstrated that, depending on assumptions regarding typical outdoo r soil/dust and indoor dust Pb con centrations, the IE UBK model either moderately overpredicted GM blood Pb levels (b y two-fold or less) or generated predictions close to NHANES summary statistics (see Section J.3.1). By contrast, predictions from the Leggett model were more than three to six ti mes higher than the age-specific NHANES IV GM
values. The third stage of the model evaluation focused on evaluating the candidate models in predicting blood Pb levels for an urban child c ohort (Appendix J, Secti on J.3.2). The dataset,
which included matched Pb media concentratio ns (outdoor soil/dust and indoor dust levels) and blood Pb levels, was collected as part of an ep idemiological study focusing on the effects of Pb exposure in children living in Rochester, NY (L anphear et al., 1995; Lanphear and Roghmann, 1997). Blood Pb levels for each child sampled in the study were predicted using IEUBK and Leggett, with the measured media Pb levels coll ected as part of the study as inputs. These3-39 predicted blood Pb levels were then compared to the measured blood Pb levels for each child. The results of this stage of the model eval uation were similar to those from the NHANES evaluation in that IEUBK results suggested a moderate overprediction (~70%) while Leggett results indicated a much greater overp rediction (a factor of 2 to 6).
Results of the model evaluation for IEUBK and Leggett suggest that IEUBK generates more representative blood Pb estimates relative to Leggett in the context of the evaluations
conducted here. Based on the results of the mo del evaluation, we decided to use IEUBK in generating the primary set of exposure and risk re sults for this analysis, and include Leggett as part of the sensitivity anal ysis but not in the primary analysis (see Section 4.3.2).

3.5.2.2 Evaluation of model-derived outd oor air Pb-to-blood Pb ratios

In deriving the current NAAQS in 1978 (43 FR 46246), USEPA used an estimate of the relationship between ambient air Pb concentration and associated blood Pb concentration (i.e., 1:2, μg/m 3 to μg/dL). In this assessment, we rely on several distinct modeling steps which, when taken together, translate ambient air Pb into blood Pb. As part of the blood Pb model performance evaluation we have extracted air-to -blood Pb ratios from the modeling completed for the three case studies for comparison to estimates reported in the literature. Ratios were developed that relate ambient air Pb to blood Pb contributed from the following different exposure pathways or pathway combinations: (a) inhalation of ambient air, (b) inhalation of ambient air plus ingestion of the Pb in indoor dust that is pr edicted to be associated with ambient air Pb levels (i.e., “r ecent air” per Section 2.4.3), and (c) inhalation of ambient air plus ingestion of indoor dust plus ingestion of outdoor soil/dust (i.e., not including the diet and drinking water inges tion pathways). The limitations of our modeling tools precluded us from parsing air-related blood contributions any more finely. The ratios (actually, the blood Pb side of the ratio) will be larger as you move from (a) to (c) because there is a progressively larger fraction of overall blood Pb (exposure) bei ng associated with air. With regard to the potential impact of ambient air Pb on blood P b, the first ratio (inhala tion pathway) is an underestimate because it excludes the important inge stion pathways to which ambient air Pb can contribute. Conversely, the third ratio, although not including any im pact of air Pb on diet (and blood), potentially includes some contributions to blood Pb that are not influenced by air (e.g., indoor paint). For the purposes of this mode l performance discussion, we have focused on the second type of ratio (those for “recent air”) deri ved using the concurrent blood Pb metric. The full set of ratios are presented fo r each case study in Appendix I In this evaluation, we have considered the ratios derived from the blood Pb estimate prior to application of the GSD reflecti ng interindividual variability in Bl ood Pb levels (i.e., the central tendency estimate of blood Pb derived for each US Census block or blockgroup for the point3-40 source case studies and for the entire study area for the genera l urban case study). Although airto-blood Pb ratios can be derive d for individual simulated childre n after application of the GSD, these would be less relevant to empirical ratios reported in the literature, which tend to capture central tendency or typical ratios for a study pop ulation (through statisti cal regression modeling, for example). While this interindividual variability in exposure parameters is not reflected in the ratios evaluated here, variation in exposure c oncentration is reflecte d by the range of ratios derived for the different exposure zones of the two point source ca se studies. For the general urban case study, ratios are presen ted for both current conditions scenarios (reflecting a mean and high-end estimate of air Pb concentration).

Table 3-10. Air-to-blood Pb ratios for “recent air” contribution to concurrent blood Pb level.

Case study Annual average ambient air Pb concentrations ( μ g/m 3 ) Air-to-Blood Pb Ratio Air-to-Blood Pb Ratios Identified from the Literature

General urban – current conditions

Current conditions (mean) 0.056 1:4 & 1:10 a

Current conditions (95 th percentile) 0.114 1:4 & 1:7 a

Primary Pb smelter – current NAAQS

Median air concentration 0.093 1:3

95 th percentile air concentration 0.458 1:7
Secondary Pb smelter- current conditions

Median air concentration 0.005 1:5

95 th percentile air concentration 0.011 1:4 - Review of studies publishedbefore 1984 reports air-toblood Pb ratios for children generally ranging from 1:3 to 1:5 (Brunekreef, 1984).

- Pooled analysis of air-toblood Pb relationship (log-log regression) based on above studies yielded ratios of 1:3 to 1:6 (Brunekreef, 1984).

- An air-to-blood ratio developed based on more recent study data (Hilts, 2003) is 1:7 (for children in the vicinity of an operating smelter which experienced a modification in facility operations, leading to a marked decrease in air Pb emissions).

a The two ratios for the general urban case study correspon d to results obtained from the two indoor dust models.

Several observations can be made regarding hi gher ratios generated for two of the case studies presented in Table 3-10:

• For the general urban case study, the higher ra tios (i.e., 1:10 and 1:7) result from application of the hybrid dust model which produces a higher indoor dust3-41 concentration per unit ambien t outdoor air Pb relative to the air-only regression model.

• For the primary Pb smelter case study, the hi gher ratio (1:7) is al so the result of a more potent dust model (the site-speci fic regression model developed for the remediation zone of this study area). That indoor dust model was used for the higherimpact (95 th percentile) block, while the regression (air plus soil) model was used for the majority of the study area, including the median block (producing the 1:3 ratio).

The air-to-blood Pb ratios from the literature presented in Table 3-10 all come from a
combination of older studies summarized in a single review from 1984 (Brunekreef, 1984) and more recent data (Hilts, 2003). The Brunekreef , 1984 review presents both (a) the ratios identified from the reviewed studies, which in cluded surveys focused on smelter and urban study areas (generally range from 1:3 to 1:5) and (b) the results of a pooled analysis where a log-log regression model was developed relating ambient air Pb directly to blood Pb levels based on the underlying study data (thi s yields the range of 1:3 to 1: 6 presented in Table 3-10). Data presented in Hilts, 2003, track Pb e xposure levels (e.g., outdoor ambient air Pb, outdoor soil Pb and indoor dust Pb) and measured blood Pb levels (annual geometric mean blood Pb levels for children 6 to 60 months of age), fo r a population of children liv ing in the vicinity of smelting operations in Trail, British Colu mbia for the period 1996 to 2001. The facility experienced a change in operations in 1997 whic h resulted in a significant decrease in Pb emissions from the facility and a resulting decrease in ambient air Pb levels in the vicinity of the facility. In deriving an air-to-blood Pb ratio from these data, we compared ambient air Pb levels for 1996 and 1999 to geometric mean blood Pb levels for the same years (all data were obtained from Table 3 in Hilts, 2003). An air-to-blood Pb ratio of 1:7 was generated based on this comparison. Note, also, that a se cond air-to-blood Pb ratio could have been generated by comparing data for 1996 and 2001 (which would ha ve tracked the continued decrease in both ambient air Pb levels and geometric blood Pb le vels). However, blood Pb measurements in 2001 focused on children 6-36 months of age, while blood Pb measurements for 1996 and 1999,
involved children 6-60 months of age. This disconnect in the age groups would have prevented a direct comparison of the ratios generated using th e two pairings of years. Therefore, we focused on the ratio developed by comparing 1996 and 1999 since the blood Pb measurements for these two years focused on an age group (6-60 month old ch ildren) closer to that used in our modeling of risks for the case studies (c hildren <7 years of age). The air-to-blood Pb ratio of 1:7 generated using the Hilts, 2003 data is slightly higher than the range presented in Brunekreef, 1984 (1:3 to 1:6). This might reflect the fact that overall exposures associated with the Hi lts, 2003 study are lower than thos e reflected in the Brunekreef,3-42 1984 study and, as noted in Brunekreef, 1984, there appe ars to be greater efficiency in translating air Pb to blood Pb at lower exposure levels, compared with higher levels. Although the blood Pb levels associated w ith Hilts, 2003 (geometric mean blood Pb levels of 4 to >10 μg/dL) are closer to those associated with our modeled case studies than are the levels in Brunekreef (1984), th e levels in Hilts are still higher. In the case of the blood Pb
levels reflected in the studies summarized in Br unekreef (1984), these measured blood Pb levels (typically > 10ug/dL) are significa ntly higher than those modele d for our case studies. This
disconnect between the studies in the literature and the modeled case studies (regarding blood Pb ranges) introduces uncertainty into performan ce evaluation of air-to-b lood Pb ratios completed here. That is, it is possible that ratios seen at blood Pb levels signifi cantly above 5 to 10 μg/dL (levels seen in the literature) ar e different from those below 5 μg /dL (those associated with the modeled case studies), although this can not be ve rified without updated ch aracterizations of airto-blood Pb ratios based on contemporary trends in Pb exposure and blood Pb levels. Despite this uncertainty, however, comparison of the air- to-blood Pb ratios developed for the three case studies against those found in the literature is considered useful. The lower air-to-blood ratios generated for th e three case studies (1:4 for the general urban case study, 1:3 for the primary Pb smelter case study and 1:4 to 1:5 for the secondary Pb smelter case study) fall within the range of ratios identified in the litera ture (1:3 to 1:7). However, the higher ratios, ranging from 1:7 to 1:10, are higher than th e bulk of Brunekreff (1984) reported ratios and just include the 1:7 ratio developed from the Hilts (2003) dataset, although a subset of the individu al studies reviewed by Brunekr eef (1984) presented air-to-blood Pb ratios that were 8.5 and higher. In several in stances, such higher ratios are associated with
lower blood Pb levels and lower ambient air Pb leve ls (both factors that would seem to be more relevant to exposure conditions found in the th ree case studies modeled for this analysis). However, the studies reporting these higher rati os are complicated by a number of factors (e.g., involving older children, use of ambient air measurement techniques which may underestimate results, thereby inflating ratios, rela tively small sample sizes, etc.).

3.5.2.3 Comparison of modeled blood Pb levels to nationally representative data

Evaluation of modeled blood Pb levels using empirical data depends on the availability of empirical datasets for populati ons with exposure similar to thos e in the case st udies. While there are many blood Pb studies reported in th e literature, they are largely composed of populations experiencing exposures from all pathways that are higher than exposures reflected in

our case studies. No datasets were identif ied for specific subpopulations with exposures corresponding to current exposures that would be appropriate for performance evaluation here.3-43 In the case of the general urban case study, wh ile it is not location-sp ecific, we have not identified any contemporary data sets for urban locations with ambient air Pb levels matching

those modeled in the current scenario that are no t heavily influenced by Pb-based paint. For the primary Pb smelter case study location, the most generally available blood Pb monitoring study was completed in 2001 and 2002, and given the change s in exposures since that time and the use of a future (current NAAQS) scenario these ol der blood Pb surveys do not correspond to the exposures modeled for this case study. In the ca se of the secondary Pb smelter, the blood Pb data available for the county containing the s econdary Pb smelter does not indicate residence location handicapping efforts to consider blood Pb levels for children in the secondary Pb smelter case study location.

While blood Pb datasets were not identified for populations representative of those
modeled for the three case studies, we have compared the modeled blood Pb levels against general national-scale blood Pb levels (for a ch ild age group matching that modeled for the case studies). While each of the cas e studies involves a population that is different than the national population (reflecting the nature of the exposur e scenario being cons idered), the background exposures for the case studies and the nationa l population are similar. Accordingly, the relationship between the modeled blood Pb levels for each study area and the national

distribution are evaluated. For th is evaluation, we have used meas ured values interpolated from the NHANES IV dataset. Specifically, we have interp olated a series of percentile estimates for 7 yr olds based on summary data presented for NHANES IV (CDC, 2005). The process of interpolating these values invol ved the following steps: (a) us e summary statistics (GM and associated confidence intervals) for 1-5 yr olds and 6-19 yr olds (for years 1999-2002) to establish lognormal distributions for each age range, (b) identify population percentiles of interest (e.g., median, 90 th , 95 th percentiles) for each age group using these lognormal distributions, and (c) interpolate a series of percentiles for a 7yr age cohort using the percentiles for the two age ranges (the 1-5 yr old and 6-19 yr old). This interpolatio n procedure resulted in the population percentile estimates for a 7 yr ol d cohort (for the years 1999-2002) presented in

Table 3-11.3-44 Table 3-11. Blood Pb levels for 7 year olds in the U.S. (interpolated from NHANES IV, 1999-2002).

Population percentile Blood Pb level (μg/dL) median 1.5

75th 2.55 90th 4.1 95th 5.25 Figure 3-2 compares modeled blood Pb levels for the three case studies with the interpolated NHANES IV blood Pb levels presen ted in Table 3-11. Modeled blood Pb levels presented in Figure 3-2 include (a) the (mean) curr ent conditions scenario for the general urban case study, including both low-end and high-end estimates reflecting application of different indoor dust models, and GSDs for this case study, and (b) the current NAAQS scenarios for both the primary and secondary Pb smelter case studies.

Figure 3-2. Comparison of NHANES IV blood Pb levels with modeled estimates.

0 2 4 6 8 10 12 14

median 75th 90th 95th Population PercentileBlood Pb level (ug/dL) NHANES-IV (interpolated 1999- 2002, 7yr old)

General urban case study (mean current conditions - low er)

General urban case study (mean current conditions - higher)

Primary Pb smelter (current NAAQS)

Primary PB smelter (smaller 1.5km study area - current NAAQS) Secondary Pb smelter (current conditions)

Secondary Pb smelter (smaller 1.5km study area - current conditions)

The following observations are made regard ing the blood Pb modeling results for the three case studies (Figure 3-2):

• General urban case study: The low- and high-end estimates for the mean current conditions scenario bracket the NHANES IV data. Of the three case studies, it is reasonable to assume that results for the current conditions (mean) scenario for this3-45 case study might be the closest to matchi ng the NHANES. Interestingly, of the results for the low- and high-end modeling ap proaches for this scenario, the high-end results appear to be closer to the NHANES data, which would indicate that modeling for this case study may not be biased high with regard to total blood Pb levels. Additionally, unlike the point source case studies, th e general urban case study included consideration of variability in background exposure pathways through application of the larger GSD in the hi gh-end modeling approach for that case study.

• Primary Pb smelter case study: It would be expected that higher percentile exposure levels for this case study might exceed the NHANES data, however, that is not seen either at the 90th or 95th pe rcentiles in Figure 3-2. There are three factors which may account for this observation. First, we have not generated higher population percentile estimates (i.e., greater than 95th percentile) and it is possible that exposures for those higher percentiles at the primary Pb smelter might begin to exceed levels seen with the NHANES-interpol ated data. Second, it is important to reiterate that modeling for this case study did not account fully for exposure variability in background Pb exposures. It is likely that if modeling for this case study had included consideration of bac kground exposure variability (e.g., relatively high paint Pb and drinking water exposures ), there would be a greater difference between high-end blood Pb levels modeled fo r this case study and those interpolated form NHANES IV, with the potential for the primary Pb smelter percentiles to exceed those derived from the NHANES IV data. Note, however, this lack of full accounting for blood Pb variability from b ackground sources is not considered a major limitation in this analysis becaus e the focus is on ambient air-related exposures. Finally, as discussed in Sec tion 4.3.1, modeling for the point source case studies did not consider other sources of ambient air Pb besides the smelter. This could produce low bias in predictions of exposure, especially for portions of the study area further from the facility.

• Secondary Pb smelter case study: Modeled blood Pb levels for this case study are significantly lower than NHANES IV le vels, especially for higher population percentiles. As with the primary Pb sm elter case study, this likely reflects three factors: (a) absence of pred ictions (due to technical li mitations) of higher population percentiles above the 95th per centile, where exposu re related to facility emissions is likely more dominant, (b) exclusion of consideration for higher background exposures to Pb in paint and drinking water, and (c) failure to consider ambient air Pb sources impacting the study area, other than the smelter facility. Note, also that, for the secondary Pb smelter case study, overall uncertainty associated with modeling of exposure and risk is considered suffici ently high to significantly reduce overall confidence in results for this case study (see Section 4.3.1).3-46 REFERENCES
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### 4 RISK ASSESSMENT

This chapter describes the approach used to ch aracterize risk for the initial analyses of the full-scale assessment, including discussion of the methodology (Section 4.1), presentation of risk estimates (Section 4.2), and uncertainty charact erization (Section 4.3). Additional analyses completed after the August 2007 CASAC mee ting (Henderson, 2007a), involving a core modeling approach, are presented in Chapter 5.

4.1 METHODS FOR DERIVING RISK ESTIMATES

Risk characterization for this assessment fo cuses on IQ loss in children. IQ loss is derived using a set of concentr ation-response functions develope d based on results from a pooled analysis of epidemiology studies (Lanphear et al., 2005). These concentration-response functions are combined with the population-leve l blood Pb distributions generated for the case studies to produce distributions of IQ loss esti mates for each study popula tion. IQ loss is also apportioned among different exposure pathways using the pathway apportionment information generated as part of the exposure analysis. Two key elements of the risk methodology are described in greater detail below: (a) the concentration-response functions used in the analysis (Sec tion 4.1.1) and (b) the stepwise analytical procedure used to generate th e IQ loss (risk) distributions (Section 4.1.2).

4.1.1 Concentration-Response Functions

As discussed in Section 2.1.5, log-linear concentr ation-response functions for IQ loss for the concurrent and lifetime average blood Pb me trics were obtained from a large pooled study (Lanphear et al., 2005) and used as the basis fo r estimating IQ changes in children in this analysis. Three types of concen tration-response functions were drawn from this study, and two equations of each type are used in this assessmen t: one for the concurrent blood Pb metric, and a second for the lifetime average blood Pb metric . The three types in clude: (a) a log-linear function with cutpoint (Section 4.1.1.1), (b) a log- linear function with low- exposure linearization (Section 4.1.1.2) and (c) a two-pie ce linear function (Sect ion 4.1.1.3). Plots of the three types of IQ loss functions developed for this assessment are presented in Figure 4-1 for the concurrent blood Pb metric. Table 4-1 presents a comparis on of total IQ loss and incremental IQ loss (IQ loss/μg/dL) for the three functions across a range of concurrent blood Pb levels. As can be seen by comparing the plots of the three functions togeth er with the results pres ented in Table 4-1, the log-linear function with low-exposure linearizati on will generate the grea test IQ change across the exposure range 0 to 10 μg/dL followed by the l og-linear function with cutpoint and then the two-piece linear function.4-2 Inclusion of these three types of concen tration-response functions in this assessment produces a range of risk estimates that is indicative of the model uncertainty related to predicting IQ loss based on modeled blood Pb levels.

Figure 4-1. Comparison of three concentration-response functions for concurrent blood Pb levels < 10 μg/dL.

0 2 4 6 8 100.111.92.83.74.65.56.47.38.29.110
Concurrent PbB (ug/dL)IQ loss log-linear with cutpoint

log-linear with low-exposure linearization two-piece linear

Table 4-1. Comparison of total and incremental IQ loss estimates below 10 μg/dL for the three concentration-response functions.

