Lead: Human Exposure and Health Risk 

Assessments for Selected Case Studies 

Volume I. Human Exposure and Health Risk 

Assessments - Full-scale 

EPA-452/R-07-014a 

October 2007 

Lead: Human Exposure and Health Risk Assessments for 

Selected Case Studies 

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

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 
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 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. 

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). 
i 

TABLE OF CONTENTS 

 

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

 List of Figures......................................................................................................................v

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

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

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

1.3 PILOT PHASE ASSESSMENT FOR THE CURRENT REVIEW ................1-5 

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

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

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

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

2.1 BACKGROUND 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......................................................... 2-11 

2.1.4.4 Renal System ....................................................................... 2-12 

2.1.4.5 Heme Synthesis.................................................................... 2-12 

2.1.4.6 Immune System ................................................................... 2-13 

2.1.5 Metric and Model for Risk Quantitation..............................................2-14 

2.2 USE OF CASE STUDIES AND LOCATION SELECTIONS ..................... 2-19 

2.2.1 General Urban Case Study................................................................... 2-20 

2.2.2 Point Source Case Studies ...................................................................2-20 

2.2.2.1 Primary Pb Smelter Case Study........................................... 2-21 

2.2.2.2 Secondary Pb Smelter Case Study....................................... 2-24 

2.3 ASSESSMENT SCENARIOS....................................................................... 2-25 

2.3.1 Air Concentrations............................................................................... 2-25 

2.3.2 Policy-relevant Background................................................................. 2-28 

2.3.3 Outdoor Soil/Dust ................................................................................ 2-29 

2.4 ANALYTICAL APPROACH ....................................................................... 2-31 

2.4.1 Temporal Aspects ................................................................................ 2-31 

2.4.2 Spatial Scale and Resolution................................................................2-31 
ii 

2.4.3 Categorization of Policy-relevant Exposure Pathways........................2-32 

2.4.4 Overview of Analytical Steps .............................................................. 2-34 

2.4.4.1 Exposure Assessment........................................................... 2-37 

2.4.4.2 Risk Characterization........................................................... 2-39 

2.4.5 Variability Characterization................................................................. 2-39 

2.4.6 Uncertainty Characterization and Sensitivity Analysis ....................... 2-40 

2.4.6.1 Performance Evaluations ..................................................... 2-41 

2.4.6.2 Generating Multiple Sets of Results .................................... 2-41 

2.4.6.3 Sensitivity Analysis ............................................................. 2-42 

2.4.6.4 Qualitative Discussion of Sources of Uncertainty...............2-43 

REFERENCES ........................................................................................................ 2-44 

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

3.1 METHODS FOR ESTIMATING 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....................................... 3-14 

3.2 METHODS FOR ESTIMATING BLOOD PB LEVELS ............................. 3-15 

3.2.1 Blood Pb Modeling..............................................................................3-15 

3.2.1.1 Primary Analysis.................................................................. 3-16 

3.2.1.2 Sensitivity Analysis ............................................................. 3-17 

3.2.2 Exposure Pathway Apportionment and Probabilistic Population 

Modeling........................................................................................................3-18 

3.2.2.1 General Urban Case Study................................................... 3-20 

3.2.2.2 Point Source Case Studies ...................................................3-20 

3.2.3 GSD for Population Blood Pb Modeling Procedure............................3-23 

3.2.3.1 General Urban Case Study................................................... 3-25 

3.2.3.2 Point Source Case Studies ...................................................3-26 

3.3 ESTIMATED MEDIA CONCENTRATIONS.............................................. 3-26 

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

3.5 UNCERTAINTY CHARACTERIZATION AND SENSITIVITY 

 ANALYSIS................................................................................................... 3-33 
iii 

3.5.1 Performance Evaluation Related to Exposure Media Modeling..........3-33 

3.5.1.1 Evaluation of Modeled Ambient Air Pb Concentrations.....3-33 

3.5.1.2 Evaluation of Modeled Outdoor Soil/Dust Pb 

 Concentrations .............................................................................. 3-34 

3.5.1.3 Evaluation of Modeled Indoor Dust Pb Concentrations...... 3-35 

3.5.2 Performance Evaluation Related to Blood Pb Modeling.....................3-38 

3.5.2.1 Evaluation of Candidate Blood Pb Models..........................3-38 

3.5.2.2 Evaluation of model-derived outdoor air Pb-to-blood Pb 

 ratios .............................................................................................. 3-39 

3.5.2.3 Comparison of modeled blood Pb levels to nationally 

 representative data ..............................................................................3-42 

REFERENCES ........................................................................................................ 3-46 

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

4.1 METHODS FOR DERIVING RISK ESTIMATES........................................ 4-1 

4.1.1 Concentration-Response Functions ....................................................... 4-1 

4.1.1.1 Log-Linear Function with Cutpoint....................................... 4-3 

4.1.1.2 Log-Linear Function with Low-Exposure Linearization.......4-3 

4.1.1.3 Two-piece Linear Function.................................................... 4-4 

4.1.2 Projection of Population Risk ................................................................ 4-5 

4.2 RISK ESTIMATES ......................................................................................... 4-6 

4.3 UNCERTAINTY CHARACTERIZATION AND SENSITIVITY 

 ANALYSIS.................................................................................................... 4-13 

4.3.1 Qualitative Discussion of Key Sources of Uncertainty ....................... 4-13 

4.3.2 Sensitivity Analysis ............................................................................. 4-17 

4.3.3 Performance Analyses ......................................................................... 4-19 

4.3.4 Uncertainty in Modeling Approaches – Multiple Sets of Results .......4-21 

REFERENCES ........................................................................................................ 4-23 

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

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

5.1.1 Assessment Scenarios ............................................................................ 5-1 

5.1.2 Analytical Approach .............................................................................. 5-1 

5.1.3 Location-Specific Urban Case Studies .................................................. 5-2 

5.2 EXPOSURE ASSESSMENT .......................................................................... 5-4 

5.2.1 Methods for General Urban and Primary Pb Smelter Case Studies ......5-4 

5.2.2 Methods for Location-specific Urban Case Studies .............................. 5-5 

5.2.2.1 Ambient Air and Inhalation Exposure Concentrations.......... 5-5 

5.2.2.2 Other media Concentrations .................................................. 5-6 

5.2.2.3 Blood Pb levels ......................................................................5-7 

5.2.3 Media concentrations............................................................................. 5-7 

5.2.3.1 Location-specific Urban Case Studies................................... 5-7 
iv 

5.2.3.2 General Urban and Primary Pb Smelter Case Studies........... 5-8 

5.2.4 Blood Pb levels .................................................................................... 5-13 

5.2.5 Uncertainty Characterization ............................................................... 5-16 

5.2.5.1 Performance Evaluation of Modeled MediaConcentrations 5-16 

5.2.5.2 Performance Evaluation of Modeled Blood Pb Levels........5-17 

5.3 RISK ASSESSMENT.................................................................................... 5-20 

5.3.1 Methods for Deriving Risk Estimates.................................................. 5-20 

5.3.1.1 Concentration-response Functions....................................... 5-20 

5.3.1.2 Projection of Population Risk .............................................. 5-24 

5.3.2 Risk Estimates...................................................................................... 5-24 

5.3.2.1 Population Risk Distribution Estimates............................... 5-25 

5.3.2.2 IQ Loss Incidence Estimates................................................5-28 

5.3.3 Uncertainty Characterization and Sensitivity Analysis ....................... 5-31 

5.3.3.1 Qualitative Discussion of Key Sources of Uncertainty .......5-31 

5.3.3.2 Performance Analyses ......................................................... 5-33 

5.3.3.3 Uncertainty in Modeling Approaches - Multiples Sets 

of Results .............................................................................................5-34 

5.3.3.4 Sensitivity Analysis – Indoor Dust Pb Modeling ................ 5-34 

REFERENCES ........................................................................................................................5-37 

List of Tables 

Table 2-1. Key aspects of primary Pb smelter case study. ....................................................... 2-23 

Table 2-2. Key aspects of secondary Pb smelter case study..................................................... 2-24 

Table 2-3. Air quality scenarios assessed for the general urban case study. ............................ 2-28 

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. ............................... 3-12 

Table 3-3. Estimated annual ambient air concentrations.......................................................... 3-27 

Table 3-4. Estimated inhalation exposure concentrations. ....................................................... 3-28 

Table 3-5. Estimated outdoor soil/dust concentrations.............................................................3-28 

Table 3-6. Estimated indoor dust concentrations...................................................................... 3-29 

Table 3-7. Summary of blood Pb estimates for medians in total-exposure blood Pb 

 distributions. ............................................................................................................ 3-31

Table 3-8. Summary of blood Pb estimates for 95th percentiles in total-exposure blood Pb 

distributions. ............................................................................................................ 3-32 

Table 3-9. Evaluation of model-predicted indoor dust Pb levels against empirical data obtained 

from the literature.................................................................................................... 3-37 

Table 3-10. Air-to-blood Pb ratios for “recent air” contribution to concurrent blood Pb level. 3-40 

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

 1999-2002). ............................................................................................................. 3-44 

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

concentration-response functions..............................................................................4-2 

Table 4-2. Summary of risk estimates for medians of total-exposure risk distributions. ......... 4-11 
v 

Table 4-3. Summary of risk estimates for 95th percentiles of total exposure risk 

 distributions. ............................................................................................................ 4-12

Table 4-4. Impact of multiple sources of uncertainty on risk results. ......................................4-22 

Table 5-1. Estimated annual average ambient air concentrations. ............................................. 5-9 

Table 5-2. Estimated inhalation exposure concentrations. ....................................................... 5-10 

Table 5-3. Estimated outdoor soil/dust concentrations.............................................................5-11 

Table 5-4. Estimated indoor dust concentrations...................................................................... 5-12 

Table 5-5. Summary of blood Pb level estimates for median total blood Pb. .......................... 5-14 

Table 5-6. Summary of blood Pb level estimates for high-end total blood Pb.........................5-15 

Table 5-7. Air-to-blood ratios derived by comparing air quality scenario air and blood Pb 

estimates. ................................................................................................................. 5-19

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

concentration-response functions............................................................................5-23 

Table 5-9. Summary of risk estimates for medians of total-exposure risk distributions. ......... 5-26 

Table 5-10. Summary of risk estimates for 95th percentiles of total exposure risk 

 distributions. ............................................................................................................ 5-27

Table 5-11. Incidence of children with >1 point IQ loss........................................................... 5-29 

Table 5-12. Incidence of children with >7 points IQ loss. ........................................................5-30 

Table 5-13. Comparison of hybrid indoor dust model with a modified form of the model. ..... 5-36 

List of Figures 

Figure 1-1. Principal pathways of human and ecological exposure to Pb. . ...............................1-3 

Figure 2-1. Conceptual model for Pb human health risk assessment. ......................................... 2-2 

Figure 2-2. Overview of analysis approach. ..............................................................................2-36 

Figure 2-3. Modeling approaches for case study analyses presented in chapters 3 and 4. ........2-42 

Figure 3-1. Procedure for generating population blood Pb distributions for point source case 

studies......................................................................................................................3-22 

Figure 3-2. Comparison of NHANES IV blood Pb levels with modeled estimates. ................. 3-44 

Figure 4-1. Comparison of three concentration-response functions for concurrent 

 blood Pb levels < 10 μg/dL. ...................................................................................... 4-2 

Figure 5-1. Core modeling approach for each case study............................................................ 5-2 

Figure 5-2. Comparison of four concentration-response functions for concurrent blood 

Pb levels < 10 μg/dL....................................................................5-23 
 1-1

1 INTRODUCTION 

This document is the first volume of the report Lead: Human Exposure and Health Risk 

Assessments for Selected Areas. This volume describes the quantitative human exposure and 

health risk assessments1 conducted to inform the U.S. Environmental Protection Agency’s 

(EPA’s) current review of the National Ambient Air Quality Standards (NAAQS) for lead (Pb). 

As with the last review of the Pb NAAQS (see Section 1.2), the human exposure and 

health risk assessments (the risk assessment)2

 for this review reflect multimedia exposure 

pathways, and their influence on blood Pb levels as an internal index of exposure or dose 

(Section 1.1). The assessment for this review, as with that for the last review, utilizes a case 

study approach wherein a set of specific locations or case studies associated with policy-relevant 

Pb exposures are evaluated in detail. The case studies have been selected to provide a 

perspective on the nature and magnitude of air source Pb exposures and risk in the United States. 

There are two phases to the risk assessment for the current review: pilot and full-scale. 

The first phase (the pilot assessment, described in Section 1.3) was presented in the first draft 

Staff Paper and accompanying technical report (USEPA, 2006a; ICF, 2006), and was the subject 

of a review by the Clean Air Scientific Advisory Committee (CASAC) on February 6 and 7, 

2007 described in Section 1.4 (Henderson, 2007a). The initial full-scale analyses were presented 

in the July 2007 draft report (USEPA, 2007b) and were the subject of a CASAC review at a 

public meeting on August 28 and 29, 2007. In response to CASAC recommendations described 

in Section 1.4 (Henderson, 2007b), additional analyses, using a core modeling approach were 

conducted to complete the full-scale assessment. 

All analyses for the full-scale assessment are described in this document, with the initial 

analyses being the focus in Chapters 3 and 4 and the additional analyses in Chapter 5. Further, 

given the significant time constraints of this review, risk results are provided in this document 

without substantial interpretation. Rather, interpretative discussion of these results is provided in 

the Staff Paper. 

 

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 Staff Paper (USEPA, 2007a). 

2

 Throughout the remainder of this document, the term "risk assessment" will be used to refer to both the 

human exposure and health risk assessments collectively, unless specific reference to either the human exposure or 

health risk assessment is required. 
 1-2

1.1 MULTIMEDIA ASPECT OF THE RISK ASSESSMENT 

The focus for this Pb NAAQS risk assessment is on Pb derived from those sources 

emitting Pb to ambient air. In designing and implementing this assessment, we have been faced 

with significant limitations and complexity that go far beyond the situation for similar 

assessments typically performed for other criteria pollutants. In addition to the constraints of the 

timeframe allowed for this review, we are also constrained by significant limitations with regard 

to data and tools needed for the assessment. The multimedia and persistent nature of Pb and the 

role of multiple exposure pathways contribute significant additional complexity to the 

assessment as compared to other assessments that focus only on the inhalation pathway. 

First, exposures to Pb emitted into the air occur via multiple pathways. As described in 

the Air Quality Criteria for Lead (USEPA, 2006b; henceforth referred to as the CD), “The 

multimedia aspects of Pb exposure can be seen in that Pb emissions to the air contribute to Pb 

concentrations in water, soil and dusts; Pb in soil and dust also can make important contributions 

to Pb concentrations in ambient air” (CD, p. 3-1). 

Inhalation exposures can result from Pb emitted to the ambient air recently or from Pb 

emitted in the past that has deposited from air to soil or dust and then become resuspended in the 

ambient air. Further, Pb emitted into the ambient air can contribute to ingestion exposures 

(associated with indoor dust, outdoor soil/dust, agricultural products and surface water) of 

recently deposited Pb and of Pb that was deposited in the past. Consequently, this is a 

multipathway risk assessment in which we are considering both airborne Pb, as it contributes to 

human exposures through direct inhalation of particles containing Pb, and also Pb that has 

deposited from air to dusts, soil and other environmental media and that contributes to human 

exposures through ingestion. Further, we are considering that Pb, once deposited, may be 

resuspended in the air, contributing to human inhalation exposures or, upon redeposition, to 

human ingestion exposures. Thus, as illustrated in Figure 1-1, pathways that are directly relevant 

to a review of the NAAQS include both newly emitted Pb from currently operating sources, and 

Pb emitted in the past, either from currently operating sources or historic sources, which are 

collectively referred to as “policy-relevant sources”. 

 
 1-3

Newly emitted Pb Historically emitted Pb

Air Outdoor Soil, Dusts

Indoor Dust

Paint 

Surface Waters

& Sediments

Policy-relevant

Background*

Policy-relevant Sources 

Non-air Pb

releases

Policy-relevant

Background*

Human Exposures

-Inhalation & ingestion

Diet** 

Ecological Exposures

Drinking

water 

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

**Dietary exposure should not be considered to be limited to policy-relevant background, as it reflects a combination

of Pb introduced into food items during processing (policy-relevant background), as well as Pb associated with 

atmospheric deposition (policy-relevant sources).

Figure 1-1. Principal pathways of human and ecological exposure to Pb. Among the 

policy-relevant pathways, heavy arrows indicate the predominant human 

exposures. 

Due to limited data, models, and time available, however, we are not able to fully and 

completely characterize in our risk assessment all of the various complexities associated with Pb. 

Consequently, in our efforts to focus on and characterize risk associated with the ambient airrelated3

 sources and exposures, we have made a number of simplifying assumptions in a number 

of areas. For example, Figure 1-1 illustrates that people are also exposed to Pb that originates 

from nonair sources, including leaded paint or drinking water distribution systems. For purposes 

of this assessment, the Pb from these nonair sources is collectively referred to as “policy-relevant 

background” 4. Although Pb in diet and drinking water sources may derive from Pb emitted into 

 

3

 Ambient air related sources are those emitting Pb into the ambient air (including resuspension of 

previously emitted Pb), and ambient air related exposures 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. 
 1-4

the ambient air, the contribution from air pathways to these exposure pathways is not explicitly 

recognized, such that these exposures are treated as policy-relevant background.

1.2 RISK ASSESSMENT FROM LAST REVIEW 

In the risk assessment conducted in support of the last review, air quality scenarios were 

compared in terms of their impact on the percentage of modeled populations that exceeded 

specific blood Pb levels chosen with consideration of the health effects evidence at that time 

(USEPA, 1986a, 1986b, 1990a). The 1990 analysis focused on both children (birth through 7 

years of age) and middle-aged men residing in three case study locations, two near secondary Pb 

smelters and one near a primary Pb smelter (USEPA, 1990b). The analysis also introduced the 

use of pharmacokinetic blood Pb modeling for children, although it used empirically derived 

slope models for adult men to relate changes in air Pb to changes in blood Pb. 

In the 1990 Staff Paper, staff concluded that at levels of 10-15 micrograms per deciliter 

(μg/dL) of blood Pb, there appeared to be “a convergence of evidence of lead-induced 

interference with diverse set of physiological functions and processes, particularly evident in 

several independent studies showing impaired neurobehavioral function and development” 

(USEPA, 1990).5 Accordingly, the staff used blood Pb levels of 10 and 15 μg/dL to evaluate 

effects of alternate NAAQS on children in the 1990 analysis (USEPA, 1990). These values were 

chosen with consideration of the full body of health effects evidence at that time. Staff then used 

dispersion modeling (the Industrial Source Complex (ISC) model) combined with source 

characterization data to generate Pb air concentrations for each case study area. Statistically 

derived relationships based on data from other industrial locations, including Pb smelters, that 

linked concentrations of Pb in air to Pb in indoor dust and outdoor soil were then used to predict 

Pb in these media for the three case study locations, based on the modeled air Pb concentrations. 

An uptake/biokinetic model was also developed to predict child blood Pb levels. This model was 

used in place of a statistical regression slope model to allow consideration of the dynamic nature 

of Pb exposure in children. EPA combined model-derived central tendency blood Pb levels with 

an estimated geometric standard deviation (GSD) reflecting interindividual variability in blood 

Pb levels, to generate population distributions of blood Pb levels. These distributions were then 

used to estimate the percentage of children at each case study location that exceeded the blood 

Pb levels 10 and of 15 μg/dL, respectively. 

For adult men, the 1990 assessment used blood Pb levels of 10 and 12 μg/dL to compare 

relative effects of alternate NAAQS (USEPA, 1990). The same approach was used for 

 

5

 As a result of a parallel activity, the U.S. Centers for Disease Control and Prevention in 1991 reduced the 

children’s blood Pb level warranting individual intervention to 15 μg/dL and identified a level of 10 μg/dL for 

implementing community-wide prevention activities (CDC, 1991; CDC, 2005). 
 1-5

generating media concentrations for the adult analysis as was used for the child assessment. 

However, rather than using a biokinetic model, as for the children’s assessment, the 1990 

analysis for adults used statistically derived slope models to relate air Pb to blood Pb levels with 

two versions of the slope models being employed: (a) the aggregate model which predicts blood 

Pb in adults based solely on air Pb levels (here a single slope factor captures both the direct 

inhalation pathway as well as the more complex pathway of Pb deposition to soil and dust 

followed by incidental ingestion) and (b) the disaggregate model which uses media-specific 

slopes to predict blood Pb based on Pb concentrations in soil, dust and air. Since the projected 

blood Pb levels were mean population levels, a GSD term was included to develop populationlevel blood Pb distributions. The GSD estimates for adults and children were derived from 

information on observed blood Pb levels in these subgroups. These population-level 

distributions were then queried to identify the percentage of adult men at each case study 

location with modeled blood Pb levels exceeding the levels of interest for adults (10 and 12 

μg/dL). 

The primary difference between the risk assessment approach used in the current pilot 

analysis and the assessment completed in 1990 involves the risk metric employed, which reflects 

the quantitative and qualitative health effects evidence available today that was not available in 

1990 (CD). Rather than estimating the percentage of study populations with exposures above 

blood Pb levels of interest as was done in the last review (i.e., 10, 12 and 15 μg/dL), the current 

pilot analysis estimates the degree of health decrement in study populations exposed to Pb. 

Specifically, the pilot analysis estimates the distribution of IQ loss associated with Pb exposure 

for child populations at each of the case study locations with that IQ loss further differentiated 

between background Pb exposure and policy-relevant exposures. 

1.3 PILOT PHASE ASSESSMENT FOR THE CURRENT REVIEW 

The pilot phase of the risk assessment for the current review is described in the first draft 

Staff Paper and accompanying technical report (USEPA 2006a; ICF 2006). The pilot assessment 

was intended primarily as a demonstration of the risk assessment methodology being developed 

for the current review. Consequently, exposure and risk results from the pilot assessment are 

considered preliminary. 

