&EPA
United States
EnviroimnU Protection
Agency
Lead Human Exposure and Health Risk
Assessments for Selected Case Studies
(Draft Report)
Volume I. Human Exposure and Health Risk
Assessments - Full-scale
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EPA-452/D-07-001a
July 2007
Lead Human Exposure and Health Risk Assessments for
Selected Case Studies (Draft Report)
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 Carolina
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DISCLAIMER
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 draft
document has been prepared by staff from the Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or
recommendations are those of the authors and do not necessarily reflect the views of the EPA
Mention of trade names or commercial products is not intended to constitute endorsement or
recommendation for use. This document is being provided to the Clean Air Scientific Advisory
Committee for their review, and made available to the public for comment. Any questions or
comments concerning this document 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).
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PREFACE
This document is part of the Environmental Protections Agency's (EPA's) review of the
National Ambient Air Quality Standards (NAAQS) for lead (Pb). As part of that review, the
Agency has prepared the Air Quality Criteria Document for Lead (the "CD", October, 2006;
available at http ://www. epa. gov/ttn/naaqs/standards/pb/s_pb_cr_cd.html), a draft Staff Paper
(Review of the National Ambient Air Quality Standards for Lead: Policy Assessment of Scientific
and Technical Information, OAQPS Staff Paper -First Draft, December, 2006; available at
http ://www. epa. gov/ttn/naaqs/standards/pb/s_pbcrsp.html), and a draft technical report of pilot
phase risk assessments (Lead Human Exposure and Health Risk Assessments and Ecological
Risk Assessment for Selected Areas., December, 2006; available at
http://www.epa.gov/ttn/naaqs/standards/pb/sjb crtd.html). These documents were developed
under our historic approach for reviewing NAAQS, which has included the completion of a
policy assessment, in the form of a Staff Paper, and of any related risk and exposure assessments
(risk/exposure reports) prior to development of notices of proposed and final rulemakings. The
policy assessment, considering the adequacy of the current standard and policy alternatives, is
intended to help "bridge the gap" between the scientific assessment contained in the CD and the
judgments required of the EPA Administrator in determining whether it is appropriate to retain
or revise the NAAQS.
The Agency is now moving forward to implement a new, improved process for
conducting NAAQS reviews (http://www.epa.gov/ttn/naaqs/) and is transitioning to that new
process during the course of the Pb NAAQS review, beginning with this document (the
risk/exposure report). Under the new process, the risk/exposure report precedes the policy
assessment (rather than accompanying it), and the policy assessment is included in an Advance
Notice of Proposed Rulemaking (ANPR) rather than a Staff Paper. Accordingly, it is the
Agency's intention that the results of the assessments described in the final risk/exposure
assessment report for Pb will be considered, in combination with an evaluation of the policy
implications of the key studies and scientific information contained in the CD and ambient Pb
analyses, in the development of the policy assessment to be published in the Federal Register in
an ANPR this fall.1
Volume I of this document has been drafted by EPA staff, and the appendices (contained
in Volume II) have been drafted by EPA staff, in conjunction with ICF International (through
Contract No. EP-D-06-115). This draft document is being provided to the Clean Air Scientific
Advisory Committee (CASAC) for their review, and is being made available to the public for
1 EPA's preference is to issue the policy assessment as part of an ANPR and not in the form of a final Staff
Paper. EPA is currently, however, under a court order to issue a final Staff Paper and has moved for modification of
that order to allow EPA to issue an ANPR in place of a final Staff Paper. In the event EPA's motion is not granted,
EPA intends to fully comply with the existing order.
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comment. A final version of this document will be prepared taking into consideration CAS AC
and public comments.
This document is limited in focus to the human exposure and risk assessments. As stated
in the December draft Staff Paper, a full-scale ecological risk assessment is not being performed
for this review. The pilot phase ecological risk assessment is presented in the December 2006
draft technical report of pilot phase risk assessments and discussed in the December 2006 draft
Staff Paper. Accordingly, the focus for this review with regard to the policy assessment for the
secondary standard will be on what we have learned from the pilot phase risk assessment, in
addition to the science assessment in the CD.
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Table of Contents
List of Tables iv
List of Figures iv
I INTRODUCTION 1-1
1.1 MULTIMEDIA ASPECT OF THE RISK ASSESSMENT 1-1
1.2 RISK ASSESSMENT FROM LAST REVIEW 1-3
1.3 PILOT PHASE ASSESSMENT FOR THE CURRENT REVIEW 1-5
1.4 CASAC ADVICE 1-5
1.5 ORGANIZATION OF THE DOCUMENT 1-7
REFERENCES 1-8
2 DESIGN OF EXPOSURE AND RISK ASSESSMENTS 2-1
2.1 BACKGROUND INFORMATION 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 2-10
2.1.4.4 Renal System 2-11
2.1.4.5 Heme Synthesis 2-12
2.1.4.6 Immune System 2-12
2.1.5 RiskMetric and Model for Risk Quantitation 2-13
2.2 USE OF CASE STUDIES AND LOCATION SELECTIONS 2-15
2.2.1 Urban Case Study 2-15
2.2.2 Point Source Case Studies 2-16
2.2.2.1 Primary Pb Smelter Case Study 2-17
2.2.2.2 Secondary Pb Smelter Case Study 2-19
2.3 ASSESSMENT SCENARIOS 2-20
2.3.1 Air Concentrations 2-20
2.3.2 Policy-relevant Background 2-22
2.3.3 Outdoor Soil/Dust 2-23
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2.4 ANALYTICAL APPROACH 2-25
2.4.1 Temporal Aspects 2-25
2.4.2 Spatial Scale and Resolution 2-25
2.4.3 Categorization of Policy-relevant Exposure Pathways 2-26
2.4.4 Overview of Analytical Steps 2-28
2.4.4.1 Exposure Assessment 2-30
2.4.4.2 Risk Characterization 2-32
2.4.5 Variability Characterization 2-32
2.4.6 Uncertainty Characterization 2-33
2.4.6.1 Performance Evaluations 2-34
2.4.6.2 Generating Multiple Sets ofResults 2-34
2.4.6.3 Sensitivity Analysis 2-35
2.4.6.4 Qualitative Discussion of Sources of Uncertainty 2-36
REFERENCES 2-37
3 EXPOSURE ASSESSMENT 3-1
3.1 METHODS FOR ESTIMATING MEDIA 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 3-13
3.2 METHODS FOR ESTIMATING BLOOD PB LEVELS 3-14
3.2.1 Blood Pb Modeling 3-14
3.2.1.1 Primary Analysis 3-16
3.2.1.2 Sensitivity Analysis 3-16
3.2.2 Exposure Pathway Apportionment and Probabilistic Population
Modeling 3-17
3.2.2.1 General Urban Case Study 3-18
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3.2.2.2 Point Source Case Studies 3-19
3.2.3 GSD for Population Blood Pb Modeling Procedure 3-22
3.2.3.1 General Urban Case Study 3-24
3.2.3.2 Point-Source Case Studies 3-25
3.3 ESTIMATED MEDIA CONCENTRATIONS 3-25
3.4 ESTIMATED BLOOD PB LEVELS 3-28
3.5 UNCERTAINTY CHARACTERIZATION 3-42
3.5.1 Performance Evaluation Related to Exposure Media Modeling 3-42
3.5.1.1 Evaluation of Modeled Ambient Air Pb Concentrations 3-42
3.5.1.2 Evaluation of Modeled Outdoor Soil/Dust Pb Concentrations 3-43
3.5.1.3 Evaluation of Modeled Indoor Dust Pb Concentrations 3-44
3.5.2 Performance Evaluation Related to Blood Pb Modeling 3-47
3.5.2.1 Evaluation of Candidate Blood Pb Models 3-47
3.5.2.2 Evaluation of model-derived outdoor air Pb-to-blood Pb ratios... 3-48
3.5.2.3 Comparison of modeled blood Pb levels to nationally
representative data 3-50
REFERENCES 3-55
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 Low-Exposure Linearization 4-3
4.1.1.3 Two-piece Linear Function 4-4
4.1.2 Project! on of Population Risk 4-5
4.2 RISK ESTIMATES 4-6
4.2.1 General Urban Case Study 4-8
4.2.2 Primary Pb Smelter Case Study 4-18
4.2.3 Secondary Pb Smelter Case Study 4-26
4.3 UNCERTAINTY CHARACTERIZATION 4-32
4.3.1 Qualitative Discussion of Key Sources of Uncertainty 4-32
4.3.2 Sensitivity Analysis 4-35
4.3.3 Performance Analyses 4-37
4.3.4 Uncertainty in Modeling Approaches - Multiple Sets of Results 4-40
REFERENCES 4-42
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List of Figures
Figure 1-1. Principal pathways of human and ecological exposure to Pb. Among the policy-
relevant pathways, heavy arrows indicate the predominant human exposures 1-3
Figure 2-1. Conceptual model for Pb human health risk assessment 2-2
Figure 2-2. Overview of analysis approach 2-29
Figure 2-3. Modeling approaches for each case study 2-35
Figure 3-1. Procedure for generating population blood Pb distributions for point source
case studies 3-21
Figure 3-2. Comparison of NHANES-IV blood Pb levels with modeled estimates 3-52
Figure 4-1. Comparison of three concentration-response functions for concurrent blood Pb
levels < 10 |ig/dL 4-2
List of Tables
Table 2-1. Key aspects of primary Pb smelter case study 2-18
Table 2-2. Key aspects of secondary Pb smelter case study 2-19
Table 2-3. Air quality scenarios assessed for the general urban case study 2-22
Table 3-1. Case study approaches for estimating media Pb concentrations 3-2
Table 3-2. Hybrid model for indoor dust Pb in general urban case study 3-11
Table 3-3. Estimated annual ambient air concentrations 3-26
Table 3-4. Estimated inhalation exposure concentrations 3-27
Table 3-5. Estimated outdoor soil/dust concentrations 3-27
Table 3-6. Estimated indoor dust concentrations 3-28
Table 3-7. General urban case study: current conditions - estimated blood Pb levels 3-31
Table 3-8. General urban case study: current NAAQS (1.5 |ig/m3, maximum quarterly
average) - estimated blood Pb levels 3-32
Table 3-9. General urban case study: alternative NAAQS (0.2 |ig/m3, maximum quarterly
average) - estimated blood Pb levels 3-33
Table 3-10.General urban case study: alternative NAAQS (0.5 |ig/m3, maximum monthly
average) - estimated blood Pb levels 3-34
Table 3-11.General urban case study: alternative NAAQS (0.2 |ig/m3, maximum monthly
average) - estimated blood Pb levels 3-35
Table 3-12.General urban case study: alternative NAAQS (0.05 |ig/m3, maximum monthly
average) - estimated blood Pb levels 3-36
Table 3-13.Primary Pb smelter case study: current NAAQS (1.5 |ig/m3, maximum quarterly
average) - estimated blood Pb levels 3-37
Table 3-14.Primary Pb smelter case study: current NAAQS attainment (0.2 |ig/m3, maximum
quarterly average) estimated blood Pb levels 3-37
Table 3-15.Primary Pb smelter case study: alternate NAAQS (0.5 |ig/m3, maximum monthly
average) - estimated blood Pb levels 3-38
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Table 3-16.Primary Pb smelter case study: alternate NAAQS (0.2 |ig/m3, maximum monthly
average) - estimated blood Pb levels 3-38
Table 3-17.Primary Pb smelter case study: alternate NAAQS (0.05 |ig/m3, maximum monthly
average) - estimated blood Pb levels 3-39
Table 3-18.Secondary Pb smelter case study: current conditions - estimated blood Pb levels. 3-39
Table 3-19. Secondary Pb smelter case study: alternative NAAQS (0.2 |ig/m3, maximum
quarterly average) - estimated blood Pb levels 3-40
Table 3-20. Secondary Pb smelter case study: alternative NAAQS (0.5 |ig/m3, maximum monthly
average) - estimated blood Pb levels 3-40
Table 3-21. Secondary Pb smelter case study: alternative NAAQS (0.2 |ig/m3, maximum
monthly average) - estimated blood Pb levels 3-41
Table 3-22. Secondary Pb smelter case study: alternative NAAQS (0.05 |ig/m3, maximum
monthly average) - estimated blood Pb levels 3-41
Table 3-23.Evaluation of model-predicted indoor dust Pb levels against empirical data
obtained from the literature 3-46
Table 3-24.Air-to-Blood Pb ratios for "recent air" contribution to concurrent blood Pb level. 3-49
Table 3-25.Blood Pb Levels for 7 year olds in the U.S. (interpolated from NHANES IV,
1999-2002) 3-52
Table 4-1. Comparison of total and incremental IQ loss estimates below 10 |ig/dL for the three
concentration-response functions 4-2
Table 4-2. Summary of risk estimates for recent air 4-9
Table 4-3. Summary of risk estimates for recent plus past air 4-11
Table 4-4. General urban case study: current conditions - estimated IQ loss 4-12
Table 4-5. General urban case study: current NAAQS - estimated IQ loss 4-13
Table 4-6. General urban case study: alternative NAAQS (0.5 |ig/m3, maximum monthly
average) - estimated IQ loss 4-14
Table 4-7. General urban case study: alternative NAAQS (0.2 |ig/m3, maximum quarterly
average) - estimated IQ loss 4-15
Table 4-8. General urban case study: alternative NAAQS (0.2 |ig/m3, maximum monthly
average) estimated IQ loss 4-16
Table 4-9. General urban case study: alternative NAAQS (0.05 |ig/m3, maximum monthly
average) estimated IQ loss 4-17
Table 4-10.Primary Pb smelter case study: current NAAQS - estimated IQ loss 4-21
Table 4-11.Primary Pb smelter study: alternative NAAQS (0.5 |ig/m3, maximum monthly
average) - estimated IQ loss 4-22
Table 4-12.Primary Pb smelter study: alternative NAAQS (0.2 |ig/m3, maximum quarterly
average) - estimated IQ loss 4-23
Table 4-13.Primary Pb smelter case study: alternative NAAQS (0.2 |ig/m3, maximum monthly
average) - estimated IQ loss 4-24
Table 4-14.Primary Pb smelter case study: alternative NAAQS (0.05 |ig/m3, maximum monthly
average) - estimated IQ loss 4-25
Table 4-15.Secondary Pb smelter case study: current conditions - estimated IQ loss 4-27
Table 4-16. Secondary Pb smelter case study: alternative NAAQS (0.5 |ig/m3, maximum monthly
average) estimated IQ loss 4-28
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Table 4-17. Secondary Pb smelter case study: alternative NAAQS (0.2 |ig/m3, maximum
quarterly average) estimated IQ loss 4-29
Table 4-18. Secondary Pb smelter case study: alternative N A AQS (0.2 |ig/m3, maximum monthly
average) estimated IQ loss 4-30
Table 4-19. Secondary Pb smelter case study: alternative N A AQS (0.05 |ig/m3, maximum
monthly average) estimated IQ loss 4-31
Table 4-20.Impact of multiple sources of uncertainly on risk results 4-40
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1 1 INTRODUCTION
2 This document is the first volume of the draft report Lead Human Exposure and Health
3 Risk Assessments for Selected Areas. This volume describes the quantitative human exposure
4 and health risk assessments1 being conducted to inform the U.S. Environmental Protection
5 Agency's (EPA's) current review of the National Ambient Air Quality Standards (NAAQS) for
6 lead (Pb). The draft risk assessment report is being provided to CASAC and the public for
7 review in advance of a public meeting of the CASAC Pb panel planned for August 28-29, 2007.
8 Following that meeting, we will take CASAC and public comments into account in preparing the
9 final document. We plan to complete the final risk assessment report in October, 2007.
10 As with the last review of the Pb NAAQS (see Section 1.1), the human exposure and
11 health risk assessments (the risk assessment)2 for this review reflect multimedia exposure
12 pathways, and their influence on blood Pb levels as an internal index of exposure or dose. The
13 assessment for this review, as with that for the last review, utilizes a case-study approach
14 wherein a set of specific locations or case studies associated with policy-relevant Pb exposures
15 are evaluated in detail. The case studies have been selected to provide a perspective on the
16 nature and magnitude of air-sourced Pb exposures and risk in the United States. There are two
17 phases to the risk assessment for the current review: pilot and full-scale. The first phase (i.e.,
18 the pilot assessment, described in Section 1.2) was presented in the first draft Staff Paper and
19 accompanying technical report (USEPA, 2006b; ICF, 2006), and was the subject of a CASAC
20 review on February 6 and 7, 2007 described in Section 1.3 (Henderson, 2007). The full-scale
21 assessment is described in this draft document.
22 1.1 MULTIMEDIA ASPECT OF THE RISK ASSESSMENT
23 The focus for this Pb NAAQS risk assessment is on Pb derived from those sources
24 emitting Pb to ambient air. The multimedia and persistent nature of Pb, however, contributes
25 several complexities to the assessment.
26 First, exposures to Pb emitted into the air occur via multiple pathways. As described in
27 the Air Quality Criteria for Lead (USEPA, 2006a; henceforth referred to as the CD), "The
28 multimedia aspects of Pb exposure can be seen in that Pb emissions to the air contribute to Pb
1 As described in the Preface to this document, the ecological 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 draft Staff Paper (USEPA, 2006b).
2 Throughout the remainder of this document, the term "risk assessment" will be used to refer to both the
human exposure and health risk assessments collectively, unless specific reference to either the human exposure or
health risk assessment is required.
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1 concentrations in water, soil and dusts; Pb in soil and dust also can make important contributions
2 to Pb concentrations in ambient air" (CD, p. 3-1).
3 That is, inhalation exposures can result from Pb emitted to the ambient air recently from
4 Pb emitted in the past that has deposited from air to soil or dust and then become resuspended in
5 the ambient air. Further, Pb emitted into the ambient air can contribute to ingestion exposures
6 (associated with indoor dust, outdoor soil/dust, agricultural products and surface water) of
7 recently deposited Pb and of Pb that was deposited in the past. Consequently, this is a multi-
8 pathway risk assessment in which we are considering both airborne Pb, as it contributes to
9 human exposures through direct inhalation of particles containing Pb, and also Pb that has
10 deposited from air to dusts, soil and other environmental media and that contributes to human
11 exposures through ingestion. Further, we are considering that Pb, once deposited, may be
12 resuspended in the air, contributing to human inhalation exposures or, upon re-deposition, to
13 human ingestion exposures. Thus, as illustrated in Figure 1-1, pathways that are directly relevant
14 to a review of the NAAQS include both newly emitted Pb from currently operating sources, and
15 Pb emitted in the past, either from currently operating sources or historic sources, which are
16 collectively referred to as "policy-relevant sources".
17 Due to limited data, models, and time available, however, we are not able to fully and
18 completely characterize in our risk assessment all of the various complexities associated with Pb.
19 Consequently, in our efforts to focus on and characterize risk associated with the ambient air-
20 related3 sources and exposures, we have made simplifying assumptions in a number of areas.
21 For example, Figure 1-1 illustrates that people are also exposed to Pb that originates from non-air
22 sources, including leaded paint or drinking water distribution systems. For purposes of this
23 assessment, the Pb from these non-air sources is collectively referred to as "policy-relevant
24 background". Although Pb in diet and drinking water sources may derive from Pb emitted into
25 the ambient air, the contribution from air pathways to these exposure pathways is not explicitly
26 recognized, such that these exposures are treated as policy-relevant background.4
3 Ambient-air related sources are those emitting Pb into the ambient air (including resuspension of
previously emitted Pb), and ambient air related exposures include 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 background exposures is not intended to convey any
particular policy decision at this stage regarding the Pb standard. Rather, it is simply intended to convey an area of
interest to this review.
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1
Policy-relevant
Background*
Policy-relevant Sources
Policy-relevant
Background*
Historically emitted Pb
Non-air Pb
releases
Outdoor Soil, Dusts
Ecological Exposures
Human Exposures
-Inhalation & ingestion
*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).
2 Figure 1-1.
3
4
Principal pathways of human and ecological exposure to Pb. Among the
policy-relevant pathways, heavy arrows indicate the predominant human
exposures.
5 1.2 RISK ASSESSMENT FROM LAST REVIEW
6 In the risk assessment conducted in support of the last review, air quality scenarios were
7 compared in terms of their impact on the percentage of modeled populations that exceeded
8 specific blood Pb levels chosen with consideration of the health effects evidence at that time
9 (USEPA, 1990). The 1990 analysis focused on both children (birth through 7 years of age) and
10 middle-aged men residing in three case study locations (two near secondary Pb smelters and one
11 near a primary Pb smelter). The analysis also introduced the use of pharmacokinetic blood Pb
12 modeling for children, although it used empirically derived slope models for adult men to relate
13 changes in air Pb to changes in blood Pb.
14 In the 1990 Staff Paper, staff concluded that at levels of 10-15 ug/dL of blood Pb, there
15 appeared to be "a convergence of evidence of lead-induced interference with diverse set of
16 physiological functions and processes, particularly evident in several independent studies
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1 showing impaired neurobehavioral function and development" (USEPA, 1990).5 Accordingly,
2 the staff used blood Pb levels of 10 and 15 ug/dL to evaluate effects of alternate NAAQS on
3 children in the 1990 analysis (USEPA, 1990). These values were chosen with consideration of
4 the full body of health effects evidence at that time. Staff then used dispersion modeling (the
5 Industrial Source Complex (ISC) model) combined with source characterization data to generate
6 Pb air concentrations for each case study area. Statistically derived relationships based on data
7 from other industrial locations, including Pb smelters, that linked concentrations of Pb in air to
8 Pb in indoor dust and outdoor soil were then used to predict Pb in these media for the three case
9 study locations, based on the modeled air Pb concentrations. An uptake/biokinetic model was
10 also developed to predict child blood Pb levels. This model was used in place of a statistically-
11 based regression slope model to allow consideration of the dynamic nature of Pb exposure in
12 children. EPA combined model-derived central tendency blood Pb levels with an estimated
13 geometric standard deviation (GSD) reflecting inter-individual variability in blood Pb levels, to
14 generate population distributions of blood Pb levels. These distributions were then used to
15 estimate the percentage of children at each case study location that exceeded the blood Pb levels
16 10 and of 15 ug/dL, respectively.
17 For adult men, the 1990 assessment used blood Pb levels of 10 and 12 ug/dL to compare
18 relative effects of alternate NAAQS (USEPA, 1990). The same approach was used for
19 generating media concentrations for the adult analysis as was used for the child assessment.
20 However, rather than via a biokinetic model, as was used for the children's assessment, the 1990
21 analysis for adults used statistically derived slope models to relate air Pb to blood Pb levels with
22 two versions of the slope models being employed: (a) the aggregate model which predicts blood
23 Pb in adults based solely on air Pb levels (here a single slope factor captures both the direct
24 inhalation pathway as well as the more complex pathway of Pb deposition to soil and dust
25 followed by incidental ingestion) and (b) the disaggregate model which uses media-specific
26 slopes to predict blood Pb based on Pb concentrations in soil, dust and air. Since the projected
27 blood Pb levels were mean population levels, a GSD term was included to develop population-
28 level blood Pb distributions. The GSD estimates for adults and children were derived from
29 information on observed blood Pb levels in these subgroups. These population-level
30 distributions were then queried to identify the percentage of adult men at each case study
31 location with modeled blood Pb levels exceeding the levels of interest for adults (10 and 12
32 ug/dL).
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 ug/dL and identified a level of 10 ug/dL for
implementing community-wide prevention activities (CDC, 1991; CDC, 2005).
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1 The primary difference between the risk assessment approach used in the current pilot
2 analysis and the assessment completed in 1990 involves the risk metric employed, which reflects
3 the quantitative and qualitative health effects evidence available today that was not available in
4 1990 (CD). Rather than estimating the percentage of study populations with exposures above
5 blood Pb levels of interest as was done in the last review (i.e., 10, 12 and 15 ug/dL), the current
6 pilot analysis estimates the degree of health decrement in study populations exposed to Pb.
7 Specifically, the pilot analysis estimates the distribution of IQ loss associated with Pb exposure
8 for child populations at each of the case study locations with that IQ loss further differentiated
9 between background Pb exposure and policy-relevant exposures.
10 1.3 PILOT PHASE ASSESSMENT FOR THE CURRENT REVIEW
11 The pilot phase of the risk assessment for the current review is described in the first draft
12 Staff Paper and accompanying technical report (USEPA 2006b, ICF 2006). The pilot assessment
13 was intended primarily as a demonstration of the risk assessment methodology being developed
14 for the current review. Consequently, exposure and risk results from the pilot assessment are
15 considered preliminary. Additionally, the pilot assessment presented exposure and risk
16 assessments for only two air quality scenarios (current conditions and attainment of the current
17 NAAQS).
18 The pilot assessment included three case studies: (a) a primary Pb smelter (in
19 Herculaneum, Missouri), (b) a secondary Pb smelter (in Troy, Alabama), and, (c) a near roadway
20 (urban) location in Houston, Texas.6 The case studies modeled for the pilot were selected to
21 provide a preliminary perspective on the nature and magnitude of air-sourced Pb exposures and
22 risk. In addition, they provided a range of exposure scenarios in which to test the risk
23 assessment methodology developed for the current review. Because of differences in the
24 exposure scenarios and available data at each of the case study locations, the approach used for
25 modeling exposure and risk differed among the case studies. Results from the pilot assessment,
26 as well as comments received from the public and CASAC (see Section 1.4) have informed
27 decisions on the design for the full-scale assessment, including the types of case studies included.
28 1.4 CASAC ADVICE
29 The staff consulted with the CASAC on the draft analysis plan for the risk assessment
30 (USEPA, 2006c) in June, 2006 (Henderson, 2006), and subsequently developed the pilot
6 Note, that the near roadway (urban) case study comprised a 1.5 mile road segment and the residents living
within 200m of that road segment. Consequently, 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.
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1 assessment, summarized in Section 1.3, and described in the first draft Staff Paper and
2 accompanying technical report (USEPA, 2006b; ICF 2006). On February 6-7, 2007, the CASAC
3 Pb panel met to discuss these documents and CASAC's written comments and recommendations
4 were provided in March 2007 (Henderson, 2007).
5 Consistent with their mandate under the Clean Air Act, CASAC provided comments on
6 both scientific aspects of the risk assessment and aspects related to the standards themselves
7 (Henderson, 2007).7 With regard to the risk assessment, they recommended that the case study
8 approach implemented for the pilot risk assessment be supplemented with a "population-based"
9 analysis, and, in discussion at the public meeting, the panel raised the general occurrence of
10 lower Pb levels in urban areas beyond more point source impacted areas as being an important
11 focus for the risk assessment. As described below, consideration of comments in this area led to
12 a significant difference in the design of the full-scale assessment as compared to the pilot
13 assessment.
14 CASAC also recommended that uncertainty be characterized with regard to the
15 relationship between a change in the NAAQS and the distribution of population blood Pb
16 concentrations, and with regard to the relationship between blood Pb concentrations and the risk
17 of adverse health effects (Henderson, 2007). With respect to alternate NAAQS for consideration
18 by EPA, CASAC recommended consideration of levels less than or about 0.2 |ig/m3
19 (micrograms per cubic meter) and of a monthly averaging time. Additionally, they indicated that
20 they consider a loss of 1-2 points in intelligence quotient (IQ) at the 99.5th percentile of the
21 population to be highly significant from a public health perspective. CASAC also recommended
22 conducting future Pb monitoring with samplers for particulate matter less than ten microns in
23 size (PMio) rather than with samplers for total suspended particulate matter (TSP) (Henderson,
24 2007).
25 In consideration of CAS AC's comments on the pilot-scale assessment (Henderson,
26 2007), we considered a number of alternate approaches for the full-scale assessment. As a result,
27 several additions and modifications to the assessment design were implemented for the full-scale
28 assessment. The most significant of these modifications is the replacement of the near-roadway
29 case study with a general urban case study. This case study was designed to provide estimates of
30 risk in urban areas associated with broad population level exposures to different ambient air
31 levels of Pb. The general urban case study is assessed in addition to the two point source case
32 studies, and differs from those case studies in basing the estimate of air quality on monitoring
33 data (rather than on results from air quality modeling). The alternate NAAQS levels were
7 Consistent with the focus of this document on the human exposure and health risk assessment, CASAC
comments regarding the ecological risk assessment and secondary standard considerations are not discussed here.
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1 selected to overlap with the range of levels suggested by CASAC. The target population and
2 endpoint for the assessment remains young children and risk of IQ decrements associated with
3 Pb exposure. To address CASAC comments on the cut-point employed in the pilot assessment
4 (Henderson, 2007), the blood Pb concentration response function was re-examined, and three
5 alternatives were included in the assessment. Additionally, in consideration of CASAC
6 recommendations regarding consideration of the geometric standard deviation used in the blood
7 Pb modeling, a range of values were included in the assessment for the general urban case study.
8 1.5 ORGANIZATION OF THE DOCUMENT
9 The remainder of this document is organized as follows. Chapter 2 describes the design
10 of the exposure and risk assessments, covering such topics as the conceptual model used in
11 designing the analysis (Section 2.1), the case studies included in the assessment (Section 2.2), the
12 air quality scenarios simulated in the assessment (Section 2.3), and an overview of the analytical
13 approach (Section 2.4). Chapter 3 describes the methods and results for the exposure
14 assessment, as well as the performance evaluation. Chapter 4 describes the methods for deriving
15 risk estimates, the resultant estimates, sensitivity analyses and a characterization of uncertainty.
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1 REFERENCES
2 Centers for Disease Control (1991) Preventing lead poisoning in young children: a statement by the Centres for
3 Disease Control. Atlanta, GA: U.S. Department of health and Human Services, Public Health Service;
4 October 1. http://wonder.cdc.gov/wonder/prevguid/p0000029/p0000029.asp
5 Centers for Disease Control and Prevention (2005) Preventing lead poisoning in young children: a statement by the
6 Centers for Disease Control and Prevention. Atlanta, GA: U.S. Department of Health and Human Services,
7 Public Health Service. August.
8 Henderson, R. (2006) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to
9 Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee (CASAC) Lead Review
10 Panel's Consultation on EPA's draft Analysis Plan for Human Health and Ecological Risk Assessment for
11 the Review of the Lead National Ambient Air Quality Standards. July 26, 2006.
12 Henderson, R. (2007) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to
13 Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee's (CASAC) Review of
14 the 1st Draft Lead Staff Paper and Draft Lead Exposure and Risk Assessments. March 27, 2007.
15 ICF International. (2006) Lead Human Exposure and Health Risk Assessments and Ecological Risk Assessment for
16 Selected Areas. Pilot Phase. Draft Technical Report. Prepared for the U. S. EPA's Office of Air Quality
17 Planning and Standards, Research Triangle Park, NC. December.
18 U.S. Environmental Protection Agency. (1990) Air Quality Criteria for Lead: Supplement to the 1986 Addendum.
19 Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and
20 Assessment Office; report no. EPA/600/8-89/049F. Available from: NTIS, Springfield, VA; PB91-138420.
21 U.S. Environmental Protection Agency. (1990) Review of the National Ambient Air Quality Standards for Lead:
22 Assessment of Scientific and Technical Information: OAQPS Staff Paper. Research Triangle Park, NC:
23 Office of Air Quality Planning and Standards; report no. EPA-450/2-89/022. Available from: NTIS,
24 Springfield, VA; PB91-206185. Available on the web:
25 http://www.epa.gov/ttn/naaqs/standards/pb/data/rnaaqsl asti.pdf
26 U.S. Environmental Protection Agency. (2006a) Air Quality Criteria for Lead. Washington, DC, EPA/600/R-
27 5/144aF. Available online at: www.epa.gov/ncea/
28 U.S. Environmental Protection Agency. (2006b). Review of the National Ambient Air Quality Standards for Lead:
29 Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper - First Draft. Office of Air
30 Quality Planning and Standards, Research Triangle Park, NC.
31 U.S. Environmental Protection Agency. (2006c) Analysis Plan for Human Health and Ecological Risk Assessment
32 for the Review of the Lead National Ambient Air Quality Standards. Office of Air Quality Planning and
33 Standards, Research Triangle Park, NC. Available online at:
34 http://www.epa.gOv/ttn/naaqs/standards/pb/s pb cr pd.html
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1 2 DESIGN OF EXPOSURE AND RISK ASSESSMENTS
2 The risk assessment design relies on the use of case studies. The types of case studies
3 included, as well as the analytical aspects of the assessment of each, reflect consideration of the
4 evidence presented in the CD, air quality analyses, and findings of the pilot assessment (Section
5 1.3), as well as comments received from CASAC (Section 1.4) and the public.
6 Drawing primarily from the CD, Section 2.1 provides background for the risk
7 assessment, with regard to key elements of Pb exposure and effects. The assessment scenarios
8 evaluated in the assessment are described in Section 2.2. Background information on the three
9 case studies is described in Section 2.3. Section 2.4 describes the analytical approach, with
10 attention to key analytical steps, and discussion of temporal and spatial aspects of the
11 assessment, as well as the categorization of policy-relevant exposure pathways, and the
12 uncertainty characterization.
13 2.1 BACKGROUND INFORMATION ON LEAD EXPOSURE AND RISK
14 As recognized in Section 1.1, there are a variety of complexities associated with the
15 assessment of air-related Pb exposure and risk. In this risk assessment, we have attempted to
16 focus effort on those aspects that are most important and feasible to address within our scope and
17 given the constraints of time, pertinent data, models, etc. With regard to some aspects,
18 simplifying assumptions have been implemented. The following subsections describe elements
19 of Pb exposure and effects pertinent to evaluating public health risks associated with Pb from
20 ambient air, and specify those that are explicitly addressed in this quantitative risk assessment.
21 This is summarized in Figure 2-1, with boxes outlined in bold indicating items included in the
22 quantitative risk assessment and sources and pathways for which ambient air has played a role
23 identified in bold text.
24
25
26
27
28
29
30
31
32
33
34
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1
2
Figure 2-1. Conceptual model for Pb human health risk assessment.
4
5
6
LU
w
Newly Emitted
Pb
SQiPDust - Outdoors
from Past Pb
Diet
i ^
RE-ENTRAINMENT
INTERNAL
DISPOSITION POPU
Ambient Air
i
DEPOSITION
4
Air * Indoor
t
Inhalaten
REENTRA1NM
-Nff
J >
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Dust -
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Lead Paint -
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1
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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.
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1 2.1.1 Sources, Pathways and Routes
2 As described in Section 1.1, policy-relevant sources (in Figure 2-1 in bold type) - for the
3 purposes of this assessment - include both sources of new Pb emissions (e.g., from active
4 stationary and mobile sources) and re-emission or resuspension of historically deposited Pb (e.g.,
5 near roadways or associated with now inactive, or now lower emitting stationary sources, as
6 discussed in Appendix A, Section A. 1.1.3).
7 There are more than 13,000 individual sources in the U.S. for which we have estimated
8 Pb emissions to the air (Appendix A, Section A. 1.2). Cumulatively, those sources, in addition to
9 mobile sources and other sources not individually quantified, emit some 1600 tons per year (tpy)
10 of Pb in the U.S. (Appendix A). The largest categories (in terms of aggregate national
11 emissions) include mobile sources (specifically combustion of leaded general aviation fuel),
12 boilers and process heaters, and metals processes, such as primary and secondary Pb smelting.
13 Of these, metals processing industries are among the largest emitters of Pb, in terms of emissions
14 from individual facilities (Appendix A, Section A.I). Another large category of Pb emissions,
15 for which we do not have quantitative estimates in our national emissions inventory, is
16 resuspension of recent and historically deposited Pb (Appendix A, Section A.I.1.3; CD, Section
17 2.3.3). Studies of emissions in southern California indicate that Pb in resuspended road dust may
18 represent between 40% and 90% of Pb emissions in some areas (Appendix A, Section A. 1.1.3).
19 Resuspension is represented to differing degrees in the three case studies included in the risk
20 assessment (Sections 2.2.1 and 2.2.2)
21 Lead in outdoor dust and soil may be derived from a range of sources including current
22 and historical air emissions sources, as well as miscellaneous non-air sources (e.g., land disposal
23 of wastes and subsequent weathering). These media may play a substantial role in human
24 exposures, particularly for children (CD, Section 3.2). Additionally, Pb in house dust, which
25 may be derived from Pb in outdoor dust and soil as well as from ambient air Pb (including
26 previously deposited Pb resuspended into ambient air), is another source of children's exposure
27 (CD, Sections 3.2 and 4.4). For example, blood Pb levels in children have been shown to be
28 particularly influenced by exposures to Pb in dust (e.g., Lanphear and Roghmann 1997;
29 Lanphear et al., 1998). As described in the CD, such findings "and other studies of populations
30 near active sources of air emissions (e.g., smelters), substantiate the effect of airborne Pb and
31 resuspended soil Pb on interior dust and blood Pb" (CD, p. 8-22).
32 In addition to airborne emissions (recent or those in the past), sources of Pb to the
33 environment or to human exposure included old leaded paint, Pb in drinking water and Pb in the
34 diet (Figure 1-1). As mentioned in Section 1.1, Pb in the diet and that from drinking water may
35 have air pathway-related (i.e., policy-relevant) contributions as well as contributions from
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1 policy-relevant background (e.g., Pb-solder on water distribution pipes and Pb in materials used
2 in food processing). Limitations in our data and modeling tools have handicapped our ability to
3 separate these contributions in the risk assessment, such that we have labeled diet and drinking
4 water policy-relevant background. Consequently, these sources of Pb exposure are depicted in
5 Figure 2-1 as policy-relevant background (in non-bold type), although this is not intended to
6 convey any particular policy decision at this stage regarding the Pb standard. Policy-relevant
7 pathways (bold text in Figure 2-1) include inhalation of newly or previously emitted Pb,
8 ingestion of outdoor soil/dust containing previously deposited Pb, and ingestion of indoor dust
9 containing newly or previously emitted Pb.