Concentration-Response Function

Log-linear with cutpoint Log-linearwith lowexposure linearization Two-piece linear Performance Metric Points, IQ loss at 2 μg/dL 1.9 4.6 0.9 at 5 μg/dL 4.3 7.0 2.3 at 7.5 μg/dL 5.4 8.1 3.4 Total IQ loss
at 10 μg/dL 6.2 8.9 4.5 <2 μg/dL 0.94 2.29 0.45 <5 μg/dL 0.87 1.41 0.45 <7.5 μg/dL 0.73 1.09 0.45 Incremental IQ loss (points per μg/dL) <10 μg/dL 0.62 0.89 0.454-3 4.1.1.1 Log-Linear Function with Cutpoint

This concentration-response function is th e log-linear function for IQ change from Lanphear et al. (2005), with incorporation of a cutpoint at the blood Pb level corresponding to the lowest levels represented by measurements in the underlying pooled analysis. The values used for cutpoints, for both the concurrent a nd lifetime average blood Pb metrics, are based on the corresponding low end of the range of values for these two indices in the pooled analysis (Hornung, 2007a, b). The values are 1.0 μg/dL and 1.47 μg/dL, for the concurrent and lifetime average blood Pb metrics, resp ectively. Slopes for th e two log-linear func tions were obtained directly from the study (Lanph ear et al., 2005). Parameteriza tion for these two log-linear functions with cutpoints is presented below, alon g with the mathematical form of the function:

• Form of function: IQ loss = beta * ln (concurrent blood Pb/cutpoint)

• Log-linear function with cutpoint (concurrent metric): - beta (slope): -2.70 - cutpoint: 1.0 μg /dL

• Log-linear function with cutpoint (lifetime average metric): - beta (slope): -3.04 - cutpoint: 1.47 μg /dL

4.1.1.2 Log-Linear Function with Low-Exposure Linearization

We also developed risk estimates that reflect the possibility th at IQ loss is associated with the entire range of Pb exposure all the wa y down to "zero" exposure, as recommended by CASAC (Henderson, 2007b). The risk assessmen t included IQ loss prediction based on a loglinear IQ change functio n with a linearization of the log- linear function taking over at lower exposure levels. The transition point from the log-linear function to the linearized slope was selected with the same basis as the cutpoint described in Section 4.1.1.1, the lower end of the range of the pertinent blood Pb index values in the pooled analys is (Lanphear et al., 2005). The linearized slope is obtained by ta king the tangent to the log-lin ear function at the point of departure, with different slopes being identified for the concurre nt and lifetime average blood Pb metrics (per Lanphear et al, 2005) . Parameterization for the two log-linear functions with lowexposure linearization is presen ted below, with the mathematic al form of the function:

• Form of function:
- For blood Pb level ≥ cutpoint: IQ loss = beta * ln (concurrent blood Pb/cutpoint) + linear slope * cutpoint - For blood Pb level < cutpoint: IQ loss = linear slope * concurrent blood Pb4-4 • Log-linear function with low-exposure linearization (concurrent metric): - beta (slope): -2.70 - linear slope to be applied below 1.0 μg /dL: -2.70

• Log-linear function with low-exposure lin earization (lifetime average metric): - beta (slope): -3.04 - linear slope to be applied below 1.47 μg /dL: -2.1

4.1.1.3 Two-piece Linear Function

This category of concentration-response function was developed by fitting a two-piece linear model to the log-linear IQ loss function obt ained from the pooled analysis (Lanphear et al., 2005). Inclusion of this function in the risk assessment is intended to address the practical problem of the shape associated with applicati on of the log-linear model at the low end of the blood Pb value range. In this case, we consider an alternate functional fo rm that allows ready prediction of IQ loss across the fu ll range of modeled Pb exposure . The procedure involved first generating blood Pb values for each of the two blood Pb metrics, concurrent and lifetime average, for a set of N = 1,333 simulated childre n representative of thos e included in the pooled analysis (Lanphear et al., 2005). This was accomplished by sampling from a blood Pb
distribution constructed from the median and 95 th percentile of the c oncurrent and lifetime average blood Pb indices, respec tively, reported in Lanphear et al. (2005). IQ values for each of the 1,333 simulated children were then estimated us ing the reported log-linear models that relate blood Pb to absolute IQ (Lanphear, et al., 2005). Nonlinear regression techniques were then used to fit two piece linear models to these tw o sets of simulated children with their matched pairs of blood Pb and IQ values. The regres sions provided parameter estimates (slopes and "hinge" point) of the best f itting two piece linear segment func tion for each blood Pb metric, concurrent and lifetime average. As discussed in Section 4.3.1, the use of a constructed dataset introduces uncertainty related to fitting of a mode l to a model. Parameterization for the two twopiece linear functions is presente d below, with the mathematic al form of the function:

• Form of function: - For blood Pb level > hinge: IQ loss = beta 2 * concurrent blood Pb
- For blood Pb level ≤ hinge: IQ loss = beta 1 * concurrent blood Pb

• Two-piece linear function (concurrent metric): - "hinge" linking two segments: 10.82 μg /dL - beta 1 (slope at ≤ 10.82 μg/dL): -0.4539 - beta 2 (slope at >10.82 μg /dL: -0.11304-5 • Two-piece linear function (lifetime average metric): - "hinge" linking two segments: 13.39 μg /dL - beta 1 (slope at ≤ 13.39 μg /dL): -0.3790 - beta 2 (slope at >13.39 μg /dL: -0.1187

4.1.2 Projection of Population Risk

Risk characterization completed for this assessment involved c onverting the populationlevel blood Pb distributions generated for the three case studies into population-level distributions of IQ loss using th e three types of concen tration-response functions described in the last section. This procedure is descri bed below for each of the functions.

• Log-linear function with cutpoint: Each modeled blood Pb level is compared against the cutpoint. If the blood Pb level is lo wer than the cutpoint, then no IQ loss is estimated because the simulated individual’ s blood Pb level is below the level for predicting IQ loss with this function. If the blood Pb level is greater than the cutpoint, then the log-linear function is used to predict IQ loss for the portion of the estimated blood Pb level exte nding above the cutpoint.

• Log-linear function with low-exposure linearization: Each modeled blood Pb level is compared against the point of linearization. If the blood Pb level is below the point where the function becomes linear, than the linear slope is used to predict IQ loss. If the modeled Pb level is above the point where the function becomes linear, than IQ loss is calculated as the sum of IQ loss across the linear portion of the cu rve plus the additional contribution from the log-linear portion of the functi on extending up to the total blood Pb level.

• Two-piece linear function: Similar to the last function, the modeled blood Pb level is compared against the blood Pb level of the “h inge”. If the blood Pb level falls below the hinge, as is the case for most simulated i ndividuals at the three case studies, then the steeper, low-exposure slope is used to es timate IQ loss. If the simulated blood Pb level falls above the hinge, then IQ loss associated with the low-exposure (steeper slope) piece of the function (for the portion of the blood Pb level up to the hinge) is combined with IQ loss estimated using th e shallower and higher exposure slope, for that portion of the blood Pb leve l extending above the hinge.

The IQ loss estimates generated using this ap proach are pooled to form a population-level distribution of IQ loss for a given study area. Each of these IQ loss estimates are pathway apportioned among policy-relevant pathways and policy-relevant background based on pathway contribution to the underlying blood Pb levels (see Sections 2. 4.3 and 3.2.2). As with pathway apportionment for blood Pb levels (Section 3.2.2), pathway apportionment of risk estimates is also at the level of exposure zone. All simulate d individuals from a given zone are assigned the4-6 same pathway apportionment. As noted with regard to blood Pb levels (Section 3.2.2), there is increased uncertainty associated with pathway apportioned IQ loss estimates for higher-end risk percentiles, since these reflect an assumption th at relative pathway contributions at central tendency exposure levels hold at the high-end percentiles of th e blood Pb level distribution. Just as with the population-level exposure estimates discussed in Section 3.2.2, risk estimates generated using the approach outlined above are used to generate several types of risk metrics, depending on the case study. For the tw o point source case studies, because they are location-specific and include a defined and e numerated receptor populat ion, two categories of risk metrics are generated:

• Population-weighted risk (IQ loss) percentiles: IQ loss (with pathway apportionment) for simulated individuals representing specific points along the population risk distribution (e.g., 50, 90, 95 percen tile simulated individuals).

• Incidence counts: Number of children within a given study area projected to experience a magnitude of IQ loss.

As for the exposure estimates, the general ur ban case study risk metric s are restricted to population-weighted risk (IQ loss) percentiles.

4.2 RISK ESTIMATES

Estimates of IQ loss resulting from Pb exposur e have been developed for each air quality scenario in each case study (see Appendix K). In addition, IQ loss results have been developed for subareas associated with each of the point -source case studies (s ee Appendix P). Further, multiple sets of risk results were generated fo r each combination of case study and air quality scenario, in an effort to consider key sources of uncertainty and their impact on blood Pb estimates (see Section 2.4.6.2). That is, twenty four separate risk distributions were generated for each air quality scenario of the general urban case study and si x distributions were generated for each air quality scenario of the poin t source case studies (see Table 2-3). As discussed in Section 2.4.6.2, generating multiple sets of risk results for each combination of case study and air quality scenar io provides a range of results reflecting the impact of key sources of uncerta inty on risk results. Howeve r, because we could not assign specific confidence levels to each modeling appr oach, these multiple sets of results are not translated into single uncertainty distributions of risk for each air quality scenario in each case study. Therefore, we consider the multiple sets of risk results to span the best estimate risk distribution. In response to CASAC comments on the July 2007 draft risk assessment report (Henderson, 2007a), however, we have omitted resu lts from the two-piece linear (with hinge at4-7 10.82 μg/dL for concurrent blood metric) from the su mmary of results presen ted in this chapter (these results are available in Appendix K). Three types of risk estimates are su mmarized in Tables 4-2 and 4-3:

• Risk estimates for recent air: This estimate is the porti on of total IQ loss derived from inhalation of ambient air Pb and ingest ion of Pb in indoor dus t that is predicted to be associated with ambient air Pb c oncentrations based on the indoor dust model (i.e., “recent air” risk per Sections 2.4.3 and 3.2.2) Given the modeling approach used in this analysis and its limitations, this set of risk estimates is expected to be most responsive to alternative NAAQS.

• Risk estimates for recent air plus pas t air (other indoor dust and outdoor soil contribution): This estimate is the portion of to tal IQ loss derived from inhalation of ambient air Pb, ingestion of indoor dust a nd ingestion of outdoor soil/dust (Sections 2.4.3 and 3.2.2). That is, this estimate includes the recent air pathways in addition to other indoor dust and outdoor soil pathways. This estimate include some contribution from indoor paint because the indoor dust m odels handicapped our ability to parse out this contribution. Otherwise, these estimates reflect contributions from all except the policy-relevant background pathways of diet and drinking water.

• Risk estimates for al l pathway contributions : This estimate is the IQ loss associated with the total blood Pb concentration.

As noted previously (Sections 2.4.3 and 3.2.2), policy-relevant pathwa ys are represented in the first two bullets above, with the true values for the policy-relevant pathways considered to
fall between the estimates for “recent air” and thos e for “recent” plus “past air”. Additionally, with regard to the urban case study “recent air” and “recent” plus “pas t air” categories, an artifact of the hybrid dust Pb model tends to mask tre nds in the two component s of dust Pb (recent air and other), which contribute to “r ecent air” and “past air” esti mates, respectively (see Section
4.3.1 discussion of this uncertain ty). For the primary Pb sm elter case study, uncertainty in parsing out the "recent air" and "other" compone nts of indoor dust (specifically for the sitespecific regression model used in the remediation z one) have led us to conclude that only "recent plus past air" IQ loss estimates should be pr esented and "recent air" results should not be separately presented for the primary Pb smelter, as is done for the other case studies (see Section 3.1.4.2). IQ loss estimates reflecting all exposure pathwa ys (total), the “recent air” pathways (see Section 3.2.2), and the recent plus past air pathwa ys are presented in Tables 4-2 and 4-3. The total estimates presented in Table 4-2 are those for the median in the distribution of total risk, and the estimates for the other two categories in that table (i.e., recent air and recent plus past air) are the values for those categories associated with the median for the total exposure pathway4-8 estimate. The corresponding estimates for the 95 th percentile in the di stribution of IQ loss estimates for total blood Pb for each case study ar e presented in Table 4-3. This presentation may lead to some seeming inconsistencies in trends for recent air and recent air plus past air risk estimates across air quality scenarios. This is because the re cent air or recent plus past air
estimates associated with the median and 95 th percentile total blood Pb estimate may not necessarily be the median and 95 th percentiles of the distribution of estimates for those specific categories. This is because the blood Pb level (and associated total IQ loss) for a simulated child reflects not only the total Pb uptake (from both background and ambient air-related pathways), but also the GSD-based adjustment factor. This means that tw o simulated children could have identical blood Pb levels (and IQ loss estimates ), but one child could have a higher recent air exposure, with the other child having their lo wer recent air exposure compensated for by a higher GSD-based adjustment factor, th ereby resulting in both children having the same total blood Pb
level. In presenting IQ loss estimates, all values are rounded to one decimal place. The complete set of risk estimates are presented in A ppendix K. The risk estimates in Tables 4-2 and 4-3 indicate the following:

• IQ loss estimates were uniformly higher for the current NAAQS air quality scenario compared with any of the alternative NAAQS or current c onditions air quality scenarios for the primary Pb smelter and ge neral urban case studies . This trend held for both the median and 95 th percentile risk results. Ri sk estimates for the secondary Pb smelter case study (both the full study ar ea and sub-area) differed little across any of the air quality scenarios.

• IQ loss estimates decreased with increasingly lower alte rnative NAAQS, as a general observation across case studies. However, this trend varied in consistency and magnitude. For example, the trend was mo re pronounced with the recent air estimates (both median and 95 th percentile) for the genera l urban case study, and was less obvious with the risk estimates for total blood Pb (in this case, it is likely that background exposures overwhelmed the recent air related exposures). The trend was also stronger for the subarea of the primary Pb smelter, compared with the full study area, reflecting the fact that the subar ea has higher ambient air Pb impacts and therefore, is likely to demonstrate greater variation in IQ impa cts across alternative NAAQS.

• As expected, risks associated with recent plus past air c ontributions are larger, across all population percentiles fo r all case studies and air quality scenarios evaluated, compared with the recent air risk estimates. This reflects that the “past air” category includes contributions from the outdoor soil/ dust contribution to i ndoor dust, historical air contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions as sociated with outdoor ambient air Pb levels, either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, includi ng resuspended, previously deposited Pb4-9 (see Section 2.4.3). As noted previously (Sections 2.4.3 and 3.2.2), policy-relevant pathways are represented in both of these categories, with the true values for the policy-relevant pathways considered to fall between the estimates for “recent air” and those for “recent” plus “past air”.

• Recent air and recent plus past air IQ loss es timates were higher for the subareas of the point source case studies. This reflects th e fact, mentioned above, that these subareas experience significantly greater ambient air- related impacts compared with the full study areas. While the full study areas include a large number of children in areas with ambient air Pb levels well below the alte rnative NAAQS considered, whose exposures consequently are less affected under altern ative NAAQS, these areas are omitted from the subarea, thus increasing the percentage of simulated children whose exposures and associated risks are notabl y reduced under alternate NAAQS.

• Risk estimates generated for both of the point source case studies (full study areas) are lower across all population per centiles (for the same air quality scenario) compared with the general urban case study. This reflec ts two factors. First, the point source case studies include spatial gradients, such that areas near the facility may be at or near a given ambient air Pb level (associated with an air quality scenario), while the majority of the study area (and consequently study population) experiences ambient air Pb levels considerably lower than the level a ssociated with the air quality scenario. By contrast, the general urban case study assume d a uniform ambient air Pb concentration at the level of the air quality scenario being evaluated. Cons equently, the entire hypothetical study population would experience that uniform air concentration. The second reason for the point source case studie s having risk estimates lower than those for the general urban case study, is that th ey only consider Pb emitted from the industrial facility of interest and do not consider em issions from any other sources within the study area, or near the study area that could im pact populations within the study area. By contrast, current conditions fo r the general urban case study is based on monitoring data for urban locations, which will by default reflect the actual mix of Pb sources influencing air Pb concentrations. Consequently, the general urban case study reflects the combined impact of different Pb sources, while the point source case studies only consider emissions from the smelter under consideration and the influence of these emissions lessens (and consequently air Pb concentrations decline) with distance from the facilities.

• For the general urban case study, the large di fference seen in the recent air risk estimates between the three higher and the lowest alternate NAAQS scenario for the 95 th percentile is not seen for the risk estimat es for recent plus past air contributions. This reflects an artifact of the approach used in the hybrid indoor dust model to characterize "other" (non-“re cent air”-associated) contribu tions to indoor dust (see Appendix G). Because of the method used to convert dust loadings to dust concentrations within the hybr id dust model and the approach used to apportion total dust concentrations between other and recen t air components, the "other" indoor dust concentration predicted by this model varies with air quality scenario, with that value increasing as the ambient air Pb level decrea ses (see Appendix C). This means that, as the recent air contribution to exposure through indoor dust in gestion decreases (as the lower alternative NAAQS levels are consid ered), the estimate of contribution of4-10 "other" indoor dust actually increases. This partially accounts for the trend noted above. That is, even though recent air risk estimates drop, they ar e partially offset by risk resulting from an incr ease in exposure to the "other " Pb contributions to indoor dust. The impact of this aspect of the hybr id dust model is iden tified in Section 4.3.1 and evaluated in a sensitivity an alysis presented in Chapter 5.

• Risk estimates for the secondary Pb smelter case study are considerably lower than risk estimates for the other case studies (for all air quality scenarios considered). Limitations in the design fo r the secondary Pb smelter cas e study (described in Section 4.3.1) contribute significant uncer tainty to these estimates. Accordingly these results should not be used to draw conclusion rega rding similar facilities risks experienced by populations living in the vicinity of other similar point sources.4-11 Table 4-2. Summary of risk estimates for medians of total-exposure risk distributions.

Points IQ loss a

Recent air b Recent plus past air b Total Pb exposure Case Study and Air Quality Scenario Low High Low High Low High General urban case study Current NAAQS (1.5 μg/m 3 , max quarterly) 2.1 4.3 2.9 6.0 3.5 7.0 Current conditions - 95 th percentile (0.87 μg/m 3 , max quarterly) 0.4 2.0 1.3 4.0 1.8 5.4 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.5 2.1 1.4 4.1 1.9 5.4 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.3 1.7 1.2 3.8 1.7 5.2 Current conditions – mean (0.14 μg/m 3 , max quarterly) 0.2 1.4 1.1 3.6 1.5 5.0 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.2 1.4 1.0 3.6 1.5 5.0 Alternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 0.6 0.9 3.1 1.3 4.6 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 0.6 2.5 1.1 3.8 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.8 2.2 1.0 3.7 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.6 2.7 0.9 3.6 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.4 2.1 0.9 3.6 Alternative NAAQS (0.05 μg/m 3 , max monthly) NA c

0.4 2.4 0.9 3.5 Primary Pb smelter - 1.5 km subarea Current NAAQS (1.5 μg/m 3 , max quarterly) 3.2 6.3 3.7 6.8 Alternative NAAQS (0.5 μg/m 3 , max monthly) 2.1 4.9 2.6 5.8 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 1.5 4.3 2.0 5.2 Alternative NAAQS (0.2 μg/m 3 , max monthly) 1.2 4.0 1.9 2.0 Alternative NAAQS (0.05 μg/m 3 , max monthly) NA c

0.9 2.9 1.4 4.6 Secondary Pb smelter - full study area Current conditions 0 d <0.1 0 d 1.0 0 d 2.8 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0 <0.1 0 1.0 0 2.7 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0 <0.1 0 1.0 0 2.7 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0 <0.1 0 0.9 0 2.7 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0 <0.1 0 1.0 0 2.7 Secondary Pb smelter - 1.5 km subarea Current conditions <0.1 0.4 0.4 2.7 0.8 3.8 Alternative NAAQS (0.5 μg/m 3 , max monthly) <0.1 0.1 0.4 1.9 0.7 3.7 Alternative NAAQS (0.2 μg/m 3 , max quarterly) <0.1 <0.1 0.3 1.7 0.6 3.6 Alternative NAAQS (0.2 μg/m 3 , max monthly) <0.1 0.1 0.4 1.9 0.6 3.6 Alternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 <0.1 0.3 2.1 0.6 3.6 a - With the exception of "true zero values" (see note d below), all estimates are rounded to one decimal place. b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air contribution to indoor dust, and outdoor soil/dust pathway s, while “recent air” refers to contributions from inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3). c – “Recent air” estimates were not developed for the primary Pb smelter case st udy (see Section 3.1.4.2). d – Table entries of “0” (in the “low” column) are truly zero values and reflect application of log-linear concentration-response function with cutp oint to blood Pb estimates below 1 μg/dL.4-12 Table 4-3. Summary of risk estimates for 95 th percentiles of total exposure risk distributions.