The pilot assessment included three case studies: (a) a primary Pb smelter (in 

Herculaneum, Missouri), (b) a secondary Pb smelter (in Troy, Alabama), and, (c) a near roadway 

(urban) location along a short road segment in Houston, Texas.6

 The case studies modeled for 

 

6

 This case study was intended to provide perspective on the near roadway exposure scenario but was not 

intended to estimate total population risk for a full urban or metropolitan area. 
 1-6

the pilot were selected to provide a preliminary perspective on the nature and magnitude of air 

sourced Pb exposures and risk. In addition, they provided a range of exposure scenarios in 

which to test the risk assessment methodology developed for the current review. Because of 

differences in the exposure scenarios and available data at each of the case study locations, the 

approach used for modeling exposure and risk differed among the case studies. Results from the 

pilot assessment, as well as comments received from the public and CASAC (see Section 1.4) 

have informed decisions on the design for the full-scale assessment, including the types of case 

studies included. 

1.4 CASAC ADVICE 

The staff consulted with the CASAC on the draft analysis plan for the risk assessment 

(USEPA, 2006c) in June 2006 (Henderson, 2006), and subsequently developed the pilot 

assessment, summarized in Section 1.3, and described in the first draft Staff Paper and 

accompanying technical report (USEPA, 2006a; ICF 2006). On February 6-7, 2007, the CASAC 

Pb panel met to discuss these documents and CASAC’s written comments and recommendations 

were provided in March 2007 (Henderson, 2007a). 

Consistent with their mandate under the Clean Air Act, CASAC provided comments on 

both scientific aspects of the risk assessment and aspects related to the standards themselves 

(Henderson, 2007a).7

 With regard to the risk assessment, they recommended that the case study 

approach implemented for the pilot risk assessment be supplemented with a “population-based” 

analysis, and, in discussion at the public meeting, the panel raised the general occurrence of 

lower Pb levels in urban areas beyond more point source impacted areas as being an important 

focus for the risk assessment. As described below, consideration of comments in this area led to 

a significant difference in the design of the full-scale assessment as compared to the pilot 

assessment. 

 CASAC also recommended that uncertainty be characterized with regard to the 

relationship between a change in the NAAQS and the distribution of population blood Pb 

concentrations, and with regard to the relationship between blood Pb concentrations and the risk 

of adverse health effects (Henderson, 2007a). With respect to alternate NAAQS for 

consideration by EPA, CASAC recommended consideration of levels less than or about 0.2 

μg/m3

 (micrograms per cubic meter) and of a monthly averaging time. Additionally, they 

indicated that they consider a population loss of 1-2 points in intelligence quotient (IQ) to be 

highly significant from a public health perspective and that the primary Pb standard should be set 

 

7

 Consistent with the focus of this document on the human exposure and health risk assessment, CASAC 

comments regarding the ecological risk assessment and secondary standard considerations are not discussed here. 
 1-7

to protect 99.5 percent of the population from exceeding that loss (Henderson, 2007a, p. 6).8 

CASAC also recommended conducting future Pb monitoring with samplers for particulate matter 

less than ten microns in size (PM10) rather than with samplers for total suspended particulate 

matter (TSP) (Henderson, 2007a, p. 7). 

In consideration of CASAC’s comments on the pilot phase assessment (Henderson, 

2007a), we considered a number of alternate approaches for the full-scale assessment. As a 

result, several additions and modifications to the assessment design were implemented for the 

initial analyses of the full-scale assessment presented in the July 2007 draft risk assessment 

report (USEPA, 2007a) and chapters 1 through 4 of this document. The most significant of these 

modifications was the replacement of the near roadway case study with a general urban case 

study. This case study was designed to provide estimates of risk in urban areas associated with 

broad population level exposures to different ambient air levels of Pb. The general urban case 

study was assessed in addition to the two point source case studies, and differs from those case 

studies in basing the estimate of air quality on monitoring data (rather than on results from air 

quality modeling). The alternate NAAQS levels were selected to overlap with the range of levels 

suggested by CASAC. The target population and endpoint for the assessment remains young 

children and risk of IQ decrements associated with Pb exposure. To address CASAC comments 

on the cutpoint employed in the pilot assessment (Henderson, 2007a), the blood Pb concentration 

response function was re-examined, and three alternatives were included in the assessment. 

Additionally, in consideration of CASAC recommendations regarding the geometric standard 

deviation used in the blood Pb modeling, two values were included in the initial analyses for the 

general urban case study. 

In their review of the July draft risk assessment report, in addition to reiterating their 

previous recommendations on aspects of the standards and concurring with several aspects of the 

draft assessment, the CASAC Pb Panel made several recommendations for additional exposure 

and health risk analyses (Henderson, 2007b), of which several notable ones are summarized here. 

First, they recommended that the general urban case study be augmented by the inclusion of risk 

analyses in specific urban areas of the U.S. Further, they recommended use of the hybrid dust Pb 

model in preference to the alternate that had additionally been employed in the initial analyses 

for the general urban case study. The Panel also recommended limiting presentation of 

population risk results to the 5th through 95th percentile of the distribution.9

 In response to 

 

8

 CASAC’s reference to 99.5 percent of the population in this statement is consistent with their recognition 

earlier in the same letter of the 99.5 percent figure being the EPA’s risk management choice when they established 

the Pb NAAQS in 1978 (Henderson, 2007a, pp. 5 and 6). 

9

 This recommendation was in recognition by Panel members of uncertainties in quantitative estimates 

from the assessment for this point on the distribution (Henderson, 2007b, Appendix D). 
 1-8

discussion by the CASAC Pb Panel associated with these recommendations at the August public 

meeting, staff has developed additional analyses that include three location-specific urban case 

studies, and focus on a core modeling approach (with regard to dust Pb model, blood metric and 

concentration-response functions). With regard to the blood Pb concentration-response function, 

CASAC recommended use of a two-piece or dual linear function that recognizes a change in 

slope (to a notably higher value) at blood Pb levels of 7.5 μg/dL and indicated less favor for the 

two-piece linear function with hinge at 10.82 μg/dL derived for the initial analyses of the fullscale assessment (see Section 4.1.1.3) (Henderson, 2007b). Accordingly, a different set of 

concentration-response functions was employed in the additional analyses (see Section 5.3.1.1), 

and risk estimates for the two-piece linear function with hinge at 10.82 μg/dL are not included in 

the range of risk estimates presented for the initial analyses in Chapter 4. 

1.5 ORGANIZATION OF THE DOCUMENT 

The remainder of this document is organized as follows. Chapter 2 describes the design 

of the exposure and risk assessments, covering such topics as the conceptual model used in 

designing the analysis (Section 2.1), the case studies included in the assessment (Section 2.2), the 

air quality scenarios simulated in the assessment (Section 2.3), and an overview of the analytical 

approach (Section 2.4). Chapter 3 describes the methods and results for the exposure 

assessment, as well as the performance evaluation. Chapter 4 describes the methods for deriving 

risk estimates, the resultant estimates, sensitivity analyses and a characterization of uncertainty. 

Chapter 5 presents additional analyses using a core modeling approach completed in response to 

the August 2007 CASAC review of initial full-scale analyses. 
 1-9

REFERENCES 

Centers for Disease Control (1991) Preventing lead poisoning in young children: a statement by the Centres for 

Disease Control. Atlanta, GA: U.S. Department of health and Human Services, Public Health Service; 

October 1. http://wonder.cdc.gov/wonder/prevguid/p0000029/p0000029.asp

Centers for Disease Control and Prevention (2005) Preventing lead poisoning in young children: a statement by the 

Centers for Disease Control and Prevention. Atlanta, GA: U.S. Department of Health and Human Services, 

Public Health Service. August. 

Henderson, R. (2006) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to 

Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee (CASAC) Lead Review 

Panel’s Consultation on EPA’s draft Analysis Plan for Human Health and Ecological Risk Assessment for 

the Review of the Lead National Ambient Air Quality Standards. July 26, 2006. 

Henderson, R. (2007a) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to 

Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of 

the 1st Draft Lead Staff Paper and Draft Lead Exposure and Risk Assessments. March 27, 2007. 

Henderson, R. (2007b) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to 

Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of 

the 2nd Draft Lead Human Exposure and Health Risk Assessments Document. September 27, 2007. 

ICF International. (2006) Lead Human Exposure and Health Risk Assessments and Ecological Risk Assessment for 

Selected Areas. Pilot Phase. Draft Technical Report. Prepared for the U.S. EPA's Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. December. 

U.S. Environmental Protection Agency. (1986a) Air Quality Criteria for Lead. Environmental Criteria and 

Assessment Office, Office of Research and Development, Research Triangle Park, NC. EPA/600/8-83-028 

a-d. June 1986. 

U.S. Environmental Protection Agency. (1986b) Lead effects on cardiovascular function, early development, and 

stature: an addendum to U.S. EPA Air Quality Criteria for Lead (1986) Environmental Criteria and 

Assessment Office, Office of Research and Development, Research Triangle Park, NC. September, 1986. 

U.S. Environmental Protection Agency. (1990a) Air Quality Criteria for Lead: Supplement to the 1986 Addendum. 

Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria and 

Assessment Office; report no. EPA/600/8-89/049F. Available from: NTIS, Springfield, VA; PB91-138420. 

U.S. Environmental Protection Agency. (1990b) Review of the National Ambient Air Quality Standards for Lead: 

Assessment of Scientific and Technical Information: OAQPS Staff Paper. Research Triangle Park, NC: 

Office of Air Quality Planning and Standards; report no. EPA-450/2-89/022. Available from: NTIS, 

Springfield, VA; PB91-206185. Available on the web: 

http://www.epa.gov/ttn/naaqs/standards/pb/data/rnaaqsl_asti.pdf

U.S. Environmental Protection Agency. (2006a). Review of the National Ambient Air Quality Standards for Lead: 

Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper – First Draft. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. EPA-452/P-06-002. Available online at: 

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

U.S. Environmental Protection Agency. (2006c) Analysis Plan for Human Health and Ecological Risk Assessment 

for the Review of the Lead National Ambient Air Quality Standards. Office of Air Quality Planning and 
 1-10

Standards, Research Triangle Park, NC. Available online at: 

http://www.epa.gov/ttn/naaqs/standards/pb/s_pb_cr_pd.html

U.S. Environmental Protection Agency. (2007a). Review of the National Ambient Air Quality Standards for Lead: 

Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. EPA-452/R-07-013. Available online at: 

http://www.epa.gov/ttn/naaqs/standards/pb/s_pb_cr_sp.html 

U.S. Environmental Protection Agency. (2007b). Lead: Human Exposure and Health Risk Assessments for Selected 

Case Studies (Draft Report). Volume I. Human Exposure and Health Risk Assessments – Full-scale. and 

Volume II. Appendices. Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA452/D-07-001a. and EPA-452/D-07-001b. Available online at: 

http://www.epa.gov/ttn/naaqs/standards/pb/s_pb_cr_td.html
 2-1

2 DESIGN OF EXPOSURE AND RISK ASSESSMENTS 

The risk assessment design relies on the use of case studies. The types of case studies 

included, as well as the analytical aspects of the assessment of each, reflect consideration of the 

evidence presented in the CD, air quality analyses, and findings of the pilot assessment (Section 

1.3), as well as comments received from CASAC (Section 1.4) and the public. 

Drawing primarily from the CD, Section 2.1 provides background for the risk 

assessment, with regard to key elements of Pb exposure and effects. The assessment scenarios 

evaluated in the assessment are described in Section 2.2. Background information on the three 

case studies is described in Section 2.3. Section 2.4 describes the analytical approach, with 

attention to key analytical steps, and discussion of temporal and spatial aspects of the 

assessment, as well as the categorization of policy-relevant exposure pathways, and the 

uncertainty characterization. 

2.1 BACKGROUND INFORMATION ON LEAD EXPOSURE AND RISK 

As recognized in Section 1.1, there are a variety of complexities associated with the 

assessment of air-related Pb exposure and risk. In this risk assessment, we have attempted to 

focus effort on those aspects that are most important and feasible to address within our scope and 

given the constraints of time, pertinent data, models, etc. With regard to some aspects, 

simplifying assumptions have been implemented. The following subsections describe elements 

of Pb exposure and effects pertinent to evaluating public health risks associated with Pb from 

ambient air, and specify those that are explicitly addressed in this quantitative risk assessment. 

This is summarized in Figure 2-1, with boxes outlined in bold indicating items included in the 

quantitative risk assessment and sources and pathways for which ambient air has played a role 

identified in bold text. 
 2-2

Figure 2-1. Conceptual model for Pb human health risk assessment. 

 

Note: Boxes outlined in bold are included in the quantitative risk assessment. Sources and pathways for 

which ambient air has played a role are in bold text. 
 2-3

2.1.1 Sources, Pathways and Routes 

As described in Section 1.1, for the purposes of this assessment, policy-relevant sources 

(in Figure 2-1 in bold type) include both sources of new Pb emissions (e.g., from active 

stationary and mobile sources) and re-emission or resuspension of historically deposited Pb (e.g., 

near roadways or associated with now inactive, or now lower emitting stationary sources, as 

discussed in Appendix A, Section A.1.1.3). 

There are more than 13,000 individual sources in the U.S. for which we have estimated 

Pb emissions to the air (Appendix A, Section A.1.2). Cumulatively, those sources, in addition to 

mobile sources and other sources not individually quantified, emit some 1600 tons per year (tpy) 

of Pb in the U.S. (Appendix A). The largest categories (in terms of aggregate national 

emissions) include mobile sources (specifically combustion of leaded general aviation fuel), 

boilers and process heaters, and metals processes, such as primary and secondary Pb smelting. 

Of these, metals processing industries are among the largest emitters of Pb, in terms of emissions 

from individual facilities (Appendix A, Section A.1). Another potentially large category of Pb 

emissions, for which we do not have quantitative estimates in our national emissions inventory, 

is resuspension of recent and historically deposited Pb (Appendix A, Section A.1.1.3; CD, 

Section 2.3.3). Studies of emissions in southern California suggest that Pb in resuspended road 

dust may represent up to 40%-90% of Pb emissions in some areas (Appendix A, Section 

A.1.1.3). Resuspension is represented to differing degrees in the three case studies included in 

the risk assessment (Sections 2.2.1 and 2.2.2) 

Lead in outdoor dust and soil may be derived from a range of sources including current 

and historical air emissions sources, as well as miscellaneous nonair sources (e.g., land disposal 

of wastes and subsequent weathering). Outdoor dust and soil may play a substantial role in 

human exposures, particularly for children (CD, Section 3.2). Additionally, Pb in house dust, 

which may be derived from Pb in outdoor dust and soil as well as from ambient air Pb (including 

previously deposited Pb resuspended into ambient air), is another source of children’s exposure 

(CD, Sections 3.2 and 4.4). For example, blood Pb levels in children have been shown to be 

particularly influenced by exposures to Pb in dust (e.g., Lanphear and Roghmann 1997; 

Lanphear et al., 1998). Such findings and “other studies of populations near active sources of air 

emissions (e.g., smelters), substantiate the effect of airborne Pb and resuspended soil Pb on 

interior dust and blood Pb” (CD, p. 8-22). 

In addition to airborne emissions (recent or those in the past), sources of Pb to the 

environment or to human exposure included old leaded paint, Pb in drinking water and Pb in the 

diet (Figure 1-1). As mentioned in Section 1.1, Pb in diet and drinking water may have air 

pathway related (i.e., policy-relevant) contributions as well as contributions from policy-relevant 
 2-4

background (e.g., Pb solder on water distribution pipes and Pb in materials used in food 

processing). Limitations in our data and modeling tools have handicapped our ability to separate 

these contributions in the risk assessment, such that we have labeled diet and drinking water 

policy-relevant background. Consequently, these sources of Pb exposure are depicted in Figure 

2-1 as policy-relevant background (in nonbold type), although this is not intended to convey any 

particular policy decision at this stage regarding the Pb standard. Policy-relevant pathways (bold 

text in Figure 2-1) include inhalation of newly or previously emitted Pb, ingestion of outdoor 

soil/dust containing previously deposited Pb, and ingestion of indoor dust containing newly or 

previously emitted Pb. 

Human exposure to environmental Pb occurs predominantly via ingestion and inhalation 

routes, with ingestion (including incidental ingestion of dust and soil) recognized as generally 

playing a larger role for the general human population (CD, Section 4.5). The dermal route is 

relatively less well characterized but is not considered to play a large role in total Pb exposure 

(CD, Section 4.5), and is not included in this assessment (Figure 2-1). 

2.1.2 At-risk Populations 

In considering populations for inclusion in the risk assessment, we considered evidence 

regarding those with increased susceptibility (i.e., physiological factors contributing to a greater 

response for the same exposure), and those with increased exposure (including that resulting 

from behavior leading to increased contact with contaminated media). A behavioral factor of 

great impact on Pb exposure is the incidence of hand-to-mouth activity that is prevalent in very 

young children (CD, Section 4.4.3). Physiological factors include both conditions contributing 

to a subgroup’s increased risk of effects at a given blood Pb level, and those that contribute to 

blood Pb levels higher than those otherwise associated with a given Pb exposure (CD, Section 

8.5.3). An additional population characterization for which evidence was considered was 

vulnerability to pollution-related effects which additionally encompasses situations of elevated 

exposure, such as residing in old housing with Pb-containing paint or near sources of ambient 

Pb, as well as socioeconomic factors, such as reduced access to health care or low 

socioeconomic status (SES) (USEPA, 2003, 2005) that can contribute to increased risk of 

adverse health effects from Pb. 

Three particular physiological factors contributing to increased risk of Pb effects at a 

given blood Pb level are recognized in the CD (e.g., CD, Section 8.5.3): age, health status, and 

genetic composition (or genotype). With regard to age, the susceptibility of young children to 

the neurodevelopmental effects of Pb is well recognized (e.g., CD, Sections 5.3, 6.2, 8.4, 8.5, 

8.6.2), although the specific ages of vulnerability have not been established (CD, pp 6-60 to 6-

64). Early childhood may also be a time of increased susceptibility for Pb immunotoxicity (CD, 
 2-5

Sections 5.9.10, 6.8.3 and 8.4.6), and childhood exposures have been associated with increased 

risk of cardiovascular and neurodegenerative effects in adulthood (CD, p. 8-74). Health status is 

another physiological factor in that subpopulations with pre-existing health conditions may be 

more susceptible (as compared to the general population) for particular Pb-associated effects, 

with this being most clear for renal and cardiovascular outcomes. For example, African 

Americans as a group, have a higher frequency of hypertension than the general population or 

other ethnic groups (NCHS, 2005), and as a result may face a greater risk of adverse health 

impact from Pb-associated cardiovascular effects. A third physiological factor relates to genetic 

polymorphisms. That is, subpopulations defined by particular genetic polymorphisms (e.g., 

presence of the δ-aminolevulinic acid dehydratase-2 [ALAD-2] allele) have also been recognized 

as sensitive to Pb toxicity, which may be due to increased susceptibility to the same internal dose 

and/or to increased internal dose associated with same exposure (CD, p. 8-71, Sections 6.3.5, 

6.4.7.3 and 6.3.6). 

Several physiological factors pertain to susceptibility by contributing to increased blood 

Pb levels (i.e., increased internal dose levels) over those otherwise associated with a given Pb 

exposure (CD, Section 8.5.3). These include nutritional status, which plays a role in Pb 

absorption from the GI tract (CD, Section 5.10.2.5); polymorphism for the vitamin D receptor, 

which studies suggest may contribute to increased Pb absorption from the GI tract (CD, Section 

8.4.2.7); presence of the ALAD-2 allele, which studies suggest contribute to increased blood Pb 

levels (Section 8.5.3); and bone demineralization, such as occurs during pregnancy, lactation, 

and aging, which appears to influence Pb release from bone into the blood (CD, Section 4.3.2). 

In summary, there are a variety of ways in which Pb exposed populations might be 

characterized and stratified for the purposes of health risk assessment. Age or lifestage is used to 

distinguish potential groups on which to focus the risk assessment (see Figure 2-1) in recognition 

of its influence on exposure and susceptibility. In consideration of the health effects evidence 

regarding endpoints of greatest public health concern and a recognition of effects on the 

developing nervous system as a sentinel endpoint for public health impacts of Pb (see Section 

2.1.4), young children have been selected as the priority population for this risk assessment (see 

Figure 2-1). We recognize, however, other population subgroups as described above may also 

be at risk of Pb-related health effects of public health concern at similar or higher exposures. As 

currently available data do not generally support quantitative modeling that differentiates blood 

Pb levels and associated health risk within a particular population group such as young children 

on the basis of enhanced or reduced susceptibility to Pb effects (e.g., concentration response 

functions for IQ loss that differentiate between populations that are calcium deficient and those 

that are not), the assessment does not develop separate risk estimates for such subpopulations of 
 2-6

young children. In the risk assessment, however, interindividual variability in blood Pb is 

quantitatively considered (Section 3.2.3). 

2.1.3 Internal Disposition 

Once inhaled or ingested and absorbed into the blood stream, Pb is distributed throughout 

the body via the blood, with bone being the predominant site of Pb accumulation and storage in 

the body (CD, Sections 4.2 and 4.3). Additionally, the epidemiologic evidence indicates that Pb 

freely crosses the placenta resulting in continued fetal exposure throughout pregnancy, and that 

exposure increases during the later half of pregnancy (CD, Section 6.6.2). 

During childhood development, bone represents approximately 70% of a child’s body 

burden of Pb, and this accumulation continues through adulthood, when more than 90% of the 

total Pb body burden is stored in the bone (CD, Section 4.2.2). Accordingly, levels of Pb in bone 

are indicative of a person’s long-term, cumulative exposure to Pb. In contrast, blood Pb levels 

are usually indicative of recent exposures. Depending on exposure dynamics, however, blood Pb 

may – through its interaction with bone - be indicative of past exposure or of cumulative body 

burden (CD, Section 4.3.1.5). 