10 Human exposure to environmental Pb occurs predominantly via ingestion and inhalation
11 routes, with ingestion (including incidental ingestion of dust and soil) recognized as generally
12 playing a larger role for the general human population (CD, Section 4.5). The dermal route is
13 relatively less well characterized but is not considered to play a large role in total Pb exposure
14 (CD, Section 4.5), and is not included in this assessment (Figure 2-1).
15 2.1.2 At-risk Populations
16 In considering populations for inclusion in the risk assessment, we considered evidence
17 regarding those with increased susceptibility (i.e., physiological factors contributing to a greater
18 response for the same exposure), and those with increased exposure (including that resulting
19 from behavior leading to increased contact with contaminated media). A behavioral factor of
20 great impact on Pb exposure is the incidence of hand-to-mouth activity that is prevalent in very
21 young children (CD, Section 4.4.3). Physiological factors include both conditions contributing
22 to a subgroup's increased risk of effects at a given blood Pb level, and those that contribute to
23 blood Pb levels higher than those otherwise associated with a given Pb exposure (CD, Section
24 8.5.3). An additional population characterization for which evidence was considered was
25 vulnerability to pollution-related effects which additionally encompasses situations of elevated
26 exposure, such as residing in old housing with Pb-containing paint or near sources of ambient
27 Pb, as well as socioeconomic factors, such as reduced access to health care or low
28 socioeconomic status (SES) (USEPA, 2003, 2005) that can contribute to increased risk of
29 adverse health effects from Pb.
30 Three particular physiological factors contributing to increased risk of Pb effects at a
31 given blood Pb level are recognized in the CD (e.g., CD, Section 8.5.3): age, health status, and
32 genetic composition. With regard to age, the susceptibility of young children to the
33 neurodevelopmental effects of Pb is well recognized (e.g., CD, Sections 5.3, 6.2, 8.4, 8.5, 8.6.2),
34 although the specific ages of vulnerability have not been established (CD, pp 6-60 to 6-64).
35 Additionally, early childhood may also be a time of increased susceptibility for Pb
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1 immunotoxicity (CD, Sections 5.9.10, 6.8.3 and 8.4.6), and childhood exposures have been
2 associated with increased risk of cardiovascular and neurodegenerative effects in adulthood (CD,
3 p. 8-74). Health status is another physiological factor in that subpopulations with pre-existing
4 health conditions may be more susceptible (as compared to the general population) for particular
5 Pb-associated effects, with this being most clear for renal and cardiovascular outcomes. For
6 example, African Americans, who, as a group, have a higher frequency of hypertension than the
7 general population or other ethnic groups (NCHS, 2005) may face a greater risk of adverse
8 health impact from Pb-associated cardiovascular effects. A third physiological factor relates to
9 genetic polymorphisms. That is, subpopulations defined by particular genetic polymorphisms
10 (e.g., presence of the 5-aminolevulinic acid dehydratase-2 [ALAD-2] allele) have also been
11 recognized with regard to susceptibility to Pb toxicity, which may be due to increased
12 susceptibility to the same internal dose and/or to increased internal dose associated with same
13 exposure (CD, p. 8-71, Sections 6.3.5, 6.4.7.3 and 6.3.6).
14 Several physiological factors pertain to susceptibility by contributing to increased blood
15 Pb levels (i.e., increased internal dose levels) over those otherwise associated with a given Pb
16 exposure (CD, Section 8.5.3). These include nutritional status, which plays a role in Pb
17 absorption from the GI tract (CD, Section 5.10.2.5); polymorphism for the vitamin D receptor,
18 which studies suggest may contribute to increased Pb absorption from the GI tract (CD, Section
19 8.4.2.7); presence of the ALAD-2 allele, which studies suggest contribute to increased blood Pb
20 levels (Section 8.5.3); and bone demineralization, such as occurs during pregnancy, lactation,
21 and aging, which appears to influence Pb release from bone into the blood (CD, Section 4.3.2).
22 Further, differences in blood Pb levels among subpopulations living in the same area
23 have also been identified that indicate an increased vulnerability to Pb exposure among some
24 subgroups, perhaps related to SES (CD, pp. 3-26 and 8-13).
25 In summary, there are a variety of ways in which Pb exposed populations might be
26 characterized and stratified for the purposes of health risk assessment. In recognition of the role
27 of age or lifestage on exposure and susceptibility, this is used in identifying potential groups in
28 Figure 2-1. In consideration of the health effects evidence regarding endpoints of greatest public
29 health concern (see Section 2.1.4), young children have been selected as the priority population
30 for this risk assessment (see Figure 2-1). As currently available data do not generally support
31 quantitative modeling that differentiates blood Pb levels and associated health risk within a
32 particular population group such as young children on the basis of enhanced or reduced
33 susceptibility to Pb effects (e.g., concentration response functions for IQ loss that differentiate
34 between populations that are calcium deficient and those that are not), the assessment does not
35 develop separate risk estimates for such subpopulations of young children.
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1 2.1.3 Internal Disposition
2 Once inhaled or ingested and absorbed into the blood stream, Pb is distributed throughout
3 the body via the blood, with bone being the predominant site of Pb accumulation and storage in
4 the body. During childhood development, bone represents approximately 70% of a child's body
5 burden, and this accumulation continues through adulthood, when more than 90% of the total Pb
6 body burden is stored in the bone (CD, Section 4.2.2). Accordingly, levels of Pb in bone are
7 indicative of a person's long-term, cumulative exposure to Pb. In contrast, blood Pb levels are
8 usually indicative of recent exposures. Depending on exposure dynamics, however, blood Pb
9 may - through its interaction with bone - be indicative of past exposure or of cumulative body
10 burden (CD, Section 4.3.1.5).
11 Throughout life, Pb in the body is exchanged between blood and bone, and between
12 blood and soft tissues (CD, Section 4.3.2), with variation in these exchanges reflecting "duration
13 and intensity of the exposure, age and various physiological variables" (CD, p. 4-1). For
14 example, resorption of bone (e.g., in pregnant or nursing women, or associated with osteoporosis
15 in postmenopausal women), results in a mobilization of Pb from bone into circulation (CD,
16 Sections 4.3.2.4 and 4.3.2.5). Past exposures that contribute Pb to the bone, consequently, may
17 influence current levels of Pb in blood. Where past exposures were elevated in comparison to
18 recent exposures, this influence may complicate interpretations with regard to recent exposure
19 (CD, Sections 4.3.1.4 to 4.3.1.6). That is, higher blood Pb concentrations are not always
20 indicative of higher body burdens or cumulative exposure, but they are generally indicative of
21 higher exposures or Pb uptake over a somewhat recent past (CD, pp. 4-34 and 4-133). For
22 example, response of the blood to reduction of a relatively brief Pb exposure appears to be faster
23 than for an exposure of several years, with estimated half-lives of approximately 9 months as
24 compared to 30 months for the longer exposure response (CD, pp. 4-25 to 4-26).
25 Bone measurements, as a result of the generally slower Pb turnover in bone, are
26 recognized as providing a better measure of cumulative Pb exposure (CD, Section 8.3.2). The
27 bone pool of Pb in children, however, is thought to be much more labile than that in adults due to
28 the more rapid turnover of bone mineral as a result of growth (CD, p. 4-27). As a result,
29 "changes in blood Pb concentration in children are thought to more closely parallel changes in
30 total body burden" (CD, p. 4-27). This is in contrast to adults, whose bone has accumulated
31 decades of Pb exposures (with past exposures often greater than current ones), and for whom the
32 bone may be a significant source long after exposure has ended (CD, Section 4.3.2.5).
33 Given the association with recent exposure and the relative ease of collection, blood Pb
34 levels are extensively used as an index or biomarker of exposure by national and international
35 health agencies (CD, Section 4.3.1.5). Although recent methods are making bone Pb
36 measurements easier to collect (CD, Section 4.3.2.2), epidemiological and toxicological studies
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1 of Pb health effects and dose-response relationships tend to be dominated by blood Pb as the
2 exposure metric (CD, Sections 4.3.1.3, 8.3.2 and Chapter 5).
3 Accordingly, blood Pb level is the index of exposure or exposure metric in this risk
4 assessment. The use of concentration-response functions that rely on blood Pb (e.g., rather than
5 ambient Pb concentration) as the exposure metric reduces uncertainty in the causality aspects of
6 Pb risk estimates, however the relationship between specific sources and pathways of exposure
7 and blood Pb level is needed in order to identify the specific risk contributions associated with
8 those sources and pathways of greatest interest to this assessment (i.e., those related to Pb
9 emitted into the air). For example, the blood Pb-response relationships developed in
10 epidemiological (or lexicological) studies do not distinguish among different sources of Pb (e.g.,
11 inhalation, ingestion of dust, ingestion of dust containing leaded paint). In this assessment,
12 models that estimate blood Pb levels associated with Pb exposure are used to inform estimates of
13 contributions to blood Pb arising from ambient air related Pb versus contributions from other
14 sources. We have employed two such models from the peer reviewed literature in the risk
15 assessment (Sections 3.2, 3.5 and 4.3.2, and Appendices H and J).
16 2.1.4 Health Endpoints
17 Lead has been demonstrated to exert "a broad array of deleterious effects on multiple
18 organ systems via widely diverse mechanisms of action" (CD, p. 8-24). This array of health
19 effects and the evidence associated with each effect is comprehensively described in the CD, and
20 includes
21 • Heme biosynthesis and related functions;
22 • Neurological development and function;
23 • Reproduction and physical development;
24 • Kidney function;
25 • Cardiovascular function; and,
26 • Immune function.
27 There is also some evidence of Pb carcinogenicity, primarily from animal studies, with limited
28 human evidence of suggestive associations (CD, Sections 5.6.2, 6.7, and 8.4.10).
29 This review is focused on those effects most pertinent to ambient exposures. Given the
30 reductions in ambient Pb levels over the past 30 years, these effects are generally those
31 associated with the lowest Pb levels of exposure. These are neurological, hematological and
32 immune effects for children, and neurological, hematological, cardiovascular and renal effects
33 for adults (CD, Tables 8-5 and 8-6), with neurological effects in children and cardiovascular
34 effects in adults appearing to be of greatest public health concern (CD, p. 8-60). The
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1 toxicological and epidemiological information available since the time of the last review
2 "includes assessment of new evidence substantiating risks of deleterious effects on certain health
3 endpoints being induced by distinctly lower than previously demonstrated Pb exposures indexed
4 by blood Pb levels extending well below 10 ug/dL in children and/or adults" (CD, p. 8-25). The
5 CD indicates some health effects associated with blood Pb levels that extend below 5 |ig/dL,
6 with some studies observing these effects at the lowest blood levels considered (i.e., threshold
7 levels for these effects cannot be discerned from the currently available studies).
8 While the other endpoints identified above are important for this review and are
9 discussed briefly in sections below, the health endpoint included for quantitative health risk
10 assessment in this review is developmental neurotoxicity in children, with IQ decrement as the
11 risk metric (Figure 2-1). Among the wide variety of health endpoints associated with Pb
12 exposures, there is consensus that the developing nervous system in young children is the most
13 sensitive and that neurobehavioral deficits, including IQ decrements, appear to occur at lower
14 levels than previously believed (i.e., at levels <10 ug/dL and possibly <5 ug/dL). For example,
15 the overall weight of the available evidence, described in the CD, provides clear substantiation of
16 neurocognitive decrements being associated in young children with blood Pb levels in the range
17 of 5 to 10 ug/dL, and some analyses appear to show Pb effects on intellectual attainment of
18 young children ranging from 2 to 8 ug/dL (CD, Sections 6.2, 8.4.2 and 8.4.2.6). That is, while
19 blood Pb levels in U.S. children ages one to five years have decreased notably since the late
20 1970s, newer studies have investigated and reported associations of effects on the
21 neurodevelopment of children with these more recent blood Pb levels (CD, Chapter 6).
22 The evidence for neurotoxic effects in children is a combination of epidemiological and
23 toxicological evidence (CD, Sections 5.3, 6.2 and 8.5). The epidemiological evidence is strongly
24 supported by animal studies that substantiate the biological plausibility of the associations, and
25 provides an understanding of mechanisms of action for the effects (CD, Section 8.4.2). The
26 selection of children's IQ for this risk assessment, thus, reflects consideration for evidence
27 presented in the CD as well as advice received from CASAC (Henderson, 2006, 2007).
28 2.1.4.1 Developing Nervous System
29 The nervous system has long been recognized as a target of Pb toxicity, with the
30 developing nervous system affected at lower exposures than the mature system (CD, Sections
31 5.3, 6.2.1, 6.2.2, and 8.4). Functional manifestations of Pb neurotoxicity during childhood
32 include sensory, motor, cognitive and behavioral impacts. Numerous epidemiological studies
33 have reported neurocognitive, neurobehavioral, sensory, and motor function effects in children at
34 blood Pb levels below 10 ug/dL (CD, Section 6.2). Studies with laboratory animals (discussed in
35 Section 5.3 of the CD) provide strong evidence for the role of Pb in producing these effects.
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1 Cognitive effects associated with Pb exposures that have been observed in
2 epidemiological studies have included decrements in intelligence test results, such as the widely
3 used IQ score, and in academic achievement as assessed by various standardardized tests as well
4 as by class ranking and graduation rates (CD, Section 6.2.16 and pp 8-29 to 8-30). Other
5 cognitive effects observed in studies of children have included effects on attention, executive
6 functions, language, memory, learning and visuospatial processing (CD, Sections 5.3.5, 6.2.5 and
7 8.4.2.1). Further, Pb-induced deficits observed in animal and epidemiological studies, for the
8 most part, have been found to be persistent in the absence of markedly reduced environmental
9 exposures (CD, Sections 5.3.5, 6.2.11, and 8.5.2). Limited animal evidence indicates that
10 environmental enrichment during development may potentially reverse these deficits.
11 Other neurological effects associated with Pb exposures (e.g., blood Pb levels near or
12 below 10 |ig/dL) include behavioral effects, such as delinquent behavior (CD, Sections 6.2.6 and
13 8.4.2.2), sensory effects, such as those related to hearing and vision (CD, Sections 6.2.7, 7.4.2.3
14 and 8.4.2.3), and deficits in neuromotor function (CD, p. 8-36).
15 Neurocognitive impact, specifically decrement in IQ in young children, is a focus of this
16 quantitative risk assessment due to the strength of evidence for association with blood Pb levels
17 below 10 ug/dL, and the strength of the dose-response information at these exposure levels.
18 As discussed in the CD (Section 8.4.2) and by Rice (1996), while there is no direct
19 animal test parallel to human IQ tests, "in animals a wide variety of tests that assess attention,
20 learning, and memory suggest that Pb exposure (of animals} results in a global deficit in
21 functioning, just as it is indicated by decrements in IQ scores in children" (CD, p. 8-27). The
22 animal and epidemiological evidence for this endpoint are consistent and complementary (CD, p.
23 8-44). Further, "epidemiologic studies of Pb and child development have demonstrated inverse
24 associations between blood Pb concentrations and children's IQ and other outcomes at
25 successively lower Pb exposure levels" over the past 30 years (CD, p. 6-64). This is supported
26 by multiple studies performed over the past 15 years (see CD, Section 6.2.13), with particularly
27 compelling evidence for decrements in IQ at blood Pb levels below 10 ug/dL provided by a
28 recent international pooled analysis of seven prospective studies (Lanphear et al., 2005; CD,
29 Section 6.2.13). For example, this pooled analysis estimated a decline of 6.2 points (with a 95%
30 confidence interval bounded by 3.8 and 8.6) in full scale IQ occurring between approximately 1
31 and 10 ug/dL blood Pb level, measured concurrent with the IQ test (CD, p. 6-76). This analysis
32 (Lanphear et al., 2005) is relied upon in the quantitative risk assessment for this endpoint
33 di scussed in Chapter 4.
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1 2.1.4.2 Adult Nervous System
2 The nervous system has long been recognized as a target of Pb toxicity (CD Sections
3 5.3.1, 8.4.2). For example, those chronically exposed in the workplace are at risk for various
4 neurological effects including peripheral sensory nerve impairment, visuomotor and memory
5 impairment, and postural sway abnormalities, with a blood Pb concentration >14 ug/dL being a
6 possible threshold (CD, p. 6-87). Past occupational exposure also increases the risk of
7 developing amyotrophic lateral sclerosis (ALS) and motor neuron disease (CD, Section 6.3.5 and
8 p. 6-87). Essential tremor is also associated with Pb exposures, particularly for those with
9 genetic susceptibility (CD, Sections 6.3.5 and 6.3.6 and p. 6-86).
10 In elderly populations, significant associations have been reported between bone Pb
11 levels and impaired cognitive performance or dysfunction (CD, Section 6.3.3 and 6.3.3.1), but
12 not with blood Pb levels, perhaps indicating a role of cumulative and/or past Pb exposures (CD,
13 p. 6-83). During demineralization of bone in the elderly, Pb may be released into the blood, thus
14 augmenting blood Pb associated with current ambient exposures (CD, Section 4.3.2.4). An
15 increased susceptibility among the elderly to Pb effects on cognitive function is supported by
16 animal evidence (Section 5.3.7). With lifetime exposure, senescent animals have exhibited an
17 increased susceptibility to Pb, due to the increased exposure from bone resorption, and an
18 apparently greater sensitivity to the biochemical effects of Pb (CD, Section 5.3.7). Laboratory
19 animal research in rats and monkeys also indicates a potential for cognitive function effects in
20 the elderly to be related to physiological effects (regulation of protein thought to play a role in
21 Alzheimer's disease) of Pb exposures in early childhood (CD, p. 5-67; Basha et al., 2006). Thus,
22 early life exposure to Pb may contribute to neurocognitive effects later in life due to the
23 redistribution of Pb body burden from bone to brain and by enhanced susceptibility caused by
24 age-related degenerative changes in various organs, including brain (CD, p. 8-40).
25 2.1.4.3 Cardiovascular System
26 Epidemiologic and experimental toxicology studies support the relationship between Pb
27 exposure and increased adverse cardiovascular outcome, including increased blood pressure,
28 increased incidence of hypertension, and cardiovascular morbidity and mortality (CD, Sections
29 5.5, 6.5 and 8.4.3). The cardiovascular effect most frequently examined in epidemiological
30 studies is increased systolic blood pressure in adults, which has been repeatedly associated with
31 Pb exposure (CD, Sections 8.4.3, 8.6.3, 6.5.2.3, and 6.5.7). The association has been observed
32 with Pb levels in bone and also, in some cohorts, with Pb in blood (including blood Pb levels
33 below 10 ug/dL). This epidemiological evidence is supported by evidence in numerous animal
34 studies of arterial hypertension with low Pb exposures, an effect that persists in animals long
35 after cessation of exposure (CD, Sections 5.5 and 8.4.3).
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1 Multiple studies reporting positive associations of blood pressure and hypertension with
2 bone Pb levels highlight the important role of cumulative past Pb exposure in development of
3 cardiovascular health effects (Sections 6.5.2.3 and 6.5.7). A study of young adults who lived as
4 children in an area of high Pb exposures also indicates the potential role of childhood exposure.
5 In this study, higher bone Pb levels were associated with higher systolic and diastolic blood
6 pressure (CD, p. 6-138), while current blood Pb levels (mean of 2.2 ug/dL) were not (CD, p. 6-
7 124).
8 Systolic blood pressure exerts a strong influence on more serious cardiovascular events
9 by its role in hypertension and its adverse cardiovascular sequelae (CD, p. 8-83). Several
10 analyses of National Health and Nutrition Examination Survey (NHANES) cohorts, including
11 some recently released, have collectively suggested a "significant effect of Pb on cardiovascular
12 mortality in the general U.S. population" (CD, p. 8-88, Sections 6.5.3.2 and 8.6.3). For example
13 recent analyses of NHANES blood Pb data from 1976 to 1980 and 1988 to 1994 provide
14 supportive evidence for an increased risk of cardiovascular mortality, consistent with projected
15 likely increases in serious cardiovascular events (stroke, heart attack) resulting from Pb-induced
16 increases in blood pressure (CD, Section 8.6.3).
17 2.1.4.4 Renal System
18 Lead nephrotoxicity is mediated by alterations in the glomerular filtration rate (CD,
19 Sections 5.7.3 and 8.4.5). The interaction of Pb with the kidney, including occurrences and
20 mechanisms of Pb uptake by and accumulation in the kidney, and associated cellular alterations,
21 is well described in animal research (CD, Section 5.7). A set of screening tests involving
22 markers of nephrotoxic effects have been established for screening individuals exposed to Pb
23 occupationally or environmentally (CD, Section 5.7.1). In the epidemiological literature,
24 associations between blood Pb and indicators of renal function impairment (e.g., measures of
25 glomerular integrity, such as creatinine levels in urine) have been found at blood Pb levels
26 extending below 10 ug/dL, to as low as ~2 to 4 ug/dL (CD, Sections 6.4.4.1.5 and 8.4.5).
27 Associations are also observed with cumulative Pb dose, assessed via bone Pb, and longitudinal
28 renal function decline (CD, p. 6-94), indicating the potential role of earlier exposures.
29 The findings regarding Pb exposures and renal effects are of particular concern with
30 regard to certain susceptible subpopulations. At levels of exposure in the general U.S.
31 population overall, Pb combined with other risk factors, such as diabetes, hypertension, or
32 chronic renal insufficiency from non-Pb related causes, can result in clinically relevant effects.
33 Notably, the size of such susceptible populations is increasing in the United States due to obesity
34 (CD, p. 6-113). That is, Pb is recognized as acting cumulatively with other renal risk factors to
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1 cause early onset of renal insufficiency and/or a steeper rate of renal function decline in
2 individuals already at risk for renal disease (CD, p. 6-107).
3 2.1.4.5 Heme Synthesis
4 It has long been recognized that Pb exposure is associated with disruption of heme
5 synthesis in both children and adults. At blood Pb levels above 30 ug/dL, such disruption leads
6 to notable reductions in hemoglobin synthesis, and, at blood Pb levels above 40 ug/dL, to frank
7 anemia, a clinical sign of severe Pb poisoning (CD, p. 8-47). The evidence regarding effects on
8 heme synthesis and other hematological parameters in animal and humans is strong, and includes
9 documented quantitative relationships between exposure and effects in children and adults.
10 Interference with heme synthesis was identified as one of the targets of low-level Pb toxicity in
11 children during the time of the last NAAQS review (USEPA, 1990), and was the primary basis
12 for the initial setting of the Pb NAAQS in 1978 (USEPA, 1978).
13 Mechanisms associated with Pb interference with heme synthesis include inhibition of
14 the enzymes ALAD and ferrochelatase (CD Sections 5.2.1, 6.9.1, 6.9.2). Inhibition of ALAD
15 has been associated with increased blood Pb concentrations across the range of 5 to 150 ug/dL.
16 The evidence, including that on associated mechanisms, is presented and discussed in detail in
17 the CD (Sections 8.4.4, 5.2.1, 6.9.1 and 6.9.2).
18 2.1.4.6 Immune System
19 Since the time of the last review, there has been substantial research on the
20 immunotoxicity of Pb. As summarized in the CD, "studies across humans and a variety of
21 animal models are in general agreement concerning both the nature of the immunotoxicity
22 induced by Pb as well as the exposure conditions that are required to produce
23 immunomodulation" (CD, p. 5-244, Section 5.9). Lead is distinguished from other
24 immunotoxicants, however, by the fact that the most sensitive biomarkers of its immunotoxicity
25 are associated with specific functional capacities that influence risk of disease, as opposed to
26 being associated with changes in immune cell numbers or pathological changes of lymphatic
27 system organs (CD, Section 5.9.1). The main immune system targets of Pb are macrophages
28 and T lymphocytes, leading to a potential for increased tissue inflammation, reduced cell-
29 mediated immunity, and increased risk of autoimmunity (See CD, Figure 5-18, Section 5.9.11).
30 Additionally, Pb exposures in both animal and human studies are associated with increased
31 production of IgE, an immunoglobulin involved in allergic responses and asthma (CD, Section
32 5.9.3.2). These effects have been reported in epidemiologic studies of children, and supported
33 by evidence in neonatal/juvenile animals, at blood Pb levels extending below 10 ug/dL (CD, p.
34 6-197 and Sections 5.9.10 and 8.4.6).
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1 2.1.5 Risk Metric and Model for Risk Quantitation
2 The epidemiological studies that have investigated blood Pb effects on IQ (see CD,
3 Section 6.2.3) have considered a variety of specific blood Pb metrics, including: 1) blood
4 concentration "concurrent" with the response assessment (e.g., at the time of IQ testing), 2)
5 average blood concentration over the "lifetime" of the child at the time of response assessment
6 (e.g., average of measurements taken over child's first 6 or 7 years), 3) peak blood concentration
7 during a particular age range and 4) early childhood blood concentration (e.g., the mean of
8 measurements between 6 and 24 months age). All four specific blood Pb metrics have been
9 correlated with IQ (see CD, p. 6-62; Lanphear et al., 2005). In the international pooled analysis
10 by Lanphear and others (2005), however, the concurrent and lifetime averaged measurements
11 were considered "stronger predictors of lead-associated intellectual deficits than was maximal
12 measured (peak) or early childhood blood lead concentrations," with the concurrent blood Pb
13 level exhibiting the strongest relationship (CD, p. 6-29).
14 Using concurrent blood Pb level as the exposure metric and IQ as the response from the
15 pooled dataset of seven international studies, Lanphear and others (2005) employed
16 mathematical models of various forms, including linear, cubic spline, the log-linear, and piece-
17 wise linear, in their investigation of the blood Pb concentration-response relationship (CD, p. 6-
18 29; Lanphear et al., 2005). They observed that the shape of the concentration-response
19 relationship is nonlinear and the log-linear model provides a better fit for the data than a linear
20 one (CD, p. 6-29 and pp. 6-67 to 6-70; Lanphear et al., 2005). In addition, they found that no
21 individual study among the seven drove the results (CD p. 6-30). Others have also analyzed the
22 same dataset and similarly concluded that, within the ranges of the dataset's blood Pb levels, a
23 log-linear relationship was a significantly better fit than the linear relationship (p=0.009) with
24 little evidence of residual confounding from included model variables (CD, Section 6.2.13;
25 Rothenberg and Rothenberg, 2005).
26 A nonlinear blood Pb concentration-response relationship is also suggested by several
27 other studies that have observed that each ug/dL increase in blood Pb may have a greater effect
28 on IQ at blood Pb levels below 10 ug/dL than at higher levels (CD, pp. 8-63 to 8-64). While this
29 may at first seem at odds with certain fundamental toxicological concepts, a number of examples
30 of non- or supra-linear dose-response relationships exist in toxicology (CD, pp. 6-76 and 8-83 to
31 8-39).l With regard to the effects of Pb on neurodevelopmental outcome such as IQ, the CD
32 suggests that initial neurodevelopmental effects at lower Pb levels may be disrupting very
33 different biological mechanisms (e.g., early developmental processes in the central nervous
1 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).
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1 system) than more severe effects of high exposures that result in symptomatic Pb poisoning and
2 frank mental retardation (CD, p. 6-76). In comparing across the individual studies and the
3 pooled analysis, it is observed that at higher blood Pb levels, the slopes derived for log-linear and
4 linear models are almost identical, and for studies with lower blood Pb levels, the slopes appear
5 to be steeper than those observed in studies involving higher blood Pb levels (CD, p. 8-78,
6 Figure 8-7).
7 Given the evidence summarized here and described in detail in the CD (Chapters 6 and
8 8), and consistent with CASAC recommendations (Henderson, 2006, 2007), the risk assessment
9 relies on the log-linear functions presented by Lanphear and others (2005) that relate absolute IQ
10 as a function of the log of concurrent blood Pb, and lifetime average blood Pb, respectively. As
11 discussed above, the slope of the concentration-response relationship described by these
12 functions is greater at the lower blood Pb levels (e.g., less than 10 ug/dL). The impact of the
13 nonlinear slope is illustrated by the estimated IQ decrements associated with increases in blood
14 IQ for different ranges of blood Pb level. The IQ changes were 3.9 (with 95% confidence
15 interval, CI, of 2.4-5.3), 1.9 (95% CI, 1.2-2.6) and 1.1 (95% CI, 0.7-1.5), for increases in
16 concurrent blood Pb from 2.4 to 10 ug/dL, 10 to 20 ug/dL, and 20 to 30 ug/dL, respectively
17 (Lanphear et al., 2005). On a change in IQ per ug/dL basis, estimates of IQ decrement
18 associated with blood Pb levels below 10 ug/dL (using the concurrent, 24-month, peak, lifetime
19 average or lifetime cumulative blood Pb metric) range from -0.4 to -1.8 (CD, Table 8.7). The
20 pooled analysis by Lanphear and others (2005) also estimated the slope for a linear function of
21 IQ change associated with concurrent blood Pb for the subset of the children in the pooled data
22 set for which maximal or peak blood Pb levels were below 7.5 ug/dL (103 of 1333 children).
23 The slope for this subset (primarily composed of children from the Rochester cohort) was -2.94,
24 compared to a slope of -0.16 for the remainder of the children with maximal blood Pb levels at or
25 above 7.6 ug/dL.
26 As discussed in the CD, threshold blood Pb levels for these effects cannot be discerned
27 from the currently available epidemiological studies, and the evidence in the animal Pb
28 neurotoxicity literature does not define a threshold for any of the toxic mechanisms of Pb (CD,
29 Sections 5.3.7 and 6.2). In applying the relationship observed with the pooled analysis
30 (Lanphear et al., 2005) to this risk assessment, which includes blood Pb levels below the range
31 represented by the pooled analysis, three alternative blood Pb concentration-response models
32 were employed for each of the two blood metrics used (see Section 4.1.1). The first model (log-
33 linear function with low-exposure linearization) applies the nonlinear relationship down to the
34 blood Pb concentration representing the lower bound of blood Pb levels for that blood metric in
35 the pooled analysis and applies the slope of the tangent at that point to blood Pb concentrations
36 estimated in the risk assessment to fall below that level. The second model (log-linear function
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1 with outpoint) also applies the nonlinear relationship at blood Pb concentrations above the lower
2 bound of blood Pb concentrations in the pooled analysis dataset for that blood metric, but then
3 applies zero risk to all lower blood Pb concentrations estimated in the risk assessment. The third
4 model (two-piece linear function) applies a two-piece linear model derived from the log-linear
5 function to all blood Pb concentrations estimated in the risk assessment. All three of these
6 alternate models are used in the risk assessment in recognition of the reduced confidence in our
7 ability to characterize the quantitative blood Pb concentration-response relationship at the lowest
8 blood Pb levels represented in the recent epidemiological studies, as well as the possibility of a
9 threshold at or below these levels.
10 2.2 USE OF CASE STUDIES AND LOCATION SELECTIONS
11 Consistent with risk assessment performed during the prior Pb NAAQS review and for
12 the pilot phase assessment, this risk assessment relies on a case study approach. This approach is
13 intended to provide a framework for considering the nature and magnitude of air-sourced Pb
14 exposures and associated risk to human health, and for comparing the impact of alternate
15 NAAQS on them.
16 With consideration of CAS AC comments on the pilot phase assessment (see Section 1.4),
17 the design for the full-scale assessment expanded from the pilot assessment by replacing the
18 urban near-roadway case study with a general, non-location-specific, case study focused on
19 population exposures in urban areas (Section 2.2.1), while retaining the two stationary source
20 case studies (Sections 2.2.2.1 and 2.2.2.2). The point source case studies are intended to
21 illustrate risks associated with Pb near point sources of significant impact on an individual basis,
22 while the general urban case study is intended to illustrate the potential for more widespread
23 risks, due to the large populations in urban areas. Background information for all three case
24 studies is presented in appendices A and B, and briefly summarized in Sections 2.2.1 and 2.2.2,
25 below.
26 2.2.1 Urban Case Study
27 In consideration of CAS AC comments on the pilot assessment (Henderson, 2007), a
28 general urban case study is included in the assessment. This case study is designed to provide
29 estimates of risk associated with the current and alternate NAAQS in urban areas, as well as with
30 current conditions. This case study differs from the point source case studies in several ways.
31 First it is not based on a specific location. Rather, it is designed to generally represent large
32 urban areas in the U.S.2 Second, as discussed further in subsequent sections (e.g., Sections 2.4.2
2 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
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1 and 3.1.1), the media concentrations are assumed to be spatially uniform throughout the case
2 study area (i.e., spatial variation within the area is not considered). Third, the ambient air quality
3 for this case study is specified, based on analyses of Pb levels in large urban areas of the U.S.
4 (Appendix A and Section 2.3.1), rather than derived from air quality modeling of particular air
5 Pb sources. As a result of this third difference, this case study includes different types of
6 uncertainties as compared to the case studies employing air quality models, and it does not
7 distinguish among the different air Pb sources influencing the air concentrations, be they
8 currently active stationary or mobile sources, or resuspension of previously deposited Pb.
9 Fourth, the case study does not rely on any specific demographic values; that is, a theoretical
10 population of unspecified size is assumed to be uniformly distributed across the study area. All
11 of these distinctions of this case study from the others have produced a platform that is a
12 simplified representation of urban areas, intended to inform our assessment of the impact of
13 changes in ambient Pb concentrations on risk.
14 2.2.2 Point Source Case Studies
15 Based on the Analysis Plan (USEPA, 2006a) and conclusions from the pilot phase risk
16 assessment (USEPA, 2006; ICF, 2006b), the point source case studies for this full-scale
17 assessment include a study area near a primary Pb smelter, and a second near another, smaller
18 but still significant, stationary Pb source. The locations for these two case studies were chosen
19 with consideration of factors described in the Analysis Plan: (a) availability of site-specific
20 monitoring data for ambient air Pb, (b) availability of measurement data for other environmental
21 media (soil and indoor dust) and for Pb exposure (i.e., blood Pb levels), and (c) demographic and
22 socioeconomic factors related to Pb exposure and risk (USEPA 2006a).
23 Both of these case studies rely on air quality modeling to estimate air Pb concentrations
24 used in the risk assessment and as a result they include different types of uncertainties as
25 compared to those associated with the air concentrations for the general urban case study. One
26 of these is related to the representation of resuspension of previously deposited Pb as a source of
27 Pb to ambient air. To the extent that emissions estimates are available for this source, this is
28 represented in these case studies. However, as recognized in Section 2.1.1 and Appendix A,
29 Section A. 1.1.3, such emissions estimates are often uncertain or unavailable. Some resuspension
residential soil Pb and indoor dust Pb, are generally representative of U.S. residential properties (see Sections
3.1.1.1, 3.1.3.1 and3.1.4.1). 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 datasets will include greater representation by urban areas,
particularly large ones, than non-urban areas. However,these datasets are not limited to urban locations.
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1 sources are included in the primary Pb smelter case study, however no such sources are explicitly
2 modeled in the secondary Pb smelter case study.
3 Background information for these case studies is briefly summarized below with regard
4 to: (a) population characteristics, (b) reported emissions, (c) ambient air Pb levels, and (d) the
5 availability of site-specific data characterizing levels of Pb in key media and Pb exposures (e.g.,
6 soil, blood Pb level data).
7 2.2.2.1 Primary Pb Smelter Case Study
8 The primary Pb smelter case study is focused on the currently operating facility in the
9 U.S., which is located in Herculaneum, Missouri. At primary Pb smelters, Pb-bearing ore
10 concentrates are smelted to produce Pb metal. This smelter is one of the largest individual
11 sources of Pb metal in the U.S., has been active for over a century and there exist a large amount
12 of site-specific data in the surrounding area characterizing both media concentrations (soil,
13 indoor dust, outdoor air) and population blood Pb levels (Appendix B, Section B.I).
14 The facility's century of operation has contributed to Pb contamination of the area
15 surrounding the facility3, which has triggered various remediation activities (e.g., removal of Pb-
16 contaminated residential soil) as well as enlargement of the facility property to encompass many
17 of the most heavily impacted private properties. The remediation activity introduces a
18 complication to the risk modeling, especially aspects involving characterization of the
19 relationship of ambient air Pb and residential soil Pb to indoor dust Pb (see Section 3.1.4).
20 Some key aspects of the background information for this case study (Appendix B) are
21 summarized briefly in Table 2-1.
3 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.
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1 Table 2-1. Key aspects of primary Pb smelter case study.
Population
As of the 2000 U.S. Census, approximately 38,000 people lived within 10 kilometers
(km) of the facility, 10% of which were children less than 8 years of age. Since 2000,
actions associated with reducing facility-related Pb exposures 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 ug/m3. 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 (Appendix A).
2
3 The area within the city limits of Herculaneum is designated non-attainment for the Pb
4 NAAQS and the existing State Implementation Plan (SIP) was approved in 2002 (67 FR 18497).
5 EPA determined the existing SIP to be inadequate to attain and maintain the current NAAQS in
6 2006 (71 FR 19432), and consequently the state of Missouri developed a revised SIP for the area.