Points IQ loss a

Recent air b Recent plus past air b Total Pb exposure Case Study and Air Quality Scenario Low High Low High Low High General urban case study Current NAAQS (1.5 μg/m 3 , max quarterly) 3.3 6.0 4.6 8.3 5.5 10.5 Current conditions - 95 th percentile (0.87 μg/m 3 , max quarterly) 1.0 3.4 3.1 6.6 4.2 8.8 Alternative NAAQS (0.5 μg/m 3 , max monthly) 1.0 3.6 3.1 6.9 4.2 9.1 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.7 2.9 2.8 6.4 4.0 8.7 Current conditions – mean (0.14 μg/m 3 , max quarterly) 0.5 2.5 2.7 6.1 3.9 8.5 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.4 2.3 2.7 6.0 3.9 8.4 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.1 1.0 2.4 5.5 3.7 8.1 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 2.3 4.2 3.7 6.8 Alternative NAAQS (0.5 μg/m 3 , max monthly) 2.5 4.5 3.4 6.6 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 1.9 5.2 3.2 6.5 Alternative NAAQS (0.2 μg/m 3 , max monthly) 2.0 4.0 3.2 6.4 Alternative NAAQS (0.05 μg/m 3 , max monthly) NA c

1.5 4.9 3.1 6.3 Primary Pb smelter - 1.5 km subarea Current NAAQS (1.5 μg/m 3 , max quarterly) 5.7 8.8 6.8 9.5 Alternative NAAQS (0.5 μg/m 3 , max monthly) 5.1 7.5 5.8 8.5 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 4.7 6.9 5.3 7.8 Alternative NAAQS (0.2 μg/m 3 , max monthly) 3.9 6.7 4.9 7.6 Alternative NAAQS (0.05 μg/m 3 , max monthly) NA c

2.8 4.7 3.6 6.5 Secondary Pb smelter - full study area Current conditions <0.1 <0.1 0.8 1.8 2.4 5.2 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.1 0.1 1.0 2.0 2.3 5.1 Alternative NAAQS (0.2 μg/m 3 , max quarterly) <0.1 <0.1 0.8 2.1 2.3 5.1 Alternative NAAQS (0.2 μg/m 3 , max monthly) <0.1 <0.1 1.1 1.8 2.3 5.1 Alternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 <0.1 0.8 1.7 2.3 5.1 Secondary Pb smelter - 1.5 km subarea Current conditions 0.5 1.4 2.1 5.0 3.2 6.3 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.4 0.5 2.4 3.9 3.2 6.3 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.2 0.1 2.3 3.2 3.1 6.1 Alternative NAAQS (0.2 μg/m 3 , max monthly) <0.1 0.2 1.4 4.3 3.1 6.1 Alternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 <0.1 1.6 4.2 3.0 6.1 a - Estimates are rounded to one decimal place. b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air contribution to indoor dust, and outdoor soil/dust pathway s, while “recent air” refers to contributions from inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3). c – “Recent air” estimates were not developed for the primary Pb smelter case st udy (see Section 3.1.4.2).4-13 4.3 UNCERTAINTY CHARACTERIZATION AND SENSITIVITY ANALYSIS

This section discusses uncertainty related to exposure and risk estimates generated for this analysis. Several methods have been used to examine uncertainty in our modeling approach and its potential impact on exposure a nd risk estimates. These include:

• qualitative discussion of ke y sources of uncertainty a nd their potential impact on exposure and risk estim ates (Section 4.3.1),

• evaluation of model performance, including comparison with empirical data (Section 4.3.3), and,

• development of multiple sets of exposure and risk estimates for each assessment scenario that illustrate the combined impact of different models and input data on risk results and the associated uncertainty (Section 4.3.4).

In addition to these methods for consideri ng uncertainty, a sensitivity analysis was also conducted to characterize the pote ntial impact of individual m odeling elements on risk results (see Section 4.3.2). Each of thes e elements of the uncertainty characterization, along with the sensitivity analysis, is briefly summarized below.

4.3.1 Qualitative Discussion of Key Sources of Uncertainty

Given the complexity of this assessment and th e range of models and input data used in completing it, there is a wide va riety of sources of uncertainty potentially impacting the exposure and risk results generated (Appendi x M, Table M-1). This secti on identifies those sources of uncertainty with the potential to have a signific ant impact on risk results (see Appendix M, Table M-1 bold text). When it was feasible with the available methods and data, these key sources of uncertainty have been quantitatively assessed for their potential impact on risk results either as part of the multiple modeling approaches implem ented (see Section 4.3.4). In addition, some of these modeling elements have been included in th e sensitivity analysis (see Section 4.3.2). Key sources of uncertainty include:

• Temporal aspects : As described in Section 2.4.1, exposure for the simulated child population begins at birth and continues for 7 years, with Pb concentrations in all exposure media remaining constant throughout the period, and child ren residing in the same exposure zone throughout the period. In characterizing exposure media concentrations, annual averages are derived and held constant through the seven year period. Exposure factors and physiological parameters vary with age of the cohort through the seven year exposure period, se veral exposure factor s and physiological parameters are varied on an annual basi s within the blood Pb modeling step (see Section 3.2). These aspects are a simp lification of populati on exposures that contributes uncertainty to our exposure and risk estimates.4-14 • General urban case study : This case study differs from the others in several ways (described in more detail in Section 2.2.1). It is by definition a general case study and not based on a specific location. There is a single exposure zone for the case study within which all media concentrations of Pb are assumed to be spatially uniform; that is, no spatial variation within the area is simulated (see Sections 2.4.2, and 3.1.1). Additionally, the case st udy does not rely on any specific demographic values. Within the single exposure zone a th eoretical population of unspecifi ed size is assumed to be uniformly distributed. Thus this case st udy is a simplified representation of urban areas intended to inform our assessment of the impact of changes in ambient Pb concentrations on risk, but which carries with it attendant uncertainties in our interpretation of the associ ated exposure and risk estimates. For example, the risk estimates for this case study, while generally representative of an urban residential population exposed to the specifi ed ambient air Pb levels, can not be readily related to a specific urban population. Speci fic urban populations are sp atially distributed in a nonuniform pattern and experien ce ambient air Pb levels that vary through time and space. Consequently, interpreta tions of the associated blood Pb and risk estimates with regard to their relevance to specific urban residential e xposures carry uncertainty and presumably an upward bias in risk, partic ularly for large areas, across which air concentrations may vary substantially.

• Point source case studies: Dispersion modeling was used to characterize ambient air Pb levels in the point source case studies. This approach simulates spatial gradients related to dispersion and deposition of Pb from emitting sources. In the case of the point sources modeled, sources were limited to those associated with the smelter operations, and did not include other sources such as resuspension of roadside Pb not immediately related to facility operations, and other stationary sources of Pb within or near the study area. This means that, with distance from the facility, there is likely underestimation of ambient ai r-related Pb exposure because with increased distance from the facility there would be increasing in fluence of other source s relative to that of the facility. We believe this limitation to have more significant impact on risk estimates associated with the full study than on those for the subareas, and to perhaps have a more significant impact on risk estimates associated with the smaller secondary Pb smelter (see below).

• Secondary Pb smelter case study: Air Pb concentration estimates derived from the air dispersion modeling completed for the second ary Pb smelter case study are subject to appreciably greater uncertainty than that for those for the primary Pb smelter case study due to a number of factors, includi ng: (a) a more limited and less detailed accounting of emissions and emi ssions sources associated with the facility (particularly fugitives), (b) a lack of prior air quality modeling analyses and performance analyses, and (c) a substantially smaller number of Pb -TSP monitors in the area that could be used to evaluate and provide confidence in model performance 1 . Further, as mentioned in the previous bullet, no air sources of Pb other than those associated with the facility were accounted for in the modeling. Give n the relatively smaller magnitude of

1 The information supporting the air dispersion modelin g for the primary Pb smelter case study (see Section 3.5.1.1) provides substantially greater co nfidence in estimates for that case study.4-15 emissions from the secondary Pb smelter, the underestimating potential of this limitation with regard to air concentrations with distance from the facility has a greater relative impact on risk estimates for this case study than for the primary Pb smelter case study. The aggregate uncertainty of all of these factors has left us with low confidence in estimates for this case study. We note that exposure and risk estimates are lower than those for the other case studies. Although we had initially intended to use this case study as an example of areas near stationary sources of intermediate size (smaller than the primary Pb smelter), our e xperience with this anal ysis indicates that substantially more data and multiple case st udies differing in several aspects would be needed to broadly characterize risks for such a category of Pb exposure scenarios.

• Indoor dust Pb modeling for th e general urban case study: The hybrid indoor dust Pb model was developed for the general urban case study due to a lack of an existing urban-focused dust model, and this hybrid mode l is subject to particular uncertainties. Key among these uncertainties is failure to consider house-to-house variability in factors related to the infiltr ation of ambient air Pb indoors and subsequent buildup of Pb on indoor surfaces. This handicaps our abil ity to predict varia tion in indoor dust Pb levels for non-typical residential conditions. In addition, a lack of comprehensive data in the literature on rates and efficiency of indoor cleaning, introduces uncertainty into the model. The method used to convert Pb loadings generated by the hybrid model to Pb concentrations is also subject to un certainty, partially due to the age of the underlying dataset. Because the underlying da taset is an older dataset (see Section 3.1.4.1), there is potential for bias toward greater representation of housing with Pbbased paint. Additionally, the component of the hybrid model that converts dust Pb loadings to concentrations (Appendix G, Sec tion G.3.4) introduces an uncertainty into the estimates of percent contribution from recent air compared to other pathways. This is related to a nonlinearity in this conver sion and results in the "other" indoor dust concentration predicted by this model varying with air qualit y scenario, with that value increasing as the ambient air Pb level decrea ses (see Appendix C). This means that, as the recent air contribution to exposure through indoor dust in gestion decreases for the lower alternative NAAQS levels, the estimat e of contribution of "other" indoor dust actually increases. This issue of the uncertainty in estimating the "other" component of indoor dust (and relating it to the "recent air" component) has been examined further as a sensitivity analysis (see Section 5.3.3.4).

• Estimates of indoor dust Pb concentrations for the primary Pb smelter case study (application of the site-sp ecific regression model): There is uncertainty associated with the site-specific regression model appl ied in the remediation zone (see Section 3.1.4.2), and relatively greater uncertainty associ ated with its applic ation to air quality scenarios that simulate notably lower air Pb levels. Limitations in the dataset from which the model was derived limited its form to that of a simple regression that predicts dust Pb concentration as a function of air Pb concentration plus a constant (intercept). We recognize, howev er, that there may be variab les in addition to air that influence dust Pb concentrations and th eir absence in the regression contributes uncertainty to the resulting estimates. To the extent that these unaccounted for variables are spatially related to the smelter facility Pb sources, our estimates could be biased, not with regard to the absolute dust Pb concentr ation, but with regard to4-16 differences in dust Pb concentration estimate between different air quality scenarios. Those differences may be overestimated becau se of potential overest imation of the air coefficient and underestimation of the intercep t in the regression model. Examples of such not-accounted-for variables are roadside dust Pb and hi storically contributions to current levels of indoor dust Pb (e.g., dust Pb contributed to a hous e in the past that continues to contribute to current dust Pb levels). See Sections 3.1.4.2 and 3.5.1.3 for additional discussion.

• Estimates of outdoor soil/dust Pb concentrations : Outdoor soil/dust Pb concentrations in all air quality scenarios have been set e qual to the values for the current conditions scenarios. That is, we are not simulating an impact of changes in air Pb concentrations on soil concentrations, or the associated impact on dust concentrations, blood Pb and risk estimates. In areas where air concen trations have been greater in the past, however, implementation of a reduced NAAQS might be expect ed to yield reduced soil Pb levels over the long term. As de scribed in Section 2.3.3, however, there is potentially uncertainty associated with this specification, particularly with regard to implications for areas in which a Pb sour ce may locate where one of comparable size had not been previously. A dditionally, we note that contro l measures implemented to meet alternative NAAQS may result in change s to soil Pb concentr ations; these are not reflected in the assessment

• Recontamination of residential yard soil near the primary Pb smelter : Although data collected in residential yards within 3⁄4 mile of the primary Pb smelter indicate a trend of increasing surface soil Pb concentrations, soil Pb estimates in this area of the primary Pb smelter case study were not adjusted using this information. Therefore, these estimates likely were underestimates of soil Pb levels associated with the current ambient air Pb levels. Because the indoor dust Pb model used in this part of this case study did not rely on outdoor soil /dust concentrations this di d not affect indoor dust Pb estimates, although as described in a previous bu llet the lack of soil data to consider in developing the indoor dust Pb model increase d uncertainty in that model’s results. Additionally, the outdoor soil/dust ingestion pa thway is a small contributor to blood Pb levels, which also reduced the impact of this factor on blood Pb estimates .

• Interindividual variability GSD : There is uncertainty associated with the GSD specified for each case study. In the case of the general urban case study, the use of a uniform ambient air Pb concentration acr oss the hypothetical st udy area complicates selection of a GSD. This is because any urban area for which GSDs can be developed would be subject to spatial vari ation in ambient air Pb levels. To the extent that these ambient air Pb levels influence children’s blood Pb levels, this influence would be reflected in the associated GSD. In the case of the two point s ource case studies, the use of the spatial templates complicates se lection of a GSD for a different reason. Because the spatial templates are intended to contribute va riability in risk results related to spatial gradients in air-related Pb media concentrations, the GSDs used for these case studies need to reflect the remain ing sources of variability in blood Pb levels (e.g., interindividual variability in behavior and biokinetics relate d to Pb exposure as well as variability in non-air related Pb exposures). The ex tent to which the specified GSDs reflect the sources of variability at play in each of these case studies is unknown.4-17 • Exposure pathway apportionment for higher pe rcentile blood Pb level and IQ loss estimates: As discussed in Section 3.2.2, pathwa y apportionment of blood Pb levels for higher population percentiles is specified to be the same as that estimated using the central tendency estimate of blood Pb in an exposure zone. This introduces uncertainty into projections of pathway apportionment for higher population pe rcentiles of blood Pb and IQ loss. In reality, pathway a pportionment may shift as you consider higher exposure percentiles. For example, pa int and/or drinking water exposures may increase in importance, with air-related contributions decreas ing as an overall percentage of blood Pb leve ls and associated risk.

• Projection of IQ loss at lower exposure levels : Because available epidemiological data are limited in their description of the relati onship of IQ decrement to blood Pb at lower blood Pb levels (e.g., < 5 μg/dL), there is uncer tainty associated w ith projecting IQ loss at these blood Pb levels which are particul arly relevant to th e current population of children in the U.S. (see discussion in S ection 2.1.5). For additional discussion of uncertainty associated with predicting IQ loss resulting from Pb exposure, see Sections 5.3.3.1.

4.3.2 Sensitivity Analysis

Sensitivity analysis techniques were used to evaluate the impact of individual modeling elements on exposure and risk estimates. Specif ically, we used a "one element at a time elasticity analysis" approach, in which the full risk model was run with one of the selected modeling elements adjusted to reflect an alternat e input value or modeling choice. The results of that run with the modified modeling element was then compared to those for the "baseline risk" run to determine the magnitude of the impact on ri sk results of selections for that one modeling
element. The sensitivity analysis described in Appe ndix L focused on the general urban case study, reflecting the fact that this cas e study has potential re levance for a larger number of Pb-exposed children compared with the two point source case studies. Additionally, we recognized that exposure patterns for urban children can be highly variable compared wi th exposures near Pb smelters and the availability of data sets for specific urban areas that reflect current blood Pb levels is limited. The modeling elements examin ed in the sensitivity analysis included those inputs and modeling steps believed to have a si gnificant potential for impacting exposure and risk results (e.g., oral uptake f actor, interindividual blood Pb vari ability GSD, biokinetic model, concentration-response function for IQ loss). The one-element-at-a-time sensitivity analysis indicates which of the modeling elements include d in the sensitivity analysis has the greatest impact on risk results, which can be used to guide future efforts to refine the overall risk model. Note, that an additional sens itivity analysis involving the hybrid indoor dust model and specifically the derivation of the "other" compone nt of indoor dust was conducted as part of the core analysis (see Section 5.3.3.4).4-18 The results of the sensitivity analysis (Appendi x L) can be considered both in terms of the impact of individual modeling element choices on (a) overall risk re sults and (b) recent air risk results, i.e., IQ loss estimates for inhalation plus ingestion of indoor dust Pb predicted to be
associated with ambient outdoor air Pb levels , where ambient outdoor air potentially includes resuspended, previously deposited Pb (see Sec tion 2.4.3). Given the rele vance of the recent air exposure pathways to the NAAQS review, the resu lts of the sensitivity analysis are summarized here in terms of their impact on rece nt air risk results, rather than to tal risk results. Results of the sensitivity analysis for total risk esti mates, however, are similar (Appendix L). Results of the sensitivity analysis showed the following modeling elements to have the greatest impact on recent air risk estimate s for the general urban case study (i.e., > 40% impact on
the 95 th percentile recent air risk estimates and an even greater impact, in terms of percent change, on median estimates). These elements ar e presented in order of decreasing magnitude of impact on risk estimates (Note, results pr esented here reflect the impact on the 95 th percentile risk results - see Appendix L for sensitivity analysis results reflecting impact on the median population percentile):

• IQ loss function: Use of the log-linear with line arization IQ loss model (the model producing the greatest IQ loss) resulted in 226% increase in recent air risk results over results for baseline which used the 2-piec e linear model. These results suggest that characterization of the relationship between Pb exposure and IQ loss has the greatest impact on high-end risk results.

• Indoor dust Pb modeling: Use of a combination of hi gh-end modeling parameters for the hybrid dust model results in a 139% increas e in recent air risk estimates over results using the hybrid model with those input va lues used in the primary analysis. In addition, use of the air-only regression mode l in place of the hybrid model results in a 60% decrease in the recent air risk results. Th ese results together suggest that predicting the relationship between outdoor ambient air Pb and indoor dust Pb is an important factor impacting risk results.

• Blood Pb modeling: Use of the Leggett model as compared to the IEUBK model, results in a 170% increase in recent air risk estimates. This indicates the importance of the blood Pb modeling step in the analysis . However, these re sults need to be considered in the context of the performance evaluation for blood Pb which suggested that the Leggett model may be significantly overpredicting blood Pb levels, and the fact that the Legget model is designed to pr ovide results over shorte r time frames (e.g., with daily time step), while the IEUBK m odel provides longer-term quasi steady-state estimates.

• Monitor-based ratio of maximum quarterly to annual average Pb-TSP concentrations: A ratio of monitored maximum quarterly average to annual average air Pb concentrations is used in the general urban case study to translate maximum quarterly average air concentrations into the annual average values used in the risk assessment.4-19 Use of the 95 th percentile of urban Pb-TSP monito r ratios as compared to the mean value, results in a 50% reduction in recent air risk estimates.

• GSD reflecting interindividual variability in blood Pb levels: Use of the larger GSD (2.1) in place of the smaller value (1.7) resu lted in a 40% increase in the recent air risk estimates. Alternate selections for the other modeling elements included in the sensitivity analysis yielded changes to the 95 th percentile risk results of less than 40% from baseline.

4.3.3 Performance Analyses

Performance evaluation for the exposure a ssessment (Section 3.5) focused on evaluation of estimates of Pb in ambient air, outdoor so il, and indoor dust (discusse d in Section 3.5.1) and estimates of Pb in blood (covered in Section 3.5.2) . Consideration of the results of performance evaluation can provide insights into potential sources of uncertainty in an analysis, by identifying those elements of the analysis that appear inconsistent with availa ble empirical data. This can, in turn, point to underlying bias or other errors associated with that particular modeling step, reflecting either parameter or model uncertainty. This section identifies key findings of the performance analysis describing the nature of associated uncertainty, including results which either supported modeling elements or suggested increased uncertainty.

• Modeled ambient air Pb levels : The evaluation of the air model performance for the primary Pb smelter case study (Sect 3.5.1.1) indicated performance generally consistent with empirical data, increasing our confidence in air-related results generated for this case study. Evaluation of air dispersion model performance for the secondary Pb smelter suggested the potential for low bias in predictions of ambient air Pb concentrations.