Throughout life, Pb in the body is exchanged between blood and bone, and between 

blood and soft tissues (CD, Section 4.3.2), with variation in these exchanges reflecting “duration 

and intensity of the exposure, age and various physiological variables” (CD, p. 4-1). For 

example, resorption of bone (e.g., in pregnant or nursing women, and associated with 

osteoporosis in postmenopausal women or, to a lesser magnitude, in older men) results in a 

mobilization of Pb from bone into circulation (CD, Sections 4.3.2.4 and 4.3.2.5). Past exposures 

that contribute Pb to the bone, consequently, may influence current levels of Pb in blood. Where 

past exposures were elevated in comparison to recent exposures, this influence may complicate 

interpretations with regard to recent exposure (CD, Sections 4.3.1.4 to 4.3.1.6). That is, higher 

blood Pb concentrations may be indicative of higher cumulative exposures or of a recent 

elevation in exposure (CD, pp. 4-34 and 4-133). 

Bone measurements, as a result of the generally slower Pb turnover in bone, are 

recognized as providing a better measure of cumulative Pb exposure (CD, Section 8.3.2). The 

bone pool of Pb in children, however, is thought to be much more labile than that in adults due to 

the more rapid turnover of bone mineral as a result of growth (CD, p. 4-27). As a result, changes 

in blood Pb concentration in children more closely parallel changes in total body burden (CD, 

pp. 4-20 and 4-27). This is in contrast to adults, whose bone has accumulated decades of Pb 

exposures (with past exposures often greater than current ones), and for whom the bone may be a 

significant source long after exposure has ended (CD, Section 4.3.2.5). 
 2-7

Given the association with exposure, particularly recent exposure, and the relative ease of 

collection, blood Pb levels are extensively used as an index or biomarker of exposure by national 

and international health agencies (CD, Section 4.3.1.5). Although recent methods are making 

bone Pb measurements easier to collect (CD, Section 4.3.2.2) and consequently, their use more 

widespread, epidemiological and toxicological studies of Pb health effects and dose-response 

relationships tend to be dominated by blood Pb as the exposure metric (CD, Sections 4.3.1.3, 

8.3.2 and Chapter 5). 

Accordingly, blood Pb level is the index of exposure or exposure metric in this risk 

assessment. The use of concentration-response functions that rely on blood Pb (e.g., rather than 

ambient Pb concentration) as the exposure metric reduces uncertainty in the causality aspects of 

Pb risk estimates. The relationship between specific sources and pathways of exposure and 

blood Pb level is needed, however, in order to identify the specific risk contributions associated 

with those sources and pathways of greatest interest to this assessment (i.e., those related to Pb 

emitted into the air). For example, the blood Pb-response relationships developed in 

epidemiological studies of Pb-exposed populations do not distinguish among different sources or 

pathways of Pb exposure (e.g., inhalation, ingestion of indoor dust, ingestion of dust containing 

leaded paint). In the exposure assessment for this review, models that estimate blood Pb levels 

associated with Pb exposure (e.g., CD, Section 4.4) are used to inform estimates of contributions 

to blood Pb arising from ambient air related Pb versus contributions from other sources. 

2.1.4 Health Endpoints 

Lead has been demonstrated to exert “a broad array of deleterious effects on multiple 

organ systems via widely diverse mechanisms of action” (CD, p. 8-24 and Section 8.4.1). This 

array of health effects, the evidence for which is comprehensively described in the CD, includes 

• Heme biosynthesis and related functions; 

• Neurological development and function; 

• Reproduction and physical development; 

• Kidney function; 

• Cardiovascular function; and, 

• Immune function. 

There is also some evidence of Pb carcinogenicity, primarily from animal studies, with limited 

human evidence of suggestive associations (CD, Sections 5.6.2, 6.7, and 8.4.10). 1 

 

1

 Lead has been classified as a probable human carcinogen by the International Agency for Research on 

Cancer, based mainly on sufficient animal evidence, and as reasonably anticipated to be a human carcinogen by the 
 2-8

This review is focused on those effects most pertinent to ambient exposures. Given the 

reductions in ambient Pb levels over the past 30 years, these effects are generally those 

associated with the lowest Pb levels of exposure. These are neurological, hematological and 

immune effects for children, and neurological, hematological, cardiovascular and renal effects 

for adults (CD, Tables 8-5 and 8-6), with neurological effects in children and cardiovascular 

effects in adults appearing to be of greatest public health concern (CD, p. 8-60). The 

toxicological and epidemiological information available since the time of the last review 

“includes assessment of new evidence substantiating risks of deleterious effects on certain health 

endpoints being induced by distinctly lower than previously demonstrated Pb exposures indexed 

by blood Pb levels extending well below 10 μg/dL in children and/or adults” (CD, p. 8-25). The 

CD indicates some health effects associated with blood Pb levels that extend below 5 μg/dL, 

with some studies observing these effects at the lowest blood levels considered (i.e., threshold 

levels for these effects cannot be discerned from the currently available studies). 

The endpoints identified above are important considerations for this review and are 

described briefly in sections below, with detailed discussion of the evidence presented in the CD. 

Of these health endpoints, the focus of the quantitative health risk assessment is developmental 

neurotoxicity in children, with IQ decrement as the risk metric (Figure 2-1), described in Section 

2.1.5 

2.1.4.1 Developing Nervous System 

The nervous system has long been recognized as a target of Pb toxicity, with the 

developing nervous system affected at lower exposures than the mature system (CD, Sections 

5.3, 6.2.1, 6.2.2, and 8.4). While blood Pb levels in U.S. children ages one to five years have 

decreased notable since the late 1970s, newer studies have investigated and reported associations 

of effects on the neurodevelopment of children with these more recent blood Pb levels (CD, 

Chapter 6). Functional manifestations of Pb neurotoxicity during childhood include sensory, 

motor, cognitive and behavioral impacts. Numerous epidemiological studies have reported 

neurocognitive, neurobehavioral, sensory, and motor function effects in children at blood Pb 

levels below 10 μg/dL (CD, Section 6.2). Further, “extensive experimental laboratory animal 

evidence has been generated that (a) substantiates well the plausibility of the epidemiologic 

findings observed in human children and adults and (b) expands our understanding of likely 

mechanisms underlying the neurotoxic effects” (CD, p. 8-25; Section 5.3). 

 

U.S. National Toxicology Program (CD, Section 6.7.2). U.S. EPA classified it in the past as a probable carcinogen 

(http://www.epa.gov/iris/subst/0277.htm). 
 2-9

Cognitive effects associated with Pb exposures that have been observed in 

epidemiological studies have included decrements in intelligence test results, such as the widely 

used IQ score, and in academic achievement as assessed by various standardized tests as well as 

by class ranking and graduation rates (CD, Section 6.2.16 and pp 8-29 to 8-30). As noted in the 

CD with regard to the latter, “Associations between Pb exposure and academic achievement 

observed in the above-noted studies were significant even after adjusting for IQ, suggesting that 

Pb-sensitive neuropsychological processing and learning factors not reflected by global 

intelligence indices might contribute to reduced performance on academic tasks” (CD, pp 8-29 to 

8-30). 

Other cognitive effects observed in studies of children have included effects on attention, 

executive functions, language, memory, learning and visuospatial processing (CD, Sections 

5.3.5, 6.2.5 and 8.4.2.1), with attention and executive function effects associated with Pb 

exposures indexed by blood Pb levels below 10 μg/dL (CD, Section 6.2.5 and pp. 8-30 to 8-31). 

The evidence for the role of Pb in this suite of effects includes experimental animal findings 

(discussed in CD, Section 8.4.2.1; p. 8-31), which provide strong biological plausibility of Pb 

effects on learning ability, memory and attention (CD, Section 5.3.5), as well as associated 

mechanistic findings. Further, Pb-induced deficits observed in animal and epidemiological 

studies, for the most part, have been found to be persistent in the absence of markedly reduced 

environmental exposures (CD, Sections 5.3.5, 6.2.11, and 8.5.2). It is additionally important to 

note that there may be long-term consequences of such deficits over a lifetime. Poor academic 

skills and achievement can have "enduring and important effects on objective parameters of 

success” later in life, as well as increased risk of antisocial and delinquent behavior (CD, 

Section 6.2.16). 

Other neurological effects associated with Pb exposures indexed by blood Pb levels near 

or below 10 μg/dL include behavioral effects, such as delinquent behavior (CD, Sections 6.2.6 

and 8.4.2.2), sensory effects, such as those related to hearing and vision (CD, Sections 6.2.7, 

7.4.2.3 and 8.4.2.3), and deficits in neuromotor function (CD, p. 8-36). 

Neurocognitive impact, specifically decrement in IQ in young children, is a focus of the 

quantitative risk assessment due to the strength of evidence for association with blood Pb levels 

below 10 μg/dL, and the strength of the dose-response information at these exposure levels. 

As discussed in the CD (Section 8.4.2) and by Rice (1996), while there is no direct 

animal test parallel to human IQ tests, “in animals a wide variety of tests that assess attention, 

learning, and memory suggest that Pb exposure {of animals} results in a global deficit in 

functioning, just as it is indicated by decrements in IQ scores in children” (CD, p. 8-27). The 

animal and epidemiological evidence for this endpoint are consistent and complementary (CD, p. 

8-44). As stated in the CD (p. 8-44): 
 2-10

Findings from numerous experimental studies of rats and of nonhuman primates, as 

discussed in Chapter 5, parallel the observed human neurocognitive deficits and the 

processes responsible for them. Learning and other higher order cognitive processes 

show the greatest similarities in Pb-induced deficits between humans and experimental 

animals. Deficits in cognition are due to the combined and overlapping effects of Pbinduced perseveration, inability to inhibit responding, inability to adapt to changing 

behavioral requirements, aversion to delays, and distractibility. Higher level 

neurocognitive functions are affected in both animals and humans at very low exposure 

levels (<10 μg/dL), more so than simple cognitive functions. 

Further, “epidemiologic studies of Pb and child development have demonstrated inverse 

associations between blood Pb concentrations and children’s IQ and other outcomes at 

successively lower Pb exposure levels” over the past 30 years (CD, p. 6-64). This is supported 

by multiple studies performed over the past 15 years (see CD, Section 6.2.13), with particularly 

compelling evidence for decrements in IQ at blood Pb levels below 10 μg/dL provided by a 

recent international pooled analysis of seven prospective studies (Lanphear et al., 2005; CD, 

Section 6.2.13). For example, this pooled analysis estimated a decline of 6.2 points (with a 95% 

confidence interval bounded by 3.8 and 8.6) in full scale IQ occurring between approximately 1 

and 10 μg/dL blood Pb level, measured concurrent with the IQ test (CD, p. 6-76). This analysis 

(Lanphear et al., 2005) is relied upon in the quantitative risk assessment for this endpoint. 

2.1.4.2 Adult Nervous System 

The nervous system has long been recognized as a target of Pb toxicity (CD Sections 

5.3.1, 8.4.2). For example, those chronically exposed in the workplace are at risk for various 

neurological effects including peripheral sensory nerve impairment, visuomotor and memory 

impairment, and postural sway abnormalities, with a blood Pb concentration >14 μg/dL being a 

possible threshold (CD, p. 6-87). Past occupational exposure also increases the risk of 

developing amyotrophic lateral sclerosis and motor neuron disease (CD, Section 6.3.5 and p. 6-

87). Essential tremor is also associated with Pb exposures, particularly for those with genetic 

susceptibility (CD, Sections 6.3.5 and 6.3.6 and p. 6-86). 

In elderly populations, significant associations have been reported between bone Pb 

levels and impaired cognitive performance or dysfunction (CD, Section 6.3.3 and 6.3.3.1), but 

not with blood Pb levels, perhaps indicating a role of cumulative and/or past Pb exposures (CD, 

p. 6-83). During demineralization of bone in the elderly, Pb may be released into the blood, thus 

augmenting blood Pb associated with current ambient exposures (CD, Section 4.3.2.4). An 

increased susceptibility among the elderly to Pb effects on cognitive function is supported by 

animal evidence (Section 5.3.7). With lifetime exposure, senescent animals have exhibited an 
 2-11

increased susceptibility to Pb, due to the increased exposure from bone resorption, and an 

apparently greater sensitivity to the biochemical effects of Pb (CD, Section 5.3.7). Laboratory 

animal research in rats and monkeys also indicates a potential for cognitive function effects in 

the elderly to be related to physiological effects (regulation of protein thought to play a role in 

Alzheimer’s disease) of Pb exposures in early childhood (CD, p. 5-67; Basha et al., 2005). Thus, 

early life exposure to Pb may contribute to neurocognitive effects later in life due to the 

redistribution of Pb body burden from bone to brain and by enhanced susceptibility caused by 

age-related degenerative changes in various organs, including brain (CD, p. 8-40). 

2.1.4.3 Cardiovascular System 

Epidemiologic and experimental toxicology studies provide strong support for the 

relationship between Pb exposure and increased adverse cardiovascular outcomes, including 

increased blood pressure, increased incidence of hypertension, and cardiovascular morbidity and 

mortality (CD, Sections 5.5, 6.5 and 8.4.3). The cardiovascular effect most frequently examined 

in epidemiological studies is increased systolic blood pressure in adults, which has been 

repeatedly associated with Pb exposure (CD, Sections 8.4.3, 8.6.3, 6.5.2.3, and 6.5.7). The 

association has been observed with Pb levels in bone and also, in some cohorts, with Pb in blood 

(including blood Pb levels below 10 μg/dL). A recent meta-analysis by Nawrot and others 

(2005), that included a range of blood Pb levels from 2.3 to 63.8 μg/dL, reported an association 

of increased systolic blood pressure and decreased diastolic pressure with increased blood Pb 

level, including levels below 10 μg/dL. The magnitude of change observed has considerable 

significance at the population level (CD, p. 8-45, Section 8.6.3). The epidemiological evidence 

is supported by evidence in numerous animal studies of arterial hypertension with low Pb 

exposures, an effect that persists in animals long after cessation of exposure (CD, Sections 5.5 

and 8.4.3). 

Multiple studies reporting positive associations of blood pressure and hypertension with 

bone Pb levels highlight the important role of cumulative past Pb exposure in development of 

cardiovascular health effects (Sections 6.5.2.3 and 6.5.7). A study of young adults who lived as 

children in an area of high Pb exposures also indicates the potential role of childhood exposure. 

In this study, higher bone Pb levels were associated with higher systolic and diastolic blood 

pressure (CD, p. 6-138), while current blood Pb levels (mean of 2.2 μg/dL) were not (CD, p. 6-

124). 

Systolic blood pressure exerts a strong influence on more serious cardiovascular events 

by its role in hypertension and its adverse cardiovascular sequelae (CD, p. 8-83). Several 

analyses of National Health and Nutrition Examination Survey (NHANES) cohorts, including 

some recently released, have collectively suggested a “significant effect of Pb on cardiovascular 
 2-12

mortality in the general U.S. population” (CD, p. 8-88, Sections 6.5.3.2 and 8.6.3). For example 

recent analyses of NHANES blood Pb data from 1976 to 1980 and 1988 to 1994 provide 

supportive evidence for an increased risk of cardiovascular mortality, consistent with projected 

likely increases in serious cardiovascular events (stroke, heart attack) resulting from Pb-induced 

increases in blood pressure (CD, Section 8.6.3). 

2.1.4.4 Renal System 

Lead nephrotoxicity is mediated by alterations in the glomerular filtration rate (CD, 

Sections 5.7.3 and 8.4.5). The interaction of Pb with the kidney, including occurrences and 

mechanisms of Pb uptake by and accumulation in the kidney, and associated cellular alterations, 

is well described in animal research (CD, Section 5.7). A set of screening tests involving 

markers of nephrotoxic effects have been established for screening individuals exposed to Pb 

occupationally or environmentally (CD, Section 5.7.1). In the epidemiological literature, 

associations between blood Pb and indicators of renal function impairment (e.g., measures of 

glomerular integrity, such as creatinine levels in urine) have been found at blood Pb levels 

extending below 10 μg/dL, to as low as ~2 to 4 μg/dL (CD, Sections 6.4.4.1.5 and 8.4.5). 

Associations are also observed with cumulative Pb dose, assessed via bone Pb, and longitudinal 

renal function decline (CD, p. 6-94), indicating the potential role of earlier exposures. 

The findings for non-occupational populations since the last review provide “strong 

evidence that renal effects occur at much lower blood Pb levels than previously recognized” 

(CD, p. 6-113). These findings of lower Pb renal effects thresholds in environmental compared 

to occupational research may be a result of potentially larger proportions of susceptible 

individuals in the general population as compared to occupational cohorts (CD, p. 6-107). The 

data available are not sufficient to determine whether these effects are related more to current 

blood-Pb levels, higher levels from past exposure, or both (CD, p. 8-49). 

The findings regarding Pb exposures and renal effects are of particular concern with 

regard to certain susceptible subpopulations. At levels of exposure in the general U.S. 

population overall, Pb combined with other risk factors, such as diabetes, hypertension, or 

chronic renal insufficiency from causes unrelated to Pb, can result in clinically relevant effects. 

Notably, the size of such susceptible populations is increasing in the United States due to obesity 

(CD, p. 6-113). That is, Pb is recognized as acting cumulatively with other renal risk factors to 

cause early onset of renal insufficiency and/or a steeper rate of renal function decline in 

individuals already at risk for renal disease (CD, p. 6-107). 

2.1.4.5 Heme Synthesis 

It has long been recognized that Pb exposure is associated with disruption of heme 

synthesis in both children and adults. The evidence regarding effects on heme synthesis and 
 2-13

other hematological parameters in animal and humans is strong, and includes documented 

quantitative relationships between exposure and effects in children and adults. Interference with 

heme synthesis was identified as one of the targets of low-level Pb toxicity in children during the 

time of the last NAAQS review (USEPA, 1990), and was the primary focus for the initial setting 

of the Pb NAAQS in 1978 (USEPA, 1978). 

Mechanisms associated with Pb interference with heme synthesis include inhibition of 

the enzymes ALAD and ferrochelatase (Table 3-1; CD Sections 5.2.1, 6.9.1, 6.9.2; USEPA 

1986). Inhibition of ALAD has been associated with increased blood Pb concentrations at and 

somewhat below 10 μg/dL, in children and adults (Tables 3-1 and 3-2; CD, Table 6-7). Blood 

Pb concentrations at and above approximately 15 μg/dL, in children, and 15-30 μg/dL, in adults, 

are associated with elevation of erythrocyte protoporphyrin (EP), and notable reductions in 

hemoglobin synthesis (Tables 3-1 and 3-1; CD, p. 8-47; USEPA, 1986). In the setting of the Pb 

NAAQS in 1978, the Agency concluded that “the state of elevated EP must be regarded as 

potentially adverse to the health of young children” (USEPA, 1978). Blood Pb concentrations at 

and above 40 ug/dL are associated with frank anemia, a clinical sign of severe Pb poisoning (CD, 

p. 8-47). The evidence regarding Pb disruption of heme synthesis and associated mechanisms is 

presented in detail in past CDs (USEPA 1986, 1977), with more recent findings, including the 

role of genetic polymorphisms, discussed in the current CD (Sections 8.4.4, 5.2.1, 6.9.1 and 

6.9.2). 

2.1.4.6 Immune System 

Since the time of the last review, there has been substantial research on the 

immunotoxicity of Pb. As summarized in the CD, “studies across humans and a variety of 

animal models are in general agreement concerning both the nature of the immunotoxicity 

induced by Pb as well as the exposure conditions that are required to produce 

immunomodulation” (CD, p. 5-244, Section 5.9). Lead is distinguished from other 

immunotoxicants, however, by the fact that the most sensitive biomarkers of its immunotoxicity 

are associated with specific functional capacities that influence risk of disease, as opposed to 

being associated with changes in immune cell numbers or pathological changes of lymphatic 

system organs (CD, Section 5.9.1). The main immune system targets of Pb are macrophages 

and T lymphocytes, leading to a potential for increased tissue inflammation, reduced cellmediated immunity, and increased risk of autoimmunity (See CD, Figure 5-18, Section 5.9.11). 

Additionally, Pb exposures in both animal and human studies are associated with increased 

production of IgE, an immunoglobulin involved in allergic responses and asthma (CD, Section 

5.9.3.2). These effects have been reported in epidemiologic studies of children, and supported 
 2-14

by evidence in neonatal and juvenile animals, at blood Pb levels extending below 10 μg/dL (CD, 

p. 6-197 and Sections 5.9.10 and 8.4.6). 

2.1.5 Metric and Model for Risk Quantitation 

The health endpoint on which we focused in the quantitative health risk assessment for 

this review is developmental neurotoxicity in children (Section 2.1.4.1), with IQ decrement as 

the risk metric. Among the wide variety of health endpoints associated with Pb exposures, there 

is general consensus that the developing nervous system in young children is the most sensitive 

and that neurobehavioral effects (specifically neurocognitive deficits), including IQ decrements, 

appear to occur at lower blood levels than previously believed (i.e., at levels <10 μg/dL). For 

example, the overall weight of the available evidence, described in the CD, provides clear 

substantiation of neurocognitive decrements being associated in young children with blood Pb 

levels in the range of 5 to 10 μg/dL, and some analyses indicate Pb effects on intellectual 

attainment of young children ranging from 2 to 8 μg/dL (CD, Sections 6.2, 8.4.2 and 8.4.2.6). 

That is, while blood Pb levels in U.S. children ages one to five years have decreased notably 

since the late 1970s, newer studies have investigated and reported associations of effects on the 

neurodevelopment of children with these more recent blood Pb levels (CD, Chapter 6). 

The evidence for neurotoxic effects in children is a robust combination of 

epidemiological and toxicological evidence (CD, Sections 5.3, 6.2 and 8.5). The 

epidemiological evidence is strongly supported by animal studies that substantiate the biological 

plausibility of the associations, and provides an understanding of mechanisms of action for the 

effects (CD, Section 8.4.2). The selection of children’s IQ for the quantitative risk assessment 

reflects consideration of the evidence presented in the CD as well as advice received from 

CASAC (Henderson, 2006, 2007a). 