7 U.S. EPA, Region 7 received Missouri's proposed SIP revision on May 31, 2007 (MDNR,
8 2007). The air dispersion modeling performed for the risk assessment described in this
9 document built on the information and modeling developed for the revised SIP.4
10 The significant amount of site-specific data available for Herculaneum, paired with air
11 dispersion modeling for the facility conducted in support of SIP development for Pb, provides a
12 substantial data set for this study area which enhances the modeling of exposure and risk. For
13 example, the Herculaneum facility has more site-specific monitoring data available to support
14 risk assessment than the second point source case study location, including residential yard soil,
15 indoor dust and road dust Pb measurements collected in areas potentially impacted by the
16 facility. In addition, the Agency for Toxic Substances and Disease Registry (ATSDR) has
17 conducted a number of health consultations which involved the collection of blood Pb
18 measurements for children (Appendix B, Section B. 1.3). Extensive air Pb monitoring data are
19 also available and were considered in the performance evaluation of the modeling setup. The
20 Herculaneum case study location also has a number of attributes that add complexity to the
21 modeling of Pb exposure and risk including (a) complex terrain and meteorology which
4 The 2007 draft SIP revision (MDNR, 2007), including the modeling, is currently under review by EPA.
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1 complicates the modeling of Pb transport in ambient air, (b) a large and complex facility with a
2 long history of operation and significant opportunity for fugitive emissions which makes source
3 characterization challenging, and (c) a history of remediation activities which has contributed to
4 widely varying residential soil Pb concentrations across the town.
5 2.2.2.2 Secondary Pb Smelter Case Study
6 The secondary Pb smelter case study, in Troy, Alabama, involves a smaller point source
7 than the primary Pb smelter case study, with relatively less site-specific data characterizing
8 media concentrations and exposure levels. Secondary Pb smelters produce Pb from scrap and
9 provide the primary means for recycling Pb-acid automotive batteries, and are among the larger
10 source categories of Pb emitters (see Appendix A, Section A. 1). The Troy facility was one of 15
11 secondary Pb smelters operating within the U.S. in 2002 (see Appendix B, Section B.2). Some
12 key aspects of the background information for this case study (Appendix B) are summarized
13 briefly in Table 2-2.
14 Table 2-2. Key aspects of secondary Pb smelter case study.
Population
As of the 2000 U.S. Census, approximately 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 facility, 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 operations, from materials storage and handling, and from
facility roadway dust. Similar to most secondary 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 facility), for which data are reported in the U.S.
Air Quality System, are approximately 0.4 and 0.1 ug/m3, 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).
15
16 In contrast to the Herculaneum facility, we have not identified soil or indoor dust Pb
17 measurements for this case study location. Additionally, although there are blood Pb
18 measurements in children available at the county level, they are not available at a more refined
19 scale that might relate more directly to this case study. The relative sparseness of site-specific
20 Pb measurements means that the exposure assessment conducted for the secondary Pb smelter
21 case study is more dependent on model projections, and consideration of measurements available
22 for similar locations, and that there is less opportunity for rigorous performance evaluation of the
23 modeling steps. The available air Pb monitoring data, however, are used in the performance
24 evaluation of the air quality modeling.
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1 2.3 ASSESSMENT SCENARIOS
2 The design of the scenarios assessed for each case study include considerations with
3 regard to air concentrations of Pb (Section 2.3.1), surface soil/dust concentrations of Pb (Section
4 2.3.3) and background (Section 2.3.2). As the scenarios are primarily distinguished by the
5 differences in air concentrations, we generally refer to the different assessment scenarios as air
6 quality scenarios in this document (including the appendices). The different air quality scenarios
7 include current conditions, meeting the current NAAQS of 1.5 |ig/m3 (maximum quarterly
8 average) and meeting several alternate, lower NAAQS.
9 2.3.1 Air Concentrations
10 The air concentrations assessed in the different air quality scenarios include current
11 conditions, meeting the current NAAQS of 1.5 |ig/m3 (maximum quarterly average) and meeting
12 several alternate, lower NAAQS. In consideration of the range of levels suggested by CASAC
13 (Henderson, 2007), the alternate NAAQS scenarios included in the assessment are: 0.5 |ig/m3,
14 0.2 |ig/m3 and 0.05 |ig/m3 as monthly averages, and 0.2 |ig/m3 as a quarterly average. While the
15 current and alternate NAAQS scenarios are characterized by quarterly or monthly averaging
16 times, it is the associated annual average ambient air concentrations that are then used in the risk
17 assessment5.
18 The current conditions scenario, performed for the general urban and secondary Pb
19 smelter case studies, is intended to generally reflect recent conditions for these case studies based
20 on data available for the characterization. For example, for the urban case study, air Pb levels for
21 current conditions are based on 2003-2005 air quality data (Appendix A). For the secondary Pb
22 smelter case study, for which we used air quality modeling, air Pb levels for current conditions
23 are based on emissions characterizations drawn from currently available emissions information
24 and recent meteorological data (see Appendix E).
25 The current NAAQS attainment air quality scenario was performed for the primary Pb
26 smelter case study because monitoring data for that study area indicate exceedance of the current
27 Pb NAAQS (Appendix B, Section B. 1.5.1).6 But this scenario is not included for the secondary
28 Pb smelter case study as the current conditions for that case study meet the current NAAQS. In
29 developing the reduced air concentrations for the alternate NAAQS scenarios for the point
5 Use of the annual average concentration 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).
6 Given the status of this area with regard to non-attainment 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.
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1 source case studies (for which air quality models are employed), the maximum monthly or
2 quarterly average (depending on averaging time for the alternate NAAQS) for each modeled
3 receptor point is compared to the NAAQS level to identify the factor by which the highest
4 average exceeds the NAAQS level. All monthly or quarterly averages are then reduced by this
5 factor (i.e., a proportional roll back is implemented) and the associated annual average
6 recalculated for each receptor point.
7 Two different current conditions scenarios are assessed for the general urban case study
8 based on air Pb concentrations for the period 2003-2005 at monitors in U.S. urban areas with
9 population greater than one million (Appendix A). One of these two scenarios is based on the
10 mean maximum calendar quarter average for these monitors, and the second is based on the 95th
11 percentile of maximum calendar quarter averages for these monitors. Additionally, although the
12 mean and 95th percentile maximum quarterly average of the large urban area monitors nationally
13 do not exceed the current NAAQS level, an increased air concentration scenario (i.e., to the level
14 of the current standard) has been included for this case study.
15 As the air Pb concentrations for this case study do not vary spatially (see Section 3.1.1.1),
16 the air Pb concentration is simply set to the level specified for each scenario as either a monthly
17 or quarterly average. The annual average air concentration (the metric used in the dust and blood
18 Pb modeling) is derived from the current conditions or NAAQS level (and averaging time) using
19 relationships based on current Pb-TSP monitoring data for monitors in large U.S. urban areas
20 (Appendix A). Lead in TSP monitoring data for the time period 2003-2005 from monitors in
21 U.S. urban areas of population size greater than one million were analyzed to derive estimates of
22 maximum quarterly average, maximum monthly average and annual average for each monitor.
23 From these estimates, the ratios of the maximum quarterly and maximum monthly averages to
24 the annual average were derived for each monitor and the arithmetic mean and 95th percentile
25 value of the monitor-specific ratios were derived. To derive the annual average air concentration
26 used in the dust and blood Pb modeling for each scenario, one of these ratios was applied to the
27 air quality scenario level. For example, the alternate NAAQS level of 0.5 (for a maximum
28 monthly averaging time) was divided by the mean monitor ratio of maximum monthly average to
29 annual average to derive the annual average air concentration estimate for that alternate NAAQS
30 scenario. The air quality values associated with the different scenarios assessed for the urban
31 case study are summarized in Table 2-3.
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1 Table 2-3. Air quality scenarios assessed for the general urban case study.
Air Quality Scenario
95tn Percentile Current Conditions
Mean Current Conditions
Alternate NAAQS
Alternate NAAQS
Alternate NAAQS
Alternate NAAQS
Current NAAQS
Level
(ug/m3)
0.87
0.14
0.5
0.2
0.2
0.05
1.5
Averaging Time
(Form)
calendar quarter
(maximum)
calendar quarter
(maximum)
Month
(maximum)
Month
(maximum)
calendar quarter
(maximum)
Month
(maximum)
calendar quarter
(maximum)
Ratio
7.6a
2.5°
4.0C
4.0C
2.5°
4.0C
2.5°
Associated Annual
Average Concentration
(ug/m3)
0.11
0.056
0.13
0.05
0.08
0.013
0.60
3 This is the 95m percentile of the ratios of maximum quarterly average to annual average for monitors at sites in
urban areas with population of one million or more people.
" This is the mean of the ratios of maximum quarterly average to annual average for monitors at sites in urban
areas with population of one million or more people.
cThis is the mean of the ratios of maximum monthly average to annual average for monitors at sites in urban areas
with population of one million or more people.
3 2.3.2 Policy-relevant Background
4 Given the multimedia, multi-pathway nature of Pb, levels of Pb in all exposure media
5 (including those other than air) are essential aspects of the scenarios assessed for each case study.
6 As discussed in Section 1.1, some of the Pb in other media may be derived from policy-relevant
7 sources, while, for our purposes here, we have categorized others as policy-relevant background.
8 Some amount of Pb in the air also derives from background sources, such as volcanoes, sea-salt,
9 wind-borne soil particles from areas free of anthropogenic activity (CD, Section 2.2.1). The
10 impact of these sources on current air concentrations is expected to be quite low and has been
11 estimated to fall within the range from 0.00002 |ig/m3 and 0.00007 |ig/m3 based on mass balance
12 calculations (CD, Section 3.1 and USEPA 1986, Section 7.2.1.1.3). The midpoint in this range,
13 0.00005 |ig/m3, has been used in the past to represent the contribution of naturally occurring air
14 Pb to total human exposure (USEPA 1986, Section 7.2.1.1.3). It is noted that the data available
15 to derive such an estimate are limited and that such a value might be expected to vary
16 geographically with the natural distribution of Pb. Comparing this to reported air Pb
17 measurements is complicated by limitations of the common analytical methods and by
18 inconsistent reporting practices. This value is one half the lowest reported non-zero value in
19 AQS. For the purposes of this assessment, however, the value of 0.00005 |ig/m3 was selected as
20 representative of policy-relevant background Pb in air. Unlike for other criteria pollutants, the
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1 role of this value for Pb is limited. In considering risk contributions from policy-relevant
2 background, the contributions from exposures to non-air media are such that any credible
3 estimate of policy-relevant background in air is likely insignificant in comparison. In developing
4 the air Pb concentrations associated with the alternate NAAQS scenarios, the estimate of policy-
5 relevant background in air was the floor below which concentrations were not lowered.
6 2.3.3 Outdoor Soil/Dust
7 With regard to surface soil7 Pb concentrations for the alternate NAAQS scenarios, the
8 presence of historically deposited Pb, associated with past periods of higher air concentrations
9 and associated atmospheric deposition, affects the soil Pb dynamics. That is, under the alternate
10 NAAQS scenarios atmospheric deposition of Pb will continue to occur, albeit it will be reduced
11 from the current rate which is reduced from historic rates. The type of response of the surface
12 soil concentrations to the changed deposition rate will depend on the relationship of current
13 surface soil concentrations at these locations to their levels associated with a steady state
14 condition (i.e., when the rate of Pb addition to the surface soil equals the rate of Pb loss from the
15 surface soil). If current surface soil concentrations are below their steady state levels for the
16 current conditions (i.e., the rate of Pb addition is greater than the rate of Pb loss) and air
17 concentrations are reduced (i.e., the rate of Pb addition via deposition is reduced), surface soil
18 concentrations might be expected to continue an increasing trend, although at a reduced rate
19 from the current rate. Alternatively, if current surface soil concentrations are above their steady
20 state levels (i.e., the rate of Pb loss is greater than the rate of Pb addition) and air concentrations
21 and associated deposition are reduced, surface soil concentrations might be expected to continue
22 a decreasing trend and do so at a greater rate than the current one.
23 Information regarding the current dynamics of Pb concentrations in surface soils is
24 limited with the predominant focus for such studies being somewhat remote forested areas (e.g.,
25 CD, Section 3.2 and 3.2.2 and pp. AX7-33 to AX7-34). Findings to date indicate that systems
26 with little influence from local point sources are still responding to reduced Pb deposition rates
27 associated with reduced atmospheric emissions of Pb, including those associated with the phase-
28 out of leaded gasoline. Studies of forest soils have concluded that surface concentrations of Pb
29 are decreasing in response to the reduced Pb deposition rates since the phase-out of leaded
30 gasoline (Miller and Friedland, 1994; Kaste and Friedland, 2003). Studies in urban areas of
7 In the risk assessment, outdoor surface soil or dust is an important exposure pathway. Use of the term
surface soil here is intended to include the terms outdoor soil and outdoor dust, with there being some overlap
between those two terms in that the surface layer of outdoor soil might be referred to as outdoor dust. Specifically,
the phrase "outdoor dust" refers to particles deposited on any outdoor surface, including, for example, soil,
sidewalks, roadways, etc.
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1 southern California, where Pb has accumulated from past sources, suggest an environment in
2 which Pb may remain at the soil surface (and other surfaces), contributing to air concentrations
3 via resuspension in the near-term (CD, pp. 2-65 to 2-67 and 3-18 to 3-19). Accordingly, the
4 temporal trend in surface soil concentrations in this environment is considered to be influenced
5 by the rate of resuspension, such that little to no reduction in soil Pb concentration in southern
6 California is expected over the next few hundred years (CD, pp. 2-65 to 2-67 and 3-18 to 3-20;
7 Harris and Davidson, 2005). Temporal trends in surface soils near established point sources are
8 not well characterized. Available information for a few areas surrounding smelters after
9 implementation of pollution controls shows a decline in Pb concentrations in outdoor dustfall,
10 street dust and indoor dustfall, but has not indicated a noticeable decline in soil Pb concentrations
11 (CD, pp. 3-23 to 3-24).
12 The above discussion suggests that a reduced air concentration in the three case studies
13 would not be expected to yield a changed surface soil concentration over the near term, yet may
14 yield a reduced surface concentration over a much longer term. An exception to this may be
15 some areas of the primary Pb smelter case study where contaminated soil has been removed and
16 replaced with "clean" soil. Measurements taken of Pb concentrations in such "clean" soil placed
17 within % mile of the facility exhibit small increasing temporal trends over a few year period8
18 (USEPA, 2006c). In lieu of additional data or a multimedia modeling analysis, however, the
19 surface soil concentrations for the current and alternate NAAQS scenarios in all case studies
20 have been set equal to those used for the current conditions scenarios. This is generally believed
21 to be a reasonable representation of soil Pb response to alternate NAAQS for at least six years,
22 and likely much longer, after a new standard might be implemented.9 A potential exception is
23 the area of the primary Pb smelter case study within 3/4 mile of the facility, where it may be that
24 surface Pb concentrations in remediated soil may increase to higher levels under the current and
25 some of the alternate NAAQS. This remains an area of uncertainty with potential implications
26 for areas in which a Pb source may locate where one of comparable size had not been previously.
27 Additionally, we recognize that implementation of some alternate NAAQS could in some
28 areas (e.g., areas of substantial past atmospheric deposition) involve control of surface soil/dust
29 to reduce surface soil/dust Pb levels. That is, in places where surface soil/dust Pb concentrations
30 contribute substantially to air concentrations, controls implemented to attain various alternate
31 lower NAAQS might include reducing soil Pb concentrations. Such specific control actions have
8 An increasing trend was not seen with soil at the two locations just beyond 3/4 miles away.
9 This was also the approach used in the risk assessment performed in the last Pb NAAQS review (USEPA,
1990).
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1 not been addressed in this assessment, and as stated above, outdoor soil/dust concentrations in all
2 air quality scenarios have been set equal to the values for the current conditions scenarios.
3 2.4 ANALYTICAL APPROACH
4 This section provides an overview of the analytical approach, describing key elements
5 including: (a) temporal aspects, (b) spatial scale of the analysis and the type of spatial template
6 used in modeling, (c) overview of the analytical steps of predicting media concentrations,
7 modeling exposure, and modeling risk, (d) performance evaluation completed in support of the
8 analysis and (e) the approach used to characterize uncertainty.
9 2.4.1 Temporal Aspects
10 The risk assessment conducted for each case study uses a simulated child population for
11 which exposure begins at birth and continues for 7 years. That is, the study population is
12 assumed to be a single group, for which exposure begins at birth and continues until the group
13 reaches 7 years of age.10 Furthermore, it is assumed that no migration or immigration of these
14 children occurs during this simulation period; that is, none of the children move out of the study
15 area and no children move in.
16 For the point source case studies, the use of modeling (with a constant emissions rate and
17 temporally varying meteorology) provides temporally varying air concentrations. However, the
18 primary blood Pb model for this assessment is limited in the temporal resolution of its inputs (see
19 Section 3.2), because the finest temporal resolution of inputs to the blood Pb model is a year.
20 Consequently, in characterizing exposure media concentrations, annual averages are used.
21 With regard to temporal variation across the seven year exposure period, several
22 exposure factors and physiological parameters are varied on an annual basis within the blood Pb
23 modeling step (see Section 3.2). Once set for the air quality scenario, however, the media
24 concentrations of Pb are held constant throughout the seven year period (see Section 3.1).
25 2.4.2 Spatial Scale and Resolution
26 The size and resolution of the study area differed among the three case studies. The
27 urban case study is not set in a specific location, and involves non-spatially-varying media
28 concentrations and population density. That is, there is no spatially-differentiated template per
29 se and instead, a single generic urban study area is assumed with uniform population density and
30 exposure concentrations. For the exposure modeling for the point source case studies, however,
31 spatial templates were developed. The templates subdivide the study area into sub-units,
10 Modeling of blood Pb levels for the child population includes contributions representative of prenatal Pb
exposure.
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1 composed of U.S. Census blocks or block groups, across which media concentrations differ.11
2 Media concentration estimates (e.g., for outdoor air, soil and indoor dust) are developed for each
3 block or block group, and from these a central tendency estimate is developed of concurrent and
4 lifetime average blood Pb levels for the resident children. Inter-individual variability of blood
5 Pb levels for children within a block or block group is considered through the use of a
6 statistically derived GSD. The specific spatial templates used for each of the point source case
7 studies are presented in Appendices D (Section D.I) and E (Section E. 1).
8 2.4.3 Categorization of Policy-relevant Exposure Pathways
9 To inform policy aspects of the Pb NAAQS review, we have attempted to parse the
10 assessment estimates for indoor dust Pb, blood Pb and IQ loss into the fraction associated with
11 policy-relevant background (e.g., diet and drinking water) versus that associated with policy-
12 relevant pathways, which include inhalation, outdoor soil/dust ingestion and indoor dust
13 ingestion (Section 2.1.1). We have further categorized the policy-relevant pathways into one of
14 two categories, "recent air" or "past air". Conceptually, the recent air category includes those
15 pathways involving Pb that is or has recently been in the air, whether or not it was also in the air
16 in the past, and the past air category includes those pathways involving Pb that was in the air in
17 the past and was not in the air recently.
18 Recent air refers to exposure contributions associated with inhalation of ambient air Pb
19 and ingestion of the fraction of indoor dust Pb derived from recent ambient air Pb. To the extent
20 that ambient air Pb includes contributions from resuspension of previously deposited Pb, that
21 source is represented in the recent air category. Thus, a "recent air" exposure may involve
22 previously deposited Pb that is (1) resuspended into the air and inhaled or (2) resuspended into
23 the air, transported into a building, deposited into the dust, contacted and ingested.
24 Past air includes exposure contributions from ingestion of outdoor soil/dust that is
25 contacted on surfaces outdoors, and ingestion of indoor dust Pb that is derived from past air
26 sources. Although Pb that is currently in outdoor soil/dust may have been in the air recently or
27 some time ago, we have assigned ingestion of outdoor soil/dust Pb contacted outdoors to the past
28 air category in recognition of our inability to maintain a dynamically changing categorization of
11 US Census block groups vary in size from several city 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.
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1 recent versus past air.12 The past air category also includes the ingestion of indoor dust Pb that
2 was in the ambient air in the past but not recently. These sources to indoor dust Pb include any
3 residual legacy of historical air Pb in the indoor dust of older homes, as well as Pb occurring in
4 indoor dust that is derived from outdoor soil/dust Pb that was not transported indoors by an air
5 pathway. This latter pathway includes outdoor soil/dust Pb that is carried indoors by human
6 contact (e.g., "tracking in").
7 To implement this categorization in the assessment, we developed estimates of the recent
8 air portion of indoor dust Pb (i.e., contributions associated with recent ambient air Pb levels), and
9 assigned the remainder of indoor dust Pb to other sources, which include those relevant to past
10 air. That is, the other sources component of indoor dust Pb refers to contributions from indoor
11 paint, outdoor soil/dust and additional sources. Among the additional sources is any residual
12 legacy of historical Pb in the indoor dust of older homes.
13 The indoor dust Pb subdivision reflects and is limited by the models and inputs used to
14 estimate indoor dust Pb levels for the different scenarios. All of them predict dust Pb
15 concentration as a function of, among other factors, ambient air Pb concentration, and all of the
16 models include a constant (e.g., the intercept in the regression-based models) that captures
17 "other" sources. One of the models (used in the secondary Pb smelter case study and in part of
18 the primary Pb smelter case study) also includes a dependency on outdoor soil/dust Pb
19 concentration. This difference among the models leads to an inconsistency across the case
20 studies in the ability to separate the contribution to indoor dust Pb from outdoor soil/dust.
21 Consequently, we have limited to two categories the subdivision of indoor dust contributions,
22 with that from outdoor soil/dust included in "other" whether it is estimated by a model that
23 includes a soil concentration coefficient or is accommodated by the constant or intercept term.
24 In presenting risk estimates associated with policy-relevant pathways (Chapter 4), we have
25 included risk associated with the "other" component of indoor dust in the past air category. We
26 recognize that the potential for this "other" component to include ambient Pb unrelated to air
27 emissions contributes to a potential for the risk estimates associated with past air to be
28 overestimates. Additionally, as the recent air portion of indoor dust Pb, which derives from
29 transport of airborne Pb into a house, depends on the estimate of ambient air Pb concentration,
30 selection of an ambient air Pb estimate that is not appropriate to this relationship (e.g., one that
31 does not represent ambient air Pb levels immediately outside the house) may contribute to an
32 under or over estimate of recent air indoor dust Pb.
12 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 this
assumption (see Section 2.3.3).
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1 There is inherent uncertainty associated with the approaches used to divide indoor dust-
2 related Pb exposures and risk into contributions from "recent ambient air" and from "other"
3 sources. Further, the uncertainty may differ among the three case studies due to the different
4 approaches used in modeling indoor dust Pb. For example, uncertainty associated with the
5 hybrid mechanistic-empirical model used in the general urban case study includes that which
6 arises from model inputs and model performance, while the empirical, regression-based,
7 statistical models used in the point source case studies entail uncertainty regarding similarity of
8 the conditions from which the model was derived to those to which it is applied, as well as
9 variables that may be correlated with those explicitly represented in the model.
10 2.4.4 Overview of Analytical Steps
11 As illustrated in Figure 2-2, the risk assessment completed for the two point source case
12 studies generally includes four analytical steps: (a) fate and transport of Pb released into outdoor
13 air, including the dispersion of Pb away from the point of release and the deposition of Pb onto
14 surfaces, (b) prediction of the resulting concentration of Pb in media of concern including
15 outdoor air, outdoor surface soil/dust and indoor dust, (c) use of these Pb concentrations together
16 with estimates of Pb in background exposure pathways, including diet, to estimate associated
17 blood Pb levels in children using biokinetic modeling and (d) use of concentration-response
18 functions derived from epidemiology studies to estimate IQ loss associated with the estimated
19 blood Pb levels. The modeling approach for the general urban case study is somewhat simpler,
20 since it does not involve fate and transport modeling for air concentration estimates and instead,
21 uses ambient monitor levels combined with an assumption of uniform ambient air Pb levels
22 across the study area. Subsequent steps in the general urban case study analysis are fairly similar
23 to what is described above for the point source case studies, with the generation of population
24 blood Pb levels being somewhat simplified for the urban case study. Figure 2-2 identifies the
25 key input data sets, modeling steps and intermediate model output in each of the four analytical
26 steps. The first three steps are employed in the exposure assessment (Section 2.4.4.1), while the
27 fourth is the risk assessment step (Section 2.4.4.2).
28 Because there is no single modeling approach that is recognized broadly as the soundest
29 set of models and inputs for each step for each case study and air quality scenario, and because
30 of the quantitative influence of certain analytical steps on the results, we employed multiple
31 approaches to perform some of the analytical steps. The multiple sets of results generated in this
32 way for a case study and air quality scenario are intended to span a range indicative of the model
33 and parameter uncertainty associated with those analytical steps of the risk analysis (see Section
34 2.4.6).
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1 Figure 2-2. Overview of analysis approach.
GO
GO
w
GO
GO
GO
2
X
w
I
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3
2
S
o
o
w
GO
2
Characterizing ambient air levels and
deposition to soil
Ambient monitoring data (mean and
high-end values) (urban)
Air dispersion modeling
(primary, secondary Pb smelters)
Ambient air
concentrations
for study areas
(
Lead deposition rates
across study area
! Characterizing soil and indoor
1 r1n«t rnnrpntratinn«
i
r
Nationally-
Combination of (a) statistical
(regression) and/or (b) mechanistic
(compartmental) indoor dust prediction
models
(all 3 case studies)
Indoor residential dust 1
concentrations j
Multi-media fate & transport
model-derived soil
concentrations scaled using
measured data from surrogate
location
(secondary Pb smelter)
Outdoor residential
soil concentrations
residential soil Pb
value selected from
literature
(urban)
Site-specific soil
monitoring data
combined with
statistical
extrapolation
(primary Pb
smelter)
Background Pb exposure levels
• diet
• drinking water
• indoor paint (actually reflected in
dust modeling)
Biokinetic blood Pb
modeling
(all 3 case studies)
Ambient air
concentrations
(see above)
Demographics (distribution of
children within study areas)
(two point source case studies)
Probabilistic modeling of blood Pb
levels for children within each case
study location
(all 3 case studies)
GSD reflecting inter-
individual variability
in behavior related to
Pb exposure and
biokinetics
,-•
I DistributionoflQ loss for
I study populations
! (partitioned between policy-
j relevant exposures and
! policy-relevant background)
\ .-
Risk estimation - IQ
change estimation
(all 3 case studies)
analysis)
• breakpoint reflecting confidence in CR
function
• effects estimation based on concurrent and
lifetime average blood Pb metric
1 . .
1 1
r
Concentration-response function
• log-normal distribution (Lanphear pooled
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2 2.4.4.1 Exposure Assessment
3 Concentrations of Pb are estimated in ambient media and indoor dust using a
4 combination of empirical data and modeling projections. The use of empirical data brings with it
5 uncertainty related to the potential inclusion of background source signals in these measurements
6 (e.g., house paint contributions to indoor dust and outdoor soil Pb). Conversely, the use of
7 modeling tools introduces other uncertainties (e.g., model and parameter uncertainties). Both of
8 these uncertainties are recognized in Section 4.3. Specific approaches used at the three case
9 study locations are briefly described below.
10 Characterization of Pb in ambient air relies on (a) dispersion modeling of stack and
11 fugitive emissions for the primary and secondary Pb smelter case studies and (b) the use of
12 ambient monitor data for the urban case study. For the urban case study, monitoring data for
13 U.S. urban areas of more than a million in population were used to identify two current
14 conditions scenarios, one "typical", and one higher-end, and to relate alternate NAAQS (of other
15 forms) to annual average levels needed for blood Pb modeling. A key aspect of the general
16 urban case study is that ambient air lead levels do not vary spatially within the study area. The
17 approach is monitor-based rather than source-based as compared to two point source case
18 studies. This means that we did not explicitly model specific source contributions for the urban
19 case study (e.g., resuspension of roadside dust, "fresh" industrial emissions) and instead, relied
20 on empirical data to define ambient air Pb levels for this general case study, with these levels
21 reflecting contributions from all contributing sources, be they currently active stationary or
22 mobile sources, resuspension of previously deposited Pb or other.
23 Characterization of Pb concentrations in outdoor surface soil/dust, resulting from
24 deposition of air-borne Pb is based on the use of (a) existing site-specific measurements (primary
25 Pb smelter case study), (b) nationally representative residential soil measurements obtained from
26 the literature (general urban case )study and (c) fate and transport 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 predict 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 that relate indoor dust to outdoor air Pb and/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
35 literature and developed based on site-specific data were used, and a customized hybrid
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1 empirical-mechanistic model was developed for the general urban case study. This reflected the
2 fact that available regression-based models had been developed largely based on residential
3 exposures near large point sources and were not considered representative of more general urban
4 exposures. Consequently, a mechanistic model, augmented with empirical data, was developed
5 for the general urban case study. Additional detail 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 (TEUBK) 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).13 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 general 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 tendency 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: The 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 factors: (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 age) across the zones comprising a given study
24 area and (c) use of a GSD characterizing inter-individual variability in blood Pb (e.g.,
25 reflecting differences in behavior and biokinetics related to Pb).
26 The step involving modeling population-level exposures for the urban case study is somewhat
27 simpler than that used for the two point source case studies in that demographic data for a
28 specific location is not considered. As discussed in Section 3.2.2, this avoids the need for
29 implementing a population-weighted probabilistic sampling procedure.
13 The Leggett model was included along with IEUBK in the pilot analyses. Findings for the model in the
pilot analyses and in subsequent performance analyses (Appendix J and Section 3.5) contributed to the decision to
use the IEUBK model as the primary model in this full-scale assessment (Section 3.2), and the Leggett model in the
sensitivity analysis (Section 3.5.2).
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1 2.4.4.2 Risk Characterization
2 The risk characterization step involves generating a distribution of IQ loss estimates for
3 the set of children simulated in the exposure assessment. Specifically, estimated blood Pb levels
4 (for the two blood Pb metrics) are combined with three blood Pb concentration-response
5 functions for IQ loss (see Section 4.1). Three differing concentration-response functions
6 (described in Section 4.1.1) have been selected to provide three different characterizations of
7 behavior at low exposures. The decision to use three different functions is in recognition of
8 uncertainty related to modeling this endpoint, particularly at lower exposure levels (e.g., blood
9 Pb levels < 5 |ig/dL). These three functions are all based on the log-normal concentration-
10 response function described in the Lanphear et al, (2005) pooled analysis of epidemiology
11 studies focusing on IQ loss in children. As these three functions were developed for each of the
12 blood Pb metrics included in the analysis, concurrent and lifetime average, six separate functions
13 were used in the analysis.
14 For each of the two point source case studies, we have produced two categories of risk
15 metrics:
16 • Population risk percentiles: The IQ loss associated with policy-relevant exposure
17 pathways for specific percentiles of the child population (e.g., the 50th, 90th, 95th,
18 99th percentile modeled child). This category of metric provides perspective on the
19 distribution of IQ loss resulting from policy-relevant exposure pathways, ranging
20 from the typical or average child (50th percentile, mean) to children experiencing
21 higher exposures (90th, 99th percentiles).
22 • Child frequency counts associated with specific risk percentiles: Number of children
23 associated with each of the population percentiles (e.g., the number of children
24 predicted to have risk levels at or above the 99th percentile). This risk metrics
25 provides a perspective on the number of children associated with various levels of IQ
26 loss for a particular case study.
27 For the urban case study, because it is not location-specific, only the first type of risk
28 metric, population risk percentiles, was developed because this case study is not location-
29 specific. Child frequency counts are not applicable, since a specific location with associated
30 demographic data was not modeled.
31 Additional detail on the risk characterization is presented in Sections 4.1 and 4.2.
32 2.4.5 Variability Characterization
33 There are a variety of sources of variability associated with the results of this assessment
34 which are presented in terms of risk estimates for specific population percentiles:
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1 • variability in the concentration of lead in key media (e.g., diet, drinking water, ambient
2 air, indoor dust),
3 • variability in behaviors which effect Pb exposure (e.g., dust ingestion, soil ingestion),
4 • variability in physiological response to Pb exposure leading to variations in blood Pb
5 levels, and
6 • variability in the toxic response to Pb, resulting in differing degrees of IQ loss for the
7 same degree of Pb exposure.
8
9 A variety of methods have been used to incorporate, to a limited extent, the above
10 sources of variability in the risk assessment such that they are reflected in the results for the three
11 case studies.
12 • Use of GIS-based spatial templates (for the two point source case studies) to reflect
13 the distribution of children across a study area (in relation to ambient air Pb and
14 related media concentrations) in projecting population exposures.
15 • For the general urban case study, inclusion of two current conditions scenarios (mean
16 and 99.5th percentile scenarios) which together, provide a degree of coverage for
17 variation in ambient air Pb levels seen across urban areas in the U.S.
18 • Use of empirically derived GSDs reflecting inter-individual variability in blood Pb
19 levels, to provide coverage for multiple sources of variability associated with Pb
20 exposure and biokinetics. Note, that the application of these GSDs provides the
21 primary means of reflecting inter-individual variability in blood Pb levels in this
22 analysis. These GSDs also reflect uncertainty associated with measuring blood Pb
23 levels and characterizing population-level distributions of those levels.
24
25 There is significant uncertainty associated with reflecting the sources of variability
26 identified above in population-level exposure and risk. For example there is uncertainty
27 associated with the GSD selected to reflect inter-individual variability in blood Pb levels for a
28 particular case study. This is considered in the uncertainty characterization (Section 2.4.6).
29 2.4.6 Uncertainty Characterization
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 include: (a) development of
32 multiple sets of exposure and risk estimates for 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) completion of a sensitivity analysis, intended to characterize the
35 potential impact of individual modeling elements on risk results, (c) evaluation of model
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1 performance (e.g., by comparison with empirical data) to provide confidence in individual
2 modeling steps and (d) qualitative discussion of key sources of uncertainty and their potential
3 impact on exposure and risk estimates. Each of these elements of the uncertainty
4 characterization is briefly summarized below.
5 2.4.6.1 Performance Evaluations
6 Performance evaluation for the exposure assessment (Section 3.5) focused on evaluation
7 of estimates of Pb in ambient air, outdoor soil, and indoor dust (discussed 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. Risk results considered across these multiple modeling
15 approaches provide perspective on the range of potential risk, given key sources of uncertainty in
16 the analysis. The multiple modeling approaches for each case study were developed by the
17 following stepwise strategy:
18 • Identification of those modeling elements believed to contribute significant
19 uncertainty to risk results,
20 • Identification of a set of plausible options for each of these key modeling elements
21 (e.g., alternative models or input parameters), and
22 • Development of alternative modeling approaches by combining individual options
23 from the previous step.
24 Identification of the modeling elements believed to contribute significant 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 the general urban case study, two different dust
33 models and two GSDs were used, compared to one model and GSD for these analytical steps in
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1 the two point source case studies. However, the same number of blood Pb metrics and IQ loss
2 functions are used for all three case studies.
3 Figure 2-3. Modeling approaches for each case study.
Case Study
General Urban
Case Study
Each Point
Source Case
Study
Elements of modeling approaches
Indoor dust
modeling
2 models:
(a) hybrid
mechanistic-
empirical
(b) statistical
(regression)
1 model:
statistical
(regression)
approach b
Blood Pb metric
2 metrics:
(a) concurrent
(b) lifetime
average
GSD
2 sets of GSDs,
representing:
(a) smaller
scale
(b) larger,
regional scale
1 set of GSDs
Concentration-
response function
3 functions:
(a) log-linear with
linearization,
(b) log-linear with
cutpoint, and
(c) two-piece linear
Number of
sets of results
2 * 2 * 2 * 3 = 24
1 * 1 * 2 * 3 =6
b Different models used for each point source case study.
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
The set of exposure and risk results generated for each case study and air quality scenario
using the alternative modeling approaches indicates to some extent the magnitude of uncertainty
surrounding the risk results. However, as discussed in Section 4.3, these sets of risk results do
not represent an uncertainty distribution, since confidence levels are not specified for each
modeling approach. In presenting the multiple sets of results generated for each case study, we
have selected the highest and lowest sets of risk results from those generated, and used them to
represent, respectively, upper- and lower-bounds on risk. The degree to which these actually
represent upper- and lower-bounds depends on the whether the various modeling approaches
evaluated in this analysis capture the largest sources of uncertainty. For example, if an important
source of uncertainty was excluded in designing the alternative modeling approaches for a given
case study, than the bounds represented by the set of risk results generated for that case study
might not be wide enough.
2.4.6.3 Sensitivity Analysis
Sensitivity analysis techniques were used to examine the uncertainty for individual
modeling elements and its impact on exposure and risk estimates. We used a "one element at a
time elasticity analysis" approach, running the full risk model with one of the selected modeling
elements adjusted to reflect an alternate input value or modeling choice. The results of that run
with the modified modeling element would then be compared to those for the "baseline risk" run
to determine the magnitude of the impact on risk results of selections for that one modeling
element.