• Estimates of outdoor soil/dust Pb concentr ation for secondary Pb smelter case study : The use of a combination of dispersion and soil mixing models to generate a spatial pattern of concentrations combined with a scaling factor based on a surrogate location contributes significant uncertain ty to the soil Pb characterization for this case study. Specifically, while the approach used is believed unlikely to significantly underestimate soil Pb levels at this type of facility, the exact exte nt to which it is representative of conditi ons at this specific lo cation is not known.

• Modeled indoor dust Pb concentrations: Evaluation of the hybrid indoor dust model used in the general urban case study sugge sted the potential for both under- and overestimation. The mechanistic ambien t air-related portion of the model may underestimate that component of dust P b, while the indoor dust loading-toconcentration conversion algorith m may contribute to overestimat e of dust Pb. It is not know to what extent these two biases cance l out each other. Overall comparison of indoor dust Pb concentrations generated for the three case studies against available empirical data suggest that: (a) for the general urban case study, estimates fall within the range of measured values from a na tional-scale study, addi ng confidence to the estimates, (b) central tendency estimates for the primary Pb smelter are close to the4-20 lower end of the range for the national-scal e dataset referenced above for the general urban case study and high-end estimates seem to fit with available data near smelters, and (c) comparison of estimates for the sec ondary Pb smelter against empirical data suggest that these estimates may be biased low.

• Evaluation of candidate blood Pb models : A number of performance evaluations were completed on the two candidate blood Pb mode ls considered for this analysis (IEUBK and Leggett), as well as the Lanphear em pirical model. The results of these performance evaluations, which included a pplication of both mode ls in replicating national-scale child blood Pb levels (NHANES IV results) and blood Pb levels for an urban child cohort, suggested that the Legge tt model consistently overpredicted blood Pb levels by a factor of 3 to 6, while IEUB K estimates were usually within a factor of 2. These findings, in addition to CASAC r ecommendations, resulted in our selecting the IEUBK model as primary blood Pb mode l for this assessment, with the Leggett model being reserved for applicati on in the sensitivity analysis.

• Outdoor air Pb-to-blood Pb ratios : Three sets of outdoor air Pb-to-blood Pb ratios were derived. These related outdoor ambient ai r Pb to blood Pb resu lting from: (1) the inhalation pathway only; (2) all recent air pathways (inhalati on plus ingestion of indoor dust Pb predicted to be associated with ambient air Pb levels, with ambient air potentially including resuspended, previously deposited Pb); and (3) all recent and past air pathways (see Section 2.4.3). All ratios were derived prior to application of the GSD reflecting interindividual variability in blood Pb levels and therefore reflect central tendency blood Pb levels and not high-end population percentiles. The modeled ratios were compared to both empiri cal data and statistically derived ratios based on a pooled analysis (Section 3.5.2.2). With the exception of the primary Pb smelter case study recent air ratio using 95 th percentile air co ncentration and the general urban case study recent air ratios for th e hybrid dust model, the ratios for recent air contribution to concurrent blood Pb level (Table 3-10) generated for the three case studies were reasonable and supported by avai lable empirical data. As described in Section 3.5.2.2, the exceptions relate to use in those case studies of the dust models predicting the greatest influence of air concentrati on on indoor dust Pb among the set of dust models used. These were the ge neral urban case study hybrid dust model and the site-specific regression dust model used in the remediation zone for the primary Pb smelter case study. In both cases, the ratios based on these models were higher than empirically derived ratios obtained from th e literature. However, the literature indicated a potential for higher ratios for lo cations with either lower ambient air Pb levels or lower blood Pb levels, both conditi ons of which are present at the two case studies. Therefore, the higher modeled rati os obtained using the site-specific indoor dust models for the general urban and primary Pb smelter case studies do not necessarily point to poten tial high bias in predicting blood Pb levels for the study populations.

• Comparison of modeled blood Pb levels to nationally representative data : Our ability to compare modeled blood Pb levels to em pirical data was handicapped by a lack of studies for populations of children directly co mparable to those in the three case studies (see Section 3.5.2.3). Therefore, we focused most of our evaluation of modeled blood Pb levels on consideration of the national- level data obtained from NHANES IV. Note4-21 that while higher-end population percentiles for the three case studies might not be reflected in a national-level dataset, bl ood Pb levels associated with background exposures for all three case studies are similar to blood Pb levels in the national dataset. Thus, we considered a comparison of modeled blood Pb levels for the three case studies against a national-scale datase t for central tendency population percentiles appropriate. In this comparison, we include d the highest and lowest of the multiple sets of blood Pb estimates for the general urban case study generated using the multiple modeling approaches employed used fo r this case study. These two blood Pb distributions bracketed the NHANES IV base d distribution, which adds confidence to blood Pb modeling conducted for this case st udy. Results for the primary Pb smelter (full study area) were similar to the NHANES IV distribution across population percentiles up to the 95 th . This result is not surprisi ng given the fact that exposure modeling for the primary Pb smelter did not fully consider variability in background Pb exposure (e.g., paint, diet, drinking water). Had background exposures been fully considered, the degree of divergence be tween the case study and national-level percentiles would likely have been much greater (with the primary Pb smelter distribution exceeding the NHANES IV distribution at highe r population percentiles). Results for the subarea of the primary Pb smelter case study were consistently higher than the NHANES IV distribu tion across all population per centiles, as is expected given that the entire study area experiences elevated facility-related ambient air Pb levels. Blood Pb levels for all population pe rcentiles for the secondary Pb smelter are lower than those for the national-scale datase t. As with the primary Pb smelter case study, this likely reflects the fact that vari ability in background Pb exposures was not fully considered in modeling this case study.

4.3.4 Uncertainty in Modeling Approaches – Multiple Sets of Results

For those more highly influential analytical steps for which it is not clear which model or input would generate “best estimate” result s, we have implemented multiple modeling approaches (see Section 2.4.6.2). Because each of the case studies uses different modeling approaches for some of the analytical steps such as the indoor dust modeling step, and these approaches are associated with differing uncerta inty, the identity and size of the areas of uncertainty associated with each case study diffe rs. The specific modeling approaches for each case study and their elements are presented in Figure 2-3. For the general urban case study, two
different dust models and two GSDs were used as compared to one model and GSD for these analytical steps in the two poi nt source case studies. However, the same number of blood Pb metrics and IQ loss functions are us ed for all three case studies. Consideration of the range of risk results generated using the multiple modeling approaches for each case study provides perspectiv e on the combined effect of key sources of uncertainty on risk results. The median and 95 th percentile estimates associated with the modeling approaches yielding the highest and lowe st risk results for specific scenarios of the three case studies are presented in Table 4-4.4-22 Table 4-4. Impact of multiple sources of uncertainty on risk results.

IQ loss (population percentile) Percent difference between highest and lowest approaches Case study Modeling approach Median 95 th Median 95 th
Lowest risk : Dust Model (Air-only Regression-based), GSD (1.7), blood Pb Metric (Concurrent), IQ Function (Two-piece Linear) 2 4 General urban (mean current conditions) Highest risk : Dust Model (Hybrid), GSD (2.0), blood Pb Metric (Lifetime), IQ Function (Log-linear with Linearization) 5 9 230% 120%

Lowest risk : blood Pb Metric (Concurrent), IQ Function (Two-piece Linear) 1 4 Primary Pb smelter (current NAAQS) Highest risk : blood Pb Metric (Lifetime), IQ Function (Log-linear with Linearization) 4 7 280% 80%

Lowest risk : blood Pb Metric (Concurrent), IQ Function (low-linear with cutpoint) 0 2 Secondary Pb smelter (current conditions) Highest risk : blood Pb Metric (Lifetime), IQ Function (Log-linear with Linearization) 3 5 >300% 120%

The lack of a consistent pattern regardi ng the magnitude of differences between the modeling approaches generating the lowest and highest risk results either across case studies, or across population percentiles (Table 4-4), is due to different infl uences of various factors specific to the modeling approaches for each case study. Fo r example, the finding with regard to the 95 th percentile results that the great est difference across approaches occurs for the secondary Pb smelter case study (>300%) reflects the fact that th e lowest predicted median blood Pb level for this case study is below the cu tpoint associated with one of the three concen tration-response functions (thereby resulting in zero pred icted IQ loss). The finding for the 95 th percentile results that there is a greater difference for the gene ral urban case study than the primary Pb smelter case study is because of the larger range of GS Ds applied in this case study (2.1 and 1.6). In summary, results presented in Table 4-4 s uggest that these key s ources of uncertainty, when acting in concert, can produce an impact on ove rall risk results on the order of a factor of 2
to 3.4-23 REFERENCES
Henderson, R. (2007a) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of the 2 nd Draft Lead Human Exposure and Health Risk Assessments Document. September 27, 2007.

Henderson, R. (2007b) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of the 1 st Draft Lead Staff Paper and Draft Lead Expo sure and Risk Assessments. March 27, 2007.

Hornung, R. (2007a) Email message to David Svendsgaard, U.S. EPA. April 24, 2007.

Hornung R. (2007b) Email message to Zachary Pekar, U.S. EPA, May 1, 2007.

Lanphear, B.P., Hornung, R., Khoury, J., Yolton, K., Bag hurst, P., Bellinger, D.C., Canfield, R.L., Dietrich, K.N., Bornschein, R., Greene, T., Rothenberg, S.J., Needleman, H.L., Schnaas, L., Wasserman, G., Graziano, J., and Robe, R. (2005) Low-level environmental Pb exposure and children's intellectual function: An international pooled analysis. Environmen tal Health Perspectives. 113(7):894-899.5-1 

### 5 ADDITIONAL ANALYSES

This chapter presents the additional anal yses performed for the Pb NAAQS risk assessment subsequent to the public meeti ng of the CASAC Pb Panel on August 28-29, 2007 (Henderson, 2007b). These analyses include rean alysis of the general urban and primary Pb smelter case studies and new analyses for three location-specific urban case studies located in Cleveland, Chicago and Los Angeles. 1 The methods used in conducting these additional analyses, as well as the resultant exposure and ri sk estimates, sensitivity analyses and uncertainty characterization are presen ted in this chapter.

5.1 DESIGN OF EXPOSURE AND RISK ASSESSMENTS

This section presents an overview of the de sign for the additional analyses. Additional details regarding the methods used in estima ting exposure for both analyses can be found in Section 5.2. Details on the concentration-respons e functions for IQ loss and methods used to generate population risk estimates for these additional an alyses can be found in Section 5.3.

5.1.1 Assessment Scenarios

The design of the assessment scenarios for the additional analyses is generally the same as that for the initial analyses (see Section 2.3 and subsections). The main difference is the
addition of an alternate NAAQS scenario of 0.02 μg/m 3 , as a maximum monthly average; otherwise, the air quality scenarios for the additi onal analyses are the same as those included in the initial analyses (S ection 2.3.1). Specification of the ai r concentrations for the current conditions location-specific urban case studies is summarized in Section 5.1.3 and described more fully in Section 5.2.2.1 and Appendix O.

5.1.2 Analytical Approach

The general analytical approach employed for the initial analyses, described in Section 2.4, was also employed for the additional analys es, including the temporal aspects (Section 2.4.1), categorization of po licy-relevant exposure pathways (S ection 2.4.3), and the analytical steps (Section 2.4.4). Additionally, the spatial scal e and resolution aspects of the reanalyses for the general urban and primary Pb smelter case studies were the same as for the initial analyses (see Section 2.4.2). The spatial sc ale and resolution aspects of th e location-specific urban case
studies are summari zed in Section 5.1.3.

1 Due to limitations with the design of the secondar y Pb smelter case study and the associated high level of uncertainty noted in Sections 4.2 and 4. 3.1,this case study was not part of the additional analyses described in this chapter.5-2 A notable difference in the approach between the initial analyses and the additional analyses is that the multiple modeling approach es described in Section 2.4.6.2 and implemented for the initial analyses were narrowed down to a core modeling approach for the additional analyses. This was done in consideration of CASAC comments on the initial analyses (Henderson, 2007). Consistent with CASAC comments, the core modeling approach includes a single indoor dust model for the ge neral and location-specific ur ban case studies, focuses on the concurrent blood Pb metric, and applies a singl e blood Pb GSD for the general urban case study. Additionally, CASAC recommended use of a linea r concentration-res ponse function derived from a subpopulation with peak blood Pb levels be low 7.5 μg/dL, and indicated less favor for the two-piece linear function employe d in the initial analyses of the full-scale assessment (Section 4.1.1.3). Accordingly, a different set of concentr ation-response functions was employed in the additional analyses (see Section 5.3.1.1). Figure 5-1 identifies th e core modeling approach for each case study.

Figure 5-1. Core modeling approach for each case study.

Elements of modeling approaches

Case Study Indoor dust modeling Blood Pb metric GSD Concentrationresponse function Number of sets of results General Urban Case Study AndLocationspecific Urban Case Studies 1 model: hybridmechanisticempirical 1 GSD, representing larger, regional scale 1 * 1 * 1 * 4 = 4

Primary Pb Smelter Case Study 1 model: statistical (regression) approach 1 metric: (a) concurrent

1 GSD 4 functions: (a) log-linear with low-exposure linearization, (b) log-linear with cutpoint, and (c) dual linear, stratified at 7.5 μg/dL (d) dual linear, stratified at 10 μg/dL 1 * 1 * 1 * 4 =4

5.1.3 Location-Specific Urban Case Studies

The primary design difference between the lo cation-specific case studies and the general urban case study, is that, unlike the spatially un iform general urban case study in which ambient
air Pb levels are uniform and fixed at the air concentration considered (see Section 2.2.1) both
the number of children and the ambient air Pb le vels vary within the location-specific urban study areas. As a result, alt hough the data on which these locatio n-specific urban case studies are based are quite limited, exposure and risk estimates for these location-specific urban case studies provide a better representa tion of some urban areas in the U.S. than do those for the general urban case study.5-3 Locations were selected for these study ar eas based on consideration of the number and spatial distribution of Pb-TSP monitors, the rela tive representation of source-oriented versus
other monitors and the relative Pb concentra tions reported for those monitors (Appendix A, Attachment A-2). Due to time constraints, th e 2003-2005 dataset of Pb-T SP monitors analyzed in Appendix A (Attachment A-2) was the focus fo r this selection process and for subsequent consideration of air quality for the selected areas. Among all metropolitan areas represented in this dataset, preference was give n to urban areas with relatively more monitors having a more uniform distribution and relatively higher Pb co ncentrations, and monitors that were not considered source oriented (see Appendix A, Secti on A.2.2.2.3). This led to selection of areas in Cleveland (6 monitors), Ch icago (11 monitors) and Lo s Angeles (7 monitors). The three study areas differ in several respects. First, they differ in Pb air levels, with the highest reported for Cleveland a nd the lowest for Los Angeles. Further, while monitors in Cleveland and Chicago included a su bset classified as source orie nted, this was not the case in Los Angeles. Last, the source oriented monitors were segregated from the other monitors in Chicago, while they were relatively more disp ersed among the other monitors in Chicago. The size of each study area is based on a polygo n encompassing the area of each city in which the monitors occur. That polygon is define d initially using those monitors as apices and
then a one mile-wide buffer is included be yond them in defining the final study area polygon (Appendix O, Exhibits O-2 through O-4). The U.S. Census blocks whose centroids fall within this polygon comprise the study area for each lo cation-specific case study (Appendix O). Using centroids as the basis for determining whether a Census block is inside or outside of the study area provides a simple way for determining member ship for those blocks that straddle the study area boundary. The target population for each study ar ea is those children less than 7 years of age located within the study area blocks. The three study areas range in size from 67 to 1091 square km, and in target population size from 13,990 to 396,511 children (less than 7 years of age). Exposure zones within each study area were defined based on assignment of census blocks to monitor sites, such that the number of exposure zones for a study area was equal to the number of monitors in a study ar ea. The total child count for each exposure zone reflects the sum of children less than seven years old in th e U.S. census blocks assigned to it, and the
ambient air Pb concentrations for that zone refl ect characteristics of data for that monitor. Consistent with the approach for all case studie s, Pb concentrations in air (as well as other media) are homogenous within each exposure zone. Within each study area, Census blocks were assigned to exposure zones differently depending on whether or not they were near a source oriented monitor. All blocks whose
centroids fall within a mile of a source oriented TSP monitor were assigned to a source oriented5-4 exposure zone defined by that monitor. All othe r blocks (i.e., those for which the centroid was
farther than a mile from a source oriented m onitor) were assigned to the nearest non source oriented monitor. This approach reflects the re cognition that air Pb con centrations associated with sizeable stationary s ources exhibit sharper gradients with distance than air Pb concentrations less influenced by such sour ces. Accordingly, source influenced Pb
concentrations are constrai ned to smaller exposure zone s than non source influenced
concentrations.
Lead concentrations in indoor dust and outdoorsoil/dust were estimated for the locationspecific urban case studies using specific elements of the core modeling approach applied in the general urban case study (e.g., th e indoor dust Pb model and Pb level in outdoor dust/soil). Once the media concentrations are assigned to the exposure zones, the design of the exposure and risk analyses for each of these locati on-specific urban case studi es is similar to that employed in the initial analyses to model the point source case studies (see Section 2.4.4.1 and 2.4.4.2), with a population-weighted simulation of exposure and risk conducted at the U.S.
Census block level.

5.2 EXPOSURE ASSESSMENT

5.2.1 Methods for General Urban and Pr imary Pb Smelter Case Studies

For estimating ambient air concentration, inhalation exposure concentration and Pb concentration in outdoor soil/dust, the methods described in Section 3.1.1, 3.1.2 and 3.1.3 for the general urban and primary Pb smelter case studies also apply to the additional analyses for those
case studies. For the general urban case study, indoor dust Pb concentrations were estimated using the hybrid model. The primary rationale for d eemphasizing the air only regression model and favoring the hybrid model in the a dditional analyses is that there is reduced confidence in using the air only regression mode l in the urban residential context, since this statistical model was developed with datasets that favored areas near Pb smelter sites (see Section 3.1.4.1 and Appendix G.1.1). By contrast, the hybrid i ndoor dust model was developed specifically for application in the urban residential context. For the primary Pb smelter case study, indoor dust Pb concentrati ons are estimated as
described in Section 3.1.4.2.
Blood Pb concentrations for the additional anal yses for the general urban and primary Pb smelter case studies were estimated using methods previously described in Section 3.2, with two distinctions. First, for these additional analyses, the core modeling approach employed included only the concurrent blood Pb metric, consis tent with CASAC recommendations (Henderson, 2007b). Secondly, for the general ur ban case study core approach we used the GSD of 2.1 which5-5 represents the GSD for the c oncurrent blood Pb metric iden tified from NHANES IV (Section 3.2.3.1). This GSD is consistent with the CASAC recommendation to use a GSD more descriptive of the variab ility in blood Pb levels reported in NHANES-IV, as compared with the smaller GSDs observed for more localized popula tions near specific Pb emitting industries (e.g., see Section 3.2.3). The rationale fo r selecting the larger GSD, is that larger urban areas are likely to include more heterogene ous populations with regard to Pb exposure and blood Pb levels that more closely resemble the national NHANES population than a smaller more homogeneous population. For the primary Pb smelter case study, which includes a more localized study population, we have retained the smaller GSD of 1.7 used in the initial analyses (see Section 3.2.3.2). The remaining elements of the core analysis , related to blood Pb modeling, including the blood Pb model, characterization of backgr ound Pb exposures, apportionment of exposure pathway contributions and proba bilistic population mode ling are those described in Section 3.2.

5.2.2 Methods for Location-specific Urban Case Studies

The methods used to estimate media and blood Pb concentrations in the location-specific
urban case studies are describe d in the following subsections. The primary difference in methods between the location-specific case st udies and the general urban case study is the inclusion of multiple exposure zones per st udy area across which numbers of children and ambient air Pb levels vary.

5.2.2.1 Ambient Air and Inhalation Exposure Concentrations

Air quality scenarios evaluate d for these case studies include d: (a) a current conditions scenario based on monitor-specific Pb-TSP stat istics for the 2003-2005 da taset (Appendix A, Attachment A-2), (b) a current NAAQS scenario based on a roll-up of Pb-TSP values to the current NAAQS level, and (c) alternative NAAQS s cenarios for levels falling below the current conditions levels for each case study. For each scenario, the annual average ambien t air Pb concentration estimate for each exposure zone that is the input to the blood Pb model for that exposure zone is based on the statistics for the monitor associated with that exposure zone, using the monitor-specific statistics presented in Appendix A, Attachment A-2. How this is derived varies with the air quality
scenario as described below.