The epidemiological studies that have investigated blood Pb effects on IQ (see CD, 

Section 6.2.3) have considered a variety of specific blood Pb metrics, including: 1) blood 

concentration “concurrent” with the response assessment (e.g., at the time of IQ testing), 2) 

average blood concentration over the “lifetime” of the child at the time of response assessment 

(e.g., average of measurements taken over child’s first 6 or 7 years), 3) peak blood concentration 

during a particular age range and 4) early childhood blood concentration (e.g., the mean of 

measurements between 6 and 24 months age). All four specific blood Pb metrics have been 

correlated with IQ (see CD, p. 6-62; Lanphear et al., 2005). In the international pooled analysis 

by Lanphear and others (2005), however, the concurrent and lifetime averaged measurements 

were considered “stronger predictors of lead-associated intellectual deficits than was maximal 

measured (peak) or early childhood blood lead concentrations,” with the concurrent blood Pb 

level exhibiting the strongest relationship (CD, p. 6-29). It is not clear in this case or for similar 
 2-15

findings in other studies, whether the cognitive deficits observed were due to Pb exposure that 

occurred during early childhood or were a function of concurrent exposure. Nevertheless, 

concurrent blood Pb levels likely reflected both ongoing exposure and preexisting body burden 

(CD, p. 6-32). 

Using concurrent blood Pb level as the exposure metric and IQ as the response from the 

pooled dataset of seven international studies, Lanphear and others (2005) employed 

mathematical models of various forms, including linear, cubic spline, log-linear, and piece-wise 

linear, in their investigation of the blood Pb concentration-response relationship (CD, p. 6-29; 

Lanphear et al., 2005). They observed that the shape of the concentration-response relationship 

is nonlinear and the log-linear model provides a better fit over the full range of blood Pb 

measurements than a linear one (CD, p. 6-29 and pp. 6-67 to 6-70; Lanphear et al., 2005). In 

addition, they found that no individual study among the seven was responsible for the estimated 

nonlinear relationship between Pb and deficits in IQ (CD p. 6-30). Others have also analyzed the 

same dataset and similarly concluded that, across the range of the dataset’s blood Pb levels, a 

log-linear relationship was a significantly better fit than the linear relationship (p=0.009) with 

little evidence of residual confounding from included model variables (CD, Section 6.2.13; 

Rothenberg and Rothenberg, 2005). 

A nonlinear blood Pb concentration-response relationship is also suggested by several 

other studies that have observed that each μg/dL increase in blood Pb may have a greater effect 

on IQ at blood Pb levels below 10 μg/dL than at higher levels (CD, pp. 8-63 to 8-64). While this 

may at first seem at odds with certain fundamental toxicological concepts, a number of examples 

of non- or supralinear dose-response relationships exist in toxicology (CD, pp. 6-76 and 8-83 to 

8-89). 2 With regard to the effects of Pb on neurodevelopmental outcome such as IQ, the CD 

suggests that initial neurodevelopmental effects at lower Pb levels may be disrupting very 

different biological mechanisms (e.g., early developmental processes in the central nervous 

system) than more severe effects of high exposures that result in symptomatic Pb poisoning and 

frank mental retardation (CD, p. 6-76). In comparing across the individual studies and the 

pooled analysis, it is observed that at higher blood Pb levels, the slopes derived for log-linear and 

linear models are almost identical, and for studies with lower blood Pb levels, the slopes appear 

to be steeper than those observed in studies involving higher blood Pb levels (CD, p. 8-78, 

Figure 8-7). 

Given the evidence summarized here and described in detail in the CD (Chapters 6 and 

8), and in consideration of CASAC recommendations (Henderson, 2006, 2007a, 2007b), the risk 

 

2

 Similarly, a nonlinear concentration-response relationship was observed for the relationship between 

blood Pb levels and blood pressure in adults (CD, pp. 8-83 to 8-89). 
 2-16

assessment for this review relies on the functions presented by Lanphear and others (2005) that 

relate absolute IQ as a function of concurrent blood Pb or of the log of concurrent blood Pb, and 

lifetime average blood Pb, respectively. As discussed above, the slope of the concentrationresponse relationship described by these functions is greater at the lower blood Pb levels (e.g., 

less than 10 μg/dL). The impact of the nonlinear slope is illustrated by the estimates of IQ 

decrements associated with increases in blood IQ for different ranges of blood Pb level reported 

for the log-linear model (Lanphear et al., 2005). These estimates were IQ decrements of 3.9 

(with 95% confidence interval, CI, of 2.4-5.3), 1.9 (95% CI, 1.2-2.6) and 1.1 (95% CI, 0.7-1.5), 

for increases in concurrent blood Pb from 2.4 to 10 μg/dL, 10 to 20 μg/dL, and 20 to 30 μg/dL, 

respectively (Lanphear et al., 2005). For an increase in concurrent blood Pb levels from <1 to 10 

ug/dL, the log-linear model estimates a decline of 6.2 points in full scale IQ which is comparable 

to the 7.4 point decrement in IQ for an increase in lifetime mean blood Pb levels up to 10 ug/dL 

observed in the Rochester study (CD, pp 6-30 to 6-31). 

Several studies have examined the relationship of IQ decrement with blood Pb, quantified 

by a variety of metrics, at lower blood Pb levels. On a change in IQ per μg/dL basis, estimates of 

IQ decrement associated with blood Pb levels (concurrent, 24-month, peak, lifetime average or 

lifetime cumulative) below 10 μg/dL range from -0.4 to -1.8 (CD, Table 8.7). At the upper end 

of this range are the slopes derived for the subsets of children in the Rochester and Boston 

cohorts for which peak blood Pb levels were <10 μg/dL; these slopes are -1.8 (for concurrent 

blood Pb influence on IQ) and -1.6 (for 24-month blood Pb influence on IQ), respectively. The 

number of children in these low blood Pb subsets of the Rochester and Boston cohorts are 101 

and 48, respectively. A similar stratification of the pooled dataset by Lanphear and others (2005) 

yielded a slope for the linear function of IQ change associated with concurrent blood Pb of -0.8 

for the subset of the children in the pooled data set for which maximal or peak blood Pb levels 

were below 10 μg/dL. Of the 1333 children in the full pooled dataset, there were 244 in this 

subset. When the full dataset was restricted to a still smaller subset of 103 children for which 

peak blood Pb levels were below 7.5 μg/dL the slope of concurrent blood Pb and IQ was -2.94 

(Lanphear et al., 2005). The analysis of this latter subset supported the authors’ conclusions that 

“for a given increase in blood lead, the lead-associated intellectual decrement for children with a 

maximal blood lead level <7.5 μg/dL was significantly greater than that observed for those with 

a maximal blood lead level > 7.5 μg/dL (p-0.015)” and that “environmental lead exposure in 

children who have maximal blood lead levels < 7.5 μg/dL is associated with intellectual 

deficits”. This subset was composed primarily of children from the Rochester cohort (69 

children), with smaller numbers of children from five of the other seven cohorts (Lanphear et al., 

2005). The Rochester data included IQ test and concurrent blood Pb measurements taken at age 

six (Lanphear et al., 2005). The linear slope observed for this subset of the pooled dataset, 
 2-17

however, was notably greater than that previously reported for the low blood Pb subset of the 

Rochester cohort at age five described above, and greater than those slopes from other studies for 

blood Pb < 10 μg/dL summarized in the CD (e.g., CD, Table 8-7), providing some uncertainty 

with regard to the precise magnitude of slope for the full range of blood Pb below 7.5 μg/dL. 

As discussed in the CD, threshold blood Pb levels for these effects cannot be discerned 

from the currently available epidemiological studies, and the evidence in the animal Pb 

neurotoxicity literature does not define a threshold for any of the toxic mechanisms of Pb (CD, 

Sections 5.3.7 and 6.2). 

In applying relationships observed with the pooled analysis (Lanphear et al., 2005) to the 

risk assessment, which includes blood Pb levels below the range represented by the pooled 

analysis, several alternative blood Pb concentration-response models were considered in 

recognition of a reduced confidence in our ability to characterize the quantitative blood Pb 

concentration-response relationship at the lowest blood Pb levels represented in the recent 

epidemiological studies. The functions considered and employed in the initial risk analyses for 

this review include the following. 

• Log-linear function with low-exposure linearization, for both concurrent and lifetime 

average blood metrics, applies the nonlinear relationship down to the blood Pb 

concentration representing the lower bound of blood Pb levels for that blood metric in 

the pooled analysis and applies the slope of the tangent at that point to blood Pb 

concentrations estimated in the risk assessment to fall below that level. 

• Log-linear function with cutpoint, for both concurrent and lifetime average blood 

metrics, also applies the nonlinear relationship at blood Pb concentrations above the 

lower bound of blood Pb concentrations in the pooled analysis dataset for that blood 

metric, but then applies zero risk to all lower blood Pb concentrations estimated in the 

risk assessment. 

• Two-piece linear function, for both concurrent and lifetime average blood metrics, 

applies a two-piece linear model derived from the log-linear function to all blood Pb 

concentrations estimated in the risk assessment. 

In the additional risk analyses performed subsequent to the August 2007 CASAC public 

meeting (Section 1.4) using the core modeling approach, the first two functions listed above and 

the following two functions were employed (see Section 5.3.1 of the Risk Assessment Report for 

details on the forms of these functions as applied in this risk assessment). 

• Population stratified dual linear function for concurrent blood Pb, derived from the 

pooled dataset stratified at peak blood Pb of 10 μg/dL and 

• Population stratified dual linear function for concurrent blood Pb, derived from the 

pooled dataset stratified at 7.5 μg/dL peak blood Pb. 
 2-18

In interpreting risk estimates derived using the various functions, consideration should be 

given to the uncertainties with regard to the precision of the coefficients used for each analysis. 

The coefficients for the log-linear model from Lanphear et al. (2005) had undergone a careful 

development process, including sensitivity analyses, using all available data from 1,333 children. 

The shape of the exposure-response relationship was first assessed through tests of linearity, then 

by evaluating the restricted cubic spline model. After determining that the log-linear model 

provided a good fit to the data, covariates to adjust for potential confounding were included in 

the log-linear model with careful consideration of the stability of the parameter estimates. After 

the multiple regression models were developed, regression diagnostics were employed to 

ascertain whether the lead coefficients were affected by collinearity or influential observations. 

To further investigate the stability of the model, a random-effects model (with sites random) was 

applied to evaluate the results and also the effect of omitting one of the seven cohorts on the lead 

coefficient was assessed. In the various sensitivity analyses performed, the coefficient from the 

log-linear model was found to be robust and stable. The log-linear model, however, is not 

biologically plausible at very low blood Pb concentrations as they approach zero; therefore, in 

the first two functions the log-linear model is applied down to a cutpoint, selected based on the 

low end of the blood Pb levels in the pooled dataset, followed by a linearization or an assumption 

of zero risk at levels below that point. 

In contrast, the coefficients from the two analyses using the population stratified dual 

linear function with cutpoints at 7.5 μg/dL and 10 μg/dL, peak blood Pb, have not undergone 

such careful development. These analyses were primarily done to compare the lead-associated 

decrement at lower blood lead concentrations and higher blood lead concentrations. For these 

analyses, the study population was stratified at the specified cutpoint and separate linear models 

were fitted to the data above and below the cutpoint. The fit of the model or sensitivity analyses 

were not conducted (or reported) on these coefficients. While these analyses are quite suitable 

for the purpose of investigating whether the slope at lower concentration levels are greater 

compared to higher concentration levels, use of such coefficients in a risk analysis to assess 

public health impact may be inappropriate. Further, only 103 children had maximal blood lead 

levels less than 7.5 μg/dL and 244 children had maximal blood lead levels less than 10 μg/dL. 

While these children may better represent current blood lead levels, not fitting a single model 

using all available data may lead to bias. Slob et al. (2005) noted that the usual argument for not 

considering data from the high dose range is that different biological mechanisms may play a 

role at higher doses compared to lower doses. However, this does not mean a single curve across 

the entire exposure range cannot describe the relationship. The fitted curve merely assumes that 

the underlying dose-response follows a smooth curve over the whole dose range. If biological 

mechanisms change when going from lower to higher doses, this change will result in a 
 2-19

gradually changing slope of the dose-response. The major strength of the Lanphear et al. (2005) 

study was the large sample size and the pooled analysis of data from seven different cohorts. In 

the case of the 7.5 μg/dL cutpoint, less than 10% of the available data is used in the analysis, 

with more than half of the data coming from one cohort (Rochester) and the six other cohorts 

contributing zero to 13 children to the analysis. Such an analysis dissipates the strength of the 

Lanphear et al. study. 

In consideration of the preceding discussion, we place greater confidence in the log-linear 

model form compared to the dual-linear stratified models for our purposes in this risk 

assessment. Further, in considering risk estimates derived from the four core functions (loglinear function with low-exposure linearization, log-linear function with cutpoint, dual linear 

function, stratified at 7.5 μg/dL peak blood Pb, and dual linear function, stratified at 10 μg/dL 

peak blood Pb), we have assigned greatest confidence to risk estimates derived using the loglinear function with low-exposure linearization since this function (a) is a nonlinear function that 

describes greater response per unit blood Pb at lower blood Pb levels consistent with multiple 

studies identified in the discussion above, (b) is based on fitting a function to the entire pooled 

dataset (and hence uses all of the data in describing response across the range of exposures), (c) 

is supported by sensitivity analyses showing the model coefficients to be robust, and (d) provides 

an approach for predicting IQ loss at the lowest exposures simulated in the assessment 

(consistent with the lack of evidence for a threshold). Note, however, that risk estimates 

generated using the other three concentration-response functions are also presented to provide 

perspective on the impact of uncertainty in this key modeling step. 

2.2 USE OF CASE STUDIES AND LOCATION SELECTIONS 

Consistent with the risk assessment performed during the prior Pb NAAQS review and 

for the pilot phase assessment, the full-scale risk assessment for the current review relies on a 

case study approach. This approach is intended to provide a framework for considering the 

nature and magnitude of Pb exposures associated with air sources and associated risk to human 

health, and for comparing the impact of alternate NAAQS on those exposures and risks. 

With consideration of CASAC comments on the pilot phase assessment (see Section 1.4), 

the design for the full-scale assessment expanded from the pilot assessment by replacing the 

urban near roadway case study with a general, non-location-specific, case study focused on 

population exposures in urban areas (Section 2.2.1), while retaining the two stationary source 

case studies (Sections 2.2.2.1 and 2.2.2.2). The point source case studies were intended to 

illustrate risks associated with Pb near point sources, with the primary Pb smelter case study 

representing one of significant impact on an individual basis, while the general urban case study 

is intended to assess more widespread Pb exposures, due to the large populations in urban areas. 
 2-20

Background information for all three case studies is presented in appendices A and B, and briefly 

summarized in Sections 2.2.1 and 2.2.2, below. 

Consideration of CASAC comments on the draft full-scale assessment (see Section 1.4) 

led to the development of additional case studies which are presented in Chapter 5. 

2.2.1 General Urban Case Study 

In consideration of CASAC comments on the pilot assessment (Henderson, 2007a), a 

general urban case study is included in the assessment. This case study is designed to provide 

estimates of risk associated with the current and alternate NAAQS in urban areas, as well as with 

current conditions. This case study differs from the point source case studies in several ways. 

First it is not based on a specific location. Rather, it is designed to generally represent large 

urban areas in the U.S.3

 Second, as discussed further in subsequent sections (e.g., Sections 2.4.2 

and 3.1.1), the media concentrations are assumed to be spatially uniform throughout the case 

study area (i.e., spatial variation within the area is not considered). Third, the ambient air quality 

for this case study is specified, based on analyses of Pb levels in large urban areas of the U.S. 

(Appendix A and Section 2.3.1), rather than derived from air quality modeling of particular air 

Pb sources. As a result of this third difference, this case study includes different types of 

uncertainties as compared to the case studies employing air quality models, and it does not 

distinguish among the different air Pb sources influencing the air concentrations, be they 

currently active stationary or mobile sources, or resuspension of previously deposited Pb. 

Fourth, the case study does not rely on any specific demographic values; that is, a theoretical 

population of unspecified size is assumed to be uniformly distributed across the study area. All 

of these distinctions of this case study from the others have produced a platform that is a 

simplified representation of urban areas intended to inform our assessment of the impact of 

changes in ambient Pb concentrations on risk. 

2.2.2 Point Source Case Studies 

Based on the Analysis Plan (USEPA, 2006a) and conclusions from the pilot phase risk 

assessment (USEPA, 2006b; ICF, 2006), the point source case studies for this full-scale 

assessment include a study area near a primary Pb smelter, and a second near another, smaller, 

stationary Pb source. The locations for these two case studies were chosen with consideration of 

 

3

 While the air monitoring data used to characterize the current conditions scenario for this case study area 

are from large urban areas, other empirical datasets used in developing this case study, such as those for outdoor 

residential soil Pb and indoor dust Pb, are generally representative of U.S. residential properties (see Sections 

3.1.1.1, 3.1.3.1 and 3.1.4.1). As most U.S. residential properties are in large urban areas because that is where a 

significant share of the U.S. population resides, these datasets will include greater representation by urban areas, 

particularly large ones, than non-urban areas. These datasets, however, are not limited to urban locations. 
 2-21

factors described in the Analysis Plan: (a) availability of site-specific monitoring data for 

ambient air Pb, (b) availability of measurement data for other environmental media (soil and 

indoor dust) and for Pb exposure (i.e., blood Pb levels), and (c) demographic and socioeconomic 

factors related to Pb exposure and risk (USEPA 2006a). 

Both of these case studies rely on air quality modeling to estimate air Pb concentrations 

used in the risk assessment and as a result they include different types of uncertainties as 

compared to those associated with the air concentrations for the general urban case study. One 

of these is related to the representation of resuspension of previously deposited Pb as a source of 

Pb to ambient air. To the extent that emissions estimates are available for this source, this is 

represented in these case studies. However, as recognized in Section 2.1.1 and Appendix A, 

Section A.1.1.3, such emissions estimates are often uncertain or unavailable. Some resuspension 

sources are included in the primary Pb smelter case study, however no such sources are explicitly 

modeled in the secondary Pb smelter case study. 

Background information for these case studies is briefly summarized below with regard 

to: (a) population characteristics, (b) reported emissions, (c) ambient air Pb levels, and (d) the 

availability of site-specific data characterizing levels of Pb in key media and Pb exposures (e.g., 

soil, blood Pb level data). 

2.2.2.1 Primary Pb Smelter Case Study 

The primary Pb smelter case study is focused on the only currently operating facility in 

the U.S., which is located in Herculaneum, Missouri. At primary Pb smelters, Pb-bearing ore 

concentrates are smelted to produce Pb metal. This smelter is one of the largest individual 

sources of Pb metal in the U.S., has been active for over a century and there exist a large amount 

of site-specific data in the surrounding area characterizing both media concentrations (soil, 

indoor dust, outdoor air) and population blood Pb levels (Appendix B, Section B.1). 

The facility’s century of operation has contributed to Pb contamination of the area 

surrounding the facility4

, which has triggered various remediation activities (e.g., removal of Pbcontaminated residential soil) as well as enlargement of the facility property to encompass many 

of the most heavily impacted private properties. The remediation activity introduces a 

complication to the risk modeling, especially aspects involving characterization of the 

relationship of ambient air Pb and residential soil Pb to indoor dust Pb (see Section 3.1.4). 

 

4

 Portions of this study area comprise an active Superfund site and are subject to ongoing evaluation under 

the Superfund program administered by the Office of Solid Waste and Emergency Response. Methods used in 

conducting the human health exposure and risk assessment for the pilot analysis have been selected to address policy 

questions relevant to the Pb NAAQS review and consequently may differ from those used by the Superfund 

program. 
 2-22

Some key aspects of the background information for this case study (Appendix B) are 

summarized briefly in Table 2-1. 
 2-23

Table 2-1. Key aspects of primary Pb smelter case study. 

Population As of the 2000 U.S. Census, 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 μg/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, 2006 and 2007 (Appendix A; AQS). 

The area within the city limits of Herculaneum is designated nonattainment for the Pb 

NAAQS and the existing State Implementation Plan (SIP) was approved in 2002 (67 FR 18497). 

EPA determined the existing SIP to be inadequate to attain and maintain the current NAAQS in 

2006 (71 FR 19432), and consequently the state of Missouri developed a revised SIP for the area. 

U.S. EPA, Region 7 received Missouri’s proposed SIP revision on May 31, 2007 (MDNR, 

2007). The air dispersion modeling performed for the risk assessment described in this 

document built on the information and modeling developed for the revised SIP.5

The significant amount of site-specific data available for Herculaneum, paired with air 

dispersion modeling for the facility conducted in support of SIP development for Pb, provides a 

substantial data set for this study area which enhances the modeling of exposure and risk. For 

example, the Herculaneum facility has more site-specific monitoring data available to support 

risk assessment than the second point source case study location, including residential yard soil, 

indoor dust and road dust Pb measurements collected in areas potentially impacted by the 

facility. In addition, the Agency for Toxic Substances and Disease Registry (ATSDR) has 

conducted a number of health consultations that involved the collection of blood Pb 

measurements for children (Appendix B, Section B.1.3). Extensive air Pb monitoring data are 

also available and were considered in the performance evaluation of the modeling setup. The 

Herculaneum case study location also has a number of attributes that add complexity to the 

modeling of Pb exposure and risk including (a) complex terrain and meteorology which 

 

5

 The 2007 draft SIP revision (MDNR, 2007), including the modeling, is currently under review by EPA. 
 2-24

complicate the modeling of Pb transport in ambient air, (b) a large and complex facility with a 

long history of operation and significant opportunity for fugitive emissions, making source 

characterization challenging, and (c) a history of remediation activities which has contributed to 

widely varying residential soil Pb concentrations across the town. 