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1 While the sensitivity analysis for the pilot was based on the primary Pb smelter case
2 study, for the full-scale analysis we have focused the sensitivity analysis on the general urban
3 case study. This reflects a desire to more fully understand the sensitivity of the modeling
4 approach used for the urban case study to key sources of uncertainty, recognizing that the results
5 of the pilot sensitivity analysis are informative with regard to the point source case studies. The
6 sensitivity analysis completed for the full-scale analysis focused on those modeling elements
7 (including input datasets and modeling steps) believed to have a significant potential for
8 impacting exposure and risk results. Those modeling elements include oral uptake factor, inter-
9 individual blood Pb variability GSD, biokinetic model, concentration-response function for IQ
10 loss. This type of sensitivity analysis indicates which of the modeling elements included in the
11 sensitivity analysis has the greatest impact on risk results, and can be used to guide future efforts
12 to refine the overall risk model.
13 2.4.6.4 Qualitative Discussion of Sources of Uncertainty
14 In addition to the quantitative analyses described above, we have also included a
15 qualitative discussion of key sources of uncertainty in the analysis. This includes sources not
16 explicitly included in any of the above quantitative analyses due to a lack of necessary data
17 (Section 4.3.1). This discussion also attempts to describe the nature of the impact of these
18 sources of uncertainty on risk results, e.g., would a particular source of uncertainty likely result
19 in an over- or under-estimation of risk. To the extent possible, the likely magnitude in
20 qualitative terms of a particular source of uncertainty is also discussed.
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1 REFERENCES
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3 Basha, M.R., Wei, W., Bakheet, S.A., Benitez, N., Sidiqi, H.K., Ge, Y.-W., Lahiri, O.K., Zawia, N.H. (2005) The
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26 26,2007.
27 National Center for Health Statistics. (2005) Health, United States, 2005. With Chartbook on Trends in the Health
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29 Rothenberg, S.J.; Rothenberg, J.C. (2005) Testing the dose-response specification in epidemiology: public health
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31 U.S. Environmental Protection Agency. (1978) National Primary and Secondary Ambient Air Quality Standards for
32 Lead. Federal Register 43(194): 46246-46263. Oct 5, 1978. Available at:
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34 U.S. Environmental Protection Agency. (1986) Air quality criteria for lead. Research Triangle Park, NC: Office of
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36 EPA-600/8-83/028aF-dF. 4v. Available from: NTIS, Springfield, VA; PB87-142378.
37 U.S. Environmental Protection Agency. (1989) Review of National Ambient Air Quality Standard for Pb: Exposure
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1 Office Of Air Quality Planning and Standards; report no. EPA-450/2-89/022. Available from: NTIS,
2 Springfield, VA; PB91-206185. http://www.epa.gov/ttn/naaqs/standards/pb/sjbjr sp.html.
3 U.S. Environmental Protection Agency. (2003) Framework for Cumulative Risk Assessment. Risk Assessment
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6 Paniculate Matter: Policy Assessment of Scientific And Technical Information: OAQPS Staff Paper.
7 Research Triangle Park, NC: Office Of Air Quality Planning and Standards. EPA-452/R-05-005a.
8 December.
9 U.S. Environmental Protection Agency. (2006a). Draft Analysis Plan for Human Health and Ecological Risk
10 Assessment for the Review of the Pb National Ambient Air Quality Standards. Office of Air Quality
11 Planning and Standards, Research Triangle Park, NC. May 31, 2006. Available at:
12 http://www.epa.gov/ttn/naaqs/standards/pb/sjb crjd.html
13 U.S. Environmental Protection Agency. (2006b). Review of the National Ambient Air Quality Standards for Lead:
14 Policy Assessment of Scientific and Technical Information. OAQPS Staff Paper - First Draft. Office of Air
15 Quality Planning and Standards. Research Triangle Park, NC. December 2006. Available at:
16 http://www.epa.gov/ttn/naaqs/standards/pb/sjb crsp.html
17 U.S. Environmental Protection Agency. (2006c) Lead soil trend analysis through May, 2006. Evaluation by
18 individual quadrant. Herculaneum lead smelter site, Herculaneum, Missouri. Prepared by TetraTech for
19 U.S. EPA, Region?. Available on the web, at:
20 http://www.epa.gov/region7/cleanup/superfund/herculaneum pbtrend thru may2006.pdf
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1 3 EXPOSURE ASSESSMENT
2 This chapter describes the methods and results of the exposure assessment and
3 performance evaluation.
4 3.1 METHODS FOR ESTIMATING MEDIA CONCENTRATIONS
5 To estimate media Pb concentrations for the three case studies, we used a combination of
6 empirical data and fate and transport modeling, reflecting the different availability of Pb
7 measurements for the two point source case studies and the non-location specific nature of the
8 general urban case study (Table 3-1). For all three case studies, media concentrations were
9 estimated for multiple air quality scenarios including a range of alternative NAAQS (see Section
10 2.3). However, outdoor dust/soil concentrations for each of the three case studies were
11 established for current conditions or the current NAAQS scenarios and those values were used
12 for the current NAAQS and each of the alternate NAAQS scenarios evaluated (see Sections 2.3.3
13 and 3.1.3). Further, as described in Section 2.4.1, media concentrations, once defined, are held
14 constant for the full exposure period simulated with the blood Pb modeling. For ambient air,
15 outdoor soil/dust and indoor dust, estimates of annual average concentration were used for this
16 purpose for all three case studies. Additionally, from the ambient air concentrations, annual
17 average inhalation exposure concentrations were estimated with consideration for daily activity
18 patterns by children and differences in outdoor (ambient) versus indoor air Pb levels (see Section
19 3.1.2).
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1 Table 3-1. Case study approaches for estimating media Pb concentrations.
Modeling Step
Spatial
template
Ambient air
concentrations
for current
conditions
and/or current
NAAQS
Performance
evaluation
Inhalation
exposure
concentrations
Outdoor soil
concentrations
Performance
evaluation
Indoor dust
concentrations
Performance
evaluation
General Urban Case Study
Single generic study area (with
spatially uniform media
concentrations and population
density)
Monitor data (mean and high-
end urban monitors selected to
represent two current conditions
scenarios)
[Monitoring data used as basis
characterizing ambient air
levels]
Primary Pb Smelter Case
Study
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^lock groups)
Secondary Pb
Smelter Case Study
U.S. Census blocks
out to a 10km radius
around the facility
(with media
concentrations and
demographics
uniform within
blocks)
Dispersion modeling
Comparison to Pb-TSP monitor data from study area
Estimates for all three case studies are based on ambient air concentrations and reflect the
application of location-specific adjustment factors that account for (a) the time spent by
children at different locations and at various activity levels and (b) differences between
indoor and outdoor ambient air Pb levels
Nationally representative
residential soil value selected
from literature (same value used
for entire study area and for all
air quality scenarios)
[Empirical data used in
characterizing soil levels]
Two approaches used: (a)
hybrid mechanistic
(compartmental) model
augmented with empirical data
developed specifically for this
analysis and (b) empirical (air-
only regression) model obtained
form the literature
Sub-components of hybrid
model evaluated, and case study
estimates compared to literature
estimates and national-scale
survey
Near facility (remediation
zone) relied on sampling
data, and regression model
used for outer portions of
study area
[Estimates based on surrogate
data]
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
Site-specific sampling data
used in deriving regression
model for remediation zone;
case study estimates
compared to literature
estimates and national-scale
survey
Multiple Pathways of
Exposure (MPE)
model used to predict
spatial distribution of
surface soil Pb levels,
then scaled using
empirical data from
surrogate location
Modeled estimates
compared to surrogate
data for other
industrial (point
source) locations
Statistical (air-only
regression) model
obtained from the
literature
Case study estimates
compared to literature
estimates and
national-scale survey
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1 3.1.1 Ambient Air Concentrations
2 Different methods were used for estimating annual average ambient air concentrations for
3 the three case studies. For the primary and secondary Pb smelter case studies, air dispersion
4 modeling of Pb emissions was performed, while Pb-TSP measurement data from the years 2003-
5 2005 for urban areas with greater than one million residents were used for the general urban case
6 study.
7 3.1.1.1 General Urban Case Study
8 The general urban case study is not site-specific and has been designed to represent
9 general Pb exposures experienced by children residing in urban areas within the United States.
10 The case study involves spatially uniform Pb concentrations in ambient air, outdoor soil/dust and
11 indoor dust with a matching uniformly distributed child resident population. This is in contrast
12 to the point source case studies which each have a relatively large number of exposure zones to
13 track potentially significant spatial gradients in their concentrations of Pb in environmental
14 media and the spatial distribution of children.
15 Two current conditions scenarios are included in the general urban case study based on
16 Pb-TSP monitoring data from urban areas in the United States. Specifically, these two scenarios
17 include: (a) a central tendency current conditions scenario based on the mean maximum
18 quarterly average Pb measurement (0.14 |ig/m3) seen, in 2003-05 time period, at Pb-TSP
19 monitors in urban areas with greater than a million people, and (b) a high-end current conditions
20 scenario based on the 95th percentile maximum quarterly average Pb measurement (0.87 |ig/m3)
21 obtained from the same urban Pb-TSP dataset. The data analysis associated with these values is
22 described in Appendix A, Section A.2.2.2.
23 In addition to these current conditions scenarios, the current NAAQS scenario (1.5
24 ng/m3, as a maximum quarterly average) and four alternate NAAQS scenarios have also been
25 evaluated (see Section 2.3 for additional details on the NAAQS scenarios). In each of these
26 instances, the specific NAAQS of interest has been evaluated by assuming that air Pb
27 concentrations in the entire study area are reduced to meet that specific ambient air Pb level and
28 form.
29 Because the blood Pb modeling (Section 3.2) is based on annual average media
30 concentrations, the maximum quarterly and maximum monthly average values used in defining
31 the air quality scenarios were translated into equivalent annual average ambient air
32 concentrations for the blood Pb modeling (Section 2.4.1). This is accomplished using ratios
33 obtained from the 2003-2005 Pb-TSP monitoring dataset that relates maximum quarterly or
34 maximum monthly averages to associated annual average values for each monitor located in an
35 urban area of population more than a million (Appendix A, Section A.2.2). Ratios were selected
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1 for each of the air quality scenarios according to the averaging time (calendar quarter versus
2 month) and the percentile represented by the air quality scenario (e.g., mean versus high-end for
3 current conditions). Consequently, the annual average Pb concentration for the central tendency
4 current conditions scenario was derived from the mean of monitor-specific maximum quarterly
5 average concentrations using the mean of the monitor-specific maximum quarterly-to-annual
6 average ratios, while the high-end current conditions scenario was derived from the 95th
7 percentile of monitor-specific maximum quarterly average concentrations using the 95th
8 percentile of the monitor-specific maximum quarterly-to-annual average ratios. The mean of the
9 maximum quarterly-to-annual average ratios was also used to derive the annual average
10 concentration estimates for the alternative NAAQS air quality scenario with a quarterly
11 averaging time. The mean of the maximum monthly-to-annual average ratios was used to derive
12 the annual average concentration estimates for the alternative NAAQS scenarios with monthly
13 averaging times. The derivation of these ratios is described in Appendix A, Section A.2.2.
14 3.1.1.2 Primary Pb Smelter Case Study
15 The air quality scenarios included for the primary Pb smelter case study were attainment
16 of the current NAAQS and four alternate NAAQS (see Section 2.3.1). As the study area of the
17 primary Pb smelter is currently in non-attainment for the current NAAQS, ambient air Pb
18 concentrations for the current NAAQS scenario were estimated using the model, emissions and
19 source parameters used in developing the 2007 proposed revision to the State Implementation
20 Plan for the area (MDNR, 2007). Annual average Pb concentration estimates for the current
21 NAAQS scenario were derived for each census block or block group from model outputs.
22 Additionally, for the purposes of developing the alternative NAAQS attainment scenarios, hourly
23 estimates from the model were used to generate quarterly average and monthly average
24 concentrations for each census block or block group. A proportional rollback procedure was
25 then used to adjust the set of maximum monthly or quarterly averages to represent attainment of
26 a particular NAAQS scenario. That is, the block or block group with the greatest exceedance
27 was reduced to meet the particular NAAQS and all locations were then reduced by that same
28 fraction. After the proportional rollback procedure had been applied to the set of location-
29 specific monthly or quarterly averages to attain a particular NAAQS, these adjusted quarterly- or
30 monthly-average values were then used to derive annual averages which, in turn, were used in
31 the exposure analysis.
32 The development of air Pb concentration estimates for this case study is described more
33 fully in Appendix D, Section D.2.
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1 3.1.1.3 Secondary Pb Smelter Case Study
2 Outdoor air concentration and deposition rates for Pb were estimated for the secondary
3 Pb smelter case study current conditions scenario using the AERMOD dispersion model1 and
4 source characterization and emissions information for the facility (See Appendix E, Section E.2
5 for details). The Pb emissions modeled reflected processes at the facility (e.g., stack emissions)
6 and fugitive dust emissions from materials storage and handling and roadway dust.
7 Annual average Pb concentrations for the current conditions scenario (in which the
8 current NAAQS is attained) were derived for each census block from model outputs. As with
9 the primary Pb smelter case study, alternative NAAQS scenarios were modeled using the
10 proportional rollback procedure (see Section 3.1.1.2).
11 The development of air Pb concentration estimates for this case study is described more
12 fully in Appendix E, Section E.2.
13 3.1.2 Inhalation Exposure Concentrations
14 Inhalation exposure concentrations for Pb were estimated for young children, the
15 population of interest, from the estimated ambient air concentrations using age group- and
16 location-specific relationships for Pb developed using the exposure modeling component of
17 EPA's 1999 national-scale air toxics assessment (USEPA 2006a), one of the U.S. EPA's
18 National Air Toxics Assessment (NATA) activities. These relationships account for air
19 concentration differences indoors and outdoors and mobility or time spent in different locations
20 (e.g., outdoors at home, inside at home etc.) for the population of interest.
21 The exposure modeling component of the NAT A national-scale assessment generated
22 inhalation exposure concentrations for sets of modeled children for each U.S. Census tract
23 (USEPA, 2006a). For the two point source case studies, we used the ratio of these NATA-based
24 inhalation exposure concentrations to the NAT A national-scale assessment's corresponding
25 estimates of ambient air Pb concentration matched by U.S. Census tract to develop adjustment
26 factors that could be used to derive inhalation exposure concentrations from our estimates of
27 ambient air Pb concentration (see Attachment D, Section D.2.3, and Attachment E, Section E.2.3
28 for additional detail on this procedure). For the general urban case study, we used the median
29 adjustment factor from the full NATA national-scale assessment. The 0-4 years old age group is
30 the closest age group to the age group of interest for this assessment for which outputs are
31 available. For this age group, the adjustment factors or ratios between NATA national-scale
32 assessment Pb inhalation exposure concentrations and ambient air concentrations ranged from
1 AERMOD is the current preferred Gaussian plume dispersion model for assessing stationary sources
under the Clean Air Act (70FR(216): 68217-68261).
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1 0.37 to 0.46 for the Census tracts within the two point source case study areas. A value of 0.43
2 was used for the general urban case study (see Appendix C, Section C. 1.2). Use of these ratios
3 for the 0 to 4 year old age group to represent the 0 to 7 year old age group modeled in the full-
4 scale analysis contributes some uncertainty in the estimate of inhalation exposure concentrations.
5 3.1.3 Outdoor Surface Soil/Dust Concentrations
6 Pb concentrations in outdoor surface soil/dust were characterized for the current
7 conditions or current NAAQS attainment scenarios for the three case studies using a combination
8 of modeling and empirical data. These estimates were also used for the alternate NAAQS
9 scenarios. That is, it was assumed that reductions in ambient air concentrations associated with
10 the alternate NAAQS scenarios did not have a significant impact on soil concentrations2.
11 3.1.3.1 General Urban Case Study
12 The outdoor surface soil concentration for the general urban case study was derived after
13 considering empirical data collected both at urban and other residential areas across the United
14 States (Appendix C, Section C.2). A single outdoor soil Pb level representative of general
15 residential yards in the U.S. was then selected for use with this case study. Specifically, a
16 nationally representative arithmetic mean of yard-wide soil Pb levels (198 |ig/g) was obtained
17 from the National Study of Lead and Allergen in Housing (NSLAH) (USEPA, 2000). The
18 NSLAH survey, which was conducted by the Department of Housing and Urban Development
19 (HUD) between 1998 and 1999, was intended to generate a nationally representative sample of
20 residential housing, including both private and public residences constructed between 1940 and
21 1998 (but excluding institutional and group housing).
22 3.1.3.2 Primary Pb Smelter Case Study
23 In the primary Pb smelter case study, a different approach was used to estimate outdoor
24 surface soil/dust Pb concentrations near the facility than that used for more distant locations.
25 This difference is in recognition of the remediation activities that have included the removal of
26 contaminated soil at many of the residential yards closest to the facility, and replacement with
27 "clean" soil. Consequently, soil Pb concentrations are estimated using a combination of
28 measurement data for blocks within the remediation zone and statistically-based predictions
29 beyond the remediation zone (see Appendix D, Section D.3 for more detail).
30 Surface soil/dust Pb levels near the facility (within the remediation zone) are based on the
31 most recent post-remediation measurements available for a given block. Pre-remediation soil
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.
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1 levels are not used in estimating soil levels within the remediation zone. Although analyses of
2 sequential (over time) post-remediation measurements have indicated recontamination of
3 remediated yards (USEPA, 2006b), the post-remediation measurement based estimates were
4 used for the current NAAQS scenario, and for the alternate NAAQS scenarios. It is recognized
5 that this approach may produce an underestimate of Pb exposure for the ingestion of surface
6 soil/dust for the current NAAQS scenario, and perhaps some alternate NAAQS scenarios.
7 However, an analysis of the impact of a reduced NAAQS on the remediation zone temporal trend
8 in surface soil/dust Pb levels was not available to inform an alternate approach. Further, the
9 impact of such a potential bias is limited to the surface soil/dust pathway and does not affect the
10 indoor dust pathway because the indoor dust Pb concentrations for this region of the primary Pb
11 smelter case study were derived using an approach that did not rely on outdoor soil/dust Pb
12 concentrations.
13 Characterization of soil levels for blocks and block groups beyond the remediation zone
14 are based on a regression model predicting soil Pb as a function of distance from the facility,
15 which was fitted to pre-remediation soil measurement data (available closer to the facility). The
16 use of pre-remediation soil data in deriving the regression equation reflects the fact that little
17 remediation has occurred in these more distant locations and consequently, spatial trends seen in
18 the pre-remediation soil levels are more likely to be representative for these outer portions of the
19 study area. The regression model used in these estimates has an r2 of 0.92 which suggests a good
20 fit and increases overall confidence in these statistical estimates. However, it should be noted
21 that this increased confidence holds for areas of interpolation (areas with sampling data used to
22 fit the model - out to about 2.3 km from the facility) more than for areas of extrapolation (areas
23 without sampling data - beyond 2.3 km from the facility).
24 The development of surface soil/dust Pb concentration estimates for this case study is
25 described more fully in Appendix D, Section D.3.
26 3.1.3.3 Secondary Pb Smelter Case Study
27 As noted in Section 2.2.2.2, soil sampling data for Pb were not identified for this case
28 study. Consequently, a hybrid model-empirical approach was used to characterize soil Pb levels
29 for this case study, with fate and transport modeling employed to derive a soil Pb concentration
30 surface for the study area and sampling data obtained from a surrogate secondary Pb smelter
31 study area employed to adjust (calibrate) that surface.
32 In lieu of historical estimates of emissions, the fate and transport modeling was
33 performed using current emissions estimates over a period consistent with the operating period
34 of the facility. As the emissions estimates used did not reflect levels of historical emissions,
35 generally believed to have been much higher than current estimates, the resultant Pb
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1 concentrations did not reflect current conditions for the location, and, as expected, were lower
2 than concentrations reported for areas near other secondary smelters (see comparisons in
3 Appendix E, Section E.3). Accordingly, the soil concentration surface was scaled up based on a
4 factor derived from empirical data from a surrogate secondary Pb smelter location. Specifically,
5 Pb concentrations across the entire modeled surface were increased by a factor of three to obtain
6 surface soil/dust Pb concentrations consistent with those reported in the literature for areas near
7 secondary Pb smelters. These estimates for the current conditions scenario were also used for
8 the alternate NAAQS scenarios. That is, it was assumed that reductions in ambient air
9 concentrations associated with the alternate NAAQS scenarios did not have a significant impact
10 on soil concentrations.
11 The development of surface soil/dust Pb concentration estimates for this case study is
12 described more fully in Appendix E, Section E.3.
13 3.1.4 Indoor Dust Concentrations
14 Pb in indoor dust can originate from a variety of sources including (a) outdoor soil which
15 is tracked into the house, (b) Pb in outdoor soil which is resuspended into the air and
16 subsequently transported indoors (c) Pb released directly into outdoor air through ongoing
17 anthropogenic activity (e.g., industrial point emissions) which is transported indoors and (d)
18 interior sources of Pb (e.g., paint, hobbies) (Adgate et al., 1998, Von Lindern, 2003). In the
19 exposure assessment conducted for the 1990 Staff Paper, indoor dust Pb concentrations were
20 predicted based on Pb concentrations in outdoor soil and ambient air (USEPA, 1989). This is
21 also the case for the default approach in the exposure component of the IEUBK model (USEPA,
22 1994a).
23 The importance of outdoor soil relative to outdoor air in influencing indoor dust Pb levels
24 appears to depend on the nature of the Pb sources involved. Investigations in urban areas and
25 near contaminated waste sites with elevated soil Pb levels without a currently active industrial
26 point source emitter of Pb have indicated a greater association of measurements of dust Pb
27 concentration with measurements of soil Pb concentration than with measurements of ambient
28 air concentration (e.g., Adgate, 1998 and Von Lindern, 2003). By contrast, investigations in
29 areas with currently operating large point sources of Pb (e.g., active Pb smelters) have implicated
30 ambient air Pb as an important source of Pb to indoor dust (Hilts, 2003). Contributions of
31 ambient air Pb to indoor dust Pb levels have also been illustrated by a deposition study
32 conducted in New York City (Caravanos et al., 2006). Caravanos and others described Pb
33 deposition indoors resulting primarily from exterior environmental sources and not from interior
34 Pb sources. For additional discussion of the relationship between indoor dust Pb, outdoor
35 ambient air Pb and other related factors, refer to Appendix G, Section G.2
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1 The prediction of indoor dust Pb based on Pb concentrations in outdoor ambient air and
2 other media (e.g., outdoor soil/dust Pb, indoor paint) can be conducted using empirically-based
3 statistical models, physically-based mechanistic models, or by a combination of both techniques.
4 Empirically-based statistical models (typically implemented as regression models) have the
5 advantage of being able to specify a relationship between indoor dust Pb and predictor variables
6 (e.g., outdoor soil/dust, outdoor air) even when these relationships are complex and highly
7 uncertain. However, statistical models have the disadvantage of requiring a significant amount
8 of site-specific data for their derivation and not being well-suited to extrapolation to scenarios
9 with conditions different from those underlying their development. Mechanistic models, by
10 contrast, can be developed in the absence of extensive site-specific data and are not as limited in
11 the types of scenarios to which they can be applied as long as those scenarios are conceptually
12 consistent with the scenario underlying their development. However, the development of
13 mechanistic models can be quite challenging and subject to significant uncertainty if the system
14 or process being predicted is complex and highly uncertain.
15 To date, efforts to predict indoor dust Pb have focused primarily on the development of
16 empirically-based statistical (regression) models (EPA, 1989). Furthermore, most of these
17 regression models have been based on data associated with large industrial point sources
18 (smelters) and their impacts on surrounding residential populations. Little progress has been
19 made in developing comparable models for areas where ambient Pb levels are not so greatly
20 influenced by a large industrial point source, such as in more general urban residential areas,
21 including either regression models specific to these more general urban scenarios, or physically-
22 based mechanistic models which could be applied more readily to different exposure scenarios
23 including urban residential populations.
24 In this assessment, a combination of (a) statistical (regression) models obtained from the
25 literature, (b) statistical regression model developed specifically for individual case studies and
26 (c) physically-based mechanistic models (developed specifically for the general urban case
27 study) was used in predicting indoor dust Pb. This reflects the fact that varying amounts of site-
28 specific data were available across the three case studies for characterizing indoor dust Pb and
29 related factors. In addition, the absence of statistical regression models in the literature
30 specifically focused on urban residential locations necessitated the development of a hybrid
31 empirical-mechanistic model for the general urban case study (see below). The approaches used
32 to predict indoor dust Pb for each of the case studies are presented below and described in
33 greater detail in Appendix G.
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1 3.1.4.1 General Urban Case Study
2 Two models are used to estimate indoor dust Pb in the general urban case study: a hybrid
3 mechanistic-empirical model (the hybrid model), and a regression-based model. Application of
4 these models with the ambient air Pb concentrations for this case study produced two sets of
5 indoor dust Pb estimates for each air quality scenario (see Appendix C, Section C.3).
6 The hybrid model uses a mechanistic model to predict indoor dust Pb resulting from the
7 infiltration of outdoor air containing Pb into indoor residential air with subsequent contribution
8 to indoor dust Pb. This portion of indoor dust Pb derived with this model is subsequently
9 referred to in this document as the recent air or recent ambient air related component or
10 contribution to indoor dust Pb. To the extent that outdoor air Pb includes contributions from
11 resuspension of historically deposited Pb, it is represented here. Other contributions to indoor
12 dust Pb (e.g., tracking of outdoor soil/dust indoors, indoor paint flaking, etc.) are addressed using
13 an empirically based estimate derived from a national-scale dataset characterizing indoor dust Pb
14 loadings in U.S. residences (the U.S. Housing and Urban Development [HUD] National Survey
15 of Lead-Based Paint in Housing - USEPA, 1995). When combined, the recent air component
16 (derived using the mechanistic model) and the other contributions component (derived using the
17 HUD dataset) provide an estimate of total indoor dust Pb for the general urban case study
18 (Appendix C, Exhibit C-7). Note that while the indoor dust Pb concentration contributed by the
19 hybrid model's recent air Pb component will vary, depending on the ambient air Pb level, the
20 concentration contributed from other sources will remain constant across the different air quality
21 scenarios. The two components of the hybrid model (the mechanistic and empirical) are
22 described in greater detail below.
23 The mechanistic model linking ambient air Pb to indoor dust Pb was developed by
24 obtaining the steady-state solution to a dynamic mass-balance equation that predicts Pb in both
25 indoor air and indoor floor dust as a function of outdoor air Pb. This dynamic mass-balance
26 equation was developed specifically for this assessment. In recognition of the complexity of the
27 larger task of simulating contributions to indoor dust Pb from all sources, the mechanistic model
28 development activity was limited to the area considered most essential to the needs of this
29 assessment, i.e., the contribution to indoor dust Pb from recent ambient air. See Appendix G,
30 Section G.3.2 for additional detail on the derivation of the mechanistic ambient air-to-indoor dust
31 Pb model.
32 As noted above, contributions of other sources to Pb in indoor dust were addressed in the
33 hybrid model using empirical data (rather than trying to model them mechanistically). The HUD
34 dataset, described above, that characterized the national distribution of residential indoor dust Pb
35 (USEPA, 1995) was selected as the basis for this other sources component of indoor dust Pb.
36 Specifically, a median value from the HUD dataset characterizing residential indoor dust Pb
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1 loadings (USEPA, 1995) was identified, and the ambient air related component of this indoor
2 dust value estimated and subtracted from the HUD median value, leaving only the other sources
3 component. This provided a central tendency estimate of the component of indoor dust Pb
4 loadings associated with sources other than recent air Pb, for typical residences in the United
5 States (see Appendix G, Section G.3.3 for additional detail). As noted earlier, this single
6 estimate of other sources indoor dust Pb is used for modeling all air quality scenarios, with no
7 change associated with the ambient air Pb level being evaluated.
8 The hybrid model generates estimates of indoor dust Pb in terms of loading, rather than
9 concentration, while concentration is the form required by the blood Pb models used in this
10 analysis. Conversion from loading to concentration was accomplished using a log-log regression
11 equation derived from the HUD dataset described above. This dataset has matched data for
12 sampled residences for both indoor dust loadings from vacuum samples, and indoor dust
13 concentrations. Use of the HUD dataset based loading to concentration conversion required an
14 additional conversion between loading estimates based on wipe samples (the form used by the
15 hybrid model) and those based on vacuum samples (the form of the HUD data). This was
16 accomplished using an equation developed by EPA (USEPA, 1997). Details on both
17 conversions are provided in Appendix G, Sections G.3.4 and G.3.4.1.
18 The individual elements of the hybrid model, including both the mechanistic and
19 empirical components as well as the loading-to-concentration conversion equations are presented
20 in Table 3-2. The final hybrid equation (including all components) is presented last.
21 Table 3-2. Hybrid model for indoor dust Pb in general urban case study.
Model component
Hybrid mechanistic equation for "recent" air
Hybrid empirical component for other sources
Converting wipe loadings to vacuum loadings
Converting vacuum loadings to concentration
Final combined hybrid equation
Formula
FLOOR LOADING (PbWipe) = 104.2 * PbAIR
Where, PbAIR is Pb in outdoor ambient air, and FLOOR
in terms of wipe sample loading.
LOADING is
1.15ug/m2
PbVAC = 0.185 * PbWIPE °921
Ln(PbCONC) = 4.92 + 0.52 * In(PbVAC)
PbDust = EXP [4.92 + 0.52 * Ln (0.185 * (104.2 * PbAir H
M.15)0931)]
22
23 Because the hybrid model has not been subject to extensive review and application
24 outside of this analysis and given the influence of indoor dust Pb on Pb exposure and risk, dust
25 Pb concentrations for the general urban case study are also estimated using an additional dust Pb
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1 model (see Appendix G, Section G.3.5). The use of two models is intended to inform our
2 characterization of model uncertainty in this key portion of the analysis. Specifically, we have
3 included the air-only regression model (EPA, 1989) as a second, parallel approach in predicting
4 indoor dust Pb. That model estimates indoor dust Pb based on (a) outdoor ambient air Pb
5 (multiplied by an air-related factor) and (b) an intercept which captures other impacts besides air
6 (e.g., indoor paint). The air-factor used in this equation is expected to capture longer-term
7 impacts of outdoor air Pb on indoor dust, including the indirect effect of air Pb on outdoor
8 soil/dust Pb with subsequent impacts of that outdoor soil/dust Pb on indoor dust Pb through other
9 mechanisms (EPA, 1989). The air-only regression model is presented below:
10 PbDUST(mg/kg or ppm) = 60 + 844 * PbAIR(ug/m3)
11 3.1.4.2 Primary Pb Smelter Case Study
12 We used different regression models for predicting Pb concentrations in indoor dust in
13 areas near the primary Pb smelter facility where soil has been remediated and more distant areas.
14 For the remediation zone near the facility, a regression equation was developed using dust Pb
15 measurements which had been collected from some of the houses within this area. These data,
16 while adequate for development of a site-specific regression model, did not have sufficient
17 spatial coverage to be used alone to represent indoor dust Pb levels for that portion of the study
18 area. For the remainder of the study area, we employed a regression equation developed for the
19 last review (USEPA, 1989). Because of the presumed impact of the remediation activity on dust
20 Pb, the site-specific dust Pb model developed for the remediation zone was not considered
21 appropriate for use in areas beyond that area.
22 The dataset used to develop the model for the remediation zone was based on indoor dust
23 samples collected in 17 houses within the remediation zone. Independent variables included in
24 the analysis were: (a) estimated annual average Pb concentrations in air at census block centroids
25 located within 200 meters of each of the 17 houses, (b) road dust Pb measurements for locations
26 within 300 meters of each house and (c) post-remediation residential soil Pb measurements for
27 the yard of each house. Pre-remediation soil Pb concentrations were not included in the
28 regression analysis since they were not expected to represent current conditions at the site.
29 Multiple samples for each medium associated with a specific house within the dataset (e.g.,
30 reflecting multiple samples collected over time) were averaged to produce a "temporally-
31 averaged" value. A number of regression models were evaluated, (see Appendix G, Section
32 G.4), and the "H6" model was ultimately selected based on goodness of fit and other
33 considerations. This model relates the natural log of indoor house dust to the natural log of
34 ambient air Pb (r2=0.701):
35 ln(house dust, mg/kg or ppm) = 8.3884 + 0.73639*ln(air Pb, ug/m3)
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1 Several points regarding the other variables considered for the remediation zone
2 regression are noted here. For example, road dust Pb concentration was not found to have
3 significant predictive power for indoor dust Pb. This may reflect the fact that the road dust Pb
4 dataset does not provide significant coverage for homes located near to the truck haul routes.
5 Additionally, yard soil Pb concentration was found to be slightly, and statistically significantly,
6 negatively correlated with indoor dust Pb levels. This counter-intuitive finding may be a result
7 of the existence within the remediation zone of a patchwork of remediated yards, such that the
8 remediation activity may have interfered with any correlation between yard soil Pb levels,
9 ambient air Pb levels and indoor dust Pb levels that might have existed previously. The resulting
10 slight negative correlation of dust Pb levels with soil Pb levels led us to exclude soil Pb from the
11 model. The y-intercept for the selected model may reflect a number of factors not correlated
12 with ambient air or distance from the facility, such as a general level of soil Pb contamination in
13 the area and indoor Pb paint.
14 For areas beyond the remediation zone, we used a regression equation developed during
15 the last review from data collected at a number of operational primary Pb smelters, including this
16 case study location (USEPA, 1989, Appendix B). This model, the "AGG" or "aggregate" model,
17 predicts indoor dust Pb concentration from both outdoor soil and ambient air Pb concentrations.
18 We have selected the AGG model for the non-remediation portion of the primary Pb smelter case
19 study area since this area has not been subjected to extensive remediation and is therefore likely
20 to resemble the locations included in the pooled dataset used in deriving this model in terms of
21 relationships among air, outdoor surface soil/dust and indoor dust. The AGG air and soil
22 regression model (USEPA, 1989), selected for areas beyond the remediation zone is the
23 following:
24 House dust (mg/kg or ppm) = 31.3 + 638*air Pb (ug/m3) + 0.364*soil Pb (mg/kg)
25 3.1.4.3 Secondary Pb Smelter Case Study
26 A version of the empirical regression model used for the primary Pb smelter (USEPA,
27 1989) was also used for the secondary Pb smelter case study. In this case study, an air-only
28 version of the model (USEPA, 1989) was employed reflecting the reduced overall confidence
29 associated with soil characterization at this case study (see Section 3.1.3.3). The AGG model for
30 estimating indoor dust (USEPA, 1989) was derived in two forms including an air-only model
31 that based indoor dust concentrations on outdoor ambient air Pb (without explicitly considering
32 outdoor soil Pb levels) and an air+soil model which based estimates on both outdoor soil and
33 ambient air Pb data. It is important to note, however, that the air-only model implicitly reflects
34 some consideration for the air-to-soil-to-indoor dust mechanism in the air signal. Specifically,
35 the larger air factor for the air-only model relative to the air+plus dust model's air factor, reflects
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1 contribution of air Pb both directly to dust through penetration indoors and subsequent
2 deposition to surfaces and indirectly to dust through deposition to outdoor soil which impacts
3 indoor dust (USEPA, 1989). This air-only regression model (USEPA, 1989) is as follows:
4 House dust (mg/kg or ppm) = 60 + 844*air Pb (ug/m3)
5 The model used for this case study was based on a number of studies focusing mainly on
6 primary Pb smelters (a number of primary Pb smelters were operational at the time of model
7 development). This may introduce uncertainty into indoor dust Pb predictions generated for this
8 case study to the extent that factors related to the dependency of indoor dust Pb on ambient air
9 Pb, such as particle size profiles and the nature of the airborne Pb compounds, differ for primary
10 versus secondary Pb smelters.
11 3.2 METHODS FOR ESTIMATING BLOOD PB LEVELS
12 This section presents the methodology used to estimate blood Pb levels in the child study
13 populations. The section begins with an overview of the primary biokinetic model used in the
14 full-scale risk analysis, the Integrated Exposure and Uptake Biokinetic model (USEPA, 1994a).
15 Findings associated with the use of both the IEUBK and Leggett models in the pilot analysis, in
16 addition to subsequent performance evaluation analyses (see Appendix J) contributed to the
17 decision to use IEUBK as the primary blood Pb model and reserve the Leggett model for use in
18 the sensitivity analyses. In addition, we have considered an empirical slope model (the Lanphear
19 model) in conducting our performance evaluation (Appendix J, Section J.2). However, this
20 model could not be used in generating an alternate set of risk estimates for this risk assessment
21 because it predicts blood Pb levels for children 18 months of age and consequently, can not
22 support the two blood Pb metrics selected for the analysis (concurrent and lifetime average).
23 Following the overview of the IEUBK biokinetic model, the probabilistic approach used
24 to generate population-level distributions of blood Pb levels for each study population is
25 described. The section ends with a discussion of the GSDs used within each case study to reflect
26 inter-individual variability in behavior related to Pb exposure and Pb biokinetics (a key
27 component in modeling population-level blood Pb).
28 3.2.1 Blood Pb Modeling
29 Blood Pb models are used in the assessment in order to (a) apportion exposure, the metric
30 for which is blood Pb, between policy-relevant Pb exposures and policy-relevant background,
31 and (b) estimate potential changes in blood Pb level distributions that would result from alternate
32 ambient air Pb levels.