• Current conditions scenario : Each exposure zone is assigned the Pb-TSP values derived from the analysis of the 2003-2005 dataset in Appendix A (Attachment A-2) for the monitor assigned to that zone. Specifically, the annual average ambient Pb concentration estimate that is the input to the blood Pb model for a given exposure5-6 zone is the three-year annual average c oncentration associated with the Pb-TSP monitor assigned to that zone.

• Current NAAQS scenario: A proportional roll-up procedur e is used to derive air concentrations for all exposure zones such that the highest maximum quarterly average concentration among all of the study area m onitors just meets the current NAAQS (maximum quarterly average of 1.5 μg/m3).

⎯ For the Cleveland and Los Angeles study ar eas, this procedure involves adjusting up the maximum quarterly average estimates for all monitors by the same factor as that used to set the highest monitor equal to the current NAAQS (max quarterly value of 1.5 μg/m 3 ). Then those adjusted maximum quart erly values are converted into annual averages using the ratio of annual average to maximum qua rterly average specific to each monitor (as presented in Appendix A, Attachment A-2).

⎯ For Chicago, given the spacing of the mon itors, we have defined two independent roll-up subareas, each represented by a separa te set of monitors. For this scenario, the highest maximum quarterly average value in each of the two subareas is rolled up to equal the current NAAQS and the other monito rs in each subarea are increased by the same proportion. Then the annual average valu es are calculated us ing monitor-specific ratios as for the other study areas.

• Alternative NAAQS scenarios : The procedure for rolling back ambient air Pb levels to reach alternative NAAQS is similar to the roll-up procedure used for the current NAAQS scenario, except the values are redu ced rather than increased. Only those alternative NAAQS that are exceeded by the current conditions for a given case study were evaluated for that case study. Conse quently, several of the higher alternative NAAQS were not evaluated at all for the locat ion-specific urban case studies. As for the roll-up for the current NAAQS scenario, roll-backs for the Chicago case study are implemented separately for each of the two independent subareas, based on the highest Pb-TSP monitor in each subarea. Inhalation exposure air concen trations were defined for both source oriented and non source oriented exposure zones using the same procedure employed for the general urban case study (see Section 3.1.2).

5.2.2.2 Other media Concentrations

Characterization of Pb exposure from inci dental outdoor soil/dust ingestion used the values used for the general urban case study (Sec tion 3.1.3.1), with the same values being used to characterize conditions in all exposure zones (sou rce oriented or not) in each of the three case
studies.
Indoor dust Pb concentrations are estimated for the location-specific urban case studies using the hybrid indoor dust m odel (see Section 3.1.4.1). Specifi cally, the hybrid indoor dust
model is combined with the annua l average ambient air Pb level for a particular exposure zone and air quality scenario to gene rate an indoor dust Pb level for that zone. Therefore, while background Pb exposure levels and outdoor soil/dus t Pb levels are fixed across each of the urban5-7 case studies, both ambient air Pb levels and indo or dust Pb levels are allowed to vary across source and non source exposure zones.

5.2.2.3 Blood Pb levels

Estimates of concurrent blood Pb are derive d from application of the IEUBK model as described in Appendix H, with characterizati on of Pb exposure from background sources (i.e., drinking water and diet) relying on the same values as for all case studies (Appendix H). The apportionment of the blood Pb levels among the various exposure pathways is described in
Section 3.2.2. Population-level exposure modeling for the loca tion-specific urban case studies utilizes essentially the same population-weighted sampli ng procedure as was used for the point source case studies (Section 3.2.2.2, including Figure 3-1). Use of th is more sophisticated, populationweighted, multizone approach is required for th e location-specific urban case studies since they utilize multiple exposure zones with differing de mographic data and ambient air Pb levels, negating the use of the simpler, single zone a pproach used in the gene ral urban case study. The remaining elements of the blood Pb modeling appr oach used for the loca tion-specific urban case
studies are identical to those us ed for the core approach for th e general urban case study (Section 5.2.1). These include: (a) use of the concurrent blood Pb metric, (b) use of the IEUBK blood Pb model and (c) use of the GSD of 2.1 to represent interindividual va riability in blood Pb levels.

5.2.3 Media concentrations

The following subsections summarize the media concentration estimate for all case studies and air quality s cenarios. Estimates presented in thes e subsections are presented to three (for air) or zero (for dust and soil) decimal pl aces, which results in various numbers of implied significant figures. This is not intended to convey greater prec ision for some estimates than others; it is simply an expedient and initial result of the software us ed for the calculation.

5.2.3.1 Location-specific Urban Case Studies

This section summarizes the media concen tration estimates for the core modeling approach for each air quality scenario for the loca tion-specific urban case studies. The complete
set of media concentration estimates for these case studies is presented in Appendix O. As discussed in Section 2.3.3, Pb concentrati on in outdoor soil/dust is not changed with the alternate air quality scenarios. Rather, outdoo r soil/dust concentration is held constant at the current conditions or current NAAQS level. Media concentrations generate d for the three location-specif ic urban case studies are summarized in Tables 5-1 through 5-4, for annua l ambient air Pb concentrations, inhalation exposure concentrations, outdoor soil/dust Pb and indoor dust Pb concentrations, respectively.5-8 5.2.3.2 General Urban and Primary Pb Smelter Case Studies

This section summarizes the media concen tration estimates for the core modeling approach for each air quality scenario for the general urban and primary Pb smelter case studies. The complete set of media concentration estim ates for these case studies are presented in Appendix N. For each air quality scenario for the primar y Pb smelter case study, a range of percentile estimates are presented for each exposure medi um. For the general urban case study, however, only a single value is presented for each exposure medium. This reflects the fact that, while the primary Pb smelter case study is modeled using spa tial templates that incl ude a large number of U.S. Census blocks and/or block groups (all owing percentile media c oncentrations to be identified), the general urban case study is mode led using a single study area with uniform media concentrations. Consequently, there is only one value presented for the general urban case study for each medium in each air quality scenario. As mentioned above, it is assumed that out door soil/dust Pb concentrations are not effected by changes in ambient air Pb levels. In the case of an area such as the remediation zone of the primary Pb smelter case study, however, wh ere soil dynamics have been changed by the
substitution of contaminated soil with clean so il, or in areas where local sources may pose a more significant source to outdoor soil/dust than historic sources – and where there may be a currently increasing trend in surface Pb c oncentration - this may underestimate soil concentrations under some alternate NAAQS. Media concentrations generate d for the primary Pb smelter and general urban case studies using the core modeling approach are summarized in Tables 5-1 through 5-4, for annual ambient air Pb concentrations, inhalation exposure con centrations, outdoor soil/dust Pb and indoor dust Pb concentrations, respectively. As with sim ilar presentations in Chapter 3, the percentiles presented here are from population weighted di stributions of the media concentrations (see description in Appendices N, O and P).5-9 Table 5-1. Estimated annual average ambient air concentrations.

Average Annual Air Pb Concentration (μg/m 3 )

Alternative NAAQS
1 2 3 4 5

Statistic* Current Conditions Current NAAQS 0.2 μ g/m 3 , Max Quarterly 0.5 μ g/m 3

, Max Monthly 0.2 μ g/m 3

, Max Monthly 0.05 μ g/m 3

, Max Monthly 0.02 μ g/m 3

, Max Monthly Location Specific Case Study(Chicago)

Maximum 0.040 0.809 0.040 0.040 0.040 0.010 0.004

95 th percentile 0.040 0.809 0.040 0.040 0.040 0.010 0.004

Median 0.027 0.428 0.027 0.027 0.021 0.005 0.002

5 th percentile 0.014 0.286 0.014 0.014 0.014 0.004 0.001

Minimum 0.011 0.225 0.011 0.011 0.011 0.003 0.001
Location Specific Case Study(Cleveland)

Maximum 0.121 0.506 0.067 0.108 0.043 0.011 0.004

95 th percentile 0.121 0.506 0.067 0.108 0.043 0.011 0.004

Median 0.021 0.085 0.011 0.018 0.007 0.002 0.001

5 th percentile 0.017 0.071 0.009 0.015 0.006 0.002 0.001

Minimum 0.017 0.071 0.009 0.015 0.006 0.002 0.001
Location Specific Case Study(Los Angeles)

Maximum 0.022 0.360 0.022 0.022 0.022 0.009 0.003

95 th percentile 0.022 0.360 0.022 0.022 0.022 0.009 0.003

Median 0.019 0.300 0.019 0.019 0.019 0.007 0.002

5 th percentile 0.015 0.239 0.015 0.015 0.015 0.006 0.002

Minimum 0.006 0.091 0.006 0.006 0.006 0.002 0.001
General Urban Case Study High-end:

0.114 NA – Single Study Area Mean: 0.056 0.600 0.080 0.125 0.050 0.013 0.005

Primary Pb SmelterCase Study- full study area

Maximum 0.740 0.161 0.326 0.130 0.033 0.013

95 th percentile 0.153 0.033 0.067 0.027 0.007 0.003

Median 0.042 0.009 0.019 0.007 0.002 0.001

5 th percentile 0.015 0.003 0.007 0.003 0.001 < 0.001

Minimum NA

0.006 0.001 0.003 0.001 < 0.001 < 0.001
Primary Pb SmelterCase Study- 1.5 km sub area

Maximum 0.740 0.161 0.326 0.130 0.033 0.013

95 th percentile 0.675 0.147 0.297 0.119 0.030 0.012

Median 0.238 0.052 0.105 0.042 0.011 0.004

5 th percentile 0.137 0.030 0.060 0.024 0.006 0.002

Minimum NA

0.098 0.021 0.043 0.017 0.004 0.002 *- The percentiles presented here are from population we ighted distributions of the media concentrations.5-10 Table 5-2. Estimated inhalation exposure concentrations.

Average Annual Inhalation Exposure Concentration of Pb (g/m 3 )

Alternative NAAQS
1 2 3 4 5

Statistic* Current Conditions Current NAAQS 0.2 μ g/m 3 , Max Quarterly 0.5 μ g/m 3

, Max Monthly 0.2 μ g/m 3

, Max Monthly 0.05 μ g/m 3

, Max Monthly 0.02 μ g/m 3

, Max Monthly Location Specific (Chicago)

Maximum 0.017 0.347 0.017 0.017 0.017 0.004 0.002
95 th percentile 0.017 0.347 0.017 0.017 0.017 0.004 0.002

Median 0.012 0.184 0.012 0.012 0.009 0.002 0.001

5 th percentile 0.006 0.123 0.006 0.006 0.006 0.002 0.001

Minimum 0.005 0.097 0.005 0.005 0.005 0.001 < 0.001
Location Specific (Cleveland)

Maximum 0.052 0.217 0.029 0.047 0.019 0.005 0.002
95 th percentile 0.052 0.217 0.029 0.047 0.019 0.005 0.002

Median 0.009 0.037 0.005 0.008 0.003 0.001 < 0.001

5 th percentile 0.007 0.030 0.004 0.006 0.003 0.001 < 0.001

Minimum 0.007 0.030 0.004 0.006 0.003 0.001 < 0.001
Location Specific (Los Angeles)

Maximum 0.010 0.154 0.010 0.010 0.010 0.004 0.001
95 th percentile 0.010 0.154 0.010 0.010 0.010 0.004 0.001

Median 0.008 0.129 0.008 0.008 0.008 0.003 0.001

5 th percentile 0.006 0.103 0.006 0.006 0.006 0.002 0.001

Minimum 0.002 0.039 0.002 0.002 0.002 0.001 < 0.001

General Urban High-end: 0.049 NA – Single Study Area Mean: 0.024 0.258 0.034 0.054 0.021 0.005 0.002

Primary Pb Smelter- full study area

Maximum 0.310 0.067 0.136 0.055 0.014 0.005
95 th percentile 0.064 0.014 0.028 0.011 0.003 0.001

Median 0.017 0.004 0.007 0.003 0.001 < 0.001

5 th percentile 0.006 0.001 0.003 0.001 < 0.001 < 0.001

Minimum NA

0.002 < 0.001 0.001 < 0.001 < 0.001 < 0.001
Primary Pb Smelter- 1.5 km subarea

Maximum 0.310 0.067 0.136 0.055 0.014 0.005
95 th percentile 0.282 0.061 0.124 0.050 0.012 0.005

Median 0.100 0.022 0.044 0.018 0.004 0.002

5 th percentile 0.057 0.012 0.025 0.010 0.003 0.001

Minimum NA

0.041 0.009 0.018 0.007 0.002 0.001 *- The percentiles presented here are from population we ighted distributions of the media concentrations.5-11 Table 5-3. Estimated outdoor soil/dust concentrations.

Projected Average Outdoor Soil/Dust Pb Concentration (mg/kg)
Statistic* (Same for all air quality scenarios)
Location Specific (Chicago) NA - Full Study Area 198
Location Specific (Cleveland) NA - Full Study Area 198
Location Specific (Los Angeles) NA - Full Study Area 198

General Urban NA – Single Study Area 198
Primary Pb Smelter- full study area

Maximum 958

95 th percentile 245

Median 85

5 th percentile 30

Minimum 17 Primary Pb Smelter- 1.5km sub area

Maximum 294

95 th percentile 223

Median 150

5 th percentile 42

Minimum 42 *-The percentiles presented here are from population weighted distributions of the media concentrations.5-12 Table 5-4. Estimated indoor dust concentrations.

Projected Average Indoor Dust Pb Concentration (mg/kg or ppm)

Alternative NAAQS
1 2 3 4 5

Statistic* Current Conditions Current NAAQS 0.2 μ g/m 3 , Max Quarterly 0.5 μ g/m 3

, Max Monthly 0.2 μ g/m 3

, Max Monthly 0.05 μ g/m 3

, Max Monthly 0.02 μ g/m 3

, Max Monthly Location Specific (Chicago)

Maximum 128 491 128 128 128 84 71
95 th percentile 128 491 128 128 128 84 71

Median 111 363 111 111 103 74 67

5 th percentile 91 300 91 91 91 70 65

Minimum 86 269 86 86 86 68 64
Location Specific (Cleveland)

Maximum 203 392 158 193 132 85 72
95 th percentile 203 392 158 193 132 85 72

Median 101 174 86 98 78 66 63

5 th percentile 96 161 82 93 75 65 63

Minimum 96 161 82 93 75 65 63
Location Specific (Los Angeles)

Maximum 104 334 104 104 104 81 68
95 th percentile 104 334 104 104 104 81 68

Median 99 307 99 99 99 78 67

5 th percentile 92 276 92 92 92 75 65

Minimum 75 179 75 75 75 67 63

General Urban High-end: 198 NA – Single Study Area Mean: 146 426 169 206 140 88 73

Primary Pb Smelter- full study area

Maximum 1944 648 1077 557 383 381
95 th percentile 219 152 172 149 138 120

Median 84 68 73 67 63 62

5 th percentile 53 45 47 44 43 42

Minimum NA

41 38 39 38 38 38
Primary Pb Smelter- 1.5km subarea

Maximum 1944 648 1077 557 205 106
95 th percentile 1819 606 1008 521 192 99

Median 860 287 477 246 91 60

5 th percentile 578 193 320 166 61 60

Minimum NA

453 151 251 130 60 60 *- The percentiles presented here are from population we ighted distributions of the media concentrations.5-13 5.2.4 Blood Pb levels

Estimates of concurrent blood Pb level derived from outputs of the IEUBK model (Section 3.2) have been developed for each air quality scenario in each case study (see Appendices N and O). Table 5- 5 presents median concurrent blood Pb estimates for the location-specific urban case stud ies (Chicago, Cleveland and Los A ngeles) and the core analysis of the primary Pb smelter (and sub-area) and th e general urban case study . Table 5-6 presents
95 th percentile concurrent blood Pb estimates for th e same set of case studies. As with blood Pb level results presented in Section 3.4, th ese tables present the median and 95 th percentile total Pb levels (i.e., simulated children for each case study area ranked based on total Pb level in order to identify population percentiles) , along with the blood Pb cont ributions to those total blood estimates from policy-relevant pathways. 2 While some of the blood Pb levels presented in Section 3.4 (those for the primary Pb smelter) included a range reflecting multiple modeling options related to blood Pb estim ation, the results presented in Tables 5-5 and 5-6 are single values, reflecting the fact that the core analys is, while including multiple concentration-response functions for IQ loss, did not include multiple modeling options related to blood Pb estimation (see Section 5.1.2). It is noted that given the various limitations of our m odeling tools (Sections 2.4.3 and 3.2.2), blood Pb levels associated with air-related exposure pathwa ys and current levels of Pb emitted to the air (including via resuspension) are likely to fall between the estimates for “recent air” and those for “recent” plus “past air”. A dditionally, with regard to the location-specific
urban case studies “recent air” a nd “recent” plus “past air” categor ies, an artifact of the hybrid
dust Pb model tends to mask trends in the tw o components of dust Pb (recent air and other), which contribute to “recent air” and “past ai r” estimates, respectively (see Section 4.3.1 discussion of this uncertainty). For the primary Pb smelter case study, uncertainty in parsing out the “recent air” and “other” components of indo or dust (specifically for the site-specific regression model used in the remediation zone) ha ve led us to conclude that only “recent plus past air” exposures should be presented and “recent air” results should not be separately presented for the primary Pb smelter, as is done for the other case st udies (see Section 3.1.4.2).

2 Seemingly inconsistent trends across air quality s cenarios seen in these two categories (recent air and recent plus past air) may be a result of presenting results for these policy-relevant cat egories based on populationweighted ranking of total blood Pb.5-14 Table 5-5. Summary of blood Pb level estimates for median total blood Pb.

Concurrent Blood Pb Level a
Air Quality Scenario

(and case study) Recent b Recent plus
Past b Total Pb Exposure Location-specific (Chicago) Current NAAQS (1.5 μg/m 3 , max quarterly) 1.9 2.5 3.0 Current conditions (0.14 μg/m 3 max quarterly; 0.31 μg/m 3 max monthly) 0.4 1.2 1.8 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.3 1.2 1.8 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.1 1.1 1.6 Alternative NAAQS (0.02 μg/m 3 , max monthly) <0.1 1.0 1.6 Location-specific (Cleveland) Current NAAQS (1.5 μg/m 3 , max quarterly) 1.2 1.8 2.1 Current conditions (0.36 μg/m 3 max quarterly; 0.56 μg/m 3 max monthly) 0.3 1.2 1.8 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.7 1.3 1.8 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.2 1.1 1.7 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.1 1.1 1.7 Alternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 1.0 1.6 Alternative NAAQS (0.02 μg/m 3 , max monthly) <0.1 1.0 1.6 Location-specific (Los Angeles) Current NAAQS (1.5 μg/m 3 , max quarterly) 1.3 2.1 2.6 Current conditions (0.09 μg/m 3 max quarterly; 0.17 μg/m 3 max monthly) 0.3 1.2 1.7 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.2 1.1 1.6 Alternative NAAQS (0.02 μg/m 3 , max monthly) 0.1 1.0 1.6 General urban Current NAAQS (1.5 μg/m 3 , max quarterly) 1.9 2.6 3.1 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.9 1.6 2.2 Current conditions –high-end (0.87 μg/m 3 , max quarterly) 0.8 1.6 2.1 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.7 1.5 2.0 Current conditions - mean (0.14 μg/m 3 , max quarterly) 0.6 1.4 1.9 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.5 1.4 1.9 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.2 1.1 1.7 Alternative NAAQS (0.02 μg/m 3 , max monthly) 0.1 1.1 1.6 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 0.8 1.5 Alternative NAAQS (0.5 μg/m 3 , max monthly) 1.1 1.4 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.7 1.4 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.5 1.4 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.7 1.4 Alternative NAAQS (0.02 μg/m 3 , max monthly) NA c

0.9 1.4 Primary Pb smelter - 1.5km study area Current NAAQS (1.5 μg/m 3 , max quarterly) 4.0 4.6 Alternative NAAQS (0.5 μg/m 3 , max monthly) 2.6 3.2 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 1.8 2.5 Alternative NAAQS (0.2 μg/m 3 , max monthly) 1.8 2.3 Alternative NAAQS (0.05 μg/m 3 , max monthly) 1.1 1.7 Alternative NAAQS (0.02 μg/m 3 , max monthly) NA c

1.1 1.6 a - Estimates are rounded to one decimal place. b -The term “past air” includes contribut ions from the outdoor soil/dust contri bution to indoor dust, historical air contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contri butions from inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with outdoor ambient air also potentially including resus pended, previously deposited Pb (see Section 2.4.3). c – “Recent air” estimates were not developed for the pr imary Pb smelter case study (see Section 3.1.4.2).5-15 Table 5-6. Summary of blood Pb level estimates for high-end total blood Pb.