2.2.2.2 Secondary Pb Smelter Case Study 

The secondary Pb smelter case study, in Troy, Alabama, involves a smaller point source 

than the primary Pb smelter case study, with relatively less site-specific data characterizing 

media concentrations and exposure levels. Secondary Pb smelters produce Pb from scrap and 

provide the primary means for recycling Pb acid automotive batteries, and are among the larger 

source categories of Pb emitters (see Appendix A, Section A.1). The Troy facility was one of 15 

secondary Pb smelters operating within the U.S. in 2002 (see Appendix B, Section B.2). Some 

key aspects of the background information for this case study (Appendix B) are summarized 

briefly in Table 2-2. 

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

Population As of the 2000 U.S. Census, 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 μg/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). 

In contrast to the Herculaneum facility, we have not identified soil or indoor dust Pb 

measurements for this case study location. Additionally, although there are blood Pb 

measurements in children available at the county level, they are not available at a more refined 

scale that might relate more directly to this case study. The relative sparseness of site-specific 

Pb measurements means that the exposure assessment conducted for the secondary Pb smelter 

case study is more dependent on model projections, and consideration of measurements available 

for similar locations, and that there is less opportunity for rigorous performance evaluation of the 

modeling steps. The available air Pb monitoring data, however, are used in the performance 

evaluation of the air quality modeling. 
 2-25

2.3 ASSESSMENT SCENARIOS 

The design of the scenarios assessed for each case study includes aspects particular to air 

concentrations of Pb (Section 2.3.1), surface soil/dust concentrations of Pb (Section 2.3.3) and 

background (Section 2.3.2). As the scenarios are primarily distinguished by the differences in air 

concentrations, we generally refer to the different assessment scenarios as air quality scenarios in 

this document (including the appendices). The different air quality scenarios include current 

conditions, meeting the current NAAQS of 1.5 μg/m3

 (maximum quarterly average) and meeting 

several alternate, lower NAAQS. 

2.3.1 Air Concentrations 

The air concentrations assessed in the different air quality scenarios include current 

conditions, meeting the current NAAQS of 1.5 μg/m3

 (maximum quarterly average) and meeting 

several alternate, lower NAAQS. In consideration of the range of levels suggested by CASAC 

(Henderson, 2007a), the alternate NAAQS scenarios included in the assessment are: 0.5 μg/m3

, 

0.2 μg/m3 and 0.05 μg/m3 as maximum monthly averages, and 0.2 μg/m3 as a maximum 

quarterly average. In response to discussion at the August 2007 meeting of the CASAC Pb Panel 

to extend lower the range of alternate levels considered, an alternate NAAQS scenario of 0.02 

μg/m3

, as a maximum monthly average, is also included in the additional analyses presented in 

Chapter 5. While the current and alternate NAAQS scenarios are characterized by quarterly or 

monthly averaging times, it is the associated annual average ambient air concentrations that are 

then used in the risk assessment6

. 

The current conditions scenario, performed for the general urban and secondary Pb 

smelter case studies, is intended to generally reflect recent conditions for these case studies based 

on data available for the characterization. For example, for the urban case study, air Pb levels for 

current conditions are based on 2003-2005 air quality data (Appendix A). For the secondary Pb 

smelter case study, for which we used air quality modeling, air Pb levels for current conditions 

are based on emissions characterizations drawn from currently available emissions information 

and recent meteorological data (see Appendix E). 

The current NAAQS attainment air quality scenario was performed for the primary Pb 

smelter case study, for which current monitoring data indicate exceedance of the current Pb 

 

6

 Use of the annual average 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). 
 2-26

NAAQS (Appendix B, Section B.1.5.1).7 Additionally, although we consider it extremely 

unlikely that air concentrations in urban areas across the U.S. that are well below the current 

standard would increase to just meeting the standard, we recognize the potential for air Pb 

concentrations in some areas currently well below the standard to increase to just meet standard 

by way of, for example, expansion of existing sources (e.g., facilities operating as secondary 

smelters exercising previously used capabilities as primary smelters) or by the congregation of 

multiple Pb sources in adjacent locations. Accordingly, we have simulated this type of scenario 

(increased Pb concentrations to just meet the current standard) in the general urban case study 

and in the additional analyses for three location-specific urban case studies described in Chapter 

5. 

In developing the reduced air concentrations for the alternate NAAQS scenarios for the 

point source case studies (for which air quality models are employed), the maximum monthly or 

quarterly average (depending on averaging time for the alternate NAAQS) for each modeled 

receptor point is compared to the NAAQS level to identify the factor by which the highest 

average exceeds the NAAQS level. All monthly or quarterly averages are then reduced by this 

factor (i.e., a proportional roll back is implemented) and the associated annual average 

recalculated for each receptor point. 

Two different current conditions scenarios are assessed for the general urban case study 

based on air Pb concentrations for the period 2003-2005 at monitors in U.S. urban areas with 

population greater than one million (Appendix A).8

 One of these two scenarios is based on the 

mean maximum calendar quarter average for these monitors, and the second is based on the 95th

percentile of maximum calendar quarter averages for these monitors. Additionally, although the 

mean and 95th percentile maximum quarterly average of the large urban area monitors nationally 

do not exceed the current NAAQS level, an increased air concentration scenario (i.e., to the level 

of the current standard) has been included for this case study. 

As the air Pb concentrations for this case study do not vary spatially (see Section 3.1.1.1), 

the air Pb concentration is simply set to the level specified for each scenario as either a monthly 

or quarterly average. The annual average air concentration (the metric used in the dust and blood 

 

7

 Given the status of this area with regard to nonattainment and SIP revision, as well as the use of the 

modeling set-up developed for the SIP attainment demonstration, a current conditions scenario was not developed 

for the primary Pb smelter case study. 

8

 In designing the general urban case study, we recognized CASAC’s interest in our development of risk 

estimates that were more informative to the larger national population and exposures related to ambient air reflecting 

the aggregate contributions from multiple sources of Pb than might be discrete populations near particular point 

sources of Pb (Henderson, 2007a). Urban areas of greater than one million population were identified as 

informative for large populations in established cities (as compared to more newly-developed urban areas). 
 2-27

Pb modeling) is derived from the current conditions or NAAQS level (and averaging time) using 

relationships based on current Pb-TSP monitoring data for monitors in large U.S. urban areas 

(Appendix A). Lead in TSP monitoring data for the time period 2003-2005 from monitors in 

U.S. urban areas of population size greater than one million were analyzed to derive estimates of 

maximum quarterly average, maximum monthly average and annual average for each monitor. 

From these estimates, the ratios of the maximum quarterly and maximum monthly averages to 

the annual average were derived for each monitor and the arithmetic mean and 95th percentile 

value of the monitor-specific ratios were derived. To derive the annual average air concentration 

used in the dust and blood Pb modeling for each scenario, one of these ratios was applied to the 

air quality scenario level. For example, the alternate NAAQS level of 0.5 (for a maximum 

monthly averaging time) was divided by the mean monitor ratio of maximum monthly average to 

annual average to derive the annual average air concentration estimate for that alternate NAAQS 

scenario. The air quality values associated with the different scenarios assessed for the urban 

case study are summarized in Table 2-3. 
 2-28

1 Table 2-3. Air quality scenarios assessed for the general urban case study. 

Air Quality Scenario Level

(μg/m3

)

Averaging Time 

(Form) 

Ratio Associated Annual 

Average Concentration 

(μg/m3)

95th Percentile Current Conditions 0.87 calendar quarter 

(maximum) 

7.6a 0.11 

Mean Current Conditions 0.14 calendar quarter 

(maximum) 

2.5b 0.056 

Alternate NAAQS 0.5 Month 

(maximum) 

4.0c 0.13 

Alternate NAAQS 0.2 Month 

(maximum) 

4.0 c 0.05 

Alternate NAAQS 0.2 calendar quarter 

(maximum) 

2.5 b 0.08 

Alternate NAAQS 0.05 Month 

(maximum) 

4.0 c 0.013 

Current NAAQS 1.5 calendar quarter 

(maximum) 

2.5 b 0.60 

a

 This is the 95th 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. 

b

 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. 

c This 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. 

2 

3 2.3.2 Policy-relevant Background 

4 Given the multimedia, multipathway 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 and windborne 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 

estimated to fall within the range from 0.00002 μg/m3

 and 0.00007 μg/m3 11 based on mass balance 

12 calculations (CD, Section 3.1 and USEPA 1986, Section 7.2.1.1.3). The midpoint in this range, 

0.00005 μg/m3 13 , 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 nonzero value in 

AQS. For the purposes of this assessment, however, the value of 0.00005 μg/m3 19 was selected as 

20 representative of policy-relevant background Pb in air. Unlike for other criteria pollutants, the 
 2-29

1 role of this value for Pb is limited. In considering risk contributions from policy-relevant 

2 background, the contributions from exposures to nonair media are such that any credible estimate 

3 of policy-relevant background in air is likely insignificant in comparison. In developing the air 

4 Pb concentrations associated with the alternate NAAQS scenarios, the estimate of policy5 relevant background in air was the floor below which concentrations were not lowered. 

6 2.3.3 Outdoor Soil/Dust 

With regard to surface soil9 7 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. 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. At any location, the type of response 

12 of the surface soil concentrations to the changed deposition rate will depend on the relationship 

13 of current surface soil concentrations to the surface soil concentrations associated with a steady 

14 state condition (i.e., when the rate of Pb addition to the surface soil equals the rate of Pb loss 

15 from the surface soil) at that location. If current surface soil concentrations are below their 

16 steady state levels for the current conditions (i.e., the rate of Pb addition is greater than the rate 

17 of Pb loss) and air concentrations are reduced (i.e., the rate of Pb addition via deposition is 

18 reduced), surface soil concentrations might be expected to continue an increasing trend, although 

19 at a reduced rate from the current rate. Alternatively, if current surface soil concentrations are 

20 above their steady state levels (i.e., the rate of Pb loss is greater than the rate of Pb addition) and 

21 air concentrations and associated deposition are reduced, surface soil concentrations might be 

22 expected to continue 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 phase28 out of leaded gasoline. For example, studies of forest soils have concluded that surface 

29 concentrations of Pb are decreasing in response to the reduced Pb deposition rates since the 

30 phase-out of leaded gasoline (Miller and Friedland, 1994; Kaste and Friedland, 2003). Studies in 

 

9

 In the risk assessment, outdoor surface soil or dust is an important exposure pathway. Use of the term 

surface soil here is intended to 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. 
 2-30

1 urban areas of southern California, where Pb has accumulated from past sources, suggest an 

2 environment in which Pb may remain at the soil surface (and other surfaces), contributing to air 

3 concentrations via resuspension in the near term (CD, pp. 2-65 to 2-67 and 3-18 to 3-19). 

4 Accordingly, the temporal trend in surface soil concentrations in this environment is considered 

5 to be influenced by the rate of resuspension, such that little to no reduction in soil Pb 

6 concentration in southern California is expected over the next few hundred years (CD, pp. 2-65 

7 to 2-67 and 3-18 to 3-20; Harris and Davidson, 2005). Temporal trends in surface soils near 

8 established point sources are not well characterized. Available information for a few areas 

9 surrounding smelters after implementation of pollution controls shows a decline in Pb 

10 concentrations in outdoor dustfall, street dust and indoor dustfall, but has not indicated a 

11 noticeable decline in soil Pb concentrations (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 3⁄4 mile of the facility exhibit small increasing temporal trends over a period of a few 

year10 18 (USEPA, 2006c). In lieu of additional data or a multimedia modeling analysis, however, 

19 the 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, 

and likely much longer, after a new standard might be implemented.11 22 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 or combination of Pb sources may locate where ones of 

27 comparable size had not been previously. 

28 Additionally, we recognize that implementation of some alternate NAAQS could in some 

29 areas (e.g., areas of substantial past atmospheric deposition) involve control of surface soil/dust 

30 to reduce surface soil/dust Pb levels. That is, in places where surface soil/dust Pb concentrations 

31 contribute substantially to air concentrations, controls implemented to attain various alternate 

32 lower NAAQS might include reducing soil Pb concentrations. Such specific control actions have 

 

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

11 This was also the approach used in the risk assessment performed in the last Pb NAAQS review 

(USEPA, 1990). 
 2-31

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 and evaluate model sensitivity. 

9 The approach described here pertains to the initial analyses of the full-scale assessment 

10 for which results are described in Chapters 3 and 4, and to some aspects of the core modeling 

11 approach employed for the additional analyses described in Chapter 5. 

12 2.4.1 Temporal Aspects 

13 The risk assessment conducted for each case study uses a simulated child population for 

14 which exposure begins at birth and continues for 7 years. That is, the study population is 

15 assumed to be a single group, for which exposure begins at birth and continues until the group 

reaches 7 years of age.12 16 Furthermore, it is assumed that no migration or immigration of these 

17 children occurs during this simulation period; that is, none of the children move out of the study 

18 area and no children move in. 

19 For the point source case studies, the use of modeling (with a constant emissions rate and 

20 temporally varying meteorology) provides temporally varying air concentrations. However, the 

21 primary blood Pb model for this assessment is limited in the temporal resolution of its inputs (see 

22 Section 3.2), because the finest temporal resolution of inputs to the blood Pb model is a year. 

23 Consequently, in characterizing exposure media concentrations, annual averages are used. 

24 With regard to temporal variation across the seven-year exposure period, several 

25 exposure factors and physiological parameters are varied on an annual basis within the blood Pb 

26 modeling step (see Section 3.2). Once set for the air quality scenario, however, the media 

27 concentrations of Pb are held constant throughout the seven year period (see Section 3.1). 

28 2.4.2 Spatial Scale and Resolution 

29 The size and resolution of the study area differed among the three case studies. The 

30 general urban case study is not set in a specific location, and involves non-spatially-varying 

31 media concentrations and population density. There is no spatially differentiated template per se

 

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

exposure. 
 2-32

1 and instead, a single generic urban study area is assumed with uniform population density and 

2 exposure concentrations. For the exposure modeling for the point source case studies, however, 

3 spatial templates were developed. The templates subdivide the study area into subunits, 

composed of U.S. Census blocks or block groups, across which media concentrations differ.13 4 

5 Media concentration estimates (e.g., for outdoor air, soil and indoor dust) are developed for each 

6 block or block group, and from these a central tendency estimate is developed of concurrent and 

7 lifetime average blood Pb levels for the resident children. Interindividual variability of blood Pb 

8 levels for children within a block or block group is considered through the use of a statistically 

9 derived GSD. The specific spatial templates used for each of the point source case studies are 

10 presented in Appendices D (Section D.1) and E (Section E.1). 

11 For the two point source case studies, population risk estimates were derived separately 

12 for a portion of the full study area in addition to the estimates derived for the full study area. The 

13 subareas extended approximately 1.5 km out from the point source locations (see Appendix P). 

14 2.4.3 Categorization of Policy-relevant Exposure Pathways 

15 To inform policy aspects of the Pb NAAQS review, we have attempted to parse the 

16 assessment estimates for indoor dust Pb, blood Pb and IQ loss into the fraction associated with 

17 policy-relevant background (e.g., diet and drinking water) versus that associated with policy18 relevant pathways, which include inhalation, outdoor soil/dust ingestion and indoor dust 

19 ingestion (Section 2.1.1). We have further categorized the policy-relevant pathways into one of 

20 two categories, “recent air” or “past air”. Conceptually, the recent air category includes those 

21 pathways involving Pb that is or has recently been in the air, whether or not it was also in the air 

22 in the past, and the past air category includes those pathways involving Pb that was in the air in 

23 the past and was not in the air recently. As discussed below, this conceptual distinction could 

24 not be entirely reflected in the risk assessment due to technical limitations. Thus, risk estimates 

25 associated with “past air” reflect some Pb that was recently in the air. Similarly, diet and 

26 drinking water are treated entirely as policy-relevant background, despite also reflecting 

27 contributions from policy-relevant pathways such as atmospheric deposition. 

28 Conceptually, recent air refers to exposure contributions associated with inhalation of 

29 ambient air Pb and ingestion of the fraction of indoor dust Pb derived from recent ambient air Pb. 

30 To the extent that ambient air Pb includes contributions from resuspension of previously 

 

13 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. 
 2-33

1 deposited Pb, that source is represented in the recent air category. Thus, a “recent air” exposure 

2 may involve previously deposited Pb that is (1) resuspended into the air and inhaled or (2) 

3 resuspended into the air, transported into a building, deposited into the dust, contacted and 

4 ingested. 

5 Conceptually, past air includes exposure contributions from ingestion of outdoor soil/dust 

6 that is contacted on surfaces outdoors, and ingestion of indoor dust Pb that is derived from past 

7 air sources. Although Pb that is currently in outdoor soil/dust may have been in the air recently 

8 or some time ago, we have assigned ingestion of outdoor soil/dust Pb contacted outdoors to the 

9 past air category in recognition of our inability to maintain a dynamically changing 

categorization of recent versus past air.14 10 The past air category also includes the ingestion of 

11 indoor dust Pb that was in the ambient air in the past but not recently. These sources to indoor 

12 dust Pb include any residual legacy of historical air Pb in the indoor dust of older homes, as well 

13 as Pb occurring in indoor dust that is derived from outdoor soil/dust Pb that was not transported 

14 indoors by an air pathway. This latter pathway includes outdoor soil/dust Pb that is carried 

15 indoors by human contact (e.g., “tracking in”). 

16 To implement this categorization in the assessment, we developed estimates of the recent 

17 air portion of indoor dust Pb (i.e., contributions associated with recent ambient air Pb levels), and 

18 assigned the remainder of indoor dust Pb to other sources, which include those relevant to past 

19 air. That is, the “other sources” component of indoor dust Pb refers to contributions from indoor 

20 paint, outdoor soil/dust and additional sources. Among the additional sources is any residual 

21 legacy of historical Pb in the indoor dust of older homes. 

22 The indoor dust Pb subdivision reflects and is limited by the models and inputs used to 

23 estimate indoor dust Pb levels for the different scenarios. All of them predict dust Pb 

24 concentration as a function of, among other factors, ambient air Pb concentration, and all of the 

25 models include a constant (e.g., the intercept in the regression-based models) that captures 

“other” sources, as well as uncertainty associated with the relationship with ambient air Pb.15 26 

27 One of the models (used in the secondary Pb smelter case study and in part of the primary Pb 

28 smelter case study) also includes a dependency on outdoor soil/dust Pb concentration. This 

29 difference among the models leads to an inconsistency across the case studies in the ability to 

30 separate the contribution to indoor dust Pb from outdoor soil/dust. Consequently, we have 

 

14 In concept, this assignment appears to inherently contribute to an underestimate of the recent air 

category. However, the reality of much higher air emissions in the past that have contributed to Pb concentrations in 

other media that are higher than those that would be associated with more recent lower emissions, complicates 

consideration of this assumption (see Section 2.3.3). 

15 The extent to which this intercept captures uncertainty about the relationship with ambient air will 

indicate presence of a recent air pathway in the past air category. 
 2-34

1 limited to two categories the subdivision of indoor dust contributions, with that from outdoor 

2 soil/dust included in “other” whether it is estimated by a model that includes a soil concentration 

3 coefficient or is accommodated by the constant or intercept term. Additionally, we have not 

4 subdivided indoor dust Pb estimates for the primary Pb smelter due to uncertainty regarding this 

5 categorization with the model used for that case study (Section 3.1.4.2). In presenting risk 

6 estimates associated with policy-relevant pathways (Chapter 4), we have included risk associated 

7 with the “other” component of indoor dust in the past air category. We recognize that the 

8 potential for this “other” component to include ambient Pb unrelated to air emissions contributes 

9 to a potential for the risk estimates associated with past air to be overestimates. Additionally, as 

10 the recent air portion of indoor dust Pb, which derives from transport of airborne Pb into a house, 

11 depends on the estimate of ambient air Pb concentration, selection of an ambient air Pb estimate 

12 that is not appropriate to this relationship (e.g., one that does not represent ambient air Pb levels 

13 immediately outside the house) may contribute to an under- or overestimate of recent air indoor 

14 dust Pb. 

15 There is inherent uncertainty associated with the approaches used to divide indoor dust16 related Pb exposures and risk into contributions from “recent ambient air” and from “other” 

17 sources. Further, the uncertainty may differ among the three case studies due to the different 

18 approaches used in modeling indoor dust Pb. For example, uncertainty associated with the 

19 hybrid mechanistic-empirical model used in the general urban case study includes that which 

20 arises from model inputs and model performance, while the empirical, regression-based, 

21 statistical models used in the point source case studies entail uncertainty regarding similarity of 

22 the conditions from which the model was derived to those to which it is applied, as well as 

23 uncertainty regarding variables that may be correlated with those explicitly represented in the 

24 model. Thus, given the various limitations of our modeling tools, blood Pb levels associated 

25 with air-related exposure pathways and current levels of Pb emitted to the air (including via 

26 resuspension) are likely to fall between the estimates derived for “recent air” pathways and those 

27 for “recent” plus “past air” pathways. 

28 2.4.4 Overview of Analytical Steps 

29 As illustrated in Figure 2-2, the risk assessment completed for the two point source case 

30 studies generally includes four analytical steps: (a) fate and transport of Pb released into outdoor 

31 air, including the dispersion of Pb away from the point of release and the deposition of Pb onto 

32 surfaces, (b) prediction of the resulting concentration of Pb in media of concern including 

33 outdoor air, outdoor surface soil/dust and indoor dust, (c) use of these Pb concentrations together 

34 with estimates of Pb in background exposure pathways, including diet, to estimate associated 

35 blood Pb levels in children using biokinetic modeling and (d) use of concentration-response 
 2-35

1 functions derived from epidemiology studies to estimate IQ loss associated with the estimated 

2 blood Pb levels. The modeling approach for the general urban case study is somewhat simpler, 

3 since it does not involve fate and transport modeling for air concentration estimates and instead, 

4 uses ambient monitor levels combined with an assumption of uniform ambient air Pb levels 

5 across the study area. Subsequent steps in the general urban case study analysis are fairly similar 

6 to what is described above for the point source case studies, with the generation of population 

7 blood Pb levels being somewhat simplified for the general urban case study. Figure 2-2 

8 identifies the key input data sets, modeling steps and intermediate model output in each of the 

9 four analytical steps. The first three steps are employed in the exposure assessment (Section 

10 2.4.4.1), while the fourth is the risk assessment step (Section 2.4.4.2). 