33 As discussed in Section 4.4.1 of the CD, there are two broad categories of blood Pb
34 models available to support exposure and risk assessment:
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1 • Statistical (regression) models, which attempt to apportion variance in measured blood Pb
2 levels for a study population to a range of determinants or control variables (e.g., surface
3 dust Pb concentrations, air Pb concentrations). The development of these models
4 requires paired predictor-outcome data which restricts these empirical models to the
5 domain of their observations (i.e., to applications involving the study population(s) and
6 exposure scenarios used in their derivation or at least to scenarios very similar to the
7 original study conditions).
8 • Mechanistic models, which attempt to model the process of transfer of Pb from the
9 environment to human tissues. While these models are considerably more complex
10 compared with the regression models (in terms of both the number of variables and their
11 computational structure), by incorporating variables that vary temporally and spatially, or
12 across individuals or populations, mechanistic models can be extrapolated to a wide
13 range of scenarios, including those outside of the original populations and exposure
14 scenarios used to develop/parameterize the models.
15
16 Given concerns over applying regression models to populations and exposure scenarios
17 other than those used in their derivation, and consistent with recommendations from CASAC
18 (see Section 1.4), we have relied primarily on mechanistic models in conducting the exposure
19 analysis for this assessment. Additionally, a regression model developed by Lanphear et al.
20 (1998) and described in Appendix H, Section H.2.3.was included in the blood Pb modeling
21 performance evaluation (Appendix J, Section J.3.2). The CD (Section 4.4) describes three
22 mechanistic (biokinetic) models developed over the past several decades including IEUBK for
23 modeling child Pb exposure (CD, Section 4.4.5 and Appendix H, Section H.2.1) and two models
24 designed to simulate Pb biokinetics and blood Pb levels from birth through adulthood, the
25 Leggett model (CD, Section 4.4.6) and the model developed by O'Flaherty (CD, Sections 4.4.7).
26 All three models have the potential for application in Pb risk assessment and have been evaluated
27 to varying degrees using empirical datasets (CD, Section 8.3.4). Based on results of the pilot
28 analysis, subsequent performance evaluations, and advice from CASAC, we selected the IEUBK
29 model as the primary blood Pb model for the full-scale analysis. The Leggett model has been
30 included as part of the sensitivity analysis intended to assess the potential impact of uncertainty
31 in blood Pb modeling on the overall analysis (See Section 4.3.2).3 A brief overview of the
32 IEUBK and Leggett models is presented below.
3 The Lanphear empirical model was considered for use in the sensitivity analysis, but was ultimately not
used, since it generate blood Pb metrics which do not match the metrics used in this risk assessment (i.e., concurrent
and lifetime average) (see Appendix J, Section J.2.1).
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1 3.2.1.1 Primary Analysis
2 The IEUBK model was selected for use in generating the primary set of exposure and
3 risk results. IEUBK is a multi-compartmental pharmacokinetics model for children 0-7 years of
4 age, which predicts average quasi-steady state blood Pb concentrations corresponding to daily
5 average exposures, averaged over periods of a year of more. The exposure module provides
6 average daily intakes of Pb (averaged over a 1 year time increment) for inhalation (air, including
7 consideration for both outdoor and indoor) and ingestion (soil, indoor dust, diet and water)
8 (Section 4.4.5.1 of the CD). The model is intended to be applied to groups of children
9 experiencing similar levels of Pb exposure and to generate a central tendency blood Pb estimate
10 for that group (USEPA, 1994a). In applications of IEUBK, inter-individual variability in
11 biokinetics and behavior (e.g., varying rates of dietary Pb ingestion) of the study population is
12 typically characterized through the incorporation of a GSD which, together with the IEUBK-
13 generated blood Pb level, provides a distribution of blood Pb levels for a group of modeled
14 children.
15 For each exposure zone in each case study air quality scenario, estimates of the
16 concurrent and lifetime average blood Pb level are derived from the outputs of the IEUBK model
17 as described in Appendix H. Briefly, the concurrent metric is derived as the average over ages
18 73 to 84 months (approximately 6 to 7 years of age)4, and the lifetime average metric is the
19 average of blood Pb levels between ages 6 and 84 months.
20 Additional detail on the IEUBK model is described in Appendix H (Section H.2.1) and in
21 Section 4.4.5 of the CD. Application of the IEUBK model is described in Appendix H, Section
22 H.3.1, and input parameter values, and their basis, are described in Appendix H, Section H.4.
23 3.2.1.2 Sensitivity Analysis
24 The Leggett model has been included as part of the sensitivity analysis (Appendix L) and
25 in model performance evaluations (Appendix J), but has not been used in generating primary
26 exposure and risk results for this assessment. Originally developed from a model designed to
27 simulate radiation doses for bone-seeking radionuclides, the Leggett biokinetic model has a
28 temporal resolution of one day and can model exposure from infancy through adulthood (CD,
29 Section 4.4.6). The day-level resolution in Leggett allows more comprehensive treatment of the
30 temporal pattern of exposure and its shorter-term impact on blood Pb levels than IEUBK,
4 As described in Appendix I (Section 1.2.1), 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 the 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).
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1 although for this assessment, which focuses on longer-term trends in Pb exposure, this
2 functionality is less relevant. The Leggett model does not include a detailed pathway-level
3 exposure module as does IEUBK. Rather the Leggett model takes total ingestion and inhalation
4 exposure estimates as inputs. However, it is possible to link the Leggett model to a more
5 detailed pathway-level exposure model, thereby allowing a more detailed treatment of Pb
6 exposure pathways and their impact on blood Pb. The use of this type of external exposure
7 model including pathway-specific modeling of exposure levels was implemented for the pilot
8 analysis. As with IEUBK, Leggett is used to derive central tendency blood Pb levels for groups
9 of similarly exposed children. The same GSD used for IEUBK is then used to produce estimates
10 of the distribution of blood Pb levels within study populations. For additional details on the
11 Leggett model see Section 4.4.6 of the CD and Appendix H.
12 3.2.2 Exposure Pathway Apportionment and Probabilistic Population Modeling
13 This section describes the method used to estimate contributions to blood Pb and IQ from
14 exposure pathways of interest (see Section 2.4.3), and the method used for probabilistic
15 population modeling. Both are applied to the central tendency estimates of blood Pb developed
16 for the two blood Pb metrics (concurrent and lifetime average) from the IEUBK model outputs.
17 To the extent feasible with the modeling tools and assessment design, estimates of the
18 contribution to blood Pb (central tendency estimate) are developed for policy-relevant
19 background versus policy-relevant exposures (Section 2.4.3). This is done by considering blood
20 Pb model estimates for an exposure zone derived using only the pathways of interest (Appendix
21 I, Section I.I). As discussed in Section 2.4.3, there are limitations on the resolution to which
22 policy-relevant exposures can be distinguished which results in some simplifying assumptions.
23 We developed estimates of contribution to blood Pb estimates (and IQ estimates) for the
24 following pathways (or pathway combinations):
25 • Inhalation of ambient air Pb (i.e., "recent air" Pb): This is derived using the blood Pb
26 estimate resulting from Pb exposure limited to the inhalation pathway.
27 • Ingestion of "recent air" indoor dust Pb: This is derived using the blood Pb estimate
28 resulting from Pb exposure limited to ingestion of the Pb in indoor dust that is
29 predicted to be associated with ambient air concentrations (i.e., via the air
30 concentration coefficient in the regression-based dust models or via the mechanistic
31 component of the hybrid blood Pb model (Section 3.1.4).
32 • Ingestion of "other" indoor dust Pb: This is derived using the blood Pb estimate
33 resulting from Pb exposure limited to ingestion of the Pb in indoor dust that is not
34 predicted to be associated with ambient air concentrations (i.e., that predicted by the
35 intercept in the dust models plus that predicted by the outdoor soil concentration
36 coefficient, for models that include one (Section 3.1.4)). This is interpreted to
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1 represent indoor paint, outdoor soil/dust, and additional sources of Pb to indoor dust
2 including historical air (see Section 2.4.3).
3 • Ingestion of outdoor soil/dust Pb: This is derived using the blood Pb estimate resulting
4 from Pb exposure limited to ingestion of outdoor soil/dust Pb.
5 • Ingestion of drinking water Pb: This is derived using the blood Pb estimate resulting
6 from Pb exposure limited to ingestion of drinking water Pb.
7 • Ingestion of dietary Pb: This is derived using the blood Pb estimate resulting from Pb
8 exposure limited to ingestion of dietary Pb.
9
10 The goal of this probabilistic exposure modeling is to generate population-level
11 distributions of blood Pb levels that allow (a) specific percentiles of exposure (e.g., 50th, 90th,
12 99th and mean) within a study population to be identified. These are presented along with the
13 differentiation by exposure pathway (e.g., policy-relevant background versus policy-relevant
14 exposures, with the latter further differentiated as to ambient air inhalation, indoor dust ingestion
15 and outdoor surface soil/dust ingestion, as indicated above). Therefore, for example, we may
16 have an estimate of exposure for the 99th percentile child in the primary Pb smelter case study,
17 with that blood Pb level differentiated as to the fraction coming from (a) diet and drinking water,
18 (b) recent air Pb, including ambient air inhalation and ingestion of the recent air component of
19 indoor dust Pb, with this fraction potentially including resuspended, previously deposited Pb (see
20 Section 2.4.3), and (c) other sources including outdoor soil/dust and historical air Pb, as well as
21 indoor paint. The effort to differentiate the recent air and other sources of Pb to indoor dust is
22 subject to different degrees of uncertainty for the various case studies, reflecting the different
23 approaches used in modeling indoor dust (see Sections 2.4.3 and 3.1.4).
24 Probabilistic exposure modeling differs for the two point source case studies and the
25 general urban case study. Because the two point source case studies are location-specific,
26 demographic data for each study area are used to evaluate the interaction between child
27 populations in those study areas and the distribution of Pb concentrations in each media (e.g.,
28 outdoor ambient air, outdoor soil/dust and indoor dust). By contrast, because the general urban
29 case study is not location-specific, probabilistic exposure modeling does not involve location-
30 specific demographic data and instead, is based on the assumption of a uniformly distributed
31 child receptor population contacting media with uniformly distributed Pb.
32 3.2.2.1 General Urban Case Study
33 The approach used to generate exposure distributions for the general urban case study is
34 simpler than that used for the two point source case studies (Section 3.2.2.2), due to the use of a
35 uniformly distributed population and spatially uniform media concentrations of Pb in the design
36 of this case study. This design negates the need for population-weighted sampling in generating
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1 the exposure distribution. Instead, the central tendency blood Pb level generated for a specific
2 air quality scenario using IEUBK, is combined deterministically with the GSD reflecting inter-
3 individual variability in Pb exposure and biokinetics to produce a population-level distribution of
4 blood Pb levels.5 Specific percentile exposure levels are then identified from that distribution,
5 with these levels interpreted as representing modeled individual children for the general urban
6 case study (i.e., estimates conceptually equivalent to those generated for the two point source
7 case studies). However, because the analysis is not location-specific, population incidence
8 estimates are not generated.
9 The study area for the general urban case study can be considered a single exposure zone
10 with a single lEUBK-derived central tendency blood Pb level that is combined with the GSD to
11 produce the population-level exposure distribution for that study area. As is the case with
12 individual exposure zones modeled for the point source case studies, the entire child population
13 in the single exposure zone modeled here is given the same pathway apportionment. As with the
14 point source case studies, this approach introduces uncertainty into the analysis, especially for
15 simulated individuals with high-end blood Pb levels, since pathway apportionment would likely
16 not be the same for all individuals in an exposure zone, even if all were exposed to the same Pb
17 concentrations in each medium.
18 3.2.2.2 Point Source Case Studies
19 Probabilistic exposure modeling for the two point source case studies relied on
20 information in three areas as summarized below:
21
22 • Central tendency blood Pb levels for each exposure zone: Outputs from the biokinetic
23 blood Pb modeling (considered to be central tendency estimates) are used to produce
24 central tendency estimates of concurrent and lifetime average blood Pb levels for each
25 exposure zone (i.e., census block or block group) in each case study area.
26 • Demographics (child distribution within study areas): The distribution of 0-7 year old
27 children within each case study area is used to insure that the generation of population-
28 level blood Pb level distributions for each case study reflects where children are located.
5 Note that as discussed in Section 3.2.3.1, the range of GSDs used in the general urban case study to reflect
inter-individual 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.
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1 • GSD reflecting inter-individual variability in blood Pb levels: A GSD is used to reflect
2 inter-individual variability for blood Pb levels in groups of similarly-exposed children
3 (i.e., within each exposure zone of a case study area). The GSD is combined with the
4 central tendency blood Pb level estimates to generate a distribution of blood Pb levels for
5 the group of children located in each exposure zone.
6
7 The step-wise procedure used to generate population-level blood Pb distributions for each
8 of the point source case studies is illustrated in Figure 3-1, with the information described in the
9 bullets above recognized as input data.
10 Several points related to implementation of this procedure are noted. For this assessment,
11 50,000 simulated individuals were generated for each point source case study in order to insure
12 that the population-level blood Pb distributions generated met target stability goals (see
13 Appendix M, Section M.2.2).6 This simulation count represents a higher total child count than
14 actually is associated with either study area. Using a higher number of simulated individuals was
15 necessary, however, to generate blood Pb distributions with "stable" higher-end exposure
16 estimates. If simulations matching the actual population count at each case study had been
17 conducted, the distributions that would have resulted would have been "unstable" at higher
18 percentiles. It is important to note, however, that child population count estimates for individual
19 percentiles (Section 3.4) have been scaled to reflect the actual child count associated with each
20 study area.
21
6 The analysis of simulation stability focused on the general urban case study and demonstrated that even
the highest population percentiles generated for this analysis (99.9th percentile) were relatively stable with
coefficients of variation of less than 3% for total IQ loss estimates.
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1 Figure 3-1.
Procedure for generating population blood Pb distributions for point source
case studies.
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.
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).
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.
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).
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1 An additional point of clarification relates to the process used in differentiating specific
2 percentile total blood Pb levels into pathway-specific fractions for the two point source case
3 studies. All simulated individuals associated with a given exposure zone (i.e., U.S. Census block
4 or block group), were assigned the same pathway-specific apportionment, which was derived
5 from the blood Pb modeling performed for the exposure zone. The set of simulated individuals
6 with a range of total blood Pb levels, produced subsequently via application of the GSD, are
7 assumed to all have the same pathway-specific apportionment of those blood Pb levels (i.e., the
8 same apportionment as that generated for the central tendency blood Pb level modeled for that
9 exposure zone). In reality, it is likely that pathway apportionment would vary across children
10 with different blood Pb levels located in the same exposure zone (e.g., the contribution of indoor
11 dust exposure to total blood Pb might differ for kids living near each other who demonstrate
12 different total blood Pb levels). The modeling approach used, however, does not provide
13 pathway apportionment within an exposure zone, only across exposure zones (i.e., each exposure
14 zone has a different pattern of pathway apportionment for its simulated children).
15 The modeling approach presented in Figure 3-1 and described above, generates a
16 population-level distribution of total blood Pb levels with pathway apportionment as described
17 above. These distributions are used to generate several types of exposure metrics including:
18 • Population-weighted exposure percentiles: total blood Pb levels (with pathway
19 apportionment) for simulated individuals representing specific points along the population
20 blood Pb distribution (e.g., 50, 90, 95, 99 and 99.5th percentile).
21 • Incidence counts: number of children within a given study area projected to experience a
22 specific degree of Pb exposure (total blood Pb level).
23 3.2.3 GSD for Population Blood Pb Modeling Procedure
24 A key aspect of the population-level blood Pb modeling for all three case studies is the
25 application of the GSD reflecting inter-individual variability in blood Pb levels. This GSD
26 reflects a number of factors which operate together to produce inter-individual variability in
27 blood Pb levels, including: (a) biokinetic variability (differences in the uptake, distribution or
28 clearance of Pb), (b) differences in behavior related to Pb exposure (e.g., varying hand-to-mouth
29 activity, tap water ingestion rates, and time spent playing indoors) and (c) differences in
30 environmental Pb exposure concentrations (e.g., spatial gradients in ambient Pb levels of a
31 resolution beyond that simulated in each case study, differences in cleaning/vacuuming rates and
32 air exchange rates).
33 GSDs will tend to be larger for more diverse populations and/or larger study areas,
34 reflecting the potential for greater variability in the factors listed above. Specifically, more
35 diverse populations will tend to demonstrate greater diversity in behavioral and biokinetic factors
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1 related to Pb exposure, thereby producing greater variation in blood Pb levels. Larger study area
2 will tend to produce greater GSDs due both to more diverse populations, with their greater
3 behavioral and biokinetic variability, as well as greater variation in Pb media concentrations
4 across the study area.
5 The dramatic reduction in blood Pb levels among children in the United States that was
6 observed between the NHANES surveys of the late 1970s and those of the 1980s and later was
7 accompanied by an increase in GSD (see Appendix H, Exhibit H-7). Possible reasons for this
8 include the likelihood that, as blood Pb levels decrease, a wider variety of exposure pathways
9 begin to play a role in determining overall blood Pb levels (at higher blood Pb levels, it likely
10 that one, or a few related pathways dominate exposure). As more pathways come into play, the
11 potential for inter-individual variability in behavior and biokinetics related to these pathways
12 increase, thereby producing greater variability in blood Pb levels (see Section 4.2.2 of EPA,
13 1994b,). Another possible explanation for the increase in GSDs is that, while overall Pb
14 exposure levels have decreased, some fraction of children nationwide continue to be exposed to
15 Pb paint and Pb in drinking water (associated with Pb-solder used in older plumbing). These
16 higher non-air related exposures can produce elevated blood Pb level, especially when compared
17 to average blood Pb levels in the current general population. Therefore, while the geometric
18 mean blood Pb level may have decreased, the tail of the distribution may have remained
19 anchored (for these paint and drinking water exposed children) resulting in a larger GSD.
20 A number of studies have been conducted over the past three decades which provide
21 insights into inter-individual variability in Pb levels under various exposure conditions. Many of
22 the studies from the 1970's and 1980's focused on populations living near smelters with fairly
23 elevated blood Pb levels compared with levels modeled for our three case studies. For example,
24 as seen in Appendix H, Exhibit H-7, geometric mean blood Pb levels for populations near active
25 smelters in the past tended to exceed 8 |ig/dL with GSDs on the order of 1.7. Note, that these
26 earlier studies do not have readily available summaries of blood Pb levels for the metrics of
27 interest in this analysis (concurrent and lifetime average metrics) and instead, provide more
28 generalized summaries based on the individual measurements collected.
29 Beginning in the 1980's and extending through the 1990's, a number of studies were
30 conducted focusing on urban populations in specific U.S. cities (e.g., Lanphear et al 2005). These
31 studies, which were intended to examine the link between Pb exposure and neurological effects
32 in children, have readily available blood Pb summary data for both concurrent and lifetime
33 average blood metrics. As can be seen in Appendix H, Exhibit H-9, geometric mean blood Pb
34 levels vary significantly across these studies, with concurrent values ranging from 5.5 |ig/dL to
35 14.5 |ig/dL and lifetime average values ranging from 4 |ig/dL to 14.2 |ig/dL. GSDs also vary
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1 across the studies with GSDs of 1.4 to 1.7 reported for the lifetime average metric and 1.5 to 1.9
2 for the concurrent metric.
3 In addition to these smelter-related and city-specific studies, the CDC has also conducted
4 several iterations of its NHANES national-scale survey over the past three decades which track
5 changes in the national distribution of blood Pb levels among children in the U.S. (see CD,
6 Section 4.3.1.3 and Appendix H). As mentioned above, between the earliest NHANES surveys
7 and the later (post 1980) surveys average blood Pb levels in children in the U.S. decreased
8 dramatically (following initiatives to remove Pb from gasoline and other products in the late
9 1970's and 1980's), and the GSD increased significantly. This is most pronounced in the
10 geometric mean blood Pb levels and associated GSDs reported for the first NHANES survey
11 (1967-1980) (CDC, 2005) and the second NHANES survey (1988-1991) (CDC, 2005) with
12 blood Pb levels decreasing from 14.9 to 3.6 and GSDs increasing from 1.4 to 2.1.
13 GSDs were selected for each case study based on consideration for the study data
14 summarized above. The selection of these GSDs reflected consideration for a number of factors
15 including: (a) the type of study area and underlying population involved (e.g., point-source
16 versus more generalized urban area), (b) the fact that all three study areas use exposure zones
17 with fairly uniform media concentrations and that the GSD selected will be used to represent
18 blood Pb variability within each of those zones (c) age of the underlying survey population (the
19 goal was to match the survey population to our study population to the extent possible), and (d)
20 date of the survey (generally there is a desire, when possible, to use studies that are more
21 contemporary to capture any underlying downward trends in blood Pb levels which have
22 occurred). The GSDs for each of the case studies, as well as the rationale for their selection, is
23 presented below.
24 3.2.3.1 General Urban Case Study
25 For this case study, two sets of population blood Pb estimates are developed for each
26 blood Pb metric. This was done using GSDs intended to reflect: (a) a more uniform population
27 living in a smaller urban area (represented by a smaller GSD for each metric) and (b) a more
28 diverse urban population living in a larger urban area (represented by a larger GSD for each
29 metric). Together, these two sets of estimates provide a range of results for each blood Pb metric
30 intended to reflect uncertainty associated with this key component of the analysis (i.e., inter-
31 individual variability in blood Pb levels). The lower-bound GSDs (representing the more
32 uniform, smaller urban population) were obtained from the Boston study (Bellinger, 1992) (i.e.,
33 concurrent GSD of 1.7 and a lifetime average value of 1.6). This study represents one of the
34 more contemporary of the urban studies focusing on a smaller population. By contrast, the
35 upper-bound GSDs (representing the larger, more diverse population) were obtained using
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1 NHANES IV (CDC, 2005). Here, the GSD from NHANES IV for 1-5 yr olds (2.1) is used for
2 the concurrent metric, and the GSD for the lifetime average metric (2.0) was derived by scaling
3 the value for the concurrent metric using the relationship between concurrent and lifetime seen in
4 the smaller urban-scale Boston study (Bellinger, 1992).
5 3.2.3.2 Point-Source Case Studies
6 A critical consideration in identifying the GSD for use in the point source case studies, is
7 the fact that each is modeled using a spatial template that divides the study area into exposure
8 zones with relatively uniform media concentrations. As noted earlier, the GSDs are used to
9 reflect inter-individual variability in blood Pb levels for the group of modeled children located
10 within each of those zones. Therefore, the GSD does not need to provide full coverage for media
11 concentration variability and its impact on exposure, since this is covered to some extent by the
12 spatial template. Therefore, a larger GSD such as that suggested by NHANES would likely
13 over-state variability and a smaller GSD, possibly in-line with values from smaller-scale studies
14 such as the smelter or city-specific studies would seem to be more appropriate. Following this
15 logic, a GSD of 1.7 was selected for the concurrent blood metric, based on consideration for the
16 range of values from these smaller-scale studies. A matching lifetime average metric GSD value
17 of 1.6 was selected, also based on the city-specific studies.
18 3.3 ESTIMATED MEDIA CONCENTRATIONS
19 This section summarizes the media concentration estimates for all air quality scenarios at
20 all three case studies (Tables 3-3 to 3-6). The complete set of media concentration estimates for
21 each air quality scenario are presented in Appendix C for the general urban case study, in
22 Appendix D (Attachments D-7 through D-l 1) for the primary Pb smelter case study, and in
23 Appendix E (Attachments E-3 through E-7) for the secondary Pb smelter case study. Estimates
24 presented in this section are presented to three (for air) or zero (for dust and soil) decimal places,
25 which results in various numbers of implied significant figures. This is not intended to convey
26 greater precision for some estimates than others; it is simply an expedient and initial result of the
27 software used for the calculation. The number of significant figures associated with most
28 estimates presented in this section is considered to be generally no more than two or three.
29 For each air quality scenario for the two point source case studies, a range of percentile
30 estimates are presented for each exposure medium. For the general urban case study, however,
31 only a single value is presented for each exposure medium. This reflects the fact that, while the
32 point source case studies are modeled using spatial templates that include a large number of US
33 Census blocks and/or block groups (allowing percentile media concentrations to be identified),
34 the general urban case study is modeled using a single study area with uniform media
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1 concentrations. Consequently, there is only one value presented for the general urban case study
2 for each medium in each air quality scenario.
3 As discussed in Section 2.3.3, Pb concentration in outdoor soil/dust is not changed with
4 the alternate air quality scenarios. Rather, outdoor soil/dust concentration is held constant at the
5 current conditions or current NAAQS attainment level. This reflects the judgement that in most
6 cases, a reduced air concentration would not yield a changed soil concentration over the near
7 term (e.g., years to decades). In the case of an area such as the remediation zone of the primary
8 Pb smelter case study, however, where soil dynamics have been changed by the substitution of
9 contaminated soil with clean soil, or in areas where local sources may pose a more significant
10 source to outdoor soil/dust than historic sources - and where there may be a currently increasing
11 trend in surface Pb concentration - this may underestimate soil concentrations under some
12 alternate NAAQS.
13 As expected, the highest media concentrations for each case study are associated with the
14 current NAAQS scenario. The relatively lower media concentrations for the alternate NAAQS
15 scenarios vary in the following order of decreasing estimates: 1) the 0.5 |J,g/m3 maximum
16 monthly average, 2) the 0.2 |J,g/m3 maximum quarterly, 3) 0.2 |J,g/m3 maximum monthly and 4)
17 the 0.05 ng/m3 maximum monthly average. In the case of the general urban case study, both of
18 the current conditions scenarios (mean and high-end) generate media concentrations below the
19 current NAAQS, since both monitor-based values are below the current NAAQS.
20 Table 3-3. Estimated annual ambient air concentrations.
Statistic
Average Annual Air Pb Concentration (jig/m3)
Current
Conditions
Current Alternative NAAQS Attainment
NAAQS 0.2 ng/m3,
Attainment max quarterly
0.5 |ig/m3
max monthly
0.2 |ig/m3
max monthly
0.05 ng/m3
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
95m percentile
Median
5tn percentile
Minimum
NA
0.740
0.419
0.068
0.013
0.006
0.161
0.091
0.015
0.003
0.001
0.326
0.185
0.030
0.006
0.003
0.130
0.074
0.012
0.002
0.001
0.033
0.018
0.003
0.001
<0.001
Secondary Pb smelter case study
Maximum
95m percentile
Median
5tn percentile
Minimum
0.126
0.015
0.003
0.001
<0.001
NAa
0.034
0.004
0.001
<0.001
<0.001
0.071
0.009
0.002
<0.001
<0.001
0.028
0.003
0.001
<0.001
<0.001
0.007
0.001
<0.001
<0.001
<0.001
aThe current conditions scenario for secondary Pb smelter case study met the current NAAQS.
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1 Table 3-4. Estimated inhalation exposure concentrations.
Statistic
Average Annual Inhalation Ex
Current
Conditions
Current
NAAQS
Attainment
posure Concentration of Pb (ng/m3)
Alternative NAAQS Attainment
0.2 (ig/m3 max
quarterly
0.5 u.g/m3
max monthly
0.2 ng/m3
max monthly
0.05 jig/m3
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
95m percentile
Median
5tn percentile
Minimum
NA
0.311
0.176
0.027
0.005
0.002
0.068
0.038
0.006
0.001
0.001
0.137
0.078
0.012
0.002
0.001
0.055
0.031
0.005
0.001
<0.001
0.014
0.008
0.001
<0.001
<0.001
Secondary Pb smelter case study
Maximum
95tn percentile
Median
5m percentile
Minimum
0.055
0.007
0.001
<0.001
<0.001
NAa
0.015
0.002
<0.001
<0.001
<0.001
0.031
0.004
0.001
<0.001
<0.001
0.012
0.002
<0.001
<0.001
<0.001
0.003
<0.001
<0.001
<0.001
<0.001
aThe 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)3
General urban case study
NA- single
study area
198
Primary Pb smelter case study
Maximum
95tn percentile
Median
5tn percentile
Minimum
958
409
108
28
17
Secondary Pb smelter case study
Maximum
95tn percentile
Median
5m percentile
Minimum
315
66
12
1
<1
a Estimates developed for current conditions (or current
NAAQS attainment) scenario were used for all alternate
NAAQS scenarios.
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1 Table 3-6. Estimated indoor dust concentrations.
Statistic
Projected Average Indoor Dust Pb Concentration (mg/kg or ppm)
Current
Conditions
Current Alternative NAAQS Attainment
NAAQS 0.2 (ng/m3)
Attainment max quarterly
0.5 (ng/m3)
max monthly
0.2 (u.g/m3)
max monthly
0.05 (n.g/m3)
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
95tn percentile
Median
5m percentile
Minimum
NA
3,523
2,318
122
50
41
1,145
754
83
43
38
1,926
1,271
95
45
39
981
645
82
43
38
383
247
76
42
38
Secondary Pb smelter case study
Maximum
95tn percentile
Median
5m percentile
Minimum
166
73
63
60
60
NAa
89
63
61
60
60
120
67
62
60
60
84
63
61
60
60
66
61
60
60
60
a The current conditions scenario for secondary Pb smelter case study met the current NAAQS.
3 3.4 ESTIMATED BLOOD PB LEVELS
4 Estimates of concurrent and average lifetime blood Pb level derived from outputs of the
5 IEUBK model (Section 3.2) have been developed for each air quality scenario in each case study
6 (see Appendix I). Further, multiple sets of blood Pb estimates were generated for each air
7 quality scenario of each case study, reflecting an effort to consider key sources of uncertainty
8 (e.g., indoor dust model, blood metric, GSD) and their impact on blood Pb estimates (see Section
9 2.4.6.2). That is, eight separate blood Pb distributions were generated for each air quality
10 scenario of the general urban case study (four for each of the two blood Pb metrics) and two
11 distributions were generated for each air quality scenario of the point source case studies (one for
12 each of the two blood Pb metrics) (see Table 2-3). The greater number of blood Pb distributions
13 for the general urban case study reflects the larger number of modeling approaches implemented
14 for this more conceptual case study, which differ by indoor dust model and GSD.
15 The general urban case study blood Pb distributions for the modeling approach yielding
16 the overall highest and lowest concurrent blood Pb estimates are presented in Tables 3-7 through
17 3-12. The distributions of concurrent blood Pb metric estimates for the primary Pb smelter case
18 study air quality scenario are presented Tables 3-13 through 3-17, and for the secondary Pb
19 smelter case study scenarios in Tables 3-18 through 3-22. Because general trends in blood Pb
20 levels across both population percentiles and air quality scenarios (for a given case study) are
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1 similar for both the concurrent and lifetime average blood metrics, we have only presented
2 results for the concurrent metric here.
3 For the general urban case study, the blood Pb distributions for the two current conditions
4 scenarios (95th percentile and mean) are quite similar. However, there is a difference of about a
5 factor of two between levels generated by the different modeling approaches (i.e., different dust
6 model and GSD value)7. For example, the 99.9th percentile concurrent blood Pb estimate for the
7 air-only regression based model and GSD of 1.7 was approximately 10 |ig/dL, compared to the
8 corresponding value of 20 |ig/dL for the hybrid dust model and GSD of 2.1. Additionally,
9 although the 99.9th percentile blood Pb estimates for the current NAAQS scenario were 1.5 to 2
10 times higher (18-29 |ig/dL) than those for the current conditions scenarios, the 99.9th percentile
11 blood Pb estimates for the alternate NAAQS scenarios (e.g., 8-16 |ig/dL for the lowest alternate)
12 were quite similar to those for the current conditions scenarios (10-20 |ig/dL). At the 50th
13 percentile, with the exception of the slightly higher values for the current NAAQS scenario (3-4
14 Hg/dL), little difference was seen among results generated for the different scenarios by the
15 different modeling approaches (all ~2 |ig/dL blood Pb).
16 The 99.9th and 50th percentile concurrent estimates for the primary Pb smelter case study
17 scenarios were similar to the lower estimates for the corresponding scenarios of the general
18 urban case study (i.e., those generated using the air-only regression-based dust model and GSD
19 value of 1.7). The blood Pb estimates for the secondary Pb smelter case study did not vary
20 among the air quality scenarios; the 99.5th and 50th percentile estimates for the concurrent metric
21 are 5 |ig/dL and 1 |ig/dL, respectively.
22 With the percentile estimates for each case study scenario, we also present estimates of
23 contributions from each pathway to blood Pb, expressed as percentages. The indoor dust
24 ingestion pathway is separated into contribution derived from recent ambient air and that from
25 other sources (e.g., indoor paint, outdoor soil/dust, and additional sources including historical
26 air), as described in Section 2.4.3 and Appendix G. For the point source case studies, these
27 contribution estimates were derived for the geometric mean (GM) blood Pb estimates for each
28 U.S. Census block or block group before the GSD is applied to generate the blood Pb
29 distributions. The blood Pb percentile estimates, however, are those resulting from application
30 of the GSD. Thus, as some of the high percentile blood Pb values are actually associated with
31 U.S. Census blocks (or block groups) with low blood Pb GMs (and vice versa), the differences in
32 pathway contribution across different percentiles may seem odd. Because there is only one GM
7 The lowest overall blood Pb distribution is generated when the air-only regression indoor dust model and
lower inter-individual GSD are used. Conversely, the highest overall blood Pb distribution is generated when the
hybrid indoor dust model and largest inter-individual GSD are used.
July 2007 3-29 Draft - Do Not Quote or Cite
-------
1 blood Pb per air quality scenario of the general urban case study, there is only one pathway
2 contribution estimate for all percentiles of the general urban case study.
3 For the general urban case study, we estimate that recent air Pb (including both direct
4 inhalation and ingestion of the Pb in indoor dust that is predicted to be associated with ambient
5 air Pb levels) contributes 23 to 38% of total blood Pb in the current conditions (95th percentile)
6 scenario. As would be expected, recent air contributes less to total blood Pb in the current
7 conditions (mean) scenario (12 to 28%) since ambient air Pb levels are less, compared with the
8 high-end scenario. This trend or reduced recent air contribution with reduced ambient air Pb
9 concentration continues with recent air only contributing 3 to 13% of total blood Pb in the lowest
10 alternate NAAQS scenario evaluated.
11 For both the primary and secondary Pb smelter case studies, the contribution of recent air
12 Pb generally increases for higher population percentiles (as would be expected, since individuals
13 with relatively higher blood Pb levels are likely to live near the facility and experience
14 significantly elevated ambient air Pb level).8 As would be expected, recent air generally
15 contributes less to total blood Pb for alternative NAAQS scenarios. Specifically, we project that
16 recent air will contribute from <1% to 14% of total blood Pb for the lowest alternative NAAQS
17 scenario. The relatively small influence of recent air Pb on total blood Pb levels can also be seen
18 in the pathway contribution estimates for the secondary Pb smelter case study (in which the
19 current air concentrations are lowest), which tend to be less than 10% across all population
20 percentiles (for all air quality scenarios).
Note, that this tracking of ambient air Pb contribution and percentile blood Pb level is not consistent,
likely reflecting the fact that the GSD is also a key factor influencing blood Pb level in our modeling. Consequently,
you can have the situation where a simulated child with a relatively elevated blood Pb level comes from a US
Census block that is towards the outside of the study area (and therefore has a relatively lower ambient air lead level
and consequently air-related uptake), but for which a high value from the extreme high-end tail of the GSD has been
selected. In this case, a high blood Pb level would have a relatively low ambient air-related signal.
July 2007 3-30 Draft - Do Not Quote or Cite
-------
1 Table 3-7. General urban case study: current conditions - estimated blood Pb levels.
Blood Pb
Percent! le
Predicted
Blood Pb
(ng/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
CURRENT CONDITIONS - 95th percentile
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99m
95m
90m
75m
Median
25tn
1st
10
8
7
5
4
3
2
1
1
17%
10%
37%
14%
22%
1%
Dust Model (Hybrid), GSD (2.1), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
22
14
12
7
6
4
2
1
<1
16%
9%
33%
4%
37%
1%
CURRENT CONDITIONS - Arithmetic Mean
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99tn
95tn
90tn
75tn
Median
25tn
1st
9
7
6
4
3
3
2
1
1
19%
11%
41%
15%
12%
1%
Dust Model (Hybrid), GSD (2.1), Blood Pb Metric (Concurrent)
99.9th
99.5th
99m
95tn
90m
75tn
Median
25tn
1st
18
13
11
6
5
3
2
1
<1
18%
10%
38%
6%
28%
1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
3-31
Draft - Do Not Quote or Cite
-------
1 Table 3-8. General urban case study: current NAAQS (1.5 ug/m3, maximum quarterly
2 average) - estimated blood Pb levels.
Blood Pb
Percentile
Predicted
Blood Pb
Gig/dL)
Pathway Contribution3
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Othera
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
18
14
13
9
7
5
4
3
1
9%
5%
19%
7%
58%
3%
Dust Model (Hybrid), GSD (2.1), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
29
21
17
11
8
5
3
2
10%
6%
22%
1%
57%
3%
1st 1
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient
air, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section
2.4.3).
July 2007
3-32
Draft - Do Not Quote or Cite
-------
1 Table 3-9. General urban case study: alternative NAAQS (0.2 ug/m3, maximum quarterly
2 average) - estimated blood Pb levels.