Concurrent Blood Pb Level a
Air Quality Scenario

(and case study) Recent b Recent plus
Past b Total Pb Exposure Location-specific (Chicago) Current NAAQS (1.5 μg/m 3 , max quarterly) 6.5 8.7 10.2 Current conditions (0.14 μg/m 3 max quarterly; 0.31 μg/m 3 max monthly) 1.1 4.1 6.0 Alternative NAAQS (0.2 μg/m 3 , max monthly) 1.5 4.2 6.0 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.3 3.6 5.5 Alternative NAAQS (0.02 μg/m 3 , max monthly) 0.2 3.6 5.4 Location-specific (Cleveland) Current NAAQS (1.5 μg/m 3 , max quarterly) 2.3 5.4 7.4 Current conditions (0.36 μg/m 3 max quarterly; 0.56 μg/m 3 max monthly) 0.9 4.1 6.1 Alternative NAAQS (0.5 μg/m 3 , max monthly) 1.5 4.2 6.0 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.7 3.9 5.8 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.4 3.8 5.7 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.1 3.5 5.4 Alternative NAAQS (0.02 μg/m 3 , max monthly) 0.1 3.5 5.3 Location-specific (Los Angeles) Current NAAQS (1.5 μg/m 3 , max quarterly) 4.6 7.1 8.9 Current conditions (0.09 μg/m 3 max quarterly; 0.17 μg/m 3 max monthly) 1.1 4.0 5.9 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.5 3.7 5.5 Alternative NAAQS (0.02 μg/m 3 , max monthly) 0.2 3.5 5.4 General urban Current NAAQS (1.5 μg/m 3 , max quarterly) 6.4 8.8 10.6 Alternative NAAQS (0.5 μg/m 3 , max monthly) 2.9 5.6 7.4 Current conditions –high-end (0.87 μg/m 3 , max quarterly) 2.7 5.4 7.2 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 2.3 5.0 6.8 Current conditions - mean (0.14 μg/m 3 , max quarterly) 1.9 4.7 6.5 Alternative NAAQS (0.2 μg/m 3 , max monthly) 1.7 4.6 6.4 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.7 3.9 5.7 Alternative NAAQS (0.02 μg/m 3 , max monthly) 0.4 3.7 5.5 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 2.8 4.6 Alternative NAAQS (0.5 μg/m 3 , max monthly) 3.1 4.2 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 2.4 4.0 Alternative NAAQS (0.2 μg/m 3 , max monthly) 2.5 4.0 Alternative NAAQS (0.05 μg/m 3 , max monthly) 1.9 3.8 Alternative NAAQS (0.02 μg/m 3 , max monthly) NA c

3.2 3.8 Primary Pb smelter - 1.5km study area Current NAAQS (1.5 μg/m 3 , max quarterly) 10.3 12.3 Alternative NAAQS (0.5 μg/m 3 , max monthly) 7.6 8.5 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 5.9 6.6 Alternative NAAQS (0.2 μg/m 3 , max monthly) 4.9 6.1 Alternative NAAQS (0.05 μg/m 3 , max monthly) 3.5 4.5 Alternative NAAQS (0.02 μg/m 3 , max monthly) NA c

3.0 4.2 a - Estimates are rounded to one decimal place. b -The term “past air” includes contribut ions from the outdoor soil/dust contri bution to indoor dust, historical air contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contri butions from inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with outdoor ambient air also potentially including resus pended, previously deposited Pb (see Section 2.4.3). c – “Recent air” estimates were not developed for the pr imary Pb smelter case study (see Section 3.1.4.2).5-16 5.2.5 Uncertainty Characterization As was done for the initial analyses (see S ection 3.5), characteriza tion of uncertainty related to the exposure assessmen t for the additional analyses focused on performance evaluation
of both modeled media concentrations and modele d blood Pb levels. The sensitivity analysis
described in Section 4.3.2 included several mode ling elements related to exposure assessment which have bearing on blood Pb leve ls generated for the analyses described in this chapter for the primary Pb smelter and general urban case st udy and the location-specif ic urban case studies (e.g., interindividual variability GSD, blood lead model selection). Additional key sources of uncertainty related to exposure assessment sp ecifically for the location-specific urban case studies (e.g., relating chil d study populations within the study ar eas to Pb-TSP monitor values in order to assign ambient air Pb levels) have not b een investigated quantita tively, but are addressed as part of the qualitative di scussion of uncertainty for the overall risk assessment (Section 5.3.3.1). Performance evaluation completed for both the modeled media concentrations and
modeled blood Pb levels is discussed below.

5.2.5.1 Performance Evaluation of Modeled Media Concentrations As discussed in Section 3.5.1 for the previ ous analyses, performance evaluation for
modeled media concentrations focuses on those es timates that were generated primarily through modeling. Media concentrations ch aracterized using empirical data , such as air concentrations estimated using monitor measurements, are no t subjected to performance evaluation. The following observations can be made regarding performance evalua tion of model-estimate media concentrations for the analysis using the core modeling approach of the primary Pb smelter, general urban and location- specific urban case studies.

• All media concentrations for the primary Pb smelter and general urban case study are presented in Chapter 3, and the reader is referred back to Section 3.5.1 for performance evaluation of those estimates.

• Ambient air Pb levels (and, indirectly, i nhalation exposure concentrations) for the location-specific urban case studies are based on Pb-TSP mo nitoring data and therefore will not be addressed quantitatively in th is performance evaluation. Note, however, that the method for assigning Pb-TSP mon itors to subsets of the child study populations in each study area is a key step in this analysis and one which is subject to uncertainty. However, characterizing ambien t air Pb levels for the study populations at the location-specific urban case studies is addressed as part of the qualitative discussion of key sources of uncertainty (see Section 5.3.3.1).

• Outdoor soil/dust Pb levels for the location- specific urban case studi es utilize the same uniform value as was used for the ge neral urban case study (see Section 3.5.1.2).5-17 • Indoor dust Pb levels for the location-specifi c urban case studies are generally similar to those generated for the general urban case study. This is expect ed since the locationspecific urban case studies utilize the same hybrid indoor dust Pb model as was used for the general urban case study. Because of the similarity of the indoor dust Pb levels generated for the location-sp ecific urban case studies and the general urban case study, the reader is referred to the previous perf ormance evaluation discussion for indoor dust Pb addressing the general urba n case study (see Section 3.5.1.3).

5.2.5.2 Performance Evaluation of Modeled Blood Pb Levels

In this section, we discuss air-to-blood rati os derived by comparing changes (deltas) in median total blood Pb level (concurrent) to associ ated changes in annual average air Pb levels as you step to the next lowest air qua lity scenario (Table 5-7). This analysis is only available for the general urban and primary Pb smelter case studies (inclu ding both the full study area and 1.5 km subarea for the latter). Note that these air-to -blood ratios are different than those presented in Section 3.5.2.2 for the general urban, primary Pb sm elter and secondary Pb smelter case studies.
The ratios derived in Section 3.5.2.2 are based on comparing annual average ambient air Pb levels (used in modeling these case studies) to the recent air portions of blood Pb levels. Therefore, these ratios reflect a generalization of the relationship between air Pb and blood Pb across the entire exposure spectrum, reflecting that relationship as an average of the two absolute values. By contrast, the air-to -blood ratios presented in this section look at the incremental change in blood Pb as ambient air Pb changes in crementally with lower alternate NAAQS levels (Table 5-7). The model-derived air-to-blood ratios presente d in Table 5-7 can be compared to ratios identified from the literature (see last column of Table 3-24). In fact, conceptually, it may be more appropriate to compare the air-to-blood ratios presented in Ta ble 5-7 (than those in Section 3.5.2.2) to ratios obtained from the review by Br unekreef (1984), since the individual ratios summarized in that analysis incl uded a large number of studies th at compared blood Pb levels of populations living in different loca tions with that associated diffe rent ambient air Pb levels to
derive air-to-blood ratios. Conceptually, this is fairly similar to the approach used in deriving the air-to-blood ratios presen ted in this section. Similarly, Hilts (2003) considered exposure levels (ambient air Pb) and associated blood Pb levels for the same popul ation at two different points in time, straddling a change in opera tion of a nearby smelter. This is conceptually similar to the way the air-to-blood ratios are ca lculated here. Comparison of the air-to-blood ratios presented
in Table 5-7 with the ratios identified in Brunekreef (1984) and cal culated based on data presented in Hilts (2003), as summarized in Table 3-24, results in the following observations:

• Air-to-blood ratios for the general urban case study for air quality scenarios ranging from current NAAQS down to the alternative NAAQS of 0.2 μg/m 3 , maximum monthly average, are similar to ratios reporte d in the literature (i.e., between 1:3 and5-18 1:5). Ratios for the two lowest alterna tive NAAQS are larger, ranging from 1:6 to 1:9. However, even these larger ra tios are within the range of some of the studies reported in Brunekreef (1984) and within the range of ratios calculated based on data presented in Hilts (2003). As noted in Section 3.5.2.2, there are a number of factors which may produce higher air-to-blood ratios, which may pertain to urban locations, such as lower blood Pb levels relative to those reflected in the meta analysis by Brunekreef (1984).

• Air-to-blood ratios for the primary Pb smelte r case study (full study area) are similar in magnitude to the general urban case study a nd the points made in the previous bullet apply to this case study.

• Air-to-blood ratios for the 1.5 km subarea of the primary Pb smelter are significantly higher than ratios for the other two study ar eas noted above, with values ranging from 1:10 to 1:19. These ratios, a nd particularly the higher values associated with the lower NAAQS levels, are significantly higher than values reported in Brunekreef (1984) and those based on data in Hilts (2003). Many of the studies cited in Brunekreef (1984) use differences in average ambient air Pb levels and blood Pb levels between clusters of individuals living in diffe rent areas to derive their ratios . It is possible that the indoor dust Pb model developed for the remediation zone of the primary Pb smelter case study captures a relatively stronger relationship between ambient air Pb and indoor dust Pb (and hence blood Pb) which exists in the vicini ty of industrial facilities. It is also possible that reservoirs of Pb associated w ith past airborne Pb are still within these houses near the facility and co rrelated with current ambient air Pb levels are reflected in the site-specific regression model establ ished between ambient air Pb levels and indoor dust Pb. Thus, a portion of indoor dust Pb that results from contributions from indoor reservoirs may be being attributed through our applicati on of the model to current ambient air Pb. The air-to-blood ratios pres ented earlier in Section 3.5.2.2 for the general urban case study and primary Pb smelter case study are also pe rtinent to the core modeling approach results for these two case studies presented in this chapte r and the reader is referenced to that earlier
section for discussion of these a lternative ratios and implications in the context of performance evaluation for blood Pb modeling. Other elemen ts of performance evaluation for blood Pb modeling presented earlier in Section 3.5.2 (e.g., evaluation of ca ndidate blood Pb models - see Section 3.5.2.1) also pertain to the an alyses described in this section.5-19 Table 5-7. Air-to-blood ratios derived by comparin g air quality scenario air and blood Pb estimates.

Air Scenario Median Total Blood Pb (μg/dL) Annual Average Ambient Air Concentration (μg/m 3 ) a Ratio b
General urban case study Current NAAQS (1.5 μg/m3, max quarterly average) 3.12 0.600 Alternative NAAQS 2 (0.5 μg/m3, max monthly average) 2.16 0.125 1:2 Current Conditions (95th percentile) 2.13 0.114 1:3 Alternative NAAQS 1 (0.2 μg/m3, max quarterly average) 2.01 0.080 1:4 Current Conditions (mean) 1.91 0.056 1:4 Alternative NAAQS 3 (0.2 μg/m3, max monthly average) 1.88 0.050 1:5 Alternative NAAQS 4 (0.05 μg/m3, max monthly average) 1.67 0.013 1:6 Alternative NAAQS 5 (0.02 μg/m3, max monthly average) 1.61 0.005 1:9 Primary Pb smelter case study (full study area) Current NAAQS (1.5 μg/m3, max quarterly average) 1.50 0.042 Alternative NAAQS 2 (0.5 μg/m3, max monthly average) 1.44 0.019 1:3 Alternative NAAQS 1 (0.2 μg/m3, max quarterly average) 1.40 0.009 1:3 Alternative NAAQS 3 (0.2 μg/m3, max monthly average) 1.40 0.007 1:4 Alternative NAAQS 4 (0.05 μg/m3, max monthly average) 1.38 0.002 1:4 Alternative NAAQS 5 (0.02 μg/m3, max monthly average) 1.37 0.001 1:7 Primary Pb smelter case study (1.5 km subarea) Current NAAQS (1.5 μg/m3, max quarterly average) 4.58 0.238 Alternative NAAQS 2 (0.5 μg/m3, max monthly average) 3.20 0.105 1:10 Alternative NAAQS 1 (0.2 μg/m3, max quarterly average) 2.50 0.052 1:13 Alternative NAAQS 3 (0.2 μg/m3, max monthly average) 2.35 0.042 1:15 Alternative NAAQS 4 (0.05 μg/m3, max monthly average) 1.75 0.011 1:19 Alternative NAAQS 5 (0.02 μg/m3, max monthly average) 1.63 0.004 1:19

a For the primary Pb smelter case study entries , these are the median air concentrations in a population weighted distribution of air conc entration estimates for each scenario. b These are air-to-blood ratios derived by comparing changes (deltas) in median total blood Pb levels (concurrent) to associated changes in annual average air Pb levels as you step to the next lowest air quality scenario. Accordingly, a rati o is not presented adjacent to the current NAAQS air quality scenario (for any of the case studies). In this case, the first ratio presented for any of the case studies is generated by comparing medi an blood Pb levels at the current NAAQS level to the median blood Pb level at the highest of the alternative NAAQS levels (the 0.05 ug/m 3 maximum monthly value).5-20 5.3 RISK ASSESSMENT

This chapter describes the approach used to characterize risk using the core modeling approach, including discussion of the methodology (Section 5.3.1), pr esentation of risk estimates (Section 5.3.2), and uncertainty ch aracterization (Section 5.3.3).

5.3.1 Methods for Deriving Risk Estimates

Risk characterization for this assessment fo cuses on IQ loss in children. IQ loss is derived using a set of concentr ation-response functions develope d based on results from a pooled analysis of epidemiology studies (Lanphear et al., 2005). These concentration-response functions are combined with the population-leve l blood Pb distributions to produce distributions of IQ loss estimates for each cas e study population. IQ loss is al so apportioned among different exposure pathways using the pathway apportionm ent information generated as part of the exposure analysis. Two key elements of the risk methodology are described in greater detail below: (a) the concentration-response functions used in the analysis (Sec tion 5.3.1.1) and (b) the stepwise analytical procedure used to generate th e IQ loss (risk) distributions (Section 5.3.1.2).

5.3.1.1 Concentration-response Functions

For the risk analyses described in this ch apter, performed subse quent to the August 2007 public meeting of the CASAC Pb Panel, four ty pes of concentration-re sponse functions for IQ
loss were used. Some of the functions in this se t differ from those used in the initial analyses (Section 4.1.1), reflecting CASAC recommendations to use a two-piece or dual linear function that recognizes a change in slope (to a notably higher valu e) at blood Pb levels of 7.5 μ g/dL and give less prominence to the two-piece linear function with hinge at 10.82 μ g/dL derived for the initial analyses of the full-s cale assessment (Henderson, 2007b). The set of four concentrationresponse functions used in the analyses pres ented in this chapter include the following:

• The log-linear function with a cutpoint at 1.0 μg/ dL (for the concurrent blood Pb metric): This function is described in Section 4.1.1.1.

• The log-linear function with low-exposure linearization (for the concurrent blood Pb metric): This function is described in Section 4.1.1.2.

• Dual linear function, stratified at 10 μg/ dL peak : This represents two linear functions reported in Lanphear et al., 2005. To de rive these, the full study population was stratified into two groups - children with peak blood Pb levels above and below 10 μg/dL - and separate linear f unctions were fit to the IQ a nd concurrent blood Pb values for these two groups (Lanphear et al., 2005). In order to utilize this model, we considered the relationship between peak and concurrent blood Pb levels in the pooled dataset, as well as the relationship in th e cohort comprising the bulk of the low blood Pb subsets. For the full pooled dataset, th e average peak blood Pb is 18.0 μg/dL, while5-21 the average concurrent blood Pb is 9.7 μg/dL, approximately a factor of two difference (Lanphear et al., 2005). For the Rochester cohor t, which comprised the majority of the subset of children with peak blood Pb valu es below 10 μg/dL, the average peak blood Pb is 9.0 μg/dL, while the average concurre nt blood Pb is 4.0 μg/dL, approximately a factor of two difference (Lanphear et al ., 2005). Accordingly, in specifying the concurrent blood Pb level at which this f unction would change slope, we assumed that the peak blood Pb of 10 μg/dL corresponded to a concurrent blood Pb of 5 μg/dL. The form of the function as applied in this risk assessment is:

- For concurrent blood Pb level ≤ 5 μg/dL: IQ loss = beta 1 * concurrent blood Pb - For concurrent blood Pb level > 5 μg/dL: IQ loss = beta 2 * concurrent blood Pb
Where:
beta 1 = -0.80
beta 2 = -0.13

• Dual Linear function, stratified at 7.5 μg/ dL peak : This represents two linear functions, and like the previous ones, these were sepa rately derived from subgroups of the full study population (Lanphear et al., 2005). In th is case, the populati on was stratified into groups of children with peak blood Pb levels above and below 7.5 μg/dL. For the same considerations of the relati onship between peak and concu rrent blood Pb levels stated for previously described function in specify ing the concurrent blood Pb level at which this function would change slope, we a ssumed that the peak blood Pb of 7.5 μg/dL corresponded to a concurrent blood Pb of 3.75 μg/dL. The form of this function as applied in this risk assessment is:

- For concurrent blood Pb level ≤ 3.75 μg/dL: IQ loss = beta 1 * concurrent blood Pb - For concurrent blood Pb level > 3.75 μg/dL: IQ loss = beta 2 * concurrent blood Pb
Where:
beta 1 = -2.94
beta 2 = -0.16

The four functions are presented in Figure 5-2 and compared in Table 5-8 with regard to total IQ loss and increm ental IQ loss (IQ loss per μg/dL) acr oss a range of concurrent blood Pb levels. A number of observations can be made by considering the plots of the four functions together with the results presen ted in Table 5-8. This discu ssion presents IQ loss estimates rounded to the nearest integer, a lthough they are presented with greater precision in Table 5-8. At blood Pb levels less than or equal to about 2 μg/dL, with 2 μg/dL being near the average blood Pb level for young children in the U.S., ba sed on NHANES-IV data and near the average5-22 blood Pb estimated in the risk assessment for th e three location-specific urban case studies, the log-linear with cutpoint model and the dual linear - strati fied at 10 μg/dL peak generate similar IQ loss estimates. Both models predict a total of 2 points IQ loss associated with a blood Pb level of 2 μg/dL. By contra st, the remaining two models ( log-linear with low-exposure linearization and dual linear - stratified at 7.5 μg/dL peak ) generate substantia lly larger IQ loss estimates, ranging from 5 to 6 points IQ loss. At blood Pb levels above 2 μg/dL, the patter n of predicted IQ loss shifts substantially across the four models, with the dual linear - stratified at 7.5 μg/dL peak maintaining a constant slope (up to 3.75 μg/dL) which is significantly higher than the othe r models, all of which either have slopes which decrease (for the log-linear models) or have a lower slope to begin with (the dual linear - stratified at 10 μg/dL peak ). At higher exposure le vels ranging from 2 and 10 μg/dL, the dual linear - stratified at 10 μg/dL peak and the log-linear with cutpoint perform similarly, with the latter predic ting somewhat higher total IQ loss at 10 μg/dL (6 points, versus 5
for the dual linear-stratified at 10 μg/dL). Within this higher exposure range, the log-linear with linearization model generates IQ loss estimates whic h are higher than the two models just discussed, but lower than the dual linear - stratified at 7.5 peak . Specifically, at 10 μg/dL, the log-linear with linearization model predicts 9 points IQ loss, while the dual linear - stratified at 7.5 peak model predicts 12 points IQ loss. As di scussed in Section 2.1.5, the dataset from which the lower blood Pb portion of the dual linear – stratified at 7.5 peak function is derived is much
smaller than those from which the other f unctions are derived and includes variable representation of six of the se ven cohorts in the pooled analys is with a dominance (~65% of total) of one cohort.
Both of the dual linear models with stratif ication have lower slopes above 5 μg/dL than either of the log-linear models. This means that reductions in blood Pb levels for modeled children with relatively high exposures (> 5 μg/dL total blood Pb) will result in relatively lower IQ benefits using the two dual-lin ear models, compared with the two log-linear models (even if, in the case of the dual linear - stratified at 7.5 μg /dL model, the overall IQ loss is higher for that simulated child than with either of the log-linear models).5-23 Figure 5-2. Comparison of four concentration-res ponse functions for concurrent blood Pb levels < 10 μg/dL.