11 Prior to focusing on a single modeling approach (see Chapter 5), and because of the 

12 quantitative influence of certain analytical steps on the results, we employed multiple approaches 

13 to perform some of the analytical steps. The multiple sets of results generated in this way for a 

14 case study and air quality scenario are intended to span a range indicative of the model and 

15 parameter uncertainty associated with those analytical steps of the risk analysis (see Section 

16 2.4.6). 
 2-36

1 Figure 2-2. Overview of analysis approach. 

Characterizing ambient air levels and 

deposition to soil

Air dispersion modeling

(primary, secondary Pb smelters)

Ambient monitoring data (mean and 

high-end values) (urban)

Stack and 

fugitive 

emissions data

Ambient air 

concentrations 

for study areas

Lead deposition rates 

across study area

Characterizing soil and indoor 

dust concentrations

Outdoor residential 

soil concentrations

Multi-media fate & transport 

model-derived soil 

concentrations scaled using 

measured data from surrogate 

location

(secondary Pb smelter) Site-specific soil 

monitoring data 

combined with 

statistical 

extrapolation 

(primary Pb

smelter)

Nationallyrepresentative 

residential soil Pb

value selected from 

literature

(urban)

Combination of (a) statistical 

(regression) and/or (b) mechanistic 

(compartmental) indoor dust prediction 

models 

(all 3 case studies)

Indoor residential dust 

concentrations

Biokinetic blood Pb

modeling 

(all 3 case studies)

Background Pb exposure levels

• diet

• drinking water

• indoor paint (actually reflected in 

dust modeling)

Probabilistic modeling of blood Pb

levels for children within each case 

study location

(all 3 case studies)

GSD reflecting interindividual variability 

in behavior related to 

Pb exposure and 

biokinetics

Characterizing blood Pb levels

Concentration-response function

• log-normal distribution (Lanphear pooled 

analysis)

• breakpoint reflecting confidence in CR 

function

• effects estimation based on concurrent and 

lifetime average blood Pb metric

Demographics (distribution of 

children within study areas)

(two point source case studies)

Distribution of IQ loss for 

study populations 

(partitioned between policyrelevant exposures and 

policy-relevant background)

Characterizing risk (IQ loss)

Ambient air 

concentrations 

(see above)

Risk estimation - IQ 

change estimation

(all 3 case studies)

EXPOSURE ASSESSMENT RISK CHARACTERIZATION

2 
 2-37

1 

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 facility-related 

11 (including fugitive) Pb emissions for the primary and secondary Pb smelter case studies and (b) 

12 the use of ambient monitor data for the general urban case study. For the general urban case 

13 study, monitoring data for U.S. urban areas of more than a million in population were used to 

14 identify two current conditions scenarios, one “typical”, and one higher end, and to relate 

15 alternate NAAQS (of other forms) to annual average levels needed for blood Pb modeling. A 

16 key aspect of the general urban case study is that ambient air lead levels do not vary spatially 

17 within the study area. The approach is monitor-based rather than source-based as compared to 

18 two point source case studies. This means that we did not explicitly model specific source 

19 contributions for the general urban case study (e.g., resuspension of roadside dust, “fresh” 

20 industrial emissions) and instead, relied on empirical data to define ambient air Pb levels for this 

21 general case study, with these levels reflecting contributions from all contributing sources, be 

22 they currently active stationary or mobile sources, resuspension of previously deposited Pb or 

23 other. 

24 Characterization of Pb concentrations in outdoor surface soil/dust, resulting from 

25 deposition of airborne Pb is based on the use of (a) existing site-specific measurements (primary 

26 Pb smelter case study), (b) nationally representative residential soil measurements obtained from 

27 the literature (general urban case )study and (c) fate and transport modeling (secondary Pb 

28 smelter case study). In the case of the primary Pb smelter case study, soil Pb concentration data 

29 were available for a zone close to the facility and statistical extrapolation from the available 

30 empirical data was used to predict soil levels for portions of the study area beyond this zone. 

31 To predict concentrations of ambient Pb in indoor dust, we have relied on a combination 

32 of (a) regression-based models that relate indoor dust to outdoor air Pb and/or outdoor soil Pb 

33 and (b) mechanistic models that predict indoor dust Pb based on key mechanisms (e.g., exchange 

34 of outdoor air with indoor air, deposition rates of Pb to indoor surfaces, house cleaning rates). 

35 For both point source case studies, a combination of regression-based models obtained from the 
 2-38

1 literature and developed based on site-specific data were used, and a customized hybrid 

2 empirical-mechanistic model was developed for the general urban case study. This reflected the 

3 fact that available regression-based models had been developed largely based on residential 

4 exposures near large point sources and were not considered representative of more general urban 

5 exposures. Consequently, a mechanistic model, augmented with empirical data, was developed 

6 for the general urban case study. Additional detail on methods used to characterize media Pb 

7 concentrations for each case study can be found in Section 3.1. 

8 Blood Pb levels are predicted from estimates of Pb contained in various media (e.g., 

9 ambient air, diet, water, indoor dust) using the Integrated Exposure and Uptake Biokinetic 

10 (IEUBK) model (Section 3.2.1.1). A second biokinetic model, the International Commission for 

11 Radiation Protection model (hereafter referred to as the “Leggett model”), is included in the 

sensitivity analysis (Section 3.2.1.2).16 12 The same fundamental approach was used to estimate 

13 population distributions of blood Pb levels for each of the two point source case studies, and a 

14 somewhat simpler approach was used for the general urban case study. The approach used for 

15 the two point source case studies involved two main steps: 

16 1) Use biokinetic model to predict central tendency blood Pb levels for children within 

17 each exposure zone: The model outputs are then aggregated into the “concurrent” 

18 and “lifetime average” blood Pb metrics used in the concentration-response functions. 

19 2) Implement probabilistic exposure model to generate a population distribution of 

20 blood Pb levels for children in each case study location: The probabilistic model 

21 generates a distribution of simulated blood Pb levels for the children in each study 

22 area based on consideration of three key factors: (a) the central tendency blood Pb 

23 levels generated for each exposure zone in the preceding step, (b) demographic data 

24 (distribution of children 0-7 years of age) across the zones comprising a given study 

25 area and (c) use of a GSD characterizing interindividual variability in blood Pb (e.g., 

26 reflecting differences in behavior and biokinetics related to Pb). 

27 The step involving modeling population-level exposures for the general urban case study is 

28 somewhat simpler than that used for the two point source case studies in that demographic data 

29 for a specific location is not considered. As discussed in Section 3.2.2, this avoids the need for 

30 implementing a population-weighted probabilistic sampling procedure. 

 

16 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). 
 2-39

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 μg/dL). These three functions are all based on the lognormal concentration10 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 

pathways for specific percentiles of the child population (e.g., the 50th, 90th, and 95th 17 

18 percentile modeled child). This category of metric provides perspective on the 

19 distribution of IQ loss resulting from policy-relevant exposure pathways, ranging 

from the typical or average child (50th 20 percentile, mean) to children experiencing 

higher exposures (90th, 95th 21 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 

predicted to have risk levels at or above the 95th 24 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 general urban case study, because it is not location-specific, only the first type of 

28 risk metric, population risk percentiles, was developed because this case study is not location29 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: 
 2-40

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 • For the two point source case studies, use of spatial templates developed using a 

13 geographic information system (GIS) to reflect the distribution of children across a 

14 study area (in relation to ambient air Pb and related media concentrations) in 

15 projecting population exposures. 

16 • For the general urban case study, inclusion of two current conditions scenarios (mean 

and 95th 17 percentile air Pb scenarios) which together, provide a degree of coverage for 

18 variation in ambient air Pb levels seen across urban areas in the U.S. 

19 • Use of empirically derived GSDs reflecting interindividual variability in blood Pb 

20 levels, to provide coverage for multiple sources of variability associated with Pb 

21 exposure and biokinetics. Note, that the application of these GSDs provides the 

22 primary means of reflecting interindividual variability in blood Pb levels in this 

23 analysis. These GSDs also reflect uncertainty associated with measuring blood Pb 

24 levels and characterizing population-level distributions of those levels. 

25 

26 There is significant uncertainty associated with reflecting the sources of variability 

27 identified above in population-level exposure and risk. For example there is uncertainty 

28 associated with the GSD selected to reflect interindividual variability in blood Pb levels for a 

29 particular case study. This is considered in the uncertainty characterization (Section 2.4.6). 

30 2.4.6 Uncertainty Characterization and Sensitivity Analysis 

31 Several methods have been used to examine uncertainty in our modeling approach and its 

32 potential impact on exposure and risk estimates (Section 4.3). These include: (a) development of 

33 multiple sets of exposure and risk estimates for each case study and air quality scenario that 

34 illustrate the combined impact of different key models and parameters on risk results and the 

35 associated uncertainty, (b) evaluation of model performance (e.g., by comparison with empirical 
 2-41

1 data) to provide confidence in individual modeling steps and (c) qualitative discussion of key 

2 sources of uncertainty and their potential impact on exposure and risk estimates. Each of these 

3 elements of the uncertainty characterization is briefly summarized below. 

4 In addition, we have completed a sensitivity analysis, intended to characterize the 

5 potential impact of individual modeling elements on risk results (see Section 2.4.6.3). 

6 2.4.6.1 Performance Evaluations 

7 Performance evaluation for the exposure assessment (Section 3.5) focused on evaluation 

8 of estimates of Pb in ambient air, outdoor soil, and indoor dust (discussed in Section 3.5.1) and 

9 estimates of Pb in blood (covered in Section 3.5.2). This evaluation focused on those estimates 

10 based on modeling. 

11 2.4.6.2 Generating Multiple Sets of Results 

12 There are multiple models or inputs that could be implemented for each of the analytical 

13 steps of the assessment. For those more highly influential analytical steps for which it is not 

14 clear which model or input would generate “best estimate” results, we have implemented 

15 multiple modeling approaches in the initial analyses presented in Chapters 3 and 4. Risk results 

16 considered across these multiple modeling approaches provide perspective on the range of 

17 potential risk, given key sources of uncertainty in the analysis. The multiple modeling 

18 approaches for each case study were developed by the following stepwise strategy: 

19 • Identification of those modeling elements believed to contribute significant 

20 uncertainty to risk results, 

21 • Identification of a set of plausible options for each of these key modeling elements 

22 (e.g., alternative models or input parameters), and 

23 • Development of alternative modeling approaches by combining individual options 

24 from the previous step. 

25 Identification of the modeling elements believed to contribute significant uncertainty (step 1) 

26 involved consideration of a number of factors including the results of the sensitivity analysis 

27 completed for the pilot analysis, and comments provided by CASAC and the public on the pilot 

28 analysis and analysis plan. 

29 Because each of the case studies uses different modeling approaches for some of the 

30 analytical steps (e.g., different indoor dust models are used for each case study, and these are 

31 associated with differing uncertainty), the identify and size of the areas of uncertainty associated 

32 with each case study differs. The specific modeling approaches for each case study and their 

33 elements are presented in Figure 2-3. For the general urban case study, two different dust 

34 models and two GSDs were used, compared to one model and GSD for these analytical steps in 
 2-42

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 case study analyses presented in chapters 3 and 4. 

Elements of modeling approaches 

Case Study 

Indoor dust 

modeling Blood Pb metric GSD 

Concentrationresponse function 

Number of 

sets of results 

General Urban 

 Case Study 

2 models: 

(a) hybrid 

mechanisticempirical 

(b) statistical 

(regression) 

2 sets of GSDs, 

representing: 

(a) smaller 

scale 

(b) larger, 

regional scale 

2 * 2 * 2 * 3 = 24 

Each Point 

Source Case 

Study 

1 model: 

statistical 

(regression) 

approach b

2 metrics: 

(a) concurrent 

(b) lifetime 

average 

1 set of GSDs 

3 functions: 

(a) log-linear with 

linearization, 

 (b) log-linear with 

cutpoint, and 

 (c) two-piece linear 

1 * 2 * 1 * 3 =6 

b

 Different models used for each point source case study. 

4 

5 The set of exposure and risk results generated for each case study and air quality scenario 

6 using the alternative modeling approaches indicates to some extent the magnitude of uncertainty 

7 surrounding the risk results. However, as discussed in Section 4.3, these sets of risk results do 

8 not represent an uncertainty distribution, since confidence levels are not specified for each 

9 modeling approach. In presenting the multiple sets of results generated for each case study, we 

10 have selected the highest and lowest sets of risk results from those generated, and used them to 

11 represent, respectively, upper and lower bounds on risk. The degree to which these actually 

12 represent upper and lower bounds depends on the whether the various modeling approaches 

13 evaluated in this analysis capture the largest sources of uncertainty. For example, if an important 

14 source of uncertainty was excluded in designing the alternative modeling approaches for a given 

15 case study, than the bounds represented by the set of risk results generated for that case study 

16 might not be wide enough. 

17 2.4.6.3 Sensitivity Analysis 

18 Sensitivity analysis techniques were used to examine the uncertainty for individual 

19 modeling elements and its impact on exposure and risk estimates. We used a "one element at a 

20 time elasticity analysis" approach, running the full risk model with one of the selected modeling 

21 elements adjusted to reflect an alternate input value or modeling choice. The results of that run 

22 with the modified modeling element would then be compared to those for the "baseline risk" run 

23 to determine the magnitude of the impact on risk results of selections for that one modeling 

24 element. 
 2-43

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 general urban case study to key sources of uncertainty, recognizing that the 

5 results of the pilot sensitivity analysis are informative with regard to the point source case 

6 studies. The sensitivity analysis completed for the full-scale analysis focused on those modeling 

7 elements (including input datasets and modeling steps) believed to have a significant potential 

8 for impacting exposure and risk results. Those modeling elements include oral uptake factor, 

9 interindividual blood Pb variability GSD, biokinetic model, concentration-response function for 

10 IQ loss. This type of sensitivity analysis indicates which of the modeling elements included in 

11 the sensitivity analysis has the greatest impact on risk results, and can be used to guide future 

12 efforts 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 underestimation of risk. To the extent possible, the likely magnitude in qualitative 

20 terms of a particular source of uncertainty is also discussed. 
 2-44

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 3-1

3 EXPOSURE ASSESSMENT 

This chapter describes the methods and results of the exposure assessment and 

performance evaluation for the initial analyses of the full-scale assessment. Additional analyses 

are described in Chapter 5. 

3.1 METHODS FOR ESTIMATING MEDIA CONCENTRATIONS 

To estimate media Pb concentrations for the three case studies, we used a combination of 

empirical data and fate and transport modeling, reflecting the different availability of Pb 

measurements for the two point source case studies and the non-location-specific nature of the 

general urban case study (Table 3-1). For all three case studies, media concentrations were 

estimated for multiple air quality scenarios including a range of alternative NAAQS (see Section 

2.3). However, outdoor dust/soil concentrations for each of the three case studies were 

established for current conditions or the current NAAQS scenarios and those values were used 

for the current NAAQS and each of the alternate NAAQS scenarios evaluated (see Sections 2.3.3 

and 3.1.3). Further, as described in Section 2.4.1, media concentrations, once defined, are held 

constant for the full exposure period simulated with the blood Pb modeling. For ambient air, 

outdoor soil/dust and indoor dust, estimates of annual average concentration were used for this 

purpose for all three case studies. Additionally, from the ambient air concentrations, annual 

average inhalation exposure concentrations were estimated with consideration for daily activity 

patterns by children and differences in outdoor (ambient) versus indoor air Pb levels (see Section 

3.1.2). 
 3-2

Table 3-1. Case study approaches for estimating media Pb concentrations. 

Modeling Step General Urban Case Study 

Primary Pb Smelter Case 

Study 

Secondary Pb 

Smelter Case Study 

Spatial 

template 

Single generic study area (with 

spatially uniform media 

concentrations and population 

density) 

Combination of U.S. Census 

blocks and block groups out 

to a 10 km radius around the 

facility (with media 

concentrations and 

demographics uniform within

blocks/block groups) 

U.S. Census blocks 

out to a 10km radius 

around the facility 

(with media 

concentrations and 

demographics 

uniform within

blocks) 

Ambient air 

concentrations 

for current 

conditions 

and/or current 

NAAQS

Monitor data (mean and highend urban monitors selected to 

represent two current conditions 

scenarios) Dispersion modeling 

Performance 

evaluation

[Monitoring data used as basis 

characterizing ambient air 

levels] 

Comparison to Pb-TSP monitor data from study area 

Inhalation 

exposure 

concentrations 

Estimates for all three case studies are based on ambient air concentrations and reflect the 

application of location-specific adjustment 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 

Outdoor soil 

concentrations

Nationally representative 

residential soil value selected 

from literature (same value used 

for entire study area and for all 

air quality scenarios) 

Near facility (remediation 

zone) relied on sampling 

data, and regression model 

used for outer portions of 

study area 

Multiple Pathways of 

Exposure (MPE) 

model used to predict 

spatial distribution of 

surface soil Pb levels, 

then scaled using 

empirical data from 

surrogate location 

Performance 

evaluation

[Empirical data used in 

characterizing soil levels] 

[Estimates based on surrogate 

data] 

Modeled estimates 

compared to surrogate 

data for other 

industrial (point 

source) locations 

Indoor dust 

concentrations

Two approaches used: (a) 

hybrid mechanistic 

(compartmental) model 

augmented with empirical data 

developed specifically for this 

analysis and (b) empirical (aironly regression) model obtained 

form the literature 

Near facility (remediation 

zone) relied on site-specific 

regression model (based on 

air) and pooled analysis 

regression model (based on 

air plus soil) for remainder of 

study area 

Statistical (air-only 

regression) model 

obtained from the 

literature 

Performance 

evaluation

Subcomponents of hybrid 

model evaluated, and case study 

estimates compared to literature 

estimates and national-scale 

survey 

Site-specific sampling data 

used in deriving regression 

model for remediation zone; 

case study estimates 

compared to literature 

estimates and national-scale 

survey 

Case study estimates 

compared to literature 

estimates and 

national-scale survey 
 3-3

3.1.1 Ambient Air Concentrations 

Different methods were used for estimating annual average ambient air concentrations for 

the three case studies. For the primary and secondary Pb smelter case studies, air dispersion 

modeling of Pb emissions was performed, while Pb-TSP measurement data from the years 2003-

2005 for urban areas with greater than one million residents were used for the general urban case 

study. 

3.1.1.1 General Urban Case Study 

The general urban case study is not site-specific and has been designed to represent 

general Pb exposures experienced by children residing in urban areas within the United States. 

The case study involves spatially uniform Pb concentrations in ambient air, outdoor soil/dust and 

indoor dust with a matching uniformly distributed child resident population. This is in contrast 

to the point source case studies which each have a relatively large number of exposure zones to 

track potentially significant spatial gradients in their concentrations of Pb in environmental 

media and the spatial distribution of children. 

Two current conditions scenarios are included in the general urban case study based on 

Pb-TSP monitoring data from urban areas in the United States. Specifically, these two scenarios 

include: (a) a central tendency current conditions scenario based on the mean maximum 

quarterly average Pb measurement (0.14 μg/m3) seen, in 2003-05 time period, at Pb-TSP 

monitors in urban areas with greater than a million people, and (b) a high end current conditions 

scenario based on the 95th percentile maximum quarterly average Pb measurement (0.87 μg/m3) 

obtained from the same urban Pb-TSP dataset. The data analysis associated with these values is 

described in Appendix A, Section A.2.2.2. 

In addition to these current conditions scenarios, the current NAAQS scenario (1.5 

μg/m3

, as a maximum quarterly average) and four alternate NAAQS scenarios have also been 

evaluated (see Section 2.3 for additional details on the NAAQS scenarios). In each of these 

instances, the specific NAAQS of interest has been evaluated by assuming that air Pb 

concentrations in the entire study area are reduced to meet that specific ambient air Pb level and 

form. 

Because the blood Pb modeling (Section 3.2) is based on annual average media 

concentrations, the maximum quarterly and maximum monthly average values used in defining 

the air quality scenarios were translated into equivalent annual average ambient air 

concentrations for the blood Pb modeling (Section 2.4.1). This is accomplished using ratios 

obtained from the 2003-2005 Pb-TSP monitoring dataset that relates maximum quarterly or 

maximum monthly averages to associated annual average values for each monitor located in an 

urban area of population more than a million (Appendix A, Section A.2.2). Ratios were selected 
 3-4

for each of the air quality scenarios according to the averaging time (calendar quarter versus 

month) and the percentile represented by the air quality scenario (e.g., mean versus high end for 

current conditions). Consequently, the annual average Pb concentration for the central tendency 

current conditions scenario was derived from the mean of monitor-specific maximum quarterly 

average concentrations using the mean of the monitor-specific maximum quarterly-to-annual 

average ratios, while the high end current conditions scenario was derived from the 95th

percentile of monitor-specific maximum quarterly average concentrations using the 95th

percentile of the monitor-specific maximum quarterly-to-annual average ratios. The mean of the 

maximum quarterly-to-annual average ratios was also used to derive the annual average 

concentration estimates for the alternative NAAQS air quality scenario with a quarterly 

averaging time. The mean of the maximum monthly-to-annual average ratios was used to derive 

the annual average concentration estimates for the alternative NAAQS scenarios with monthly 

averaging times. The derivation of these ratios is described in Appendix A, Section A.2.2. 

3.1.1.2 Primary Pb Smelter Case Study 

The air quality scenarios included for the primary Pb smelter case study were the current 

NAAQS and four alternate NAAQS (see Section 2.3.1). As the study area of the primary Pb 

smelter is currently in nonattainment for the current NAAQS and emissions profiles from the 

facility are being modified as additional controls are put in place, ambient air Pb concentrations 

for the current NAAQS scenario were estimated using the model, emissions and source 

parameters used in developing the 2007 proposed revision to the State Implementation Plan for 

the area (MDNR, 2007a, 2007b). Annual average Pb concentration estimates for the current 

NAAQS scenario were derived for each census block or block group from model outputs. 