Blood Pb
Percent! le
Predicted
Blood Pb
(Mfl/dL)
Pathway Contribution a
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
9
7
6
4
4
3
2
1
1
18%
11%
39%
15%
16%
1%
Dust Model (Hybrid), GSD (2.1), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
20
14
11
7
5
3
2
1
<1
17%
10%
36%
5%
33%
1%
3 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
3-33
Draft - Do Not Quote or Cite
-------
1 Table 3-10. General urban case study: alternative NAAQS (0.5 ug/m3, maximum monthly
2 average) - estimated blood Pb levels.
Blood Pb
Percentile
Predicted
Blood Pb
(mg/dL)
Pathway Contributions
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
10
8
7
5
4
3
2
1
1
17%
10%
36%
13%
23%
1%
Dust Model (Hybrid), GSD (2.1), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
19
13
11
7
5
3
2
1
15%
9%
33%
3%
38%
1%
1st <1
3 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient
air, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section
2.4.3).
July 2007
3-34
Draft - Do Not Quote or Cite
-------
1 Table 3-11. General urban case study: alternative NAAQS (0.2 ug/m3, maximum monthly
2 average) - estimated blood Pb levels.
Blood Pb
Percentile
Predicted
Blood Pb
(mg/dL)
Pathway Contributions
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
9
7
6
4
3
2
2
1
1
20%
11%
42%
16%
11%
1%
Dust Model (Hybrid), GSD (2.1), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
18
13
11
6
5
3
2
1
18%
10%
38%
6%
27%
0%
1st <1
3 "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient
air, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section
2.4.3).
July 2007
3-35
Draft - Do Not Quote or Cite
-------
1 Table 3-12. General urban case study: alternative NAAQS (0.05 ug/m , maximum monthly
2 average) - estimated blood Pb levels.
Blood Pb
Percentile
Predicted
Blood Pb
(mg/dL)
Pathway Contribution3
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
8
6
6
4
3
2
2
1
<1
22%
13%
46%
17%
3%
0%
Dust Model (Hybrid), GSD (2.1), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
16
11
10
6
4
3
2
1
21%
12%
44%
11%
13%
0%
1st <1
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient
air, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section
2.4.3).
July 2007
3-36
Draft - Do Not Quote or Cite
-------
1 Table 3-13. Primary Pb smelter case study: current NAAQS (1.5 ug/m , maximum
2 quarterly average) - estimated blood Pb levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(Hg/dl_)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
4
19
39
194
388
970
1940
2910
19
12
9
5
4
2
2
1
3%
4%
6%
22%
15%
33%
11%
37%
2%
2%
4%
13%
9%
19%
6%
21%
11%
15%
7%
39%
48%
23%
54%
19%
27%
36%
57%
17%
17%
17%
17%
18%
56%
43%
26%
9%
10%
8%
11%
5%
1%
1%
1%
1%
1%
1%
1%
0%
1st 3841 <1 31% 18% 27% 18% 6% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
Table 3-14. Primary Pb smelter case study: alternate NAAQS (0.2 ug/m , maximum
quarterly average) estimated blood Pb levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(Hg/dl_)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
4
19
39
194
388
970
1940
2910
11
8
7
4
3
2
1
1
5%
10%
12%
16%
24%
12%
33%
16%
3%
6%
7%
10%
14%
7%
19%
10%
27%
63%
59%
53%
41%
59%
29%
53%
16%
19%
19%
19%
18%
19%
18%
19%
49%
2%
3%
2%
2%
3%
1%
2%
0%
0%
0%
0%
0%
0%
0%
0%
1st 3841 <1 38% 22% 20% 18% 1% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
3-37
Draft - Do Not Quote or Cite
-------
1 Table 3-15. Primary Pb smelter case study: alternate NAAQS (0.5 ug/m , maximum
2 monthly average) - estimated blood Pb levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(Hg/dl_)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
4
19
39
194
388
970
1940
2910
14
9
7
4
3
2
1
1
5%
5%
10%
26%
19%
16%
36%
34%
3%
3%
6%
15%
11%
9%
21%
20%
19%
19%
7%
37%
48%
52%
23%
24%
24%
24%
50%
18%
18%
18%
18%
18%
49%
49%
26%
4%
4%
5%
2%
4%
1%
1%
0%
0%
0%
0%
0%
0%
1st 3841 <1 36% 21% 23% 18% 2% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
Table 3-16. Primary Pb smelter case study: alternate NAAQS (0.2 ug/m , maximum
monthly average) - estimated blood Pb levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(Hg/dl_)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
4
19
39
194
388
970
1940
2910
11
8
7
4
3
2
1
1
12%
17%
14%
20%
14%
26%
35%
24%
7%
10%
8%
12%
8%
15%
21%
14%
59%
53%
56%
48%
56%
38%
24%
41%
19%
19%
19%
19%
19%
18%
18%
19%
3%
2%
3%
2%
3%
3%
1%
2%
0%
0%
0%
0%
0%
0%
0%
0%
1st 3841 <1 35% 20% 25% 18% 1% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
3-38
Draft - Do Not Quote or Cite
-------
1 Table 3-17. Primary Pb smelter case study: alternate NAAQS (0.05 ug/m3, maximum
2 monthly average) - estimated blood Pb levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(Hg/dl_)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
4
19
39
194
388
970
1940
2910
10
7
6
4
3
2
1
1
16%
8%
17%
17%
23%
28%
33%
14%
10%
4%
10%
10%
13%
17%
19%
8%
45%
68%
54%
54%
44%
36%
29%
57%
15%
19%
19%
19%
19%
19%
19%
19%
14%
0%
0%
1%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
1st 3841 <1 19% 11% 38% 17% 14% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
Table 3-18. Secondary Pb smelter case study: current conditions - estimated blood Pb
levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(Hg/dl_)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
2
8
17
85
170
425
849
1274
5
4
3
2
2
1
1
1
40%
33%
42%
41%
29%
38%
40%
42%
23%
19%
24%
24%
17%
22%
23%
24%
4%
18%
0%
2%
25%
8%
4%
1%
31%
26%
33%
32%
23%
30%
31%
33%
1%
3%
0%
0%
5%
2%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1st 1681 <1 42% 24% 1% 33% 0% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
3-39
Draft - Do Not Quote or Cite
-------
1 Table 3-19. Secondary Pb smelter case study: alternative NAAQS (0.2 ug/m , maximum
2 quarterly average) - estimated blood Pb levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(Hg/dl_)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
2
8
17
85
170
425
849
1274
5
4
3
2
2
1
1
1
40%
40%
41%
42%
42%
41%
39%
40%
23%
23%
24%
24%
24%
24%
23%
23%
5%
5%
3%
1%
1%
3%
6%
5%
32%
31%
32%
33%
33%
32%
31%
32%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1st 1681 <1 42% 24% 1% 33% 0% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
Table 3-20. Secondary Pb smelter case study: alternative NAAQS (0.5 ug/m , maximum
monthly average) - estimated blood Pb levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(ng/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
2
8
17
85
170
425
849
1274
5
4
3
2
2
1
1
1
15%
40%
42%
35%
41%
39%
32%
41%
9%
23%
24%
20%
24%
23%
18%
24%
51%
4%
1%
15%
1%
6%
22%
2%
12%
32%
33%
28%
33%
31%
25%
32%
12%
1%
0%
2%
0%
1%
3%
0%
1%
0%
0%
0%
0%
0%
0%
0%
1st 1681 <1 39% 23% 6% 31% 1% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
3-40
Draft - Do Not Quote or Cite
-------
1 Table 3-21. Secondary Pb smelter case study: alternative NAAQS (0.2 ug/m , maximum
2 monthly average) - estimated blood Pb levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(Hg/dl_)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
2
8
17
85
170
425
849
1274
5
4
3
2
2
1
1
1
19%
42%
32%
34%
42%
39%
39%
39%
11%
24%
19%
20%
24%
23%
23%
23%
52%
1%
23%
18%
0%
6%
7%
7%
15%
33%
25%
27%
33%
31%
31%
31%
2%
0%
2%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1st 1681 <1 36% 21% 13% 29% 1% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
Table 3-22. Secondary Pb smelter case study: alternative NAAQS (0.05 ug/m3, maximum
monthly average) - estimated blood Pb levels.
Blood Pb
Percentile
Population
Above
Predicted
Blood Pb
(ng/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent
Air)
Dust Model (Air-only Regression-based), GSD (1.7), Blood Pb Metric (Concurrent)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
2
8
17
85
170
425
849
1274
5
4
3
2
2
1
1
1
42%
42%
38%
42%
17%
40%
40%
42%
24%
24%
22%
24%
10%
23%
23%
24%
1%
1%
11%
1%
58%
6%
6%
1%
33%
33%
30%
33%
13%
31%
31%
33%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1st 1681 <1 38% 22% 11% 30% 0% 0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources,
including historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air,
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
5
6
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1 3.5 UNCERTAINTY CHARACTERIZATION
2 The characterization of uncertainty associated with exposure assessment included
3 performance evaluation which is discussed in this section. In addition, uncertainty in exposure
4 assessment is also considered as part of characterizing uncertainty in the risk estimates and is
5 reflected in the sensitivity analysis, inclusions of multiple modeling approaches and the
6 qualitative discussion of key sources of uncertainty which are discussed in Section 4.3.
7 Performance evaluation for the exposure assessment focused on evaluation of projections
8 of Pb in exposure media (i.e., ambient air, outdoor soil, and indoor dust) (discussed in Section
9 3.5.1) and projections of Pb in blood (covered in Section 3.5.2). Those case studies (or case
10 study elements) for which media concentrations were estimated using empirical data as the basis
11 are not considered here; only those estimates based directly on modeling were included.
12 Comparing model output to monitoring data is often considered the most desirable form
13 of performance evaluation. In addition to monitoring data, or in the absence of such data,
14 outputs from other models and expert opinion about how outputs should look can be used as
15 comparison benchmarks in performance evaluation. The performance evaluation activities
16 reported here have focused on the comparison of model outputs to measurements. Favorable
17 comparisons, including a lack of systematic trends in either over- or underestimation of modeled
18 results relative to empirical data, provide confidence in the modeling estimates.
19 3.5.1 Performance Evaluation Related to Exposure Media Modeling
20 This section discusses performance evaluation conducted for modeling of Pb
21 concentrations in exposure media including ambient air, outdoor soil and indoor dust.
22 3.5.1.1 Evaluation of Modeled Ambient Air Pb Concentrations
23 Performance evaluation for ambient air Pb predictions focused on the two point source
24 case studies, in which air dispersion modeling was used.
25 For the primary Pb smelter case study current NAAQS scenario, the ISCST3-Prime
26 model was used with input files (e.g., source characterization, meteorological data) used in
27 developing the 2007 proposed revision to the SIP for that location (Appendix D). The
28 submission to EPA for the proposed SIP revision included a model performance evaluation,
29 focused on the "actual value" modeling scenario (MDNR 2007a). The actual value modeling
30 included three separate comparisons based on relating model predictions to measured Pb
31 concentrations at five monitor sites in the study area. These comparisons included:
32 • Day-to-day evaluation of modeling output compared to monitor values: The review
33 of the model performance evaluation conducted by the state of Missouri concluded
34 that all sites demonstrated a pattern of overall accuracy for directional prediction (i.e.,
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1 high modeled days being high monitored days and low modeled days being low
2 monitored days), suggesting that the model was performing well in relating wind
3 direction to Pb transport (MDNR, 2007a).
4 • Comparison of source contribution analysis using chemical mass balance (CMB) with
5 dispersion model predicted relative contributions: Source contribution analysis using
6 CMB of monitor filter residue to identify significant sources of Pb for each monitor
7 (e.g., in-plant roads and yard dust, blast furnace) were compared with relative
8 contributions predicted by the dispersion model for individual modeled sources. The
9 review of the model performance evaluation concluded that there was generally good
10 agreement between the CMB results and the air dispersion results in terms of major
11 sources contributing Pb at each monitor (MDNR, 2007a).
12 • Comparison of overall average modeled results with monitored levels: This
13 performance evaluation involved comparing modeled results (for 247 days simulated
14 for 2005) at six monitor locations with actual measured values for that same period at
15 those locations. Results of this evaluation suggested a slight over-prediction bias
16 (<10%) for those sites likely to have the greatest impacts from the facility.
17 This evaluation of model performance for the actual value modeling scenario increases
18 confidence in estimates developed for the current NAAQS attainment scenario with this
19 modeling setup and inputs particular to the 2007 proposed SIP revision.
20 Performance evaluation of air dispersion modeling for the secondary Pb smelter is
21 discussed in Appendix E (Section E.2.4), and involved comparing modeled ambient air Pb levels
22 to measured levels at two monitoring locations within the study area. Results of that evaluation
23 suggest that the model might be slightly under-predicting levels at the closest monitor, and
24 slightly more under-predictive of levels at the more distant monitors. The use of meteorological
25 data that is not site-specific and may not be fully representative of actual wind patterns in the
26 area may be contributing to this. When modeled concentrations at distances matching those of
27 the monitors, and for all directional points around the facility, are compared with monitor values,
28 modeled values are identified which match or exceed the measured values.
29 3.5.1.2 Evaluation of Modeled Outdoor Soil/Dust Pb Concentrations
30 Modeling was used in estimating the spatial pattern of outdoor soil/dust Pb
31 concentrations for the secondary Pb smelter case study (see Section 3.1.3.3). As the modeling
32 did not use estimates of historical Pb emissions (presumed to be higher than current emissions),
33 absolute values of the model predictions were not expected to be representative of current Pb
34 concentrations in soil/dust in this case study. Although measurements were not available for the
35 case study location, comparison of model predicted soil Pb levels with Pb concentrations
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1 reported for a surrogate secondary Pb smelter location confirmed this expectation (see Appendix
2 E, Section E.3). Model predictions were - on average - a factor of 3 lower than Pb
3 concentrations at the surrogate location. Accordingly, we used a hybrid modeled-empirical
4 approach for characterizing outdoor soil Pb levels for this case study, scaling the model estimates
5 of soil Pb concentration across the study area by a factor of 3 (see Appendix E, Section E.3).
6 3.5.1.3 Evaluation of Modeled Indoor Dust Pb Concentrations
7 Performance evaluation completed in support of indoor dust Pb prediction involved two
8 components. First, the site-specific hybrid model developed for the general urban case study was
9 evaluated using available data from the literature (see Appendix G, Section G.3.6). Results of
10 this evaluation help us assess the overall reasonableness of this model in supporting indoor dust
11 prediction for the general urban case study in particular. Additionally, predicted indoor dust Pb
12 levels for all three case studies were compared with data identified in the literature, including
13 measurements from individual studies focusing on smaller areas and data from national-scale
14 surveys. The general urban case study model and model projections are discussed here first
15 The hybrid indoor dust model developed for the general urban case study (Section 3.1.4.1
16 and Appendix G) is a combination of mechanistic model (to relate outdoor ambient air Pb to
17 indoor dust Pb) and empirical data (to characterize the non-air related fraction of indoor dust Pb
18 in the residential urban setting). Components of the mechanistic portion of the model were
19 subjected to a range of evaluations based on available data in the literature. In addition, the
20 conversion of model-generated indoor dust Pb loadings to concentrations (a key step required
21 prior to blood Pb modeling) was evaluated. Finally, model predictions of indoor dust Pb levels
22 were evaluated using data from several studies for specific cities.
23 Evaluation of the mechanistic component of the hybrid dust model focused on (a)
24 predicted deposition fluxes of Pb to indoor surfaces and (b) prediction of relationship between
25 indoor air Pb and ambient outdoor air Pb (Appendix G, Section G.3.6). Generally, modeled
26 indoor deposition fluxes for Pb appear to be in-line with values reported for a location in
27 Manhattan with closed windows (Caravanos et al., 2006). The model predicted ratio of indoor
28 air Pb to outdoor ambient air Pb are lower (-50%) than ratios based on data collected from
29 residences in the Midwest (Roy et al., 2003) but similar to ratios generated by a different hybrid
30 model which combines empirical data with a mass balance modeling approach in predicting
31 indoor ambient air concentrations (Riley et al., 2002). The under-estimate of this ratio, when
32 compared with the empirical data from Roy et al. (2003), may result from the fact that the hybrid
33 model does not consider resuspension of indoor dust Pb. Generally, these findings with regard to
34 substeps of the mechanistic (and air-focused) component of the hybrid model (Appendix G,
35 Section G.3.6) suggest a potential to under-predict the influence of outdoor ambient air Pb on
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1 indoor dust Pb. The conversion for predicted indoor dust loadings to concentrations, however,
2 may over-predict concentrations based on a comparison data collected in residences in the
3 Midwest (Roy et al., 2003; Appendix G, Section G.3.6). This finding may counter-balance any
4 potential for the mechanistic component of the model to under-estimate the influence of ambient
5 air Pb on indoor dust Pb.
6 Indoor dust Pb concentrations (in terms of mass per mass) were identified from a few
7 studies in the literature (e.g., CD, Table 3-8; Tang et al., 2004), as well as the HUD National
8 Survey of Lead-Based Paint in Housing (USEPA, 1995) described in Section 3.1.4.1. These
9 studies are identified in Table 3-23. Although the HUD survey was used for the empirical
10 component of the hybrid model, it is considered here with regard to the total dust Pb
11 concentrations predicted by both the hybrid and air-only models used in the general urban case
12 study (use of these models is described in Section 3.1.4.1).9 Comparison of the model-predicted
13 indoor dust concentrations for the current conditions scenarios (122 and 198 ppm for the hybrid
14 model and 107-157 ppm for the air-only model) to empirical data collected in Jersey City, NJ,
15 and Ottawa Canada (Table 3-23) suggest that the model may be under-predicting indoor dust Pb,
16 although the housing stock in these studies was much older than housing generally in U.S.,
17 which may mean that the measurements are impacted to a greater extent by indoor Pb paint than
18 would be the general case in the U. S. Thus, it would be expected that those reported values
19 would be higher than the model predictions. Additionally, the model-predicted values fall
20 between the medians for the youngest and oldest houses sampled in the HUD national survey
21 (Table 3-23). Given that indoor dust Pb modeling completed for the general urban case study
22 was aimed at capturing central tendency indoor dust Pb levels (and is not expected to, for
23 example, capture variability related to cleaning rates or indoor paint Pb levels), this finding
24 provides confidence in the estimates.
9 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-related 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 the evaluation.
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1 Table 3-23. Evaluation of model-predicted indoor dust Pb levels against empirical data
2 obtained from the literature.
Case study
Primary Pb smelter
case study
Secondary Pb
case study
Modeled indoor dust Pb levels (ppm)
Air quality
scenario
Current
NAAQS
Current
conditions
Median
122
63
5tn to 95tn
Percent! le
(min-max)
50-2,318
(41 -3,523)
60-73
(60-166)
Indoor Dust Pb Observations reported in
the literature
- 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 housing (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).
4 For the two point source case studies, the model-predicted indoor dust Pb concentrations
5 were compared to observations from the literature (see Table 3-23). An important factor to keep
6 in mind when reviewing the modeled results presented in Table 3-24 is that the current NAAQS
7 attainment scenario modeled for the primary Pb smelter involved regions of the study area with
8 ambient air Pb levels significantly higher than either of the other case studies.
9 In consideration of indoor dust Pb levels predicted for the primary Pb smelter, we have
10 focused more on the central tendency values. This reflects the fact that the higher-end modeled
11 values for this case study likely reflect significant ambient air impacts which are not captured in
12 any of the empirical data identified in the literature, thereby reducing the utility of performance
13 evaluations for these higher-end predictions. The media indoor dust Pb concentration generated
14 for the primary Pb smelter case study (122 ppm) compares well to the findings of the HUD
15 dataset (i.e., falls between the medians for the youngest and oldest housing, 87 and 406 ppm),
16 particularly in light of the fact that a significant fraction of the study area for this case study has
17 ambient air Pb levels not significantly different from ambient air Pb levels seen across the U.S.
18 (see Table 3-4).
19 Regarding the secondary Pb smelter case study, the median value for this case study (67
20 ppm) is below the range of values reported in the past for smelters (CD, Table 3-8), and also just
21 below median value reported for the youngest housing in the HUD national data set (USEPA,
22 1995), suggesting that indoor dust Pb levels may be under-predicted for this case study.
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1 3.5.2 Performance Evaluation Related to Blood Pb Modeling
2 Performance evaluation completed in support of blood Pb modeling involved three steps:
3 (a) evaluation of candidate blood Pb models for the analysis, (b) comparison of ambient outdoor
4 air Pb-to-bloodPb ratios generated for the three case studies against ratios obtained from the
5 literature and (c) comparison of modeled blood Pb levels for these three case studies against
6 NHANES IV data. Each of these evaluation steps is discussed below.
7 3.5.2.1 Evaluation of Candidate Blood Pb Models
8 Evaluation of candidate blood Pb models (IEUBK and Leggett) involved three separate
9 stages: (a) application of the candidate models to three hypothetical individual-child exposure
10 scenarios used previously by EPA and others in evaluations of blood Pb models, (b) comparison
11 of candidate model predictions for a general U.S. childhood exposure scenario (using typical Pb
12 exposures for key pathways) to NHANES IV empirical data, and (c) evaluation of candidate
13 model performance in replicating measurements of urban child blood Pb levels obtained in
14 Rochester. Detailed results of the evaluation of candidate blood Pb models are presented in
15 Appendix J (and summarized in Section J.4).
16 The first stage (focusing on reproducing results of previous performance evaluations)
17 demonstrated that we were applying the candidate models correctly. Tests of the models against
18 specific individual exposure scenarios (Section J.I.3) reproduced, to a high degree, the results of
19 previous model comparison.
20 The second stage of the model evaluation (focusing on reproducing general US child
21 blood Pb levels presented in NHANES IV) demonstrated that, depending on assumptions
22 regarding typical outdoor soil/dust and indoor dust Pb concentrations, the IEUBK model either
23 moderately over-predicted child GM blood Pb levels (by two-fold or less) or generated
24 predictions close to NHANES summary statistics (see Section J.3.1). By contrast, predictions
25 from the Leggett model were more than three to six times higher than the age-specific NHANES
26 IV GM values.
27 The third stage of the model evaluation focused on evaluating the candidate models in
28 predicting blood Pb levels for an urban child cohort (Appendix J, Section J.3.2). The dataset,
29 which included matched Pb media concentrations (outdoor soil/dust and indoor dust levels) and
30 blood Pb levels, was collected as part of an epidemiological study focusing on the effects of Pb
31 exposure in children living in Rochester, NY (Lanphear et al., 1995; Lanphear and Roghmann,
32 1997). Blood Pb levels for each child sampled in the study were predicted using IEUBK and
33 Leggett, with the measured media Pb levels collected as part of the study as inputs. These
34 predicted blood Pb levels were then compared to the measured blood Pb levels for each child.
35 The results of this stage of the model evaluation were similar to those from the NHANES
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1 evaluation in that IEUBK results suggested a moderate over-prediction (-70%) while Leggett
2 results indicated a much greater over-prediction (a factor of 2 to 6).
3 Results of the model evaluation for IEUBK and Leggett suggest that IEUBK generates
4 more representative blood Pb estimates relative to Leggett in the context of the evaluations
5 conducted here. Based on the results of the model evaluation, we decided to use IEUBK in
6 generating the primary set of exposure and risk results for this analysis, and include Leggett as
7 part of the sensitivity analysis but not in the primary analysis.
8 3.5.2.2 Evaluation of model-derived outdoor air Pb-to-blood Pb ratios
9 In deriving the current NAAQS in 1978 (43 FR 46246), USEPA used an estimate of the
10 relationship between ambient air Pb concentration and associated blood Pb concentration (i.e.,
11 1:2, |ig/m3 to |ig/dL). In this assessment, we rely on several distinct modeling steps which, when
12 taken together, translate ambient air Pb into blood Pb. As part of the blood Pb model
13 performance evaluation we have extracted air-to-blood Pb ratios from the modeling completed
14 for the three case studies for comparison to estimates reported in the literature.
15 Ratios were developed that relate ambient air Pb to blood Pb contributed from the
16 following different exposure pathways or pathway combinations: (a) inhalation of ambient air,
17 (b) inhalation of ambient air plus ingestion of the Pb in indoor dust that is predicted to be
18 associated with ambient air Pb levels (i.e., "recent air" per Section 2.4.3), and (c) inhalation of
19 ambient air plus ingestion of indoor dust plus ingestion of outdoor soil/dust (i.e., not including
20 the diet and drinking water ingestion pathways). The limitations of our modeling tools precluded
21 us from parsing air-related blood contributions any more finely. The ratios (actually, the blood
22 Pb side of the ratio) will be larger as you move from (a) to (c) because there is a progressively
23 larger fraction of overall blood Pb (exposure) being associated with air. With regard to the
24 potential impact of ambient air Pb on blood Pb, the first ratio (inhalation pathway) is an
25 underestimate because it excludes the important ingestion pathways to which ambient air Pb can
26 contribute. Conversely, the third ratio, although not including any impact of air Pb on diet (and
27 blood), potentially includes some contributions to blood Pb that are not influenced by air (e.g.,
28 indoor paint). For the purposes of this model performance discussion, we have focused on the
29 second type of ratio (those for "recent air") derived using the concurrent blood Pb metric. The
30 full set of ratios are presented for each case study in Appendix I
31 In this evaluation, we have considered the ratios derived from the blood Pb estimate prior
32 to application of the GSD reflecting inter-individual variability in Blood Pb levels (i.e., the
33 central tendency estimate of blood Pb derived for each US Census block or blockgroup for the
34 point source case studies and for the entire study area for the general urban case study).
35 Although air-to-blood Pb ratios can be derived for individual simulated children after application
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1
2
3
4
5
6
7
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 population (through statistical regression
modeling, for example). While this inter-individual variability in exposure parameters is not
reflected in the ratios evaluated here, variation in exposure concentration is reflected by the
range of ratios derived for the different exposure zones of the two point source case studies. For
the general urban case study, ratios are presented for both current conditions scenarios (reflecting
a mean and high-end estimate of air Pb concentration).
Table 3-24. Air-to-Blood Pb ratios for "recent air" contribution to concurrent blood Pb
level.
Case study
Annual average
ambient air
Pb concentrations
(ug/m3)
Air-to-Blood
Pb
Ratio
General urban - current conditions
Current conditions (mean)
Current conditions (95th percentile)
0.056
0.114
1:4&1:10a
1:4&1:7a
Primary Pb smelter - current NAAQS
Median air concentration
95th percentile air concentration
0.093
0.740
1:3
1:11
Secondary Pb smelter- current conditions
Median air concentration
95th percentile air concentration
0.003
0.009
1:5
1:4
Air-to-Blood Pb Ratios
Identified from the
Literature
- Review of studies published
before 1984 reports air-to-
blood Pb ratios for children
generally ranging from 1 :3 to
1:5(Brunekreef, 1984).
- Pooled analysis of air-to-
blood Pb relationship (log-log
regression) based on above
studies yielded ratios of 1:3 to
1:6(Brunekreef, 1984).
a The two ratios for the general urban case study correspond to results obtained from the two indoor dust models.
10
11
12
13
14
15
16
17
18
Several observations can be made regarding higher ratios generated for two of the case
studies presented in Table 3-24:
• For the general urban case study, the higher ratios (i.e., 1:10 and 1:7) result from
application of the hybrid dust model which produces a higher indoor dust
concentration per unit ambient outdoor air Pb relative to the air-only regression
model.
• For the primary Pb smelter case study, the higher ratio (1:11) is also the result of a
more potent dust model (the site-specific regression model developed for the
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1 remediation zone of this study area). That indoor dust model was used for the higher-
2 impact (95th percentile) block, while the regression (air plus soil) model was used for
3 the majority of the study area, including the median block (producing the 1:3 ratio).
4
5 The air-to-blood Pb ratios from the literature presented in Table 3-24 all come from older
6 studies summarized in a single review from 1984 (Brunekreef, 1984). This review presents both
7 (a) the ratios identified from the reviewed studies, which included surveys focused on smelter
8 and urban study areas (generally range from 1:3 to 1:5) and (b) the results of a pooled analysis
9 where a log-log regression model was developed relating ambient air Pb directly to blood Pb
10 levels based on the underlying study data (this yields the range of 1:3 to 1:6 presented in Table 3-
11 25). The studies reported in Brunekreef (1984) generally involved blood Pb levels significantly
12 greater than 10 |ig/dL. This means that they are generally not representative of the range of
13 modeled blood Pb levels associated with the three case studies, which, prior to application of the
14 GSD, are generally well below 10 ug/dL. This disconnect between the studies in the literature
15 and the modeled case studies (regarding blood Pb ranges) introduces uncertainty into
16 performance evaluation of air-to-blood Pb ratios completed here. That is, it is possible that ratios
17 seen at blood Pb levels significantly above 10 |ig/dL are different from those below 10 |ig/dL,
18 although this can not be verified without updated characterizations of air-to-blood Pb ratios
19 based on contemporary trends in Pb exposure and blood Pb levels). Despite this uncertainty,
20 however, comparison of the air-to-blood Pb ratios developed for the three case studies against
21 those found in the literature is considered useful.
22 The lower air-to-blood ratios generated for the three case studies (1:4 for the general
23 urban case study, 1:3 for the primary Pb smelter case study and 1:4 to 1:5 for the secondary Pb
24 smelter case study) fall within the range of ratios identified in the literature (1:3 to 1:6).
25 However, the higher ratios ranging from 1:7 to 1:11) are higher than the bulk of Brunekreff
26 reported ratios, although a subset of the individual studies reviewed by Brunekreef presented
27 air-to-blood Pb ratios that were 8.5 and higher. In several instances, such higher ratios are
28 associated with lower blood Pb levels and lower ambient air Pb levels (both factors that would
29 seem to be more relevant to exposure conditions found in the three case studies modeled for this
30 analysis). However, the studies reporting these higher ratios are complicated by a number of
31 factors (e.g., involving older children, use of ambient air measurement techniques which may
32 underestimate results, thereby inflating ratios, relatively small sample sizes, etc.).
33 3.5.2.3 Comparison of modeled blood Pb levels to nationally representative data
34 Evaluation of modeled blood Pb levels using empirical data depends on the availability
35 of empirical datasets for populations with exposure similar to those in the case studies. While
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1 there are many blood Pb studies reported in the literature, they are largely composed of
2 populations experiencing exposures from all pathways that are higher than current exposures.
3 No datasets were identified for specific subpopulations with exposures corresponding to current
4 exposures that would be appropriate for performance evaluation here. In the case of the general
5 urban case study, while it is not location-specific, we have not identified any contemporary
6 datasets for urban locations with ambient air Pb levels matching those modeled in the current
7 scenario that are not heavily influenced by Pb-paint. For the primary Pb smelter case study
8 location, the most generally available blood Pb monitoring study was completed in 2001 and
9 2002, and given the changes in exposures since that time and the use of a future (current NAAQS
10 attainment) scenario these older blood Pb surveys do not correspond to the exposures modeled
11 for this case study. In the case of the secondary Pb smelter, the blood Pb data available for the
12 county containing the secondary Pb smelter does not indicate residence location handicapping
13 efforts to consider blood Pb levels for children in the secondary Pb smelter case study location.
14 While blood Pb datasets were not identified for populations representative of those
15 modeled for the three case studies, we have compared the modeled blood Pb levels against
16 general national-scale blood Pb levels (for a child age group matching that modeled for the case
17 studies). While each of the case studies involves a population that is different than the national
18 population (reflecting the nature of the exposure scenario being considered), the background
19 exposures for the case studies and the national population are similar. Accordingly, the
20 relationship between the modeled blood Pb levels for each study area and the national
21 distribution are evaluated. For this evaluation, we have used measured values interpolated from
22 the NHANES IV dataset. Specifically, we have interpolated a series of percentile estimates for 7
23 yr olds based on summary data presented for NHANES IV (CDC, 2005). The process of
24 interpolating these values involved the following steps: (a) use summary statistics (GM and
25 associated confidence intervals) for 1-5 yr olds and 6-19 yr olds (for years 1999-2002) to
26 establish log-normal distributions for each age range, (b) identify population percentiles of
27 interest (e.g., median, 90th, 95th, 99.5th percentiles) for each age group using these log-normal
28 distributions, and (c) interpolate a series of percentiles for a 7yr age cohort using the percentiles
29 for the two age ranges (the 1-5 yr old and 6-19 yr old). This interpolation procedure resulted in
30 the population percentile estimates for a 7 yr old cohort (for the years 1999-2002) presented in
31 Table 3-25.
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1
2
4
5
6
7
Table 3-25. Blood Pb Levels for 7 year olds in the U.S. (interpolated from NHANES IV,
1999-2002).
Population
percentile
median
75th
90th
95th
99th
99.5th
99.9th
Blood Pb level
(ug/dL)
1.5
2.55
4.1
5.25
8.75
10.55
15.2
Figure 3-2 compares modeled blood Pb levels for the three case studies with the
interpolated NHANES IV blood Pb levels presented in Table 3-25. Modeled blood Pb levels
presented in Figure 3-2 include (a) the (mean) current 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 attainment
scenarios for both the primary and secondary Pb smelter case studies.
Figure 3-2. Comparison of NHANES IV blood Pb levels with modeled estimates.
10
11
-o--- NHANES-IV (interpolated 1999-
2002, 7yr old)
-a—General urban case study
(mean current conditions -
lower)
-•—General urban case study
(mean current conditions -
higher)
-e—Primary Pb smelter (current
NAAQS attainment)
-A—Secondary Pb smelter (current
conditions)
median 75th
90th 95th 99th
Population Percentile
99.5th 99.9th
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1 The following observations are made regarding the blood Pb modeling results for the
2 three case studies (Figure 3-2):
3 • General urban case study: The low- and high-end estimates for the mean current
4 conditions scenario bracket the NHANES IV data. Of the three case studies, it is
5 reasonable to assume that results for the current conditions (mean) scenario for this
6 case study might be the closest to matching the NHANES. Interestingly, of the
7 results for the low- and high-end modeling approaches for this scenario, the high-end
8 results appear to be closer to the NHANES data, which would indicate that modeling
9 for this case study may not be biased high with regard to total blood Pb levels.
10 Additionally, unlike the point source case studies, the general urban case study
11 included a consideration of variability in background exposure pathways through
12 application of the larger GSD in the high-end modeling approach for that case study.
13 • Primary Pb smelter case study: It would be expected that higher percentile exposure
14 levels for this case study might exceed the NHANES data and that is evident in
15 Figure 3-2. The difference, however, is not that large. For percentiles above the 90th,
16 NHANES values are not that much lower than those for this case study. However, it
17 is important to reiterate that modeling for this case study did not account fully for
18 exposure variability in background Pb exposures. It is likely that if modeling for this
19 case study had included consideration of background exposure variability (e.g.,
20 relatively high paint Pb and drinking water exposures), there would be a greater
21 difference between high-end blood Pb levels modeled for this case study and those
22 interpolated form NHANES IV. This lack of full accounting for blood Pb variability
23 from background sources is not considered a major limitation in this analysis because
24 the focus is on ambient air-related exposures.
25 • Secondary Pb smelter case study: Modeled blood Pb levels for this case study are
26 significantly lower than NHANES IV levels, especially for higher population
27 percentiles. As with the primary Pb smelter case study, this likely reflects the fact
28 that background Pb exposure variability is not fully reflected in modeling. As noted
29 above, however, this lack of full accounting for blood Pb variability from background
30 sources is not considered a major limitation in this analysis because the focus is on
July 2007 3-53 Draft - Do Not Quote or Cite
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1 ambient air-related exposures, particularly those associated with the secondary
2 smelter. Additionally, air Pb estimates for much of the study area were significantly
3 lower than the mean current conditions levels modeled for the general urban case
4 study, which may be a result of only modeling sources directly related to the smelter
5 and not including other non-smelter related contributions to ambient air (e.g.,
6 resuspension of roadside dust containing Pb).
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1 REFERENCES
2 Adgate, J. L.; Willis, R. D.; Buckley, T. I; Chow, J. C.; Watson, J. G.; Rhoads, G. G.; Lioy, P. J. (1998) Chemical
3 mass balance source apportionment of Pb in house dust. Environ. Sci. Technol. 32: 108-114.
4 Bellinger, D.C., Stiles, K.M., Needleman, H.L. (1992) Low-level lead exposure, intelligence and academic
5 achievement: A long-term follow-up study. Pediatrics 90(6): 855-861.
6 Brunekreef, B. (1984) The relationship between air lead and blood lead in children: a critical review. Science of the
7 total environment, 38: 79-123.
8 Caravanos, J.; Weiss, A. L.; Jaeger, R. J. (2006) An exterior and interior Pbed dust deposition survey in New York
9 City: results of a 2-year study. Environ. Res. 100: 159-164.
10 Centers for Disease Control and Prevention (CDC). (2005) Blood Lead Levels - United States, 1999-2002.
11 Available online at: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5420a5.htm.
12 Gustavsson, N., Bolviken, B., Smith, D.B., and Severson, R.C. (2001) Geochemical Landscapes of the
13 Conterminous United States - New Map Presentations of 22 Elements. U.S. Department of the Interior,
14 U.S. Geological Survey. Professional Paper 1648. Available at http://pubs.usgs.gov/pp/pl648.
15 Hattis, D. (2005) Preliminary Analysis of Blood Pb Distributional Data from Various Years of the National Health
16 and Nutrition Surveys (NHANES). Prepared for ICF International. October.