0 2 4 6 8 10 12 14

0 1 2 3 4 5 6 7 8 9 10 Concurrent blood Pb (ug/dL)IQ loss log-linear w ith cutpoint

dual linear - stratified at 10 ug/dL, peak
dual linear - stratified at 7.5 ug/dL, peak log-linear w ith low - exposure linearization

Table 5-8. Comparison of total and incremental IQ loss estimates below 10 μg/dL for the four concentration-response functions.

Concentration-Response FunctionLoglinear with cutpoint Log-linearwith lowexposure linearization Dual linear - stratified at 10 μg/dL peak Dual linear - stratified at 7.5 μg/dL peak Performance Metric Points, IQ loss at 2 μg/dL 1.9 4.6 1.6 5.9 at 5 μg/dL 4.3 7.0 3.9 11.1 at 7.5 μg/dL 5.4 8.1 4.3 11.5 Total IQ loss
at 10 μg/dL 6.2 8.9 4.6 11.9 <2 μg/dL 0.94 2.29 0.80 2.94 <5 μg/dL 0.87 1.41 0.80 2.24 <7.5 μg/dL 0.73 1.09 0.58 1.55 Incremental IQ loss (average # points per μg/dL) <10 μg/dL 0.62 0.89 0.47 1.205-24 5.3.1.2 Projection of Population Risk

The methods used to project population-level risk for the additiona l analyses are those
described in Section 4.1.2. With regard to population-leve l risk metrics, risk estimat es for different population percentiles are generated for all case studies. For the location-specific urban case studies, an additional type of population risk metric is also presented. Speci fically, we have estimated the
number of children associated with different magnitudes of IQ loss resulting from total Pb exposure under different ambient ai r quality scenarios (i.e., IQ lo ss incidence estimates). This metric illustrates (a) the overa ll number of children within a given urban case study location projected to experience various leve ls of IQ loss due to Pb exposu re and (b) how that distribution of incidence changes with alternate NAAQS levels.

5.3.2 Risk Estimates

Estimates of IQ loss resulting from Pb exposur e have been developed for each air quality scenario in each case study (see Appendices N and O). Further, multiple sets of risk results were generated for each combination of case study and air quality scenario, in an effort to consider uncertainty specifically related to specifying the relationship be tween Pb exposure and IQ loss (see Section 5.1.2). That is, four separate risk distributions were generated for each air quality scenario for each of the case studies presented he re (see Table 5-1). These risk estimates include (a) population-risk distribution estimates (in the form of estimated IQ loss for specific population percentiles) and (b) IQ loss incidence estimates, as described below. As noted earlier in presenting exposure resu lts (see Section 5.2.4), IQ loss estimates associated with air pathways and current levels of Pb emitted to the air (including via resuspension) are likely to fall be tween the estimates for “recent ai r” and those for “recent” plus “past air”. Additionally, with regard to the lo cation-specific urban case studies “recent air” and “recent” plus “past air” categories, an artifact of the hybrid dust Pb model tends to mask trends in the two components of dust Pb (re cent air and other), which cont ribute to “recent air” and “past air” estimates, respectively (see Section 4.3.1 discussion of this uncer tainty). For the primary Pb smelter case study, uncertainty in parsing out th e "recent air" and "other" components of indoor dust (specifically for the site-spe cific regression model used in th e remediation zone) have led us to conclude that only "recent pl us past air" IQ loss estimates should be presented and "recent air" results should not be separately presented for th e primary Pb smelter, as is done for the other case studies (see Section 3.1.4.2).5-25 5.3.2.1 Population Risk Distribution Estimates

Risk estimates for specific popula tion percentiles of the risk distribution associated with total blood Pb levels are presented in Tables 5-9 and 5-10. These include the median (Table 5-9)
and 95 th percentile (Table 5-10) IQ loss estimates based on total blood Pb. Two additional types
of risk estimates are summarized in Tables 5-9 and 5-10 in addition to IQ loss based on total blood Pb. Also presented are the contributions to IQ loss from the recent air and recent plus past air exposure pathways (see Section 3.2.2), whic h as noted previously (Sections 2.4.3 and 3.2.2) represent the policy-releva nt pathways, with the true values for the policy-relevant pathways
considered to fall between the estimates for “recen t air” and those for “recent” plus “past air”. More detailed pathway apportionment estimates (i ncluding percentile contributions to total IQ loss from individual exposure pathways) are presen ted in the detailed risk results tables in Appendices N, O and P. These more detailed ri sk results are, however, subject to the same limitations related to presentation of pathway appo rtionment, as noted above for individual case studies (e.g., reduced confidence in parsing out recent air-related ri sk from recent plus past air risk for the primary Pb smelter case study). The total Pb exposure risk estimates presente d in Table 5-9 are those for the median in the distribution of total risk, and the estimates for the other two categories in that table (i.e., recent air and recent plus past air) are the values for those categories associated with the median for the total exposure pathway estimate. The corresponding estimates for the 95 th percentile in the distribution of IQ loss estimates for total bl ood Pb for each case study are presented in Table 5-10. This presentation may lead to some seemi ng inconsistencies in tre nds for recent air and recent air plus past air risk estimates across air qu ality scenarios. This is because the recent air or recent plus past air estimates associated with the median and 95 th percentile total blood Pb estimate may not necessarily be the median and 95 th percentiles of the di stribution of estimates for those specific categories. Th is is because the blood Pb level (a nd associated total IQ loss) for
a simulated child reflects not onl y the total Pb upta ke (from both background and ambient airrelated pathways), but also the GSD-based adjust ment factor. This means that two simulated children could have identical blood Pb levels (and IQ loss estimates), but one child could have a higher recent air exposure, with the other child having their lower recent air exposure compensated for by a higher GSD-based adjustme nt factor, thereby re sulting in both children having the same total blood Pb level. In presen ting IQ loss estimates in tables 5-9 and 5-10, all values are rounded to one decimal place. As mentioned in Chapter 1, risk result s are provided here without substantial interpretation. Rather, interpreta tive discussion of these results is provided in the Staff Paper.5-26 Table 5-9. Summary of risk estimates for medians of total-exposure risk distributions.

Points IQ Loss a Recent Air b (low, LLL and high C-R

function estimates) Recent plus Past Air b (low, LLL and high C-R

function estimates) Total Pb Exposure (low, LLL and high C-R

function estimates) C ase Study and Air Quality Scenario

Low LLL c High Low LLL High Low LLL High Location-specific (Chicago) Current NAAQS (1.5 μg/m 3 , max quarterly) 1.4 3.4 5.6 2.0 4.7 7.4 2.4 5.6 8.8 Current conditions (0.14 μg/m 3 max quarterly; 0.31 μg/m 3 max monthly) 0.3 0.6 0.7 1.0 2.9 3.5 1.4 4.2 5.2

A lternative NAAQS (0.2 μg/m 3 , max monthly) 0.3 0.6 0.9 1.0 2.9 3.6 1.4 4.2 5.2 A lternative NAAQS (0.05 μg/m 3 , max monthly) 0.1 0.2 0.2 0.9 2.6 3.2 1.3 4.0 4.8 A lternative NAAQS (0.02 μg/m 3 , max monthly) <0.1 0.1 0.1 0.8 2.6 3.1 1.3 4.0 4.7 Location-specific (Cleveland) Current NAAQS (1.5 μg/m 3 , max quarterly) 0.6 2.8 2.1 1.3 3.9 4.6 1.7 4.7 6.3
Current conditions (0.36 μg/m 3 max quarterly; 0.56 μg/m 3 max monthly) 0.2 0.7 0.9 1.0 2.9 3.6 1.4 4.2 5.2

A lternative NAAQS (0.5 μg/m 3 , max monthly) 0.2 0.6 1.9 1.0 2.9 3.9 1.4 4.2 5.2 A lternative NAAQS (0.2 μg/m 3 , max quarterly) 0.1 0.4 0.5 0.9 2.8 3.3 1.4 4.1 5.0 A lternative NAAQS (0.2 μg/m 3 , max monthly) 0.1 0.6 0.4 0.9 2.8 3.2 1.3 4.1 4.9 A lternative NAAQS (0.05 μg/m 3 , max monthly) <0.1 0.1 0.1 0.8 2.6 3.1 1.3 4.0 4.7 A lternative NAAQS (0.02 μg/m 3 , max monthly) <0.1 <0.1 0.1 0.8 2.6 3.0 1.2 3.9 4.6 Location-specific (Los Angeles) Current NAAQS (1.5 μg/m 3 , max quarterly) 1.1 2.7 4.0 1.7 4.2 6.2 2.1 5.3 7.7 Current conditions (0.09 μg/m 3 max quarterly; 0.17 μg/m 3 max monthly) 0.2 0.7 0.9 0.9 2.9 3.5 1.4 4.2 5.1

A lternative NAAQS (0.05 μg/m 3 , max monthly) 0.1 0.3 0.4 0.9 2.7 3.2 1.3 4.0 4.8 A lternative NAAQS (0.02 μg/m 3 , max monthly) <0.1 0.1 0.2 0.8 2.6 3.1 1.3 4.0 4.7 General urban Current NAAQS (1.5 μg/m 3 , max quarterly) 1.5 3.5 5.6 2.1 4.8 7.7 2.5 5.8 9.2 Alternative NAAQS (0.5 μg/m 3 , max monthly) 0.7 1.9 2.5 1.3 3.6 4.8 1.7 4.8 6.4 Current conditions (0.87 μg/m 3 max quarterly) 0.6 1.8 2.4 1.3 3.6 4.7 1.7 4.7 6.3 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 0.5 1.5 2.0 1.2 3.4 4.3 1.6 4.6 5.9 Current conditions (0.14 μg/m 3 max quarterly) 0.4 1.3 1.6 1.1 3.2 4.1 1.5 4.5 5.6 Alternative NAAQS (0.2 μg/m 3 , max monthly) 0.4 1.2 1.5 1.1 3.2 4.0 1.5 4.4 5.6 Alternative NAAQS (0.05 μg/m 3 , max monthly) 0.2 0.5 0.6 0.9 2.8 3.3 1.3 4.1 5.0 Alternative NAAQS (0.02 μg/m 3 , max monthly) 0.1 0.3 0.3 0.9 2.6 3.1 1.3 4.0 4.8 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 0.6 1.9 2.3 1.2 3.8 4.4 A lternative N A

A QS (0.5 μg/m 3 , max monthly) 0.8 2.9 2.7 1.0 3.7 4.2 A lternative NAAQS (0.2 μg/m 3 , max quarterly) 0.6 2.3 2.6 0.9 3.6 4.2 A lternative NAAQS (0.2 μg/m 3 , max monthly) 0.4 2.7 3.0 0.9 3.6 4.1 A lternative NAAQS (0.05 μg/m 3 , max monthly) 0.4 1.7 1.9 0.9 3.6 4.0 A lternative NAAQS (0.02 μg/m 3 , max monthly) NA d

0.6 2.6 3.0 0.9 3.6 4.1 Primary Pb smelter - 1.5 km subarea Current NAAQS (1.5 μg/m 3 , max quarterly) 3.2 6.0 9.4 3.7 6.8 11.2 A lternative NAAQS (0.5 μg/m 3 , max monthly) 2.1 4.5 7.7 2.6 5.8 9.4 A lternative NAAQS (0.2 μg/m 3 , max quarterly) 1.5 3.8 5.6 2.0 5.2 7.4 A lternative NAAQS (0.2 μg/m 3 , max monthly) 1.2 3.7 5.1 1.9 5.0 6.9 A lternative NAAQS (0.05 μg/m 3 , max monthly) 0.9 2.8 3.4 1.4 4.2 5.1 A lternative NAAQS (0.02 μg/m 3 , max monthly) NA d

0.9 2.9 3.3 1.3 4.0 4.8 a - Estimates are rounded to one decimal place. b -The term “past air” includes contributio ns from the outdoor soil/dust contribution to indoor dust, histor ical air contributi on to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from inhal ation of ambient air Pb or i ngestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with outdoor ambient air also potentia lly including resuspended, previously deposited Pb (see Section 2. 4.3). c - Log-linear with low-ex posure linearization concentration-response function. d – “Recent air” estimates were not developed for the pr imary Pb smelter case study (see Section 3.1.4.2).5-27 Table 5-10. Summary of risk estimates for 95 th percentiles of total exposure risk distributions.

Points IQ Loss a C ase Study and Air Quality Scenario Recent Air b (low, LLL and high C-R

function estimates) Recent plus Past Air b (low, LLL and high C-R

function estimates) Total Pb Exposure (low, LLL and high C-R

function estimates) Low LLL c High Low LLL High Low LLL High Location-s p ecific ( Chica g o ) Current NAAQS (1.5 μg/m 3 , max quarterly) 3.0 5.8 7.7 4.0 7.6 10.3 4.7 9.0 12.1 Current conditions (0.14 μg/m 3 max quarterly; 0.31 μg/m 3 max monthly) 0.7 1.3 2.0 2.8 5.2 7.8 4.1 7.5 11.4

A lternative NAAQS (0.2 μg/m 3 , max monthly) 1.0 1.9 2.8 2.9 5.3 8.1 4.1 7.5 11.4 A lternative NAAQS (0.05 μg/m 3 , max monthly) 0.2 0.4 0.7 2.7 4.8 7.5 4.1 7.3 11.3 A lternative NAAQS (0.02 μg/m 3 , max monthly) 0.2 0.3 0.5 2.7 4.8 7.4 4.1 7.3 11.3 Location-specific (Cleveland) Current NAAQS (1.5 μg/m 3 , max quarterly) 1.4 2.6 3.7 3.1 5.9 8.5 4.3 8.1 11.6 Current conditions (0.36 μg/m 3 max quarterly; 0.56 μg/m 3 max monthly) 0.6 1.2 1.8 2.8 5.2 7.8 4.1 7.6 11.4

A lternative NAAQS (0.5 μg/m 3 , max monthly) 1.1 1.9 2.9 2.9 5.3 8.1 4.1 7.5 11.4 A lternative NAAQS (0.2 μg/m 3 , max quarterly) 0.5 0.9 1.4 2.8 5.0 7.6 4.1 7.4 11.3 A lternative NAAQS (0.2 μg/m 3 , max monthly) 0.3 0.6 0.9 2.7 4.9 7.5 4.1 7.4 11.3 A lternative NAAQS (0.05 μg/m 3 , max monthly) 0.1 0.2 0.3 2.6 4.7 7.4 4.0 7.2 11.3 A lternative NAAQS (0.02 μg/m 3 , max monthly) <0.1 0.1 0.1 2.6 4.7 7.2 4.0 7.2 11.3 Location-specific (Los Angeles) Current NAAQS (1.5 μg/m 3 , max quarterly) 2.3 4.4 6.1 3.6 6.9 9.5 4.5 8.6 11.8 Current conditions (0.09 μg/m 3 max quarterly; 0.17 μg/m 3 max monthly) 0.8 1.4 2.1 2.8 5.2 7.8 4.1 7.5 11.4

A lternative NAAQS (0.05 μg/m 3 , max monthly) 0.4 0.7 1.1 2.7 4.9 7.6 4.1 7.3 11.3 A lternative NAAQS (0.02 μg/m 3 , max monthly) 0.1 0.2 0.4 2.7 4.7 7.4 4.0 7.2 11.3 General urban Current NAAQS (1.5 μg/m 3 , max quarterly) 2.9 5.5 7.3 3.9 7.6 10.1 4.7 9.1 12.1 A lternative NAAQS (0.5 μg/m 3 , max monthly) 1.7 3.2 4.6 3.3 6.1 8.8 4.3 8.1 11.6 Current conditions

– high-end (0.87 μg/m 3 max quarterly) 1.6 3.1 4.4 3.2 6.0 8.7 4.3 8.0 11.6 A lternative NAAQS (0.2 μg/m 3 , max quarterly) 1.4 2.6 3.8 3.1 5.8 8.5 4.2 7.9 11.5 Current conditions - mean (0.14 μg/m 3 max quarterly) 1.2 2.2 3.3 3.0 5.6 8.3 4.2 7.7 11.5 A lternative NAAQS (0.2 μg/m 3 , max monthly) 1.1 2.1 3.1 3.0 5.6 8.2 4.2 7.7 11.4 A lternative NAAQS (0.05 μg/m 3 , max monthly) 0.5 0.9 1.4 2.8 5.0 7.7 4.1 7.4 11.3 A lternative NAAQS (0.02 μg/m 3 , max monthly) 0.3 0.5 0.7 2.7 4.8 7.5 4.1 7.3 11.3 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 2.3 4.2 6.8 3.7 6.8 11.2 A lternative NAAQS (0.5 μg/m 3 , max monthly) 2.5 4.9 8.3 3.4 6.6 11.1 A lternative NAAQS (0.2 μg/m 3 , max quarterly) 1.9 3.9 6.6 3.2 6.5 11.1 A lternative NAAQS (0.2 μg/m 3 , max monthly) 2.0 4.0 7.0 3.2 6.4 11.1 A lternative NAAQS (0.05 μg/m 3 , max monthly) 1.5 3.1 5.4 3.1 6.3 11.0 A lternative NAAQS (0.02 μg/m 3 , max monthly) NA d

2.5 5.3 9.2 3.1 6.3 11.0 Primary Pb smelter - 1.5 km subarea Current NAAQS (1.5 μg/m 3 , max quarterly) 4.2 8.0 10.4 5.0 9.5 12.4 A lternative N A

A QS (0.5 μg/m 3 , max monthly) 4.0 7.5 10.5 4.5 8.5 11.8 A lternative NAAQS (0.2 μg/m 3 , max quarterly) 3.7 6.9 10.2 4.2 7.8 11.5 A lternative NAAQS (0.2 μg/m 3 , max monthly) 3.4 6.1 9.2 4.1 7.6 11.4 A lternative NAAQS (0.05 μg/m 3 , max monthly) 2.8 5.3 8.7 3.6 6.8 11.1 A lternative NAAQS (0.02 μg/m 3 , max monthly) NA d

2.4 4.7 7.9 3.3 6.5 11.1 a - Estimates are rounded to one decimal place. b -The term “past air” includes contributio ns from the outdoor soil/dust contribution to indoor dust, histor ical air contributi on to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from inhal ation of ambient air Pb or i ngestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with outdoor ambient air also potentia lly including resuspended, previously deposited Pb (see Section 2. 4.3). c - Log-linear with low-ex posure linearization concentration-response function. d – “Recent air” estimates were not developed for the pr imary Pb smelter case study (see Section 3.1.4.2).5-28 5.3.2.2 IQ Loss Incidence Estimates