Additionally, for the purposes of developing the alternative NAAQS scenarios, hourly estimates 

from the model were used to generate quarterly average and monthly average concentrations for 

each census block or block group. A proportional rollback procedure was then used to adjust the 

set of maximum monthly or quarterly averages to represent meeting a particular NAAQS 

scenario. That is, the block or block group with the greatest exceedance was reduced to meet the 

particular NAAQS and all locations were then reduced by that same fraction. After the 

proportional rollback procedure had been applied to the set of location-specific monthly or 

quarterly averages to meet a particular NAAQS, these adjusted quarterly or monthly average 

values were then used to derive annual averages which, in turn, were used in the exposure 

analysis. 

The development of air Pb concentration estimates for this case study is described more 

fully in Appendix D, Section D.2. 
 3-5

3.1.1.3 Secondary Pb Smelter Case Study 

Outdoor air concentration and deposition rates for Pb were estimated for the secondary 

Pb smelter case study current conditions scenario using the AERMOD dispersion model1 and 

source characterization and emissions information for the facility (See Appendix E, Section E.2 

for details). The Pb emissions modeled reflected processes at the facility (e.g., stack emissions) 

and fugitive dust emissions from materials storage and handling and roadway dust. 

Annual average Pb concentrations for the current conditions scenario were derived for 

each census block from model outputs. As with the primary Pb smelter case study, alternative 

NAAQS scenarios were modeled using the proportional rollback procedure (see Section 3.1.1.2). 

The development of air Pb concentration estimates for this case study is described more 

fully in Appendix E, Section E.2. 

3.1.2 Inhalation Exposure Concentrations 

Inhalation exposure concentrations for Pb were estimated for young children, the 

population of interest, from the estimated ambient air concentrations using age group- and 

location-specific relationships for Pb developed using the exposure modeling component of 

EPA’s 1999 national-scale air toxics assessment (USEPA 2006a), one of the U.S. EPA’s 

National Air Toxics Assessment (NATA) activities. These relationships account for air 

concentration differences indoors and outdoors and mobility or time spent in different locations 

(e.g., outdoors at home, inside at home etc.) for the population of interest. 

The exposure modeling component of the NATA national-scale assessment generated 

inhalation exposure concentrations for sets of modeled children for each U.S. Census tract 

(USEPA, 2006a). For the two point source case studies, we used the ratio of these NATA 

national-scale assessment inhalation exposure concentrations to that assessment’s corresponding 

estimates of ambient air Pb concentration matched by U.S. Census tract to develop adjustment 

factors that could be used to derive inhalation exposure concentrations from our estimates of 

ambient air Pb concentration (see Attachment D, Section D.2.3, and Attachment E, Section E.2.3 

for additional detail on this procedure). Although analyses of the ambient air concentrations 

predicted in the NATA national-scale assessment indicate potential underpredictions, and there 

are particular uncertainties in the assessment predictions at small scales, the relationship between 

ambient air concentrations and exposure concentrations (i.e., the comparison used here) is not 

expected to be affected by these factors. For the general urban case study, we used the median 

adjustment factor from the full NATA national-scale assessment. The 0-4 years old age group is 

 

1

 AERMOD is the current preferred Gaussian plume dispersion model for assessing stationary sources 

under the Clean Air Act (70FR(216): 68217-68261). 
 3-6

the closest age group to the age group of interest for this assessment for which outputs are 

available. For this age group, the adjustment factors or ratios between NATA national-scale 

assessment Pb inhalation exposure concentrations and ambient air concentrations ranged from 

0.37 to 0.46 for the Census tracts within the two point source case study areas. A value of 0.43 

was used for the general urban case study (see Appendix C, Section C.1.2). Use of these ratios 

for the 0 to 4 year old age group to represent the 0 to 7 year old age group modeled in this 

assessment contributes some uncertainty in the estimate of inhalation exposure concentrations. 

3.1.3 Outdoor Surface Soil/Dust Concentrations 

Pb concentrations in outdoor surface soil/dust were characterized for the current 

conditions or current NAAQS scenarios for the three case studies using a combination of 

modeling and empirical data. These estimates were also used for the alternate NAAQS 

scenarios. That is, it was assumed that reductions in ambient air concentrations associated with 

the alternate NAAQS scenarios did not have a significant impact on soil concentrations2

. 

3.1.3.1 General Urban Case Study 

The outdoor surface soil concentration for the general urban case study was derived after 

considering empirical data collected both at urban and other residential areas across the United 

States (Appendix C, Section C.2). A single outdoor soil Pb level representative of general 

residential yards in the U.S. was then selected for use with this case study. Specifically, a 

nationally representative arithmetic mean of soil Pb levels in residential yards (198 μg/g) was 

obtained from the National Study of Lead and Allergen in Housing (NSLAH) (USEPA, 2000). 

The NSLAH survey, which was conducted by the Department of Housing and Urban 

Development (HUD) between 1998 and 1999, was intended to generate a nationally 

representative sample of residential housing, including both private and public residences 

constructed between 1940 and 1998 (but excluding institutional and group housing). 

3.1.3.2 Primary Pb Smelter Case Study 

In the primary Pb smelter case study, a different approach was used to estimate outdoor 

surface soil/dust Pb concentrations near the facility than that used for more distant locations. 

This difference is in recognition of the remediation activities that have included the removal of 

contaminated soil at many of the residential yards closest to the facility, and replacement with 

"clean" soil. Consequently, soil Pb concentrations are estimated using a combination of 

 

2

 As mentioned in Section 2.3.3, this also presumes that implementation methods for any of the alternate 

NAAQS do not involve taking action to separately change soil Pb concentrations. 
 3-7

measurement data for blocks within the remediation zone and statistically based predictions 

beyond the remediation zone (see Appendix D, Section D.3 for more detail). 

Surface soil/dust Pb levels near the facility (within the remediation zone) are based on the 

most recent postremediation measurements available for a given block (obtained between 2001 

and 2004 - see Appendix B Section, B.1.5.2). Preremediation soil levels are not used in 

estimating soil levels within the remediation zone. Although analyses of sequential (over time) 

postremediation measurements have indicated recontamination of remediated yards (USEPA, 

2006b), the postremediation measurement based estimates were used for the current NAAQS 

scenario, and for the alternate NAAQS scenarios. It is recognized that this approach may 

produce an underestimate of Pb exposure for the ingestion of surface soil/dust for the current 

NAAQS scenario, and perhaps some alternate NAAQS scenarios. However, an analysis of the 

impact of a reduced NAAQS on the remediation zone temporal trend in surface soil/dust Pb 

levels was not available to inform an alternate approach. Further, the impact of such a potential 

bias is limited to the surface soil/dust pathway and does not affect the indoor dust pathway 

because the indoor dust Pb concentrations for this region of the primary Pb smelter case study 

were derived using an approach that did not rely on outdoor soil/dust Pb concentrations. 

Characterization of soil levels for blocks and block groups beyond the remediation zone 

are based on a regression model predicting soil Pb as a function of distance from the facility, 

which was fitted to preremediation soil measurement data (available closer to the facility). The 

use of preremediation soil data in deriving the regression equation reflects the fact that little 

remediation has occurred in these more distant locations and consequently, spatial trends seen in 

the preremediation soil levels are more likely to be representative for these outer portions of the 

study area. The regression model used in these estimates has an r2

 of 0.92 which suggests a good 

fit and increases overall confidence in these statistical estimates. However, it should be noted 

that this increased confidence holds for areas of interpolation (areas with sampling data used to 

fit the model – out to about 2.3 km from the facility) more than for areas of extrapolation (areas 

without sampling data – beyond 2.3 km from the facility). 

The development of surface soil/dust Pb concentration estimates for this case study is 

described more fully in Appendix D, Section D.3. 

3.1.3.3 Secondary Pb Smelter Case Study 

As noted in Section 2.2.2.2, soil sampling data for Pb were not identified for this case 

study. Consequently, a hybrid mechanistic-empirical modeling approach was used to 

characterize soil Pb levels for this case study, with fate and transport modeling employed to 

derive a soil Pb concentration surface for the study area and sampling data obtained from a 

surrogate secondary Pb smelter study area employed to adjust (calibrate) that surface. 
 3-8

In lieu of historical estimates of emissions, the fate and transport modeling was 

performed using current emissions estimates over a period consistent with the operating period 

of the facility. As the emissions estimates used did not reflect levels of historical emissions, 

generally believed to have been much higher than current estimates, the resultant Pb 

concentrations did not reflect current conditions for the location, and, as expected, were lower 

than concentrations reported for areas near other secondary smelters (see comparisons in 

Appendix E, Section E.3). Accordingly, the soil concentration surface was scaled up based on a 

set of factors (differing with distance from the facility) derived from empirical data for a 

surrogate secondary Pb smelter location. Specifically, Pb concentrations across the entire 

modeled surface were increased by a factor ranging from1 to 13 (depending on distance from the 

facility) to obtain surface soil/dust Pb concentrations consistent with those reported in the 

literature for areas near other secondary Pb smelters. These estimates for the current conditions 

scenario were also used for the alternate NAAQS scenarios. That is, it was assumed that 

reductions in ambient air concentrations associated with the alternate NAAQS scenarios did not 

have a significant impact on soil concentrations. 

The development of surface soil/dust Pb concentration estimates for this case study is 

described more fully in Appendix E, Section E.3. 

3.1.4 Indoor Dust Concentrations 

Pb in indoor dust can originate from a variety of sources including (a) outdoor soil which 

is tracked into the house, (b) Pb in outdoor soil which is resuspended into the air and 

subsequently transported indoors (c) Pb released directly into outdoor air through ongoing 

anthropogenic activity (e.g., industrial point emissions) which is transported indoors and (d) 

interior sources of Pb (e.g., paint, hobbies) (Adgate et al., 1998, Von Lindern, 2003). In the 

exposure assessment conducted for the 1990 Staff Paper, indoor dust Pb concentrations were 

predicted based on Pb concentrations in outdoor soil and ambient air (USEPA, 1989). This is 

also the case for the default approach in the exposure component of the IEUBK model (USEPA, 

1994a). 

The importance of outdoor soil relative to outdoor air in influencing indoor dust Pb levels 

appears to depend on the nature of the Pb sources involved. Investigations in urban areas and 

near contaminated waste sites with elevated soil Pb levels without a currently active industrial 

point source emitter of Pb have indicated a greater association of measurements of dust Pb 

concentration with measurements of soil Pb concentration than with measurements of ambient 

air concentration (e.g., Adgate, 1998 and Von Lindern, 2003). By contrast, investigations in 

areas with currently operating large point sources of Pb (e.g., active Pb smelters) have implicated 

ambient air Pb as an important source of Pb to indoor dust (Hilts, 2003). Contributions of 
 3-9

ambient air Pb to indoor dust Pb levels have also been illustrated by a deposition study 

conducted in New York City (Caravanos et al., 2006). Caravanos and others described Pb 

deposition indoors resulting primarily from exterior environmental sources and not from interior 

Pb sources. For additional discussion of the relationship between indoor dust Pb, outdoor 

ambient air Pb and other related factors, refer to Appendix G, Section G.2 

The prediction of indoor dust Pb based on Pb concentrations in outdoor ambient air and 

other media (e.g., outdoor soil/dust Pb, indoor paint) can be conducted using empirical models, 

mechanistic models, or by a combination of both techniques. Empirical models (typically 

implemented as regression models) have the advantage of being able to specify a relationship 

between indoor dust Pb and predictor variables (e.g., outdoor soil/dust, outdoor air) even when 

these relationships are complex and uncertain. However, statistical models have the 

disadvantage of requiring a significant amount of site-specific data for their derivation and not 

being well suited to extrapolation to scenarios with conditions different from those underlying 

their development. Mechanistic models, by contrast, can be developed in the absence of 

extensive site-specific data and are not as limited in the types of scenarios to which they can be 

applied as long as those scenarios are conceptually consistent with the scenario underlying their 

development. However, the development of mechanistic models can be quite challenging and 

subject to uncertainty if the system or process being predicted is complex and uncertain. 

To date, efforts to predict indoor dust Pb have focused primarily on the development of 

empirically-based regression models (EPA, 1989). Furthermore, most of these regression 

models have been based on data associated with large industrial point sources (smelters) and 

their impacts on surrounding residential populations. Little progress has been made in 

developing comparable models for areas where ambient Pb levels are not so greatly influenced 

by a large industrial point source, such as in more general urban residential areas, including 

either regression models specific to these more general urban scenarios, or mechanistic models 

which could be applied more readily to different exposure scenarios including urban residential 

populations. 

In this assessment, a combination of (a) statistical (regression) models obtained from the 

literature, (b) statistical regression model developed specifically for individual case studies and 

(c) mechanistic models (developed specifically for the general urban case study) was used in 

predicting indoor dust Pb. This reflects the fact that varying amounts of site-specific data were 

available across the three case studies for characterizing indoor dust Pb and related factors. In 

addition, the absence of statistical regression models in the literature specifically focused on 

urban residential locations necessitated the development of a hybrid mechanistic-empirical 

model for the general urban case study (see below). The approaches used to predict indoor dust 
 3-10

Pb for each of the case studies are presented below and described in greater detail in Appendix 

G. 

3.1.4.1 General Urban Case Study 

Two models are used to estimate indoor dust Pb in the general urban case study: a hybrid 

mechanistic-empirical model (the hybrid model), and a regression-based model. Application of 

these models with the ambient air Pb concentrations for this case study produced two sets of 

indoor dust Pb estimates for each air quality scenario (see Appendix C, Section C.3). 

The hybrid model uses a mechanistic model to predict indoor dust Pb resulting from the 

infiltration of outdoor air containing Pb into indoor residential air with subsequent contribution 

to indoor dust Pb. This portion of indoor dust Pb derived with this model is subsequently 

referred to in this document as the recent air or recent ambient air related component or 

contribution to indoor dust Pb. To the extent that outdoor air Pb includes contributions from 

resuspension of historically deposited Pb, it is represented here. Other contributions to indoor 

dust Pb (e.g., tracking of outdoor soil/dust indoors, indoor paint flaking, etc.) are addressed using 

an empirically based estimate derived from a national-scale dataset characterizing indoor dust Pb 

loadings in U.S. residences (the U.S. Housing and Urban Development [HUD] National Survey 

of Lead-based Paint in Housing - USEPA, 1995). When combined, the recent air component 

(derived using the mechanistic model) and the other contributions component (derived using the 

HUD dataset) provide an estimate of total indoor dust Pb for the general urban case study 

(Appendix C, Exhibit C-7). Note that while the indoor dust Pb concentration contributed by the 

hybrid model's recent air Pb component will vary, depending on the ambient air Pb level, the 

concentration contributed from other sources will remain constant across the different air quality 

scenarios. The two components of the hybrid model (the mechanistic and empirical) are 

described in greater detail below. 

The mechanistic model linking ambient air Pb to indoor dust Pb was developed by 

obtaining the steady state solution to a dynamic mass balance equation that predicts Pb in both 

indoor air and indoor floor dust as a function of outdoor air Pb. This dynamic mass balance 

equation was developed specifically for this assessment. In recognition of the complexity of the 

larger task of simulating contributions to indoor dust Pb from all sources, the mechanistic model 

development activity was limited to the area considered most essential to the needs of this 

assessment, i.e., the contribution to indoor dust Pb from recent ambient air. See Appendix G, 

Section G.3.2 for additional detail on the derivation of the mechanistic ambient air-to-indoor dust 

Pb model. 

As noted above, contributions of other sources to Pb in indoor dust were addressed in the 

hybrid model using empirical data (rather than trying to model them mechanistically). The HUD 
 3-11

dataset, described above, that characterized the national distribution of residential indoor dust Pb 

(USEPA, 1995) was selected as the basis for this other sources component of indoor dust Pb. 

Specifically, a median value from the HUD dataset characterizing residential indoor dust Pb 

loadings (USEPA, 1995) was identified, and the ambient air related component of this indoor 

dust value estimated and subtracted from the HUD median value, leaving only the other sources 

component. This provided a central tendency estimate of the component of indoor dust Pb 

loadings associated with sources other than recent air Pb, for typical residences in the United 

States (see Appendix G, Section G.3.3 for additional detail). As noted earlier, this single 

estimate of other sources indoor dust Pb is used for modeling all air quality scenarios, with no 

change associated with the ambient air Pb level being evaluated. 

The hybrid model generates estimates of indoor dust Pb in terms of loading, rather than 

concentration, while concentration is the form required by the blood Pb models used in this 

analysis. Conversion from loading to concentration was accomplished using a log-log regression 

equation derived from the HUD dataset described above. This dataset has matched data for 

sampled residences for both indoor dust loadings from vacuum samples, and indoor dust 

concentrations. Use of the HUD dataset based loading to concentration conversion required an 

additional conversion between loading estimates based on wipe samples (the form used by the 

hybrid model) and those based on vacuum samples (the form of the HUD data). This was 

accomplished using an equation developed by EPA (USEPA, 1997). Details on both 

conversions are provided in Appendix G, Sections G.3.4 and G.3.4.1. 

The individual elements of the hybrid model, including both the mechanistic and 

empirical components as well as the loading-to-concentration conversion equations are presented 

in Table 3-2. The final hybrid equation (including all components) is presented last. 
 3-12

Table 3-2. Hybrid model for indoor dust Pb in general urban case study. 

Model component Formula 

Hybrid mechanistic equation for “recent” air 

FLOOR LOADING (PbWipe) = 104.2 * PbAIR 

Where, PbAIR is Pb in outdoor ambient air, and FLOOR LOADING is 

in terms of wipe sample loading. 

Hybrid empirical component for other sources 1.15 μg/m2

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

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

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

Because the hybrid model has not been subject to extensive review and application 

outside of this analysis and given the influence of indoor dust Pb on Pb exposure and risk, dust 

Pb concentrations for the general urban case study are also estimated using an additional dust Pb 

model (see Appendix G, Section G.3.5). The use of two models is intended to inform our 

characterization of model uncertainty in this key portion of the analysis. Specifically, we have 

included the air-only regression model (EPA, 1989) as a second, parallel approach in predicting 

indoor dust Pb. That model estimates indoor dust Pb based on (a) outdoor ambient air Pb 

(multiplied by an air-related factor) and (b) an intercept which captures other impacts besides air 

(e.g., indoor paint). The air factor used in this equation is expected to capture longer-term 

impacts of outdoor air Pb on indoor dust, including the indirect effect of air Pb on outdoor 

soil/dust Pb with subsequent impacts of that outdoor soil/dust Pb on indoor dust Pb through other 

mechanisms (EPA, 1989). The air-only regression model is presented below: 

PbDUST(mg/kg or ppm) = 60 + 844 * PbAIR(μg/m3

) 

3.1.4.2 Primary Pb Smelter Case Study 

We used different regression models for predicting Pb concentrations in indoor dust in 

areas near the primary Pb smelter facility where soil has been remediated and more distant areas. 

For the remediation zone near the facility, a regression equation was developed using dust Pb 

measurements which had been collected from some of the houses within this area. These data, 

while adequate for development of a site-specific regression model, did not have sufficient 

spatial coverage to be used alone to represent indoor dust Pb levels for that portion of the study 

area. For the remainder of the study area, we employed a regression equation developed for the 
 3-13

last review (USEPA, 1989). Because of the presumed impact of the remediation activity on dust 

Pb, the site-specific dust Pb model developed for the remediation zone was not considered 

appropriate for use in areas beyond that area. 

The dataset used to develop the model for the remediation zone was based on indoor dust 

samples collected in 17 houses within the remediation zone. Independent variables included in 

the analysis were: (a) estimated annual average Pb concentrations in air at census block centroids 

located within 200 meters of each of the 17 houses, (b) road dust Pb measurements for locations 

within 300 meters of each house and (c) postremediation residential soil Pb measurements for the 

yard of each house. Preremediation soil Pb concentrations were not included in the regression 

analysis since they were not expected to represent current conditions at the site. Multiple 

samples for each medium associated with a specific house within the dataset (e.g., reflecting 

multiple samples collected over time) were averaged to produce a "temporally averaged" value. 

A number of regression models were evaluated, (see Appendix G, Section G.4), and the “H5” 

model was ultimately selected based on goodness of fit and other considerations. This model 

relates the natural log of indoor house dust to the natural log of ambient air Pb (r2

=0.625): 

ln(house dust, mg/kg or ppm) = 7.7892 + 0.7200*ln(air Pb, μg/m3) 

In applying the H5 model to the remediation zone portion of the study area, a constraint 

was applied in recognition of the our application of this regressions to scenarios with much lower 

air Pb than that for the dataset on which it is based, and findings for similar towns of residual 

levels of Pb in house dust that reflect historical contributions (e.g., von Lindern et al., 2003; 

Hilts, 2003). A floor value for the predicted dust Pb for alternative NAAQS scenarios was 

assigned to any model predictions falling below it. This value was 60 ppm, which is the 

intercept for the air only regression model derived at the time of the last review, see previous 

section. While this value may be an underestimate of the historical component of Pb in house 

dust of existing houses or houses existing prior to remediation, its use is intended to balance the 

need for a floor for such houses and the potential for the population to also have newer houses. 

The use of and need for this floor recognizes the uncertainty associated with this model. 

Additionally, the regression model used here does not lend itself to partitioning the recent air 

related Pb from other contributions (discussed in Section 2.4.3); accordingly, this partitioning is 

not done for the primary Pb smelter case study. 

Several points regarding the other variables considered for the remediation zone 

regression are noted here. For example, road dust Pb concentration was not found to have 

significant predictive power for indoor dust Pb. This may reflect the fact that the road dust Pb 

dataset does not provide significant coverage for homes located near to the truck haul routes. 

Additionally, yard soil Pb concentration was found to be slightly, and statistically significantly, 

negatively correlated with indoor dust Pb levels. This counterintuitive finding may be a result of 
 3-14

the existence within the remediation zone of a patchwork of remediated yards, such that the 

remediation activity may have interfered with any correlation between yard soil Pb levels, 

ambient air Pb levels and indoor dust Pb levels that might have existed previously. The resulting 

slight negative correlation of dust Pb levels with soil Pb levels led us to exclude soil Pb in 

predicting indoor dust Pb, leading to the selection of the H5 model, which only considers 

ambient air Pb in predicting indoor dust Pb. The y-intercept for the selected model may reflect a 

number of factors not correlated with ambient air or distance from the facility, such as a general 

level of soil Pb contamination in the area and indoor Pb paint. 