17 Hilts, S. R. (2003) Effect of smelter emission reductions on children's blood Pb levels. Sci. Total Environ. 303: 51-
18 58.
19 Lanphear, B. P.; Emond, M; Jacobs, D. E.; Weitzman, M; Tanner, M; Winter, N. L.; Yakir, B.; Eberly, S. (1995)
20 A Side-by-Side Comparison of Dust Collection Methods for Sampling Lead-Contaminated House Dust.
21 Environ. Res. 68(2): 114-123.
22 Lanphear, B.P. and Roghmann, K. J. (1997) Pathways of Lead Exposure in Urban Children. Environ. Res. 74(67):
23 73.
24 Lanphear, B. P.; Matte, T. D.; Rogers, J.; Clickner, R. P.; Dietz, B.; Bornschein, R. L.; Succop, P.; Mahaffey, K. R.;
25 Dixon, S.; Galke, W.; Rabinowitz, M.; Farfel, M.; Rohde, C.; Schwartz, J.; Ashley, P.; Jacobs, D. E. (1998)
26 The Contribution of Lead-Contaminated House Dust and Residential Soil to Children's Blood Lead Levels:
27 A Pooled Analysis of 12 Epidemiologic Studies. Environmental Research. 79: 51-68.
28 Lanphear, B.P., Hornung, R., Khoury, J., Yolton, K., Baghurst, P., Bellinger, D.C., Canfield, R.L., Dietrich, K.N.,
29 Bornschein, R., Greene, T., Rothenberg, S.J., Needleman, H.L., Schnaas, L., Wasserman, G., Graziano, J.,
30 and Robe, R. (2005) Low-level environmental Pb exposure and children's intellectual function: An
31 international pooled analysis. Environmental Health Perspectives. 113(7):894-899.
32 Missouri Department of Natural Resources (MDNR). (2007a) Doe Run - Herculaneum State Implementation Plan
33 (SIP) Dispersion Modeling Review. Memorandum from Jeffry D. Bennett to John Rustige. February 12,
34 2007. Available at: http://www.dnr.mo.gov/env/apcp/herculaneumsip.htm
3 5 Missouri Department of Natural Resources (MDNR). (2007b) 2007 Revision of the State Implementation Plan for
3 6 the Herculaneum Lead Nonattainment Area, as adopted by the Missouri Air Conservation Commission
37 April 26, 2007.
3 8 Riley, W. J., McKone, T. E., Lai, A. C., Nazaroff, W. W. (2002) Indoor Paniculate Matter of Outdoor Origin:
39 Importance of Size-Dependent Removal Mechanisms. Environ. Sci. Technol. 36(2): 200-207.
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2 Roy, A., Georgopoulos, P. G., Ouyang, M, Freeman, N., Lioy, P. J. (2003) Environmental, Dietary, Demographic,
3 and Activity Variables Associated With Biomarkers of Exposure for Benzene and Lead. J. Expo. Anal.
4 Environ. Epidemiol. 13(6): 417-426.
5 Tang, K. M., Nace, C. G., Jr., Lynes, C. L., Maddaloni, M. A., LaPosta, D., Callahan, K. C. (2004) Characterization
6 of Background Concentrations in Upper Manhattan, New York Apartments for Select Contaminants
7 Identified in World Trade Center Dust. Environ. Sci. Technol. 38(24): 6482-6490.
8 U.S. Environmental Protection Agency. (1989) Review of National Ambient Air Quality Standard for Pb: Exposure
9 Analysis Methodology and Validation. Research Triangle Park, NC: Office of Air Quality Planning and
10 Standards. EPA-450/2-89-011. June.
11 U.S. Environmental Protection Agency. (1994a) Technical Support Document: Parameters and Equations Used in
12 the Integrated Exposure Uptake Biokinetic Model for Pb in Children (v.099d). EPA 540/R-94/040.
13 U.S. Environmental Protection Agency. (1994b) Guidance Manual for the IEUBK Model for Lead in Children.
14 EPAPB93-963510 (OSWER #9285.7-15-1).
15 U.S. Environmental Protection Agency (USEPA). (1995) Report on the National Survey of Lead-Based Paint in
16 Housing: Appendix I: Design and Methodology. EPA 747-R95-004. Office of Pollution Prevention and
17 Toxics.
18 U.S. Environmental Protection Agency (USEPA). (1997) Conversion Equations for Use in Section 403 Rulemaking.
19 EPA 747-R-96-012. Office of Pollution, Prevention, and Toxics.
20 U.S. Environmental Protection Agency (USEPA). (2000) Hazard Standard Risk Analysis Supplement - TSCA
21 Section 403. Available online at: http://www.epa.gov/lead/pubs/403risksupp.htm.
22 U.S. Environmental Protection Agency. (2006a) 1999 National-Scale Air Toxics Assessment. Available at
23 http://www.epa.gov/ttn/atw/natal999/nsata99.html.
24 U.S. Environmental Protection Agency. (2006b) Lead soil trend analysis through May, 2006. Evaluation by
25 individual quadrant. Herculaneum lead smelter site, Herculaneum, Missouri. Prepared by TetraTech for
26 U.S. EPA, Region 7.
27 VonLindern, I. H.; Spalinger, S. M.; Bero, B. N.; Petrosyan, V; VonBraun, M. C. (2003a) The influence of soil
28 remediation on lead in house dust. Sci. Total Environ. 303(1-2): 59-78.
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1 4 RISK ASSESSMENT
2 This chapter describes the approach used to characterize risk, including discussion of the
3 methodology (Section 4.1), presentation of risk estimates (Section 4.2), and uncertainty
4 characterization (Section 4.4).
5 4.1 METHODS FOR DERIVING RISK ESTIMATES
6 Risk characterization for this assessment focuses on IQ loss in children. IQ loss is
7 derived using a set of concentration-response functions developed based on results from a pooled
8 analysis of epidemiology studies (Lanphear et al., 2005). These concentration-response
9 functions are combined with the population-level blood Pb distributions generated for the case
10 studies to produce distributions of IQ loss estimates for each study population. IQ loss is also
11 apportioned among different exposure pathways using the pathway apportionment information
12 generated as part of the exposure analysis.
13 Two key elements of the risk methodology are described in greater detail below: (a) the
14 concentration-response functions used in the analysis (Section 4.1.1) and (b) the step-wise
15 analytical procedure used to generate the IQ loss (risk) distributions (Section 4.1.2).
16 4.1.1 Concentration-Response Functions
17 As discussed in Section 2.1.5, log-linear concentration-response functions for IQ loss for
18 the concurrent and lifetime average blood Pb metrics were obtained from a large pooled study
19 (Lanphear et al., 2005) and used as the basis for estimating IQ changes in children in this
20 analysis. Three types of concentration-response functions were drawn from this study, and two
21 equations of each type are used in this assessment: one for the concurrent blood Pb metric, and a
22 second for the lifetime average blood Pb metric. The three types include: (a) a log-linear
23 function with cutpoint (Section 4.1.1.1), (b) a log-linear function with low-exposure linearization
24 (Section 4.1.1.2) and (c) a two-piece linear function (Section 4.1.1.3). Plots of the three types of
25 IQ-loss functions developed for this assessment are presented in Figure 4-1 for the concurrent
26 blood Pb metric. Table 4-1 presents a comparison of total IQ loss and incremental IQ loss (IQ
27 loss/|ig/dL) for the three functions across a range of concurrent blood Pb levels. As can be seen
28 by comparing the plots of the three functions together with the results presented in Table 4-1, the
29 log-linear function with low-exposure linearization will generate the greatest IQ change across
30 the exposure range 0 to 10 |ig/dL followed by the log-linear function with cutpoint and then the
31 two-piece linear function.
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1
2
3
4
5
Inclusion of these three types of concentration-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 ug/dL.
log-linear with outpoint
log-linearwith low-exposure
linearization
-two-piece linear
Concurrent PbB (ug/dL)
6
7
Table 4-1. Comparison of total and incremental IQ loss estimates below 10 ug/dL for the
three concentration-response functions.
Performance Metric
Total IQ lost
Incremental IQ loss
(points per ug/dL)
at 2 ug/dL
at 5 ug/dL
at 7.5 ug/dL
at10ug/dl_
<2 ug/dL
<5 ug/dL
<7.5 ug/dL
<10ug/dl_
Concentration-Response Function
Log-linear
with
outpoint
2
4
5
6
0.94
0.87
0.73
0.62
Log-linear
with low-
exposure
linearization
5
7
8
9
2.29
1.41
1.09
0.89
Two-piece
linear
1
2
3
5
0.45
0.45
0.45
0.45
10
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1 4.1.1.1 Log-Linear Function with Cutpoint
2 This concentration-response function is the log-linear function for IQ change from
3 Lanphear et al. (2005), with incorporation of a cutpoint at the blood Pb level corresponding to
4 the lowest levels represented by measurements in the underlying pooled analysis. The values
5 used for cutpoints, for both the concurrent and lifetime average blood Pb metrics, are based on
6 the corresponding low end of the range of values for these two indices in the pooled analysis
7 (Hornung, 2007a, b). The values are 1.0 |ig/dL and 1.47 |ig/dL, for the concurrent and lifetime
8 average blood Pb metrics, respectively. Slopes for the two log-linear functions were obtained
9 directly from the study (Lanphear et al., 2005). Parameterization for these two log-linear
10 functions with cutpoints is presented below, along with the mathematical form of the function:
11 • Form of function: IQ loss = beta * In (concurrent blood Pb/cutpoint)
12 • Log-linear function with cutpoint (concurrent metric):
13 - beta (slope):-2.70
14 -cutpoint: 1.0 |jg/dL
15 • Log-linear function with cutpoint (lifetime average metric):
16 -beta (slope):-3.04
17 -cutpoint: 1.47 |jg/dL
18 4.1.1.2 Log-Linear Function with Low-Exposure Linearization
19 We also developed risk estimates that reflect the possibility that IQ loss is associated with
20 the entire range of Pb exposure all the way down to "zero" exposure, as recommended by
21 CAS AC (Henderson, 2007). The risk assessment included IQ loss prediction based on a log-
22 linear IQ change function with a linearization of the log-linear function taking over at lower
23 exposure levels. The transition point from the log-linear function to the linearized slope was
24 selected with the same basis as the cutpoint described in Section 4.1.1.1, the lower end of the
25 range of the pertinent blood Pb index values in the pooled analysis (Lanphear et al., 2005). The
26 linearized slope is obtained by taking the tangent to the log-linear function at the point of
27 departure, with different slopes being identified for the concurrent and lifetime average blood Pb
28 metrics (per Lanphear et al, 2005). Parameterization for the two log-linear functions with low-
29 exposure linearization is presented below, with the mathematical form of the function:
30 • Form of function:
31 - For blood Pb level > cutpoint:
32 IQ loss = beta * In (concurrent blood Pb/cutpoint) + linear slope * cutpoint
33 - For blood Pb level < cutpoint:
34 IQ loss = linear slope * concurrent blood Pb
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1 • Log-linear function with low-exposure linearization (concurrent metric):
2 - beta (slope): -2.70
3 - linear slope to be applied below 1.0 |jg/dL: -2.70
4 • Log-linear function with low-exposure linearization (lifetime average metric):
5 -beta (slope):-3.04
6 - linear slope to be applied below 1.47 |jg/dL: -2.1
7 4.1.1.3 Two-piece Linear Function
8 This category of concentration-response function was developed by fitting a two-piece
9 linear model to the log-linear IQ-loss function obtained from the pooled analysis (Lanphear et
10 al., 2005). Inclusion of this function in the risk assessment is intended to address the practical
11 problem of the shape associated with application of the log-linear model at the low end of the
12 blood Pb value range. In this case, we consider an alternate functional form that allows ready
13 prediction of IQ loss across the full range of modeled Pb exposure. The procedure involved first
14 generating blood Pb values for each of the two blood Pb metrics, concurrent and lifetime
15 average, for a set of N = 1,333 simulated children representative of those included in the pooled
16 analysis (Lanphear et al., 2005). This was accomplished by sampling from a blood Pb
17 distribution constructed from the median and 95th percentile of the concurrent and lifetime
18 average blood Pb indices, respectively, reported in Lanphear et al. (2005). IQ values for each of
19 the 1,333 simulated children were then estimated using the reported log-linear models that relate
20 blood Pb to absolute IQ (Lanphear, et al., 2005). Nonlinear regression techniques were then
21 used to fit two piece linear models to these two sets of simulated children with their matched
22 pairs of blood Pb and IQ values. The regressions provided parameter estimates (slopes and
23 "hinge" point) of the best fitting two piece linear segment function for each blood Pb metric,
24 concurrent and lifetime average. As discussed in Section 4.3.1, that the use of a constructed
25 dataset introduces uncertainty related to fitting of a model to a model. Parameterization for the
26 two two-piece linear functions is presented below, with the mathematical form of the function:
27 • Form of function:
28 - For blood Pb level > hinge:
29 IQ loss = beta 2 * concurrent blood Pb
30 - For blood Pb level < hinge:
31 IQ loss = beta 1 * concurrent blood Pb
32 • Two-piece linear function (concurrent metric):
33 - "hinge" linking two segments: 10.82 |jg/dL
34 - beta 1 (slope at <10.82 pg/dL): -0.4539
35 -beta2(slopeat>10.82|jg/dL: -0.1130
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1 • Two-piece linear function (lifetime average metric):
2 - "hinge" linking two segments: 13.39 |jg/dL
3 - beta 1 (slope at <13.39 pg/dL): -0.3790
4 -beta2(slopeat>13.39|jg/dL: -0.1187
5 4.1.2 Projection of Population Risk
6 Risk characterization completed for this assessment involved converting the population-
7 level blood Pb distributions generated for the three case studies into population-level
8 distributions of IQ loss using the three types of concentration-response functions described in the
9 last section. This procedure is described below for each of the functions.
10 • Log-linear function with cutpoint: Each modeled blood Pb level is compared against
11 the cutpoint. If the blood Pb level is lower than the cutpoint, then no IQ loss is
12 estimated because the simulated individual's blood Pb level is below the level for
13 predicting IQ loss with this function. If the blood Pb level is greater than the
14 cutpoint, then the log-linear function is used to predict IQ loss for the portion of the
15 estimated blood Pb level extending above the cutpoint.
16 • Log-linear function with low-exposure linearization: Each modeled blood Pb level is
17 compared against the point of linearization. If the blood Pb level is below the point
18 where the function becomes linear, than the linear slope is used to predict IQ loss. If
19 the modeled Pb level is above the point where the function becomes linear, than IQ
20 loss is calculated as the sum of IQ loss across the linear portion of the curve plus the
21 additional contribution from the log-linear portion of the function extending up to the
22 total blood Pb level.
23 • Two-piece linear function: Similar to the last function, the modeled blood Pb level is
24 compared against the blood Pb level of the "hinge". If the blood Pb level falls below
25 the hinge, as is the case for most simulated individuals at the three case studies, then
26 the steeper, low-exposure slope is used to estimate IQ loss. If the simulated blood Pb
27 level falls above the hinge, then IQ loss associated with the low-exposure (steeper
28 slope) piece of the function (for the portion of the blood Pb level up to the hinge) is
29 combined with IQ loss estimated using the shallower and higher exposure slope, for
30 that portion of the blood Pb level extending above the hinge.
31
32 The IQ loss estimates generated using this approach are pooled to form a population-level
33 distribution of IQ loss for a given study area. Each of these IQ loss estimates are pathway
34 apportioned based on pathway contribution to the underlying blood Pb levels. That is, IQ loss
35 estimates are apportioned among policy-relevant background pathways, recent air-related
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1 pathways, and other (including older historical air-related) pathways (see Section 2.4.3). As with
2 pathway apportionment for blood Pb levels (Section 3.2), pathway apportionment of risk
3 estimates is also at the exposure zone-level. All simulated individuals from a given zone are
4 assigned the same pathway apportionment. As noted with regard to blood Pb levels (Section
5 3.2.3), there is increased uncertainty associated with pathway apportioned IQ loss estimates for
6 higher-end risk percentiles, since these reflect an assumption that relative pathway contributions
7 at central tendency exposure levels hold at the high-end percentiles of the blood Pb level
8 distribution.
9 Just as with the population-level exposure estimates discussed in Section 3.2.2, risk
10 estimates generated using the approach outlined above are used to generate several types of risk
11 metrics, depending on the case study. For the two point source case studies, because they are
12 location-specific and include a defined and enumerated receptor population, two categories of
13 risk metrics are generated:
14 • Population-weighted risk (IQ loss) percentiles: IQ loss (with pathway apportionment)
15 for simulated individuals representing specific points along the population risk
16 distribution (e.g., 50, 90, 95, 99 and 99.5th percentile simulated individuals).
17 • Incidence counts: Number of children within a given study area projected to experience a
18 magnitude of IQ loss.
19
20 As for the exposure estimates, the general urban case study risk metrics are restricted to
21 population-weighted risk (IQ loss) percentiles.
22 4.2 RISK ESTIMATES
23 Estimates of IQ loss resulting from Pb exposure have been developed for each air quality
24 scenario in each case study (see Appendix K). Further, multiple sets of risk results were
25 generated for each combination of case study and air quality scenario, in an effort to consider
26 key sources of uncertainty and their impact on blood Pb estimates (see Section 2.4.6.2). That is,
27 twenty four separate risk distributions were generated for each air quality scenario of the general
28 urban case study and six distributions were generated for each air quality scenario of the point
29 source case studies (see Table 2-3).
30 As discussed in Section 2.4.6.2, generating multiple sets of risk results for each
31 combination of case study and air quality scenario provides a range of results reflecting the
32 impact of key sources of uncertainty on risk results. However, because we could not assign
33 specific confidence levels to each modeling approach, these multiple sets of results are not
34 translated into single uncertainty distributions of risk for each air quality scenario in each case
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1 study. Therefore, we consider the multiple sets of risk results to span the best estimate risk
2 distribution.
3 The discussions of risk results in the following subsections are focused on the following
4 three types of tables:
5 • Summary of risk estimates for recent air: This table focuses on risk estimates derived
6 from inhalation of ambient air Pb and ingestion of Pb in indoor dust that is predicted
7 to be associated with ambient air Pb concentrations based on the indoor dust model
8 (i.e., "recent air" risk per Sections 2.4.3 and 3.2.2) Given the overall modeling
9 approach used in this analysis, this set of risk estimates is expected to be most
10 responsive to alternative NAAQS.
11 • Summary of risk estimates for recent air plus past air (other indoor dust and outdoor
12 soil contribution: This table focuses on risk estimates derived from inhalation of
13 ambient air Pb, ingestion of indoor dust and ingestion of outdoor soil/dust (Sections
14 2.4.3 and 3.2.2). That is, this table includes the pathways represented in previous
15 summary table in addition to other indoor dust and outdoor soil pathways. These
16 estimates include some contribution from indoor paint because the indoor dust
17 models handicapped our ability to parse out this contribution. Otherwise, these
18 estimates reflect contributions from all except the policy-relevant background
19 pathways of diet and drinking water.
20 • Expanded set of risk estimates: This set of tables presents the population percentile
21 risk results with full pathway apportionment information for the low-risk and high-
22 risk modeling approaches for each air quality scenario in each case study.
23
24 Although there are a variety of uncertainties associated with these risk estimates
25 including the estimates of pathway apportionment (see Section 4.3), an uncertainty specific to
26 the higher population percentile pathway contribution estimates for all three case studies is noted
27 here. Pathway apportionment for higher population percentiles are subject to significant
28 uncertainty for all three case studies. This is because the pathway apportionment estimates are
29 derived using central tendency blood Pb levels (prior to application of the GSD). Consequently,
30 pathway apportionment results for higher-end population percentiles reflect an assumption that
31 pathway apportionment for lower (central tendency) estimates hold for higher population
32 percentiles. There is uncertainty in this assumption since there is the potential for higher-end
33 population percentiles to demonstrate significantly different pathway apportionment (e.g., have
34 their exposures dominated by particular sources such as Pb paint or Pb in drinking water).
35 In presenting IQ loss estimates, we have presented all values below 0.5 as <1 and values
36 between 0.5 and 1.0 as 1.0. All values above 1.0 are rounded to the nearest whole number.
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1 4.2.1 General Urban Case Study
2 For the general urban case study, IQ loss estimates estimated for recent air in the two
3 current conditions scenarios are relatively similar. For example, IQ loss estimates for the 99.5th
4 percentile range from <1 to 4 IQ points (Table 4-2). The similarity between these scenarios
5 likely reflects the similarity of the annual average ambient Pb air concentrations forming the
6 basis for the scenarios (0.056 and 0.11 ug/m3). As would be expected, given that both the mean
7 and 95th percentile current conditions scenarios have ambient air Pb levels significantly lower
8 than the current NAAQS, recent air IQ loss estimates for the current NAAQS scenario are
9 significant higher than those for the current conditions scenarios and range from 1 to 4 IQ points
10 lost for the typical child, to 3 to 7 IQ points lost for the 99.5th percentile child.
11 With regard to alternative NAAQS scenarios, both median and high-end recent air risks
12 for the first three highest alternative NAAQS are not significantly different from estimates
13 generated for the current conditions air quality scenarios. For example, for the 0.2 ug/m3
14 maximum monthly alternative NAAQS, recent air IQ loss estimates for the median child range
15 from <1 to 1 point, while high-end estimates range from <1 to 3 points. These risk ranges are
16 similar to those for the mean current conditions air quality scenario. The similarity of the current
17 conditions air quality risk estimates with the three highest alternative NAAQS standards is not
18 surprising given that the ambient air Pb levels associated with the current conditions are similar
19 to those associated with the first three alternative NAAQS levels (see Table 4-2).
20 While the current conditions air quality scenarios and the highest alternative NAAQS
21 scenarios demonstrate similar recent air IQ loss estimates, there is a clear reduction in recent air
22 risk at the high-end population percentiles for the lowest alternative NAAQS evaluated (0.05
23 ug/m3 maximum monthly average). Recent air IQ loss estimates for this air quality scenario falls
24 between <1 and 1 point for the high-end percentiles.
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1 Table 4-2. Summary of risk estimates for recent air.
Case Study and Air Quality Scenario
Predicted IQ loss for recent air at
specific population percentiles a'b
(range: low to high modeling approach)
Median
95th
99.5th
General urban case study
Current NAAQS (1.5 |jg/m3, max quarterly)
Current conditions - 95th% (0.87 ug/m3, max quarterly)
Current conditions - mean (0.14 ug/m3, max quarterly)
Alternative NAAQS (0.5 ug/m3, max monthly)
Alternative NAAQS (0.2 ug/m3, max quarterly)
Alternative NAAQS (0.2 ug/m3, max monthly)
Alternative NAAQS (0.05 ug/m3, max monthly)
1 to 4
<1 to 2
<1 to 1
<1 to 2
<1 to 2
<1 to1
<1 to 1
2 to 6
<1 to 3
<1 to 2
1 to 4
<1 to 3
<1 to 2
<1 to 1
3 to 7
1 to 4
<1 to 3
1 to 4
1 to 4
<1 to 3
<1 to1
Primary Pb smelter case study
Current NAAQS (1.5 ug/m3, max quarterly)
Alternative NAAQS (0.5 ug/m3, max monthly)
Alternative NAAQS (0.2 ug/m3, max quarterly)
Alternative NAAQS (0.2 ug/m3, max monthly)
Alternative NAAQS (0.05 ug/nf, max monthly)
<1
<1
<1
<1
<1
<1 to 1
<1 to 2 c
<1 to 3
<1 to2c
<1
2 to 4
2 to 6
<1-5C
<1 to 4
<1
Secondary Pb smelter case study
Current conditions
Alternative NAAQS (0.5 ug/m3, max monthly)
Alternative NAAQS (0.2 ug/m3, max quarterly)
Alternative NAAQS (0.2 ug/m3, max monthly)
Alternative NAAQS (0.05 ug/m3, max monthly)
<1
<1
<1
<1
<1
<1
<1
<1
<1
<1
<1
<1
<1
<1
<1
a - "recent air" refers to contributions associated 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),
with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
b - Estimates below 0.5 are reported as <1.
c - Due to variation in the primary Pb smelter case study estimates resulting from a combination of spatial
variation in media concentrations and application of the GSD (see text), values for adjacent lower percentiles
have been substituted here. That is, the 95th percentile values which are both <1 have been replaced with
the value for the 90th percentile (<1-2), and the 99.5th percentile value which is <1 was replaced with the
value for the 95th percentile (<1-5
2
O
4
5
6
As expected, risks associated with recent plus past air contributions are significantly
larger (across all population percentiles) compared with the recent air risk estimates discussed
above (Tables 4-2 and 4-3). The term "past air" includes contributions from the outdoor
soil/dust contribution to indoor dust, historical air contribution to indoor dust, and outdoor
July 2007
4-9
Draft - Do Not Quote or Cite
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1 soil/dust pathways, while "recent air" refers to contributions associated with outdoor ambient air
2 Pb levels (either by inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be
3 associated with outdoor ambient air Pb levels), with outdoor ambient air potentially also
4 including resuspended, previously deposited Pb (see Section 2.4.3). For the current conditions
5 and three highest alternative NAAQS scenarios, these risks are about two to three times higher
6 than the recent air risk estimates. For example, the 99.5th percentile IQ loss for the mean current
7 conditions scenario from Table 4-3 ranges from 2 to 8 IQ points lost, while the corresponding
8 estimate in Table 4-2 (reflecting only recent air contributions) ranges from <1 to 3 IQ points
9 lost). As mentioned earlier, however, the results presented in Table 4-3 include some
10 contribution to indoor dust Pb and outdoor soil Pb from Pb paint. It was not possible for this
11 analysis, to parse out contributions from Pb paint to these media and consequently, the risk
12 results presented in Table 4-3 may overestimate risks associated with policy relevant sources
13 (see Section 2.4.3).
14 The large difference seen in the recent air risk estimates between the three higher and the
15 lowest alternate NAAQS scenario for the 99.5th percentile is not seen for the risk estimates in
16 Table 4-3 that include recent plus past air contributions. This reflects an artifact of the approach
17 used in the hybrid indoor dust model to characterize "other" (non- recent air associated)
18 contributions to indoor dust (see Appendix G). Because of the method used to convert dust
19 loadings to dust concentrations within the hybrid dust model and the approach used to apportion
20 total dust concentrations between other and recent air components, the "other" indoor dust
21 concentration predicted by this model varies with air quality scenario, with that value increasing
22 as the ambient air Pb level decreases (see Appendix C). This means that, as the recent air
23 contribution to exposure through indoor dust ingestion decreases (as the lower alternative
24 NAAQS levels are considered), the estimate of contribution of "other" indoor dust actually
25 increases. This partially accounts for the trend noted above. That is, even though recent air risk
26 estimates drop, they are partially offset by risk resulting from an increase in exposure to the
27 "other" Pb contributions to indoor dust.
28 The expanded set of risk results for this case study are presented in Tables 4-4 to 4-9 and
29 the fuller detailed results are in Appendix K.
30
31
July 2007 4-10 Draft - Do Not Quote or Cite
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1 Table 4-3. Summary of risk estimates for recent plus past air.
Air Quality Scenario
Predicted IQ loss for recent plus
past aira
for specific population
percentilesa'b
(range: low to high modeling approach)
Median
95th
99.5th
General urban case study
Current NAAQS (1.5 ug/nrr', max quarterly)
Current conditions - 95th% (0.87 ug/mj, max quarterly)
Current conditions - mean (0.14 ug/mj, max quarterly)
Alternative NAAQS (0.5 ug/nrr', max monthly)
Alternative NAAQS (0.2 ug/mj, max quarterly)
Alternative NAAQS (0.2 ug/nf, max monthly)
Alternative NAAQS (0.05 ug/nf, max monthly)
1 to 5
<1 to 4
<1 to 4
<1 to 4
<1 to 4
<1 to 4
<1 to 3
3 to 8
2 to 7
1 to 6
2 to 7
1 to 6
1 to 6
1 to 5
5 to 10
3 to 8
2 to 8
3 to 8
2 to 8
2 to 7
2 to 7
Primary Pb smelter case study**
Current NAAQS (1.5 ug/mj, max quarterly)
Alternative NAAQS (0.5 ug/mj, max monthly)
Alternative NAAQS (0.2 ug/mj, max quarterly)
Alternative NAAQS (0.2 ug/mj, max monthly)
Alternative NAAQS (0.05 ug/nf, max monthly)
<1 to 4
<1 to 4
<1 to 3
<1 to 4
<1 to 3
2 to 7
2 to 7
2 to 5
1 to 6
2 to 5
5 to 9
4 to 9
3 to 7
3 to 8
3 to 7
Secondary Pb smelter case study
Current conditions
Alternative NAAQS (0.5 ug/mj, max monthly)
Alternative NAAQS (0.2 ug/nf, max quarterly)
Alternative NAAQS (0.2 ug/mj, max monthly)
Alternative NAAQS (0.05 ug/nf, max monthly)
<1 to 1
<1 to1
<1 to1
<1 to1
<1 to1
1 to 2
<1 to 2
<1 to 2
<1 to 2
<1 to 2
1 to 3
1 to 2
1 to 2
1 to 3
1 to 2
a - 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
associated 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), with outdoor ambient air also
potentially including resuspended, previously deposited Pb (see Section 2.4.3).
b - Estimates below 0.5 are reported as <1.
July 2007
4-11
Draft - Do Not Quote or Cite
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1 Table 4-4. General urban case study: current conditions - estimated IQ loss.
IQ Loss
Percentile
Predicted
IQ Loss
Predicted
PbB (ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
CURRENT CONDITIONS - 95th percentile
Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
5
3
3
2
2
1
<1
<1
<1
10
8
7
5
4
3
2
1
1
17%
10%
37%
14%
22%
1%
Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
12
11
10
9
8
7
5
4
1
27
18
16
10
8
5
3
2
1
16%
9%
33%
4%
37%
1%
CURRENT CONDITIONS - Arithmetic Mean
Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
3
3
2
2
1
<1
<1
<1
9
7
6
4
3
3
2
1
1
19%
11%
41%
15%
12%
1%
Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
11
10
10
9
8
6
5
4
1
22
16
14
9
7
4
3
2
1
18%
10%
38%
6%
28%
1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-12
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1
2
Table 4-5. General urban case study: current NAAQS - estimated IQ loss.
IQ Loss
Percentile
Predicted
IQ Loss
Predicted
PbB (ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
6
5
5
4
3
2
2
1
<1
18
14
13
9
7
5
4
3
1
9%
5%
19%
7%
58%
3%
Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
13
12
11
10
9
8
7
5
2
36
27
22
14
11
7
5
3
1
10%
6%
22%
1%
57%
3%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-13
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1
2
3
4
Table 4-6. General urban case study: alternative NAAQS (0.5 ug/m3, maximum monthly
average) - estimated IQ loss.
IQ Loss
Percentile
Predicted
IQ Loss
Predicted
PbB (ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
5
3
3
2
2
1
<1
<1
<1
10
8
7
5
4
3
2
1
1
17%
10%
36%
13%
23%
1%
Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
12
11
11
9
8
7
5
4
1
31
21
17
10
8
5
3
2
1
15%
9%
33%
3%
38%
1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-14
Draft - Do Not Quote or Cite
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1
2
3
4
Table 4-7. General urban case study: alternative NAAQS (0.2 ug/m3, maximum quarterly
average) - estimated IQ loss.
IQ Loss
Percentile
Predicted
IQ Loss
Predicted
PbB (ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
3
3
2
2
1
<1
<1
<1
9
7
6
4
4
3
2
1
1
18%
11%
39%
15%
16%
1%
Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
12
11
10
9
8
7
5
4
1
25
18
15
9
7
5
3
2
1
17%
10%
36%
5%
33%
1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-15
Draft - Do Not Quote or Cite
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Table 4-8. General urban case study: alternative NAAQS (0.2 ug/m3, maximum monthly
average) estimated IQ loss.
IQ Loss
Percentile
Predicted
IQ Loss
Predicted
PbB (ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
3
3
2
2
1
<1
<1
<1
9
7
6
4
3
2
2
1
1
20%
11%
42%
16%
11%
1%
Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
11
10
10
8
8
6
5
4
1
23
16
14
8
7
4
3
2
1
18%
10%
38%
6%
27%
0%
4
5
6
7
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-16
Draft - Do Not Quote or Cite
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1
2
3
4
Table 4-9. General urban case study: alternative NAAQS (0.05 ug/m , maximum monthly
average) estimated IQ loss.
IQ Loss
Percentile
Predicted
IQ Loss
Predicted
PbB (ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air-only Regression-based), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
3
2
2
1
1
<1
<1
<1
8
6
6
4
3
2
2
1
<1
22%
13%
46%
17%
3%
0%
Dust Model (Hybrid), GSD (2.0), PbB Metric (Lifetime), IQ Function (Log-linear with Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
11
10
10
8
7
6
5
3
1
20
14
12
8
6
4
2
1
<1
21%
12%
44%
11%
13%
0%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-17
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1 4.2.2 Primary Pb Smelter Case Study
2 Risk estimates for recent air for the primary Pb smelter case study are generally similar to
3 those generated for the general urban case study. As with the general urban case study, the
4 alternate NAAAQS scenarios produce lower risk estimates compared to the current NAAQS
5 scenario. The 99.5th percentile recent air IQ loss estimates for the current NAAQS scenario
6 range from 2 to 4 points, while the same population percentile for the current NAAQS scenario
7 for the general urban case study ranges from 3 to 7 points (see Table 4-2). The reason for this
8 difference relates to the use of spatially varying media concentrations in the primary Pb smelter
9 case study such that much of the study area is well below the current NAAQS, while all of the
10 general urban case study area is set equal to the current NAAQS in that scenario. As with the
11 general urban case study, while recent air risks are similar for the first three alternative NAAQS
12 evaluated, risks for the high-end population percentiles for the lowest alternative NAAQS (0.05
13 Hg/m3 maximum monthly) are noticeably lower than the other alternative NAAQS, with risks for
14 the high-end modeled child being < 1 IQ point lost.
15 Although the recent air risks for the primary Pb smelter are similar to those for the
16 general urban case study, there are differences between the two sets of risk results (Table 4-2).
17 For example, generally, the median and 95th percentile recent air risk estimates are somewhat
18 higher for the general urban case study compared with the primary Pb smelter case study.
19 However, the trend is reversed for the 99.5th percentile, where the primary Pb smelter case study
20 has slightly higher upper bound risk estimates across the air quality scenarios. These differences
21 in risk estimates generated for the two case studies reflect a number of factors including:
22 • Use of a study area with uniform ambient air Pb levels for the general urban case
23 study compared with a study area having significant spatial gradients in air Pb levels
24 for the primary Pb smelter. This results in the general urban case study having larger
25 central tendency risk estimates compared with the primary Pb smelter case study,
26 since all modeled children for the general urban case study experience the ambient air
27 Pb level associated with a particular air quality scenario, while only a relatively small
28 portion of the study area for the primary Pb smelter experiences that level.
29 • Differences in the way ambient air Pb levels for the different alternate NAAQS
30 scenarios are generated, with application of empirically derived ratios for the general
31 urban case study and use of proportional rollback methods for the primary Pb smelter.
32 The exact impact of this difference may differ depending on the population percentile
33 being considered.
34 • Different indoor dust models used at the two case studies, with hybrid and regression
35 models for the general urban case study and generic and site-specific regression
July 2007 4-18 Draft - Do Not Quote or Cite
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1 models at the primary Pb smelter. While the use of the hybrid dust model with its
2 greater air-related loading of indoor dust will effect the entire set of modeled children
3 under a given modeling approach, the site-specific regression model used for the
4 primary Pb smelter case study with its greater air-related loading of indoor dust is
5 only applied within the remediation zone and therefore, its higher air-related loading
6 will only effect the highest percentile of modeled children. It is likely that the higher
7 risks seen at the 99.5th percentile for the primary Pb smelter case study reflect the
8 impact of this site-specific indoor dust model with its strong air-to-dust signal.
9 • Inclusion of a higher GSD reflecting inter-individual variability in blood Pb levels for
10 the general urban case study, along with the same lower GSD used for the primary Pb
11 smelter: Inclusion of a higher GSD for the general urban case study will push the
12 high-end modeled blood Pb levels higher relative to application of the lower GSD in
13 the case of the primary Pb smelter case study. Note, however, that other factors seem
14 to overwhelm this GSD effect for the extreme high-end population percentiles, since
15 as noted earlier, the 99.5th percentile risks for the primary Pb smelter (using the lower
16 GSD) are somewhat higher than the same population percentile risks generated for
17 the general urban case study (with its higher GSD).
18
19 As with the general urban case study, risks associated with recent plus past air
20 contributions (Table 4-3) are 2 to 3 times higher than the recent air risks (see Tables 4-2 and 4-
21 3). However, as described in Section 2.4.3, results presented in Table 4-3 likely represent an
22 overestimate of risks associated with policy-relevant sources because these risk estimates include
23 some contribution from Pb paint.