Incidence of different levels of IQ loss has been estimated for the three location-specific urban case studies (Appendix O). These estima tes are summarized in Tables 5-11 and 5-12. Specifically, Table 5-11 shows the number of childre n projected to have IQ loss greater than one point under the current conditions scenario and the change in that metric under the current NAAQS and the alternate NAAQS considered for each location-specific urban case study. The value of one IQ point was selected as consis tent with CASAC recomm endations as to the magnitude of IQ loss they considered to be significant from a public health perspective (Henderson, 2007b). Similarly, Table 5-12 presen ts incidence estimates associated with a greater magnitude of cognitive impact, the numbe r of children exceeding 7 points IQ loss. The value of 7 IQ points is the magnit ude of IQ loss projected for the 95 th population percentile (using the log-linear with low-e xposure linearization function) unde r current condit ions at the case studies evaluated in this analysis (See Table 5-10). As mentioned in Chapter 1, risk results are provided here without substantial interpreta tion. Rather, interpretative discussion of these results is provided in the Staff Paper.5-29Table 5-11.Incidence of children with >1 point IQ loss.1dual linear - stratified at7.5 μg/dL peaklog-linear withlinearizationdual linear - stratifiedat 10 μg/dL peaklog-linear with cutpointAir Quality Scenario(for location-specific urban case studies)Incidenceof>1 pointIQ lossDelta(change inincidencecompared tocurrentconditions)Incidenceof>1 point,IQ lossDelta(change inincidencecomparedto currentconditions)Incidence of>1 point,IQ lossDelta(change inincidencecomparedto currentconditions)Incidenceof>1 point,IQ lossDelta(change inincidencecomparedto currentconditions)Chicago(total modeled population: 396,511)Chicago Current Conditions391,602389,754 271,031 236,257Current NAAQS (1.5 μg/m3Maximum Quarterly)395,797 4,195395,528 5,773 347,415 76,384 314,053 77,795Alternative NAAQS (0.2 μg/m3Maximum Monthly)391,158 -444389,461 -293 271,444 412 235,559 -698Alternative NAAQS (0.05 μg/m3Maximum Monthly)389,572 -2,030387,407 -2,347 253,775 -17,256 224,394 -11,864Alternative NAAQS (0.02 μg/m3Maximum Monthly)389,176 -2,427386,630 -3,125 249,865 -21,166 219,294 -16,963Cleveland(total modeled population: 13,990)Cleveland Current Conditions13,80913,745 9,526 8,515Current NAAQS (1.5 μg/m3Maximum Quarterly)13,893 8413,857 112 10,664 1,137 9,769 1,254Alternative NAAQS (0.2 μg/m3Maximum Quarterly)13,770 -3813,703 -42 9,221 -305 8,160 -354Alternative NAAQS (0.5 μg/m3Maximum Monthly)13,789 -2013,720 -25 9,497 -29 8,464 -51Alternative NAAQS (0.2 μg/m3Maximum Monthly)13,759 -5013,694 -51 9,083 -443 8,010 -505Alternative NAAQS (0.05 μg/m3Maximum Monthly)13,729 -8013,642 -103 8,785 -741 7,720 -795Alternative NAAQS (0.02 μg/m3Maximum Monthly)13,720 -8813,628 -117 8,736 -790 7,668 -846Los Angeles(total modeled population: 372,252)Los Angeles Current Conditions282,216280,711 191,675 170,474Current NAAQS (1.5 μg/m3Maximum Quarterly)285,272 3,056284,945 4,234 240,988 49,313 226,608 56,134Alternative NAAQS (0.05 μg/m3Maximum Monthly)281,112 -1,104279,658 -1,053 183,395 -8,280 161,914 -8,560Alternative NAAQS (0.02 μg/m3Maximum Monthly)280,740 -1,476279,057 -1,654 180,745 -10,929 158,234 -12,24025-30Table 5-12.Incidence of children with >7 points IQ loss.1dual linear - stratified at 7.5μg/dL peaklog-linear withlinearizationdual linear - stratified at10 μg/dL peaklog-linear with cutpointAir Quality Scenario(location-specific urban case studies)Incidence of> 7 pointsIQ lossDelta(change inincidencecompared tocurrentconditions)Incidenceof> 7 pointsIQ lossDelta(change inincidencecomparedto currentconditions)Incidence of> 7 pointsIQ lossDelta(change inincidencecompared tocurrentconditions)Incidenceof> 7 pointsIQ lossDelta(change inincidencecompared tocurrentconditions)Chicago(total modeled population: 396,511)Chicago Current Conditions 136,709 33,664 63 1,015Current NAAQS (1.5 μg/m3Maximum Quarterly) 244,401 107,692 100,159 66,495 555 492 5,226 4,211Alternative NAAQS (0.2 μg/m3Maximum Monthly) 136,067 -642 32,546 -1,118 48 -16 1,007 -8Alternative NAAQS (0.05 μg/m3Maximum Monthly) 120,706 -16,003 27,367 -6,297 16 -48 864 -151Alternative NAAQS (0.02 μg/m3Maximum Monthly) 117,819 -18,890 26,027 -7,637 8 -56 690 -325Cleveland(total modeled population: 13,990)Cleveland Current Conditions 4,834 1,212 3 46Current NAAQS (1.5 μg/m3Maximum Quarterly) 6,139 1,305 1,858 647 4 2 105 59Alternative NAAQS (0.2 μg/m3Maximum Quarterly) 4,525 -309 1,073 -139 1 -2 40 -6Alternative NAAQS (0.5 μg/m3Maximum Monthly) 4,806 -28 1,180 -31 1 -2 43 -3Alternative NAAQS (0.2 μg/m3Maximum Monthly) 4,424 -410 1,026 -186 1 -2 43 -3Alternative NAAQS (0.05 μg/m3Maximum Monthly) 4,106 -728 886 -326 0 -3 24 -22Alternative NAAQS (0.02 μg/m3Maximum Monthly) 4,051 -783 866 -345 0 -3 27 -18Los Angeles(total modeled population: 372,252)Los Angeles Current Conditions 94,684 22,665 23 732Current NAAQS (1.5 μg/m3Maximum Quarterly) 158,171 63,487 57,834 35,168 183 160 3,771 3,038Alternative NAAQS (0.05 μg/m3Maximum Monthly) 87,303 -7,382 19,781 -2884 11 -11 624 -109Alternative NAAQS (0.02 μg/m3Maximum Monthly) 83,909 -10,775 17,939 -4726 17 -6 498 -2355-31 5.3.3 Uncertainty Characterization and Sensitivity Analysis

This section discusses uncertainty related to exposure and risk estimates generated for the results for the core modeling approach applied to the primary Pb smelter, general urban and location-specific urban case studies. It also discusses sensitivity analyses completed to inform application of the core modeling ap proach to these case studies. Several methods have been used to examine uncertainty in our modeling appro ach and its potential im pact on exposure risk estimates. These methods for uncertainty evaluation include the following:

• Qualitative discussion of key sources of un certainty and their potential impact on exposure and risk estimates (Section 5.3.3.1). This qual itative discussion focuses on factors particular to the ne w analyses presented in this section - those sources of uncertainty that these new analyses share with the analyses presented in Chapters 3 and 4 are not repeated here (see S ection 4.3.1 for that discussion).

• Evaluation of model performance, including comparison with empirical data (Section 5.3.3.2). As with the qualitative discussion of uncertainty, only those performance evaluation elements particular to the anal yses in this chapter (i.e., performance evaluation of modeled blood Pb levels - s ee Section 5.2.5.2) are discussed here. The reader is referred back to Section 4.3.3 for a more complete discussion of performance evaluation related to modeled media concentrat ions and blood Pb modeling for this risk
assessment.

• Development of multiple sets of risk estimates for each assessment scenario that illustrate the impact of different concentra tion-response models relating Pb exposure to IQ loss and the associated uncertainty (see Section 5.3.3.3).

In addition to these methods for evaluating uncertainty in the m odeling approach, an additional sensitivity analysis has been completed for this portion of the analysis, specifically focused on the hybrid indoor dust Pb model us ed in the general urban case study and the location-specific urban case stud ies. This sensitivity analysis is discussed in Section 5.3.3.4. The results of the sensitivity analysis describe d in Section 4.3.2 for the general urban case study are broadly applicable to that case study, as well as the loca tion-specific urban case studies.

5.3.3.1 Qualitative Discussion of Key Sources of Uncertainty

This section provides qualitativ e discussion of key sources of uncertainty related to the analyses presented in this secti on. Specifically, it addresses sources of uncertainty specific to the location-specific urban ca se studies; sources of uncertainty re lated to the primary Pb smelter and general urban case studies have al ready been discussed (see Secti on 4.3.1). In addition, many of the sources of uncertainty that impact the general urban case study al so impact the locationspecific urban case studies, since both share si milar modeling elements (e.g., hybrid indoor dust Pb model, IQ loss functions, blood Pb GSD). Th e reader is referred back to Section 4.3.1 for5-32 discussion of those sources of uncertainty. Sour ces of uncertainty particular to the locationspecific urban case studies include:

• Location-specific urban case studies : As recognized in Appendix A, the Pb-TSP monitoring network is currently quite limite d. The number of monitors available to represent air concentrations in these case studies ranged from six for Cleveland to 11 for Chicago. Accordingly, our estimates of the magnitude of and spatial variation of air Pb concentrations are subject to uncertain ty associated with the limited data. In applying the available data to each of these case studies, exposure zones, one corresponding to each monitor, were creat ed and each U.S. Census block (and the children within that demographic unit) were distributed among the exposure zones. The details of the approach used are described in Section 5.1.3. Although this approach provides a spatial gradient across the study ar ea due to differences in monitor values for each exposure zone, this approach assumes a constant concentration within each exposure zone (i .e., no spatial gradient within a zone). Additionally, the nearest ne ighbor approach to assign blocks to exposure zones assumes that a monitor pertains to all locations that are closer to that monitor than to any of the others in the st udy area. In reality, there may be different and more variable spatial gradients in a study area th an those reflected in the approach used here. This introduces uncertainty into the characterization of risk for the urban case studies.

• Current NAAQS air quality scenarios : For the location-specific urban case studies, proportional roll-up procedures were used to adjust ambien t air Pb concentrations up to just meet the current NAAQS (s ee Sections 2.3.1 and 5.2.2.1 for detailed discussion). Staff rec ognizes that it is extremely unlikel y that Pb concentrations in urban areas would rise to meet the current NAAQS and that there is uncertainty with our simulation of such conditions. In th ese case studies we have simulated a proportional roll-up, such that it is assumed th at the current spatial distribution of air concentrations (as characterized by the curr ent data) is maintained and increased Pb emissions contribute to increased Pb concen trations, the highest of which just meets the current standard. There are many other types of changes within a study area that could result in a similar result such as incr eases in emissions from just one specific industrial operation that led to air concentrations in a part of the study area that just meet the current NAAQS, while the remainder of the study area remained largely unchanged (at current conditions). For th e primary Pb smelter case study, where current conditions exceed the current NAAQS, reaching the current NAAQS was simulated using air quality modeling, emi ssions and source parameters used in developing the 2007 proposed revision to the State Implementation Plan for the area (see Section 3.1.1.2 for details).

• Alternative NAAQS air quality scenarios : In all case studies, proportional roll-down procedures were used to adjust ambient air Pb concentrations downward to reach alternative NAAQS (see Sections 2.3.1 a nd 5.2.2.1). We recognize that there is uncertainty in simulating condi tions associated with the implementation of emissions reduction actions to meet a lower standard. There are a variety of changes other than that represented by a proportional roll-down th at could result in air concentrations5-33 that just meet lower alternative standards. For example, control measures might be targeted only at the specific area exceeding standard, result ing in a reduct ion of air Pb concentrations to the alternat e standard while concentrations in the rest of the study area remain unchanged (at current conditions). Consequently, there is uncertainty associated with estimates for the alternate NAAQS scenarios.

• Relating blood Pb levels to IQ loss : Specification of the quantitative relationship between blood Pb level (exposur e) and IQ loss is subject to uncertainty, especially in the projection of IQ loss at lower blood Pb levels (below 5 μg/dL concurrent blood Pb). As discussed in Section 2.1.5, this refl ects limitations in the data available for characterizing the concentration-response relationship. For example, the pooled international dataset analyzed by Lanphear and others (2005) includes relatively few children with blood Pb levels below 5 μg /dL and no children with levels below 1 μg/dL (see Section 2.1.5). As presented in Section 5.2.4, blood Pb levels in this region are a particular focus in this review . For example, as is the case for mean blood Pb levels nationally in the U.S. (CD, Section 4.3.1.3), concurrent blood Pb estimates for the median of the populations simulated in this assessment fall below 5 μg/dL. In recognition of the uncertainty in specifying a quantitative concentrationresponse relationship at such levels, our core modeling approach involves the application of four differen t functions to generate a range of risk estimates (see Sections 4.2.6 and 5.3.1). The range of ab solute IQ loss seen for a given case study/air quality scenario combination whenmodeled using the four concentrationresponse functions is typically close to a fact or of 3. However, it is important to point out that the relative (propor tional) change in IQ loss acro ss air quality scenarios (i.e., the pattern of risk reduction across air quality scenarios) is fairly consistent across all four models. Note, however, that the func tion producing higher overall risk estimates (the dual linear function, stratified at 7.5 μg/dL, peak) will also produce larger absolute reductions in IQ loss comp ared with the other three functions.

5.3.3.2 Performance Analyses

This section discusses the performance evaluation completed for blood Pb modeling for the core analysis of the general urban and primar y Pb smelter case studies. As mentioned above, the performance evaluation of media concentrations for these case studies is discussed in Section
4.3.3. The additional performance evaluation discusse d in this chapter focused on a set of airto-blood ratios developed using a different appro ach than that used in Chapter 3– (see Section 5.2.5.2). The results of this analysis suggested that ratios generated fo r the general urban case study and for the primary Pb smelter (full study area) were generally similar to those identified in the literature, with th e exception of ratios asso ciated with the lowest alternative NAAQS, which were larger than the typical range of ratios seen cited in the literature. By contrast, the set of ratios generated for all air qua lity scenarios for the 1.5 km suba rea of the primary Pb smelter were noticeably larger than the values cited in the literature. A number of plau sible explanations for this discrepancy are presented in Section 5.2.5.2.5-34 5.3.3.3 Uncertainty in Modeling Approaches - Multiples Sets of Results

For the case studies included in the additional an alyses presented in this chapter, four sets of risk estimates were generated for each air quality scenario, reflecting consideration of four concentration-response functions relating Pb expo sure to IQ loss. The resulting median and 95 th percentile risk estimates for each case study and air quality scenario combination are presented in
Tables 5-9 and 5-10. As can be seen from these tables, estimates based on these four functions result in a risk range spanning a factor of 4 for a particular ca se study and air quality scenario, although the range among results for three of the four functions is much tighter.

5.3.3.4 Sensitivity Analysis – Indoor Dust Pb Modeling

One additional sensitivity analysis was completed as part of the analyses based on the core modeling approach. Specifically, an altern ate version of the hybrid indoor dust Pb model used for the general urban a nd location-specific urban case st udies was considered. This

alternate form of the model used a different approach for partitioning total dust Pb estimates

between recent air and "other" categories (see Secti on 3.1.4.1). Note, that consideration for

alternative concentration -response functions for IQ loss is not discussed here as part of the sensitivity analysis, since all four concentration-response functions were used to evaluate the full

set of case studies and th ose results can be reviewed to identif y differences in the performance of these alternate models. The sensitivity analys is focusing on the hybrid indoor dust Pb model is presented here.

Comments received from a member of the public regarding the full-scale analysis methodology suggested a modification to the hybrid indoor dust Pb model us ed in evaluating the general urban case study (see Section 3.1.4.1 for de tails on the hybrid ind oor dust Pb model). Specifically, rather than establis hing the nonair fraction of indoor dust Pb as part of overall Pb loading , as is currently done in the hybrid model, the recommendation was made that EPA take the final version of the hybrid model, wh ich predicts indoor dust Pb concentration , set the air

term to zero and solve for nonair indoor dust Pb (this providing an estima te of nonair indoor dust Pb). This recommendation was made, in part, to address concerns raised by the commenter that the nonair indoor dust Pb concentrations generate d by the hybrid model, were not constant across air quality scenarios. EPA recognizes that the h ybrid indoor dust Pb model, as used in the fullscale analysis, does produce nonair indoor dust Pb concentrations which vary by air quality scenarios. This results from nonlinearity in the loading to concentr ation conversion algorithm5-35 within the model, which genera tes a greater per unit concentr ation at lower overall loading

levels. To inform our understanding of model uncerta inty related to pred icting indoor dust Pb levels for the urban scenarios, we have included this alternative hybrid model in this sensitivity analysis. However, it is important to point out a key limitation in this formulation of the hybrid dust model. By setting ambient air Pb levels to zero and then solving fo r indoor dust Pb, one is using the steepest part of the loading-to-c oncentration curve to c onduct the key step of translating indoor dust Pb loading to equivalent concentration. In reality, we would expect to always have a mixture of indoor dust Pb loadings from nonair and air sources, thereby resulting in a larger total loading value, which would in turn be tran slated into an indoor dust Pb concentration at a flatter portion of the curve. Solving of the hybrid indoor dust Pb model for an ambient air Pb level of zero, yields the
fixed nonair indoor dust Pb level of 61 ppm. For the alternative hybrid model, this value has
been used as the nonair indoor dust Pb level. The ambient air-related portion of indoor dust Pb concentration was then generated for each simula ted individual by subtracting this value of 61 ppm from the total indoor dust Pb level generated by the hybrid model. If the total indoor dust Pb concentration was lower than 61 ppm, then we fixed the concentration at 61 ppm and assigned all of it to nonair sources. Table 5-13 presents the median and 95 th percentile IQ estimates associated with total blood Pb estimates associated with applying the two versions of the hybrid indoor dust Pb model to the general urban case study. The table also includes the IQ loss apportioned to recent air (i.e., inhalation plus ingestion of indoor dust loaded from air) for the median and 95 th percentile IQ
estimates associated with total blood Pb estimates . All IQ loss estimates presented in Table 5-13 were generated using the log-li near with low-exposure linear ization concentration-response
model.5-36 Table 5-13. Comparison of hybrid indoor dust model with a modified form of the model.

Points, IQ loss

Hybrid indoor dust model (from full-scale analysis) Alternate hybrid indoor dust model (with fixed "other" dust value) Median 95 th percentile Median 95 th percentile Air quality scenario (General urban case study) Total Recent air Total Recent air Total Recent air Total Recent air Current NAAQS (1.5 μg/m 3 , max quarterly) 5.8 3.5 9.1 5.5 5.8 3.1 9.1 4.8 Current conditions–high-end (0.87 μg/m 3 , max quarterly) 4.7 1.8 8.0 3.1 4.7 1.4 8.0 2.3
Current conditions – mean (0.14 μg/m 3 , max quarterly) 4.5 1.3 7.7 2.2 4.4 0.9 7.7 1.6
Alternative NAAQS (0.5 μg/m 3 , max monthly) 4.8 1.9 8.1 3.2 4.8 1.5 8.1 2.4
Alternative NAAQS (0.2 μg/m 3 , max quarterly) 4.6 1.5 7.9 2.6 4.6 1.1 7.9 1.9
Alternative NAAQS (0.2 μg/m 3 , max monthly) 4.4 1.2 7.7 2.1 4.4 0.8 7.7 1.5
Alternative NAAQS (0.05 μg/m 3 , max monthly) 4.1 0.5 7.4 0.9 4.1 0.3 7.4 0.5
Alternative NAAQS (0.02 μg/m 3 , max monthly) 4.0 0.3 7.3 0.5 4.0 0.1 7.3 0.3

Results presented in Table 5-13 suggest that the alternate hybrid i ndoor dust model (with the fixed "other indoor dust value") does produ ce indoor dust Pb concentrations for recent air that are noticeably lower than va lues generated using the origin al hybrid indoor dust model used in the full-scale analysis. However, it is important to point out that because both versions of the hybrid model generate the same total indoor dust Pb value (and only differ in the way that value is apportioned between other and re cent air), the trend in total IQ loss due to Pb exposure across the air quality scenarios (moving from curre nt NAAQS to the lowest alternative NAAQS evaluated) is the same for both versions of the hybrid model.5-37 REFERENCES

Brunekreef, B. (1984) The relationship betw een air lead and blood lead in childre n: a critical review. Science of the total environment, 38: 79–123.

Henderson, R. (2007a) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of the 2 nd Draft Lead Human Exposure and Risk Asse ssments Document. September 27, 2007.

Henderson, R. (2007b) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of the 1 st Draft Lead Staff Paper and Draft Lead Expo sure and Risk Assessments. March 27, 2007.

Hilts, S. R. (2003) Effect of smelter emission reductions on children's blood Pb levels. Sci. Total Environ. 303: 51- 58.

Lanphear, B.P., Hornung, R., Khoury, J., Yolton, K., Bag hurst, P., Bellinger, D.C., Canfield, R.L., Dietrich, K.N., Bornschein, R., Greene, T., Rothenberg, S.J., Needleman, H.L., Schnaas, L., Wasserman, G., Graziano, J., and Robe, R. (2005) Low-level environmental Pb exposure and children's intellectual function: An international pooled analysis. Environmen tal Health Perspectives. 113(7):894-899.United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-07-014aEnvironmental Protection Health and Environmental Impacts Division October 2007Agency Research Triangle Park, NC