24 The trend in recent air risk estimates across air quality scenarios for the 95th and 99.5th
25 percentiles in this case study sometimes appears inconsistent (Table 4-2). Specifically, several
26 of the alternative NAAQS scenarios have <1 point IQ loss (noted with a "b" footnote in Table 4-
27 2) when lower alternative NAAQS scenarios yield higher values. This inconsistent trend relates
28 to the influence of inter-Census block and block group variation in exposure concentrations and
29 of the GSD on high-end population exposure and risk. For the two point source case studies,
30 high-end exposure and risk can result from either (a) an individual being located in a block or
31 block group with the highest modeled media Pb concentrations and/or (b) that individual having
32 a GSD-based adjustment factor (reflecting inter-individual variability in Pb exposure) that is
33 relatively high. The fact that either of these factors can produce a high exposure and risk level is
34 reflected in the fact that the pathway apportionment estimates for the high-end population
35 percentiles for the two point source case studies are inconsistent (see expanded set of risk
36 results). Accordingly, as seen in Table 4-2, two adjacent high-end population percentiles (e.g.,
July 2007 4-19 Draft - Do Not Quote or Cite
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1 99th and 99.5th) for a given combination of case study and air quality scenario may have quite
2 different contributions from recent air. This variation in recent air contributions to total Pb
3 exposure is not seen for the general urban case study, since that case study is modeled using a
4 single exposure zone with uniform ambient air Pb levels. That is, for that case study variation in
5 blood Pb levels, for a given modeling approach, results exclusively from the GSD and not from
6 the location of a modeled child relative to an emissions source).
July 2007 4-20 Draft - Do Not Quote or Cite
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1 Table 4-10. Primary Pb smelter case study: current NAAQS - estimated IQ loss.
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
6
5
4
2
2
1
<1
<1
<1
19
12
9
5
4
2
2
1
<1
3%
4%
6%
22%
15%
33%
31%
37%
31%
2%
2%
4%
13%
9%
19%
18%
21%
18%
11%
15%
7%
39%
48%
23%
27%
19%
27%
27%
36%
57%
17%
17%
17%
18%
18%
18%
56%
43%
26%
9%
10%
8%
6%
5%
6%
1%
1%
1%
1%
1%
1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
12
10
10
8
7
5
4
3
1
24
15
13
6
5
3
2
1
1
5%
5%
18%
12%
18%
33%
37%
33%
32%
3%
3%
10%
7%
10%
19%
21%
19%
18%
10%
8%
45%
49%
45%
23%
19%
23%
28%
45%
44%
17%
16%
17%
17%
18%
17%
18%
37%
39%
8%
14%
9%
8%
5%
8%
4%
1%
1%
1%
1%
1%
1%
<1%
1%
<1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-21
Draft - Do Not Quote or Cite
-------
1 Table 4-11. Primary Pb smelter study: alternative NAAQS (0.5 ug/m , maximum monthly
2 average) - estimated IQ loss.
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust)
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
5
4
3
2
2
1
<1
<1
<1
14
9
7
4
3
2
1
1
0
5%
5%
10%
26%
19%
16%
36%
34%
36%
3%
3%
6%
15%
11%
9%
21%
20%
21%
19%
19%
7%
37%
48%
52%
23%
24%
23%
24%
24%
50%
18%
18%
18%
18%
18%
18%
49%
49%
26%
4%
4%
5%
2%
4%
2%
1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
11
10
9
7
7
5
4
3
1
18
12
10
6
5
3
2
1
1
12%
4%
5%
12%
8%
16%
32%
34%
26%
7%
2%
3%
7%
5%
9%
19%
20%
15%
56%
19%
19%
58%
16%
51%
28%
24%
37%
18%
18%
24%
18%
39%
18%
18%
18%
18%
7%
57%
49%
5%
32%
5%
3%
4%
4%
1%
1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-22
Draft - Do Not Quote or Cite
-------
1 Table 4-12. Primary Pb smelter study: alternative NAAQS (0.2 ug/m , maximum quarterly
2 average) - estimated IQ loss.
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
5
4
3
2
1
<1
<1
<1
<1
11
8
7
4
3
2
1
1
<1
5%
10%
12%
16%
24%
12%
33%
16%
38%
3%
6%
7%
10%
14%
7%
19%
10%
22%
27%
63%
59%
53%
41%
59%
29%
53%
20%
16%
19%
19%
19%
18%
19%
18%
19%
18%
49%
2%
3%
2%
2%
3%
1%
2%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
10
9
9
7
6
5
4
3
1
14
10
9
6
4
3
2
1
1
7%
16%
6%
8%
12%
25%
34%
23%
32%
4%
9%
4%
4%
7%
15%
20%
13%
19%
68%
54%
17%
17%
59%
40%
26%
43%
29%
19%
19%
18%
23%
19%
19%
18%
19%
18%
1%
2%
55%
48%
3%
1%
1%
2%
2%
<1%
<1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-23
Draft - Do Not Quote or Cite
-------
1 Table 4-13. Primary Pb smelter case study: alternative NAAQS (0.2 ug/m3, maximum
2 monthly average) - estimated IQ loss.
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
5
4
3
2
1
<1
<1
<1
<1
11
8
7
4
3
2
1
1
<1
12%
17%
14%
20%
14%
26%
14%
24%
35%
7%
10%
8%
12%
8%
15%
8%
14%
20%
59%
53%
56%
48%
56%
38%
56%
41%
25%
19%
19%
19%
19%
19%
18%
19%
19%
18%
3%
2%
3%
2%
3%
3%
3%
2%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
10
9
8
7
6
5
4
3
1
14
10
8
5
4
3
2
1
1
10%
6%
17%
17%
12%
35%
26%
17%
35%
6%
3%
10%
10%
7%
21%
15%
10%
21%
63%
30%
53%
53%
24%
24%
38%
53%
24%
19%
15%
19%
19%
31%
18%
19%
19%
18%
1%
46%
2%
2%
26%
1%
2%
2%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-24
Draft - Do Not Quote or Cite
-------
1 Table 4-14. Primary Pb smelter case study: alternative NAAQS (0.05 ug/m , maximum
2 monthly average) - estimated IQ loss.
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
4
3
3
2
1
<1
<1
<1
<1
10
7
6
4
3
2
1
1
<1
16%
8%
17%
17%
23%
28%
33%
14%
19%
10%
4%
10%
10%
13%
17%
19%
8%
11%
45%
68%
54%
54%
44%
36%
29%
57%
38%
15%
19%
19%
19%
19%
19%
19%
19%
17%
14%
<1%
<1%
1%
<1%
<1%
<1%
1%
14%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
4
19
39
194
388
970
1940
2910
3841
10
9
8
7
6
5
4
3
1
12
9
8
5
4
3
2
1
1
14%
14%
14%
14%
9%
36%
36%
19%
36%
8%
8%
8%
8%
5%
21%
21%
11%
21%
57%
57%
57%
57%
67%
25%
25%
51%
24%
19%
19%
19%
19%
19%
19%
19%
19%
19%
1%
1%
1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-25
Draft - Do Not Quote or Cite
-------
1 4.2.3 Secondary Pb Smelter Case Study
2 Recent air risk for this case study across all population percentiles and air quality
3 scenarios is less than 1IQ point lost (see Table 4-2). This reflects the fact that ambient air Pb
4 levels across this study area for all air quality scenarios modeled are significantly lower than
5 those associated with the other study areas (see Table 3-3). As shown in the expanded set of risk
6 results (Tables 4-15 to 4-19), the percent contribution of the recent air pathways to total Pb
7 exposure and risk are below 7% for all scenarios in this case study. This is in contrast to the
8 other two case studies where the percent contribution to total risk from recent air ranges up to
9 60% for some air quality scenarios. Therefore, in the case of the secondary Pb smelter case
10 study, while absolute IQ loss estimates for the recent air pathways (Table 4-2) are reduced with
11 the lowest alternative NAAQS relative to higher NAAQS levels, all of the shifts in those IQ
12 reductions are associated with IQ values significantly lower than 1.0 and consequently are not
13 reflected in the summary tables presented here (i.e., they are reported as <1 IQ point).
14 As with the other two case studies, risks associated with recent plus past air contributions
15 are 2 to 3 times higher than the recent air risks (see Table 4-3).
16
July 2007 4-26 Draft - Do Not Quote or Cite
-------
1 Table 4-15. Secondary Pb smelter case study: current conditions - estimated IQ loss.
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
2
2
2
1
<1
<1
<1
<1
<1
5
4
3
2
2
1
1
1
<1
40%
33%
42%
41%
29%
38%
42%
42%
42%
23%
19%
24%
24%
17%
22%
24%
24%
24%
4%
18%
0%
2%
25%
8%
1%
1%
1%
31%
26%
33%
32%
23%
30%
33%
33%
33%
1%
3%
<1%
<1%
5%
2%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
7
7
6
5
5
4
3
2
<1
6
5
4
3
2
2
1
1
<1
14%
39%
39%
42%
39%
40%
41%
40%
42%
8%
23%
23%
24%
23%
23%
24%
24%
24%
47%
6%
5%
1%
6%
5%
1%
3%
1%
11%
31%
31%
33%
31%
31%
33%
32%
33%
19%
2%
1%
<1%
2%
1%
1%
1%
<1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
2
3
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-27
Draft - Do Not Quote or Cite
-------
1
2
3
4
Table 4-16. Secondary Pb smelter case study: alternative NAAQS (0.5 ug/m
monthly average) estimated IQ loss.
maximum
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
2
2
2
1
<1
<1
<1
<1
<1
5
4
3
2
2
1
1
1
<1
15%
40%
42%
35%
41%
39%
35%
41%
39%
9%
23%
24%
20%
24%
23%
21%
24%
23%
51%
4%
1%
15%
1%
6%
14%
2%
6%
12%
32%
33%
28%
33%
31%
28%
32%
31%
12%
1%
<1%
2%
<1%
1%
2%
<1%
1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
7
7
6
5
5
4
3
2
<1
6
5
4
3
2
2
1
1
<1
35%
39%
18%
39%
39%
42%
40%
41%
40%
21%
23%
11%
23%
23%
24%
23%
24%
23%
13%
6%
50%
6%
6%
1%
4%
2%
5%
28%
31%
14%
31%
31%
33%
32%
32%
31%
2%
1%
6%
1%
1%
<1%
1%
<1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-28
Draft - Do Not Quote or Cite
-------
1 Table 4-17. Secondary Pb smelter case study: alternative NAAQS (0.2 ug/m
2 quarterly average) estimated IQ loss.
maximum
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
2
2
2
1
<1
<1
<1
<1
<1
5
4
3
2
2
1
1
1
<1
40%
40%
41%
42%
42%
41%
39%
40%
42%
23%
23%
24%
24%
24%
24%
23%
23%
24%
5%
5%
3%
1%
1%
3%
7%
5%
1%
32%
31%
32%
33%
33%
32%
31%
32%
33%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
7
6
6
5
5
4
3
2
<1
6
4
4
3
2
2
1
1
<1
33%
40%
37%
38%
40%
39%
41%
40%
41%
19%
24%
22%
22%
23%
23%
24%
23%
24%
21%
4%
11%
10%
5%
8%
3%
5%
2%
26%
32%
29%
30%
32%
31%
32%
31%
33%
1%
<1%
1%
1%
<1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
July 2007
4-29
Draft - Do Not Quote or Cite
-------
1 Table 4-18. Secondary Pb smelter case study: alternative NAAQS (0.2 ug/m
2 monthly average) estimated IQ loss.
maximum
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
2
2
2
1
<1
<1
<1
<1
<1
5
4
3
2
2
1
1
1
<1
19%
42%
32%
34%
42%
39%
39%
39%
36%
11%
24%
19%
20%
24%
23%
23%
23%
21%
52%
1%
23%
18%
0%
6%
7%
7%
13%
15%
33%
25%
27%
33%
31%
31%
31%
29%
2%
<1%
2%
1%
<1%
<1%
<1%
<1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
7
6
6
5
5
4
3
2
<1
6
4
4
3
2
2
1
1
<1
39%
38%
41%
41%
34%
39%
42%
37%
42%
23%
22%
24%
24%
20%
22%
24%
22%
24%
6%
10%
2%
2%
19%
8%
1%
12%
0%
31%
30%
33%
33%
27%
30%
33%
29%
33%
<1%
<1%
<1%
<1%
1%
1%
<1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
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1 Table 4-19. Secondary Pb smelter case study: alternative NAAQS (0.05 ug/m3, maximum
2 monthly average) estimated IQ loss.
IQ Loss
Percentile
Population
Above
Predicted
IQ Loss
Predicted
PbB
(ug/dL)
Pathway Contribution
Ingestion
Diet
Drinking
Water
Outdoor
Soil/Dust
Indoor Dust
Other3
Recent
Air
Inhalation
(Recent Air)
Dust Model (Air+Soil Regression-based and H6), GSD (1.7), PbB Metric (Concurrent), IQ Function (Two-piece
Linear)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
2
2
2
1
<1
<1
<1
<1
<1
5
4
3
2
2
1
1
1
<1
42%
42%
38%
42%
17%
40%
40%
42%
38%
24%
24%
22%
24%
10%
23%
23%
24%
22%
1%
1%
11%
1%
58%
6%
6%
1%
11%
33%
33%
30%
33%
13%
31%
31%
33%
30%
<1%
<1%
<1%
<1%
1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
Dust Model (Air+Soil Regression-based and H6), GSD (1.6), PbB Metric (Lifetime), IQ Function (Log-linear with
Linearization)
99.9th
99.5th
99th
95th
90th
75th
Median
25th
1st
2
8
17
85
170
425
849
1274
1681
7
6
6
5
5
4
3
2
<1
6
4
4
3
2
2
1
1
<1
40%
40%
39%
42%
40%
40%
40%
40%
42%
23%
23%
23%
24%
23%
23%
23%
23%
24%
5%
4%
6%
1%
5%
5%
5%
5%
1%
32%
32%
31%
33%
32%
31%
32%
32%
33%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
<1%
a "Other" refers to Pb contributions to indoor dust from outdoor soil/dust, indoor paint, and additional sources, including
historical air, while "recent air" refers to contributions associated with recent/current outdoor ambient air, with outdoor
ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
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1 4.3 UNCERTAINTY CHARACTERIZATION
2 This section discusses uncertainty related to exposure and risk estimates generated for
3 this analysis. Several methods have been used to examine uncertainty in our modeling approach
4 and its potential impact on exposure and risk estimates. These include the following:
5 • qualitative discussion of key sources of uncertainty and their potential impact on
6 exposure and risk estimates (Section 4.3.1),
7 • sensitivity analysis, intended to characterize the potential impact of individual
8 modeling elements on risk results (Section 4.3.2),
9 • evaluation of model performance, including comparison with empirical data (Section
10 4.3.3) and
11 • development of multiple sets of exposure and risk estimates for each assessment
12 scenario that illustrate the combined impact of different models and input data on risk
13 results and the associated uncertainty (Section 4.3.4).
14 Each of these elements of the uncertainty characterization is briefly summarized below.
15 4.3.1 Qualitative Discussion of Key Sources of Uncertainty
16 Given the complexity of this assessment and the range of models and input data used in
17 completing it, there is a wide variety of sources of uncertainly potentially impacting the exposure
18 and risk results generated (Appendix M, Table M-l). This section identifies those sources of
19 uncertainty with the potential to have a significant impact on risk results (see Appendix M, Table
20 M-l bold text). When it was feasible with the available methods and data, these key sources of
21 uncertainty have been quantitatively assessed for their potential impact on risk results either as
22 part of the multiple modeling approaches implemented (see Section 4.3.4) or as part of the
23 sensitivity analysis (see Section 4.3.2). Key sources of uncertainty include:
24 • Non-location specific aspect of the general urban case study: The use of a non-
25 location specific approach for the general urban case study, including the assumption
26 of uniform media concentrations across a single hypothetical study area, means that
27 the risk results for this case study, while generally representative of an urban
28 residential population exposed to these ambient air Pb levels, cannot be readily
29 related to a specific urban population. Furthermore, an actual urban population would
30 experience a range of ambient air Pb levels rather than a constant unvarying level
31 through time and space. Thus, while a uniform ambient air Pb level is useful for the
32 purposes of this assessment, is not representative of actual urban residential
33 exposures. Any use for that purpose would carry with it high uncertainty and
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1 presumably an upward bias in risk, particularly for large areas, where specification of
2 the same air concentration across the study becomes less applicable.1
3 • Indoor dust Pb modeling for the general urban case study: The hybrid indoor dust
4 Pb model was developed for the general urban case study due to a lack of an existing
5 urban-focused dust model, and this hybrid model is subject to particular uncertainties.
6 Key among these uncertainties is failure to consider house-to-house variability in
7 factors related to the infiltration of ambient air Pb indoors and subsequent buildup of
8 Pb on indoor surfaces. This handicaps our ability to predict variation in indoor dust
9 Pb levels for non-typical residential conditions. In addition, a lack of comprehensive
10 data in the literature on rates and efficiency of indoor cleaning, introduces significant
11 uncertainty into the model. The method used to convert Pb loadings generated by the
12 hybrid model to Pb concentrations is also subject to significant uncertainty, partially
13 due to the age of the underlying dataset. Because the underlying dataset is an older
14 dataset (see Section 3.1.4.1), there is potential for bias toward greater representation
15 of housing with Pb-based paint. Additionally, the component of the hybrid model
16 that converts dust Pb loadings to concentrations (Appendix G, Section G.3.4)
17 introduces an uncertainty into the estimates of percent contribution from recent air
18 compared to other pathways. This is related to a non-linearity in this conversion and
19 results in the "other" indoor dust concentration predicted by this model varying with
20 air quality scenario, with that value increasing as the ambient air Pb level decreases
21 (see Appendix C). This means that, as the recent air contribution to exposure through
22 indoor dust ingestion decreases for the lower alternative NAAQS levels, the estimate
23 of contribution of "other" indoor dust actually increases.
24 • Indoor dust Pb modeling for the primary Pb smelter case study: The site-specific
25 regression-based indoor dust model developed for the remediation zone of the
26 primary Pb smelter case study is subject to significant uncertainty resulting from
27 several factors. This model predicts indoor dust Pb based only on ambient air
28 concentration and an intercept. Because the soil Pb levels in the remediation zone are
29 not representative of soil Pb levels that would be associated with the current air
30 concentrations, we were unable to derive a regression relating yard soil Pb to indoor
31 dust Pb even thought it is likely that a relationship exists. Similarly, the remediation
32 zone empirical dataset did not include roadway Pb dust measurements matched
33 spatially to indoor dust Pb measurements, which may be particularly useful for
1 Additionally, in applying the results of this case study to a specific urban area, one would need to identify
the appropriate GSD, with a smaller GSD usually being more appropriate for a smaller study area.
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1 housing near the haul routes. This meant that the dataset could not be used to evaluate
2 a potential association between Pb in roadway dust and indoor dust Pb, another
3 potentially important relationship in predicting indoor dust Pb.
4 • Outdoor soil/dust concentrations in alternate NAAQS scenarios: Outdoor soil/dust
5 Pb concentrations in all air quality scenarios have been set equal to the values for the
6 current conditions scenarios. In areas where air concentrations have been greater in
7 the past, however, implementation of a reduced NAAQS might be expected to yield
8 reduced soil Pb levels over the long-term. As described in Section 2.3.3, however,
9 there is potentially significant uncertainty associated with this specification,
10 particularly with regard to implications for areas in which a Pb source may locate
11 where one of comparable size had not been previously.
12 • Recontaminationof residential yard soil near the primary Pb smelter: Although data
13 collected in residential yards within 3/4 mile of the primary Pb smelter indicate a trend
14 of increasing surface soil Pb concentrations, soil Pb estimates in this area of the
15 primary Pb smelter case study were not adjusted using this information. Therefore,
16 these estimates likely were underestimates of soil Pb levels associated with the
17 current ambient air Pb levels. Because the indoor dust Pb model used in this part of
18 this case study did not rely on outdoor soil/dust concentrations this did not affect
19 indoor dust Pb estimates, although as described in previous bullet the lack of soil data
20 to consider in developing the indoor dust Pb model increased uncertainty in that
21 model's results. Additionally, the outdoor soil/dust ingestion pathway is a small
22 contributor to blood Pb levels, which also reduced the impact of this factor on blood
23 Pb estimates
24 • Inter-individual variability GSD: There is uncertainty associated with the GSD
25 specified for each case study. In the case of the general urban case study, the use of a
26 uniform ambient air Pb concentration across the hypothetical study area complicates
27 selection of a GSD. This is because any urban area for which GSDs can be
28 developed would be subject to spatial variation in ambient air Pb levels. To the
29 extent that these ambient air Pb levels influence children's blood Pb levels, this
30 influence would be reflected in the associated GSD. In the case of the two point
31 source case studies, the use of the spatial templates complicates selection of a GSD
32 for a different reason. Because the spatial templates are intended to contribute
33 variability in risk results related to spatial gradients in air-related Pb media
34 concentrations, the GSDs used for these case studies need to reflect the remaining
35 sources of variability in blood Pb levels (e.g., inter-individual variability in behavior
36 and biokinetics related to Pb exposure as well as variability in non-air related Pb
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1 exposures). The extent to which the specified GSDs reflect the sources of variability
2 at play in each of these case studies is unknown.
3 • Exposure pathway apportionment for higher percentile blood Pb level andlQ loss
4 estimates: As discussed in Section 3.2.2, pathway apportionment of blood Pb levels
5 for higher population percentiles is specified to be the same as that estimated using
6 the central tendency estimate of blood Pb in an exposure zone. This introduces
7 significant uncertainty into proj ections of pathway apportionment for higher
8 population percentiles of blood Pb and IQ loss. In reality, pathway apportionment
9 may shift as you consider higher exposure percentiles. For example, paint and/or
10 drinking water exposures may increase in importance, with air-related contributions
11 decreasing as an overall percentage of blood Pb levels and associated risk.
12 • Projection oflQ loss at lower exposure levels: Because available epidemiological
13 data are limited in their description of the relationship of IQ decrement to blood Pb at
14 lower blood Pb levels (< 5 jig/dL), there is significant uncertainty associated with
15 projecting IQ loss at these blood Pb levels which are particularly relevant to the
16 current population of children in the U.S.
17 4.3.2 Sensitivity Analysis
18 Sensitivity analysis techniques were used to examine the uncertainty for individual
19 modeling elements and their impact on exposure and risk estimates. Specifically, we used a "one
20 element at a time elasticity analysis" approach, in which the full risk model was run with one of
21 the selected modeling elements adjusted to reflect an alternate input value or modeling choice.
22 The results of that run with the modified modeling element was then compared to those for the
23 "baseline risk" run to determine the magnitude of the impact on risk results of selections for that
24 one modeling element.
25 The sensitivity analysis described in Appendix L focused on the general urban case study,
26 reflecting the fact that this case study has potential relevance for a larger number of Pb-exposed
27 children compared with the two point source case studies. Additionally, we recognized that
28 exposure patterns for urban children can be highly variable compared with exposures near Pb
29 smelters and the availability of data sets for specific urban areas that reflect current blood Pb
30 levels is limited. The modeling elements examined in the sensitivity analysis included those
31 inputs and modeling steps believed to have a significant potential for impacting exposure and
32 risk results (e.g., oral uptake factor, inter-individual blood Pb variability GSD, biokinetic model,
33 concentration-response function for IQ loss). The one element at a time sensitivity analysis
34 indicates which of the modeling elements included in the sensitivity analysis has the greatest
35 impact on risk results, which can be used to guide future efforts to refine the overall risk model.
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1 The results of the sensitivity analysis (Appendix L) can be considered both in terms of
2 the impact of individual modeling element choices on (a) overall risk results and (b) recent air
3 risk results, i.e., IQ loss estimates for inhalation plus ingestion of indoor dust Pb predicted to be
4 associated with ambient outdoor air Pb levels, where ambient outdoor air potentially includes
5 resuspended, previously deposited Pb (see Section 2.4.3). Given the relevance of the recent air
6 exposure pathways to the NAAQS review, the results of the sensitivity analysis are summarized
7 here in terms of their impact on recent air risk results, rather than total risk results. Results of the
8 sensitivity analysis for total risk estimates, however, are similar (Appendix L).
9 Results of the sensitivity analysis showed the following modeling elements to have the
10 greatest impact on recent air risk estimates for the general urban case study (i.e., >50% impact on
11 the 99.5th percentile recent air risk estimates and an even greater impact, in terms of percent
12 change, on median estimates). These elements are presented in order of decreasing magnitude of
13 impact on risk estimates:
14 • IQ loss function: Use of the log-linear with linearization IQ loss model (the model
15 producing the greatest IQ loss) resulted in 137% increase in recent air risk results
16 over results for baseline which used the 2-piece linear model. These results suggest
17 that characterization of the relationship between Pb exposure and IQ loss has the
18 greatest impact on high-end risk results.
19 • Indoor dustPb modeling: Use of a combination of high-end modeling parameters for
20 the hybrid dust model results in a 136% increase in recent air risk estimates over
21 results using the hybrid model with those input values used in the primary analysis. In
22 addition, use of the air-only regression model in place of the hybrid model results in a
23 61% decrease in the recent air risk results. These results together suggest that
24 predicting the relationship between outdoor ambient air Pb and indoor dust Pb is an
25 important factor impacting risk results.
26 • Blood Pb modeling: Use of the Leggett model as compared to the IEUBK model,
27 results in a 99% increase in recent air risk estimates. This indicates the importance of
28 the blood Pb modeling step in the analysis. However, these results need to be
29 considered in the context of the performance evaluation for blood Pb which suggested
30 that the Leggett model may be significantly over-predicting blood Pb levels, thereby
31 decreasing our confidence in considering it as an alternative to the IEUBK model.
32 • Monitor-based ratio of maximum quarterly to annual average Pb-TSP
33 concentrations: A ratio of monitored maximum quarterly average to annual average
34 air Pb concentrations is used in the general urban case study to translate maximum
35 quarterly average air concentrations into the annual average values used in the risk
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1 assessment. Use of the 95th percentile of urban Pb-TSP monitor ratios as compared to
2 the mean value, results in a 50% reduction in recent air risk estimates.
3 • GSD reflecting inter-individual variability in blood Pb levels: Use of the larger GSD
4 (2.1) in place of the smaller value (1.7) resulted in a 50% increase in the recent air
5 risk estimates.
6
7 Alternate selections for the other modeling elements included in the sensitivity analysis
8 yielded changes to the 99.5th percentile risk results of less than 50% from baseline.
9 4.3.3 Performance Analyses
10 Performance evaluation for the exposure assessment (Section 3.5) focused on evaluation
11 of estimates of Pb in ambient air, outdoor soil, and indoor dust (discussed in Section 3.5.1) and
12 estimates of Pb in blood (covered in Section 3.5.2). Consideration of the results of performance
13 evaluation can provide insights into potential sources of uncertainty in an analysis, by identifying
14 those elements of the analysis that appear inconsistent with available empirical data. This can, in
15 turn, point to underlying bias or other errors associated with that particular modeling step,
16 reflecting either parameter or model uncertainty. This section identifies key findings of the
17 performance analysis describing the nature of associated uncertainty, including results which
18 either supported modeling elements or suggested increased uncertainty.
19 • Modeled ambient air Pb levels: The evaluation of the air model performance for the
20 primary Pb smelter case study (Sect 3.5.1.1) indicated performance generally
21 consistent with empirical data, increasing our confidence in air-related results
22 generated for this case study. Evaluation of air dispersion model performance for the
23 secondary Pb smelter suggested the potential for low bias in predictions of ambient
24 air Pb concentrations.
25 • Estimates of outdoor soil/dust Pb concentration for secondary Pb smelter case study:
26 The use of a combination of dispersion and soil mixing models to generate a spatial
27 pattern of concentrations combined with a scaling factor based on a surrogate location
28 contributes significant uncertainty to the soil Pb characterization for this case study.
29 Specifically, while the approach used is unlikely to significantly underestimate soil
30 Pb levels at this type of facility, the degree to which it is representative of conditions
31 at this specific location is not known.
32 • Modeled indoor dust Pb concentrations: Evaluation of the hybrid indoor dust model
33 used in the general urban case study suggested the potential for both under- and
34 overestimation. The mechanistic ambient air-related portion of the model may
35 underestimate that component of dust Pb, while the indoor dust loading-to-
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1 concentration conversion algorithm may contribute to overestimate of dust Pb. It is
2 not know to what extent these two biases cancel out each other. Overall comparison
3 of indoor dust Pb concentrations generated for the three case studies against available
4 empirical data suggest that: (a) for the general urban case study, estimates fall within
5 the range of measured values from a national-scale study, adding confidence to the
6 estimates, (b) central tendency estimates for the primary Pb smelter also fall within
7 the range for the national-scale dataset referenced above for the general urban case
8 study and high-end estimates seem to fit with available data near smelters, and (c)
9 comparison of estimates for the secondary Pb smelter against empirical data suggest
10 that these estimates may be biased low.
11 • Evaluation of candidate blood Pb models: A number of performance evaluations
12 were completed on the two candidate blood Pb models considered for this analysis
13 (IEUBK and Leggett). The results of these performance evaluations, which included
14 application of both models in replicating national-scale child blood Pb levels
15 (NHANES IV results) and blood Pb levels for an urban child cohort, suggested that
16 the Leggett model consistently over-predicted blood Pb levels by a factor of 3 to 6,
17 while IEUBK estimates were usually within a factor of 2. These findings resulted in
18 our selecting the IEUBK model as primary blood Pb model for this assessment, with
19 the Leggett model being reserved for application in the sensitivity analysis. Note also
20 that the empirical Lanphear Pb model was considered for use in the analysis but not
21 selected because the child cohort to which it applies (16 month olds) does not match
22 either of the blood Pb metrics used in the analysis (i.e., concurrent or lifetime
23 average).
24 • Outdoor air Pb-to-bloodPb ratios: Three sets of outdoor air Pb-to-blood Pb ratios
25 were derived. These related outdoor ambient air Pb to blood Pb resulting (1)
26 inhalation pathway only, (2) all recent air pathways (inhalation plus ingestion of
27 indoor dust Pb predicted to be associated with ambient air Pb levels, with ambient air
28 potentially included resuspended, previously deposited Pb), and (3) all recent and past
29 air pathways (see Section 2.4.3). All ratios were derived prior to application of the
30 GSD reflecting inter-individual variability in blood Pb levels and therefore reflect
31 central tendency blood Pb levels and not high-end population percentiles. The
32 modeled ratios were compared to both empirical data and statistically derived ratios
33 based on a pooled analysis (Section 3.5.2.2). With the exception of the primary Pb
34 smelter case study recent air ratio using 95th percentile air concentration and the
35 general urban case study recent air ratios for the hybrid dust model, the ratios for
36 recent air contribution to concurrent blood Pb level (Table 3-24) generated for the
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1 three case studies were reasonable and supported by available empirical data. As
2 described in Section 3.5.2.2, the exceptions relate to use in those case studies of the
3 dust models predicting the greatest influence of air concentration on indoor dust Pb
4 among the set of dust models used. These were the general urban case study hybrid
5 dust model and the site-specific regression dust model used in the remediation zone
6 for the primary Pb smelter case study. In both cases, the ratios based on these models
7 were significantly higher than empirically derived ratios obtained from the literature.
8 However, the literature indicated a potential for higher ratios for locations with either
9 lower ambient air Pb levels or lower blood Pb levels, both conditions of which are
10 present at the two case studies. Therefore, the higher modeled ratios obtained using
11 the site-specific indoor dust models for the general urban and primary Pb smelter case
12 studies do not necessarily point to potential high bias in predicting blood Pb levels for
13 the study populations.
14 • Comparison of modeled blood Pb levels to nationally representative data: Curability
15 to compare modeled blood Pb levels to empirical data was handicapped by a lack of
16 studies for populations of children directly comparable to those in the three case
17 studies (see Section 3.5.2.3). Therefore, we focused most of our evaluation of
18 modeled blood Pb levels on consideration of the national-level data obtained from
19 NHANES IV. Note that while higher-end population percentiles for the three case
20 studies might not be reflected in a national-level dataset, blood Pb levels associated
21 with background exposures for all three case studies are similar to blood Pb levels in
22 the national dataset. Thus, we considered a comparison of modeled blood Pb levels
23 for the three case studies against a national-scale dataset for central tendency
24 population percentiles appropriate. In this comparison, we included the highest and
25 lowest of the multiple sets of blood Pb estimates for the general urban case study
26 generated using the multiple modeling approaches employed used for this case study.
27 These two blood Pb distributions bracketed the NHANES IV based distribution,
28 which adds confidence to blood Pb modeling conducted for this case study. Results
29 for the primary Pb smelter were similar to the NHANES IV distribution across
30 population percentiles up to the 99th%, but then diverged with the case study
31 percentiles exceeding those for the national-level dataset. This result is not surprising
32 given the fact that exposure modeling for the primary Pb smelter did not fully
33 consider variability in background Pb exposure (e.g., paint, diet, drinking water). The
34 difference between the high-end population percentiles likely reflects primarily air-
35 related exposures for the primary Pb smelter case study population and, had
36 background exposures been fully considered, the degree of divergence between the
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1 case study and national-level percentiles would likely have been much greater. Blood
2 Pb levels for all population percentiles for the secondary Pb smelter are lower than
3 those for the national-scale dataset. As with the primary Pb smelter case study, this
4 likely reflects the fact that variability in background Pb exposures was not fully
5 considered in modeling this case study.
6 4.3.4 Uncertainty in Modeling Approaches - Multiple Sets of Results
7 For those more highly influential analytical steps for which it is not clear which model or
8 input would generate "best estimate" results, we have implemented multiple modeling
9 approaches (see Section 2.4.6.2). Because each of the case studies uses different modeling
10 approaches for some of the analytical steps such as the indoor dust modeling step, and these
11 approaches are associated with differing uncertainty, the identity and size of the areas of
12 uncertainty associated with each case study differs. The specific modeling approaches for each
13 case study and their elements are presented in Figure 2-3. For the general urban case study, two
14 different dust models and two GSDs were used as compared to one model and GSD for these
15 analytical steps in the two point source case studies. However, the same number of blood Pb
16 metrics and IQ loss functions are used for all three case studies.
17 Consideration of the range of risk results generated using the multiple modeling
18 approaches for each case study provides perspective on the combined effect of key sources of
19 uncertainty on risk results. The median and 99.5th percentile estimates associated with the
20 modeling approaches yielding the highest and lowest risk results for specific scenarios of the
21 three case studies are presented in Table 4-20.
22 Table 4-20. Impact of multiple sources of uncertainty on risk results.
Case study
General
urban
(mean current
conditions)
Primary Pb
smelter
(current
NAAQS)
Secondary Pb
smelter
(current
conditions)
Modeling approach
Lowest risk: Dust Model (Air-only Regression-based),
GSD (1.7), blood Pb Metric (Concurrent), IQ Function
(Two-piece Linear)
Highest risk: Dust Model (Hybrid), GSD (2.0), blood Pb
Metric (Lifetime), IQ Function (Log-linear with
Linearization)
Lowest risk: blood Pb Metric (Concurrent), IQ Function
(Two-piece Linear)
Highest risk: blood Pb Metric (Lifetime), IQ Function
(Log-linear with Linearization)
Lowest risk: blood Pb Metric (Concurrent), IQ Function
(Two-piece Linear)
Highest risk: blood Pb Metric (Lifetime), IQ Function
(Log-linear with Linearization)
IQ loss
(population
percentile)
Median
<1
5
<1
4
<1
3
99.5th
7
16
5
10
2
7
Percent difference
between highest
and lowest
approaches
Median
>400%
>300%
>200%
99.5th
140%
100%
260%
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1 The lack of a consistent pattern regarding the magnitude of differences between the
2 modeling approaches generating the lowest and highest risk results either across case studies, or
3 across population percentiles (Table 4-20), is due to different influences of various factors
4 specific to the modeling approaches for each case study. For example, the finding with regard to
5 the 99.5th percentile results that the greatest difference across approaches occurs for the
6 secondary Pb smelter case study (260%) reflects the influence of the lower absolute blood Pb
7 levels estimated via both modeling approaches for that case study which are translated into IQ
8 response using the relatively steeper portion of the concentration-response function, thus
9 magnifying the difference. The finding for the 99.5th percentile results that there is a greater
10 difference for the general urban case study than the primary Pb smelter case study is because of
11 the larger range of GSDs applied in this case study (2.1 and 1.6).
12 In summary, results presented in Table 4-20 suggest that these key sources of uncertainty,
13 when acting in concert, can produce an impact on overall risk results on the order of a factor of 2
14 to 3.
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1 REFERENCES
2
3 Henderson, R. (2007) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to
4 Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee's (CASAC) Review of
5 the 1st Draft Lead Staff Paper and Draft Lead Exposure and Risk Assessments. March 27, 2007.
6 Hornung, R. (2007a) Email message to David Svendsgaard, U.S. EPA. April 24, 2007.
7 Hornung R. (2007b) Email message to Zachary Pekar, U.S. EPA, May 1, 2007.
8 Lanphear, B.P., Hornung, R., Khoury, I, Yolton, K., Baghurst, P., Bellinger, D.C., Canfield, R.L., Dietrich, K.N.,
9 Bornschein, R., Greene, T., Rothenberg, S.J., Needleman, H.L., Schnaas, L., Wasserman, G., Graziano, I,
10 and Robe, R. (2005) Low-level environmental Pb exposure and children's intellectual function: An
11 international pooled analysis. Environmental Health Perspectives. 113(7):894-899.
July 2007 4-42 Draft - Do Not Quote or Cite
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