For areas beyond the remediation zone, we used a regression equation developed during 

the last review from data collected at a number of operational primary Pb smelters, including this 

case study location (USEPA, 1989, Appendix B). This model, the "AGG" or "aggregate" model, 

predicts indoor dust Pb concentration from both outdoor soil and ambient air Pb concentrations. 

We have selected the AGG model for the nonremediation portion of the primary Pb smelter case 

study area since this area has not been subjected to extensive remediation and is therefore likely 

to resemble the locations included in the pooled dataset used in deriving this model in terms of 

relationships among air, outdoor surface soil/dust and indoor dust. The AGG air and soil 

regression model (USEPA, 1989), selected for areas beyond the remediation zone is the 

following: 

 House dust (mg/kg or ppm) = 31.3 + 638*air Pb (μg/m3

) + 0.364*soil Pb (mg/kg) 

3.1.4.3 Secondary Pb Smelter Case Study 

A version of the empirical regression model used for the primary Pb smelter (USEPA, 

1989) was also used for the secondary Pb smelter case study. In this case study, an air-only 

version of the model (USEPA, 1989) was employed reflecting the reduced overall confidence 

associated with soil characterization at this case study (see Section 3.1.3.3). The AGG model for 

estimating indoor dust (USEPA, 1989) was derived in two forms including an air-only model 

that based indoor dust concentrations on outdoor ambient air Pb (without explicitly considering 

outdoor soil Pb levels) and an air+soil model which based estimates on both outdoor soil and 

ambient air Pb data. It is important to note, however, that the air-only model implicitly reflects 

some consideration for the air-to-soil-to-indoor dust mechanism in the air signal. Specifically, 

the larger air factor for the air-only model relative to the air+plus dust model's air factor, reflects 

contribution of air Pb both directly to dust through penetration indoors and subsequent 

deposition to surfaces and indirectly to dust through deposition to outdoor soil which impacts 

indoor dust (USEPA, 1989). This air-only regression model (USEPA, 1989) is as follows: 

 House dust (mg/kg or ppm) = 60 + 844*air Pb (μg/m3

) 
 3-15

The model used for this case study was based on a number of studies focusing mainly on 

primary Pb smelters (a number of primary Pb smelters were operational at the time of model 

development). This may introduce uncertainty into indoor dust Pb predictions generated for this 

case study to the extent that factors related to the dependency of indoor dust Pb on ambient air 

Pb, such as particle size profiles and the nature of the airborne Pb compounds, differ for primary 

versus secondary Pb smelters. 

3.2 METHODS FOR ESTIMATING BLOOD PB LEVELS 

This section presents the methodology used to estimate blood Pb levels in the child study 

populations. The section begins with an overview of the primary biokinetic model used in this 

full-scale risk analysis, the Integrated Exposure and Uptake Biokinetic model (USEPA, 1994a). 

Findings associated with the use of both the IEUBK and Leggett models in the pilot analysis, in 

addition to subsequent performance evaluation analyses (see Appendix J) contributed to the 

decision to use IEUBK as the primary blood Pb model and reserve the Leggett model for use in 

the sensitivity analyses. In addition, we have considered an empirical slope model (the Lanphear 

model) in conducting our performance evaluation (Appendix J, Section J.2). 

Following the overview of the IEUBK biokinetic model, the probabilistic approach used 

to generate population-level distributions of blood Pb levels for each study population is 

described. The section ends with a discussion of the GSDs used within each case study to reflect 

interindividual variability in behavior related to Pb exposure and Pb biokinetics (a key 

component in modeling population-level blood Pb). 

3.2.1 Blood Pb Modeling 

Blood Pb models are used in the assessment in order to (a) apportion exposure, the metric 

for which is blood Pb, between policy-relevant Pb exposures and policy-relevant background, 

and (b) estimate potential changes in blood Pb level distributions that would result from alternate 

ambient air Pb levels. 

As discussed in Section 4.4.1 of the CD, there are two broad categories of blood Pb 

models available to support exposure and risk assessment: 

• Statistical (regression) models, which attempt to apportion variance in measured blood Pb 

levels for a study population to a range of determinants or control variables (e.g., surface 

dust Pb concentrations, air Pb concentrations). The development of these models 

requires paired predictor-outcome data which restricts these empirical models to the 

domain of their observations (i.e., to applications involving the study population(s) and 

exposure scenarios used in their derivation or at least to scenarios very similar to the 

original study conditions). 
 3-16

• Mechanistic models, which attempt to model the process of transfer of Pb from the 

environment to human tissues. While these models are considerably more complex 

compared with the regression models (in terms of both the number of variables and their 

computational structure), by incorporating variables that vary temporally and spatially, or 

across individuals or populations, mechanistic models can be extrapolated to a wide 

range of scenarios, including those outside of the original populations and exposure 

scenarios used to develop/parameterize the models. 

Given concerns over applying regression models to populations and exposure scenarios 

other than those used in their derivation, and consistent with recommendations from CASAC 

(see Section 1.4), we have relied primarily on mechanistic models in conducting the exposure 

analysis for this assessment. Additionally, a regression model developed by Lanphear et al. 

(1998) and described in Appendix H, Section H.2.3.was included in the blood Pb modeling 

performance evaluation (Appendix J, Section J.3.2). The CD (Section 4.4) describes three 

mechanistic (biokinetic) models developed over the past several decades including IEUBK for 

modeling child Pb exposure (CD, Section 4.4.5 and Appendix H, Section H.2.1) and two models 

designed to simulate Pb biokinetics and blood Pb levels from birth through adulthood, the 

Leggett model (CD, Section 4.4.6) and the model developed by O’Flaherty (CD, Sections 4.4.7). 

The Leggett and O’Flaherty models simulate smaller time steps and consequently demonstrate 

blood Pb responses on much shorter time scales than the IEUBK model, the outputs for which 

are described as indicative of quasi steady-state conditions (CD, Sections 4.4.5-4.4.7). All three 

models have the potential for application in Pb risk assessment and have been evaluated to 

varying degrees using empirical datasets (CD, Section 8.3.4). Based on results of the pilot 

analysis, subsequent performance evaluations, and advice from CASAC, we selected the IEUBK 

model as the primary blood Pb model for the full-scale analysis. The Leggett model has been 

included as part of the sensitivity analysis intended to assess the potential impact of uncertainty 

in blood Pb modeling on the overall analysis (See Section 4.3.2). A brief overview of the 

IEUBK and Leggett models is presented below. 

3.2.1.1 Primary Analysis 

The IEUBK model was selected for use in generating the primary set of exposure and 

risk results. IEUBK is a multicompartment pharmacokinetics model for children 0 through 84 

months of age (the first 7 years of life), which predicts average quasi-steady state blood Pb 

concentrations corresponding to daily average exposures, averaged over periods of a year of 

more. The exposure module provides average daily intakes of Pb (averaged over a 1 year time 

increment) for inhalation (air, including consideration for both outdoor and indoor) and ingestion 

(soil, indoor dust, diet and water) (Section 4.4.5.1 of the CD). The model is intended to be 

applied to groups of children experiencing similar levels of Pb exposure and to generate a central 
 3-17

tendency blood Pb estimate for that group (USEPA, 1994a). In applications of IEUBK, 

interindividual variability in biokinetics and behavior (e.g., varying rates of dietary Pb ingestion) 

of the study population is typically characterized through the incorporation of a GSD which, 

together with the IEUBK-generated blood Pb level, provides a distribution of blood Pb levels for 

a group of modeled children. 

For each exposure zone in each case study air quality scenario, estimates of the 

concurrent and lifetime average blood Pb level are derived from the outputs of the IEUBK model 

as described in Appendix H. Briefly, the concurrent metric is derived as the average over ages 

73 to 84 months (approximately 6 to 7 years of age)3

, and the lifetime average metric is the 

average of blood Pb levels between ages 6 and 84 months. 

Additional detail on the IEUBK model is described in Appendix H (Section H.2.1) and in 

Section 4.4.5 of the CD. Application of the IEUBK model is described in Appendix H, Section 

H.3.1, and input parameter values, and their basis, are described in Appendix H, Section H.4. 

3.2.1.2 Sensitivity Analysis 

The Leggett model has been included as part of the sensitivity analysis (Appendix L) and 

in model performance evaluations (Appendix J), but has not been used in generating primary 

exposure and risk results for this assessment. Originally developed from a model designed to 

simulate radiation doses for bone-seeking radionuclides, the Leggett biokinetic model has a 

temporal resolution of one day and can model exposure from infancy through adulthood (CD, 

Section 4.4.6). The daily resolution in Leggett allows more comprehensive treatment of the 

temporal pattern of exposure and its shorter-term impact on blood Pb levels than IEUBK, 

although for this assessment, which focuses on longer-term trends in Pb exposure, this 

functionality is less relevant. The Leggett model does not include a detailed pathway-level 

exposure module as does IEUBK. Rather the Leggett model takes total ingestion and inhalation 

exposure estimates as inputs. However, it is possible to link the Leggett model to a more 

detailed pathway-level exposure model, thereby allowing a more detailed treatment of Pb 

exposure pathways and their impact on blood Pb. The use of this type of external exposure 

model including pathway-specific modeling of exposure levels was implemented for the pilot 

analysis. As with IEUBK, Leggett can be used to derive central tendency blood Pb levels for 

groups of similarly exposed children. The same GSD used for IEUBK is then used to produce 

 

3

 As described in Appendix I (Section I.2.1), the concurrent metric is calculated by averaging blood Pb 

estimates generated for ages 75 and 81 months with these estimates representing the first and second halves of the 

7th year of life, respectively. Use of 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). 
 3-18

estimates of the distribution of blood Pb levels within study populations. For additional details 

on the Leggett model see Section 4.4.6 of the CD and Appendix H. 

3.2.2 Exposure Pathway Apportionment and Probabilistic Population Modeling 

This section describes the method used to estimate contributions to blood Pb and IQ from 

exposure pathways of interest (see Section 2.4.3), and the method used for probabilistic 

population modeling. Both are applied to the central tendency estimates of blood Pb developed 

for the two blood Pb metrics (concurrent and lifetime average) from the IEUBK model outputs. 

To the extent feasible with the modeling tools and assessment design, estimates of the 

contribution to blood Pb (central tendency estimate) are developed for policy-relevant 

background versus policy-relevant exposures (Section 2.4.3). This is done by considering blood 

Pb model estimates for an exposure zone derived using only the pathways of interest (Appendix 

I, Section I.1). As discussed in Section 2.4.3, there are limitations on the resolution to which 

policy-relevant exposures can be distinguished which results in some simplifying assumptions. 

We developed estimates of contribution to blood Pb estimates (and IQ estimates) for the 

following pathways (or pathway combinations): 

• Inhalation of ambient air Pb (i.e., “recent air” Pb): This is derived using the blood Pb 

estimate resulting from Pb exposure limited to the inhalation pathway. 

• Ingestion of “recent air” indoor dust Pb: This is derived using the blood Pb estimate 

resulting from Pb exposure limited to ingestion of the Pb in indoor dust that is 

predicted to be associated with ambient air concentrations (i.e., via the air 

concentration coefficient in the regression-based dust models or via the mechanistic 

component of the hybrid blood Pb model (Section 3.1.4). For the primary Pb smelter 

case study, estimates for this pathway are not separated from estimates for the pathway 

described in the subsequent bullet due to uncertainty regarding this categorization with 

the model used for this case study (Section 3.1.4.2). 

• Ingestion of “other” indoor dust Pb: This is derived using the blood Pb estimate 

resulting from Pb exposure limited to ingestion of the Pb in indoor dust that is not

predicted to be associated with ambient air concentrations (i.e., that predicted by the 

intercept in the dust models plus that predicted by the outdoor soil concentration 

coefficient, for models that include one (Section 3.1.4)). This is interpreted to 

represent indoor paint, outdoor soil/dust, and additional sources of Pb to indoor dust 

including historical air (see Section 2.4.3). As the intercept in regression dust models 

will be inclusive of error associated with the model coefficients, this category also 

includes some representation of dust Pb associated with current ambient air 

concentrations (described in previous bullet). For the primary Pb smelter case study, 

estimates for this pathway are not separated from estimates for the pathway described 

above due to uncertainty regarding this categorization with the model used for this case 

study (Section 3.1.4.2). 
 3-19

• Ingestion of outdoor soil/dust Pb: This is derived using the blood Pb estimate resulting 

from Pb exposure limited to ingestion of outdoor soil/dust Pb. 

• Ingestion of drinking water Pb: This is derived using the blood Pb estimate resulting 

from Pb exposure limited to ingestion of drinking water Pb. 

• Ingestion of dietary Pb: This is derived using the blood Pb estimate resulting from Pb 

exposure limited to ingestion of dietary Pb. 

The goal of this probabilistic exposure modeling is to generate population-level 

distributions of blood Pb levels that allow (a) specific percentiles of exposure (e.g., 50th, 90th, 

and 95th) within a study population to be identified. These are presented along with the 

differentiation by exposure pathway (e.g., policy-relevant background versus policy-relevant 

exposures, with the latter further differentiated as to ambient air inhalation, indoor dust ingestion 

and outdoor surface soil/dust ingestion, as indicated above). Therefore, for example, we may 

have an estimate of exposure for the 95th percentile child in the primary Pb smelter case study, 

with that blood Pb level differentiated as to the fraction coming from (a) diet and drinking water, 

(b) recent air Pb, including ambient air inhalation and ingestion of the recent air component of 

indoor dust Pb, with this fraction potentially including resuspended, previously deposited Pb (see 

Section 2.4.3), and (c) other sources including outdoor soil/dust and historical air Pb, as well as 

indoor paint. The effort to differentiate the recent air and other sources of Pb to indoor dust is 

subject to different degrees of uncertainty for the various case studies, reflecting the different 

approaches used in modeling indoor dust (see Sections 2.4.3 and 3.1.4). Furthermore, it is noted 

that given the various limitations of our modeling tools (Sections 2.4.3), blood Pb levels 

associated with air-related exposure pathways and current levels of Pb emitted to the air 

(including via resuspension) are likely to fall between the estimates for “recent air” and those for 

“recent” plus “past air”. 

Probabilistic exposure modeling differs for the two point source case studies and the 

general urban case study. Because the two point source case studies are location-specific, 

demographic data for each study area are used to evaluate the interaction between child 

populations in those study areas and the distribution of Pb concentrations in each media (e.g., 

outdoor ambient air, outdoor soil/dust and indoor dust). By contrast, because the general urban 

case study is not location-specific, probabilistic exposure modeling does not involve locationspecific demographic data and instead, is based on the assumption of a uniformly distributed 

child receptor population contacting media with uniformly distributed Pb. 
 3-20

3.2.2.1 General Urban Case Study 

The approach used to generate exposure distributions for the general urban case study is 

simpler than that used for the two point source case studies (Section 3.2.2.2), due to the use of a 

uniformly distributed population and spatially uniform media concentrations of Pb in the design 

of this case study. This design negates the need for population-weighted sampling in generating 

the exposure distribution. Instead, the central tendency blood Pb level generated for a specific 

air quality scenario using IEUBK, is combined deterministically with the GSD reflecting 

interindividual variability in Pb exposure and biokinetics to produce a population-level 

distribution of blood Pb levels.4

 Specific percentile exposure levels are then identified from that 

distribution, with these levels interpreted as representing modeled individual children for the 

general urban case study (i.e., estimates conceptually equivalent to those generated for the two 

point source case studies). However, because the analysis is not location-specific, population 

incidence estimates are not generated. 

The study area for the general urban case study can be considered a single exposure zone 

with a single IEUBK-derived central tendency blood Pb level that is combined with the GSD to 

produce the population-level exposure distribution for that study area. As is the case with 

individual exposure zones modeled for the point source case studies, the entire child population 

in the single exposure zone modeled here is given the same pathway apportionment. As with the 

point source case studies, this approach introduces uncertainty into the analysis, especially for 

simulated individuals with high-end blood Pb levels, since pathway apportionment would likely 

not be the same for all individuals in an exposure zone, even if all were exposed to the same Pb 

concentrations in each medium. 

3.2.2.2 Point Source Case Studies 

Probabilistic exposure modeling for the two point source case studies relied on 

information in three areas as summarized below: 

• Central tendency blood Pb levels for each exposure zone: Outputs from the biokinetic 

blood Pb modeling (considered to be central tendency estimates) are used to produce 

central tendency estimates of concurrent and lifetime average blood Pb levels for each 

exposure zone (i.e., census block or block group) in each case study area. 

 

4

 Note that as discussed in Section 3.2.3.1, the range of GSDs used in the general urban case study to reflect 

interindividual variability in lead exposure and biokinetics has been selected to include some degree of coverage for 

small-scale variation in media concentrations. Therefore, while the overall scenario modeled in the general urban 

case study is based on the assumption of generally uniform media concentrations (e.g., an ambient air concentration 

level which is generally constant across the study area), the use of larger GSDs provides some coverage for 

residence-to-residence variation in these media concentrations. 
 3-21

• Demographics (child distribution within study areas): The distribution of children (<7 

yrs old) within each case study area is used to insure that the generation of populationlevel blood Pb level distributions for each case study reflects where children are located. 

• GSD reflecting interindividual variability in blood Pb levels: A GSD is used to reflect 

interindividual variability for blood Pb levels in groups of similarly exposed children 

(i.e., within each exposure zone of a case study area). The GSD is combined with the 

central tendency blood Pb level estimates to generate a distribution of blood Pb levels for 

the group of children located in each exposure zone. 

The stepwise procedure used to generate population-level blood Pb distributions for each 

of the point source case studies is illustrated in Figure 3-1, with the information described in the 

bullets above recognized as input data. 

Several points related to implementation of this procedure are noted. For this assessment, 

50,000 simulated individuals were generated for each point source case study in order to insure 

that the population-level blood Pb distributions generated met target stability goals (see 

Appendix M, Section M.2.2).5

 This simulation count represents a higher total child count than 

actually is associated with either study area. Using a higher number of simulated individuals was 

necessary, however, to generate blood Pb distributions with "stable" higher-end exposure 

estimates. If simulations matching the actual population count at each case study had been 

conducted, the distributions that would have resulted would have been "unstable" at higher 

percentiles. It is important to note, however, that child population count estimates for individual 

percentiles (Section 3.4) have been scaled to reflect the actual child count associated with each 

study area. 

 

5 The analysis of simulation stability focused on the general urban case study and demonstrated that even 

the highest population percentiles generated for this analysis (95th percentile) were relatively stable with coefficients 

of variation of less than 1% for total IQ loss estimates (see Appendix M, Section M.2.2).
 3-22

Figure 3-1. Procedure for generating population blood Pb distributions for point source 

case studies. 

Step 1 – select a central-tendency blood Pb level for a specific 

exposure zone based on population-weighted random sampling: 

The central-tendency blood Pb level dataset is combined with the 

demographic dataset to conduct population-weighted sampling of a 

central-tendency blood Pb level for a specific exposure zone (i.e., 

sampling proportional to child count within each exposure zone is 

used to select a specific zone and the central-tendency blood Pb level 

for that zone is chosen as the output form Step 1). 

Step 2 – select an adjustment factor reflecting inter-individual 

variability in behavior and biokinetics related to Pb exposure for 

a single simulated child: A value is sampled randomly from the 

adjustment factor distribution described above. This single value 

represents the behavior and biokinetics (related to Pb exposure) for a 

single simulated child. 

Step 3 – generate a blood Pb level for a single simulated 

individual: The central-tendency blood Pb level selected in Step 1 is 

multiplied by the adjustment factor in Step 2 to produce an “adjusted”

blood Pb level for a single simulated child within the exposure zone 

selected in Step 1 (this value reflects both the central-tendency PbB

for that exposure zone as well as consideration for inter-individual 

variability in PbB levels). 

Repeat procedure 

50,000 times to 

generate set of 50,000 

simulated individuals

(Note, this set can be 

weighted down to 

reflect the actual 

population of children 

(0-7 years) within 

each study area in 

generating 

population-count 

related risk metrics). 

Input distributions and datasets used in the Monte Carlo-based population-exposure modeling: 

• Central-tendency blood Pb level dataset: Blood Pb modeling (described in Section 3.2.1) 

generates a central-tendency blood Pb level for each exposure zone (e.g., block, or blockgroup) 

within each study area (results dimensioned on blood metric and blood Pb model). 

• Demographic dataset: The child count (children <7 yrs of age) for each exposure zone is used 

to support population-weighted sampling of exposure zones.

• Adjustment factor distribution representing inter-individual variability in behavior and 

biokinetics related to Pb exposure: These adjustment factor distributions are derived by 

centering the GSDs (representing inter-individual variability in PbB levels) on a median value of 

1.0. This results in distributions that can be used to obtain adjustment factors reflecting the 

behavior and biokinetics (related to Pb exposure) for a single simulated child. 

Step 4 – place simulated child blood Pb level (output of Step 3) in 

pool of modeled blood Pb levels for that study area: This pool of 

simulated child blood Pb levels represents the distribution of blood Pb

levels across the study area and reflects (a) the demographic 

distribution of children across that study area (and their relation to Pb

levels in contact media) and (b) inter-individual variability in behavior 

and biokinetics related to Pb exposure.

Monte Carlo-Based Population Blood Pb Level Modeling Procedure:

Step 1 – select a central-tendency blood Pb level for a specific 

exposure zone based on population-weighted random sampling: 

The central-tendency blood Pb level dataset is combined with the 

demographic dataset to conduct population-weighted sampling of a 

central-tendency blood Pb level for a specific exposure zone (i.e., 

sampling proportional to child count within each exposure zone is 

used to select a specific zone and the central-tendency blood Pb level 

for that zone is chosen as the output form Step 1). 

Step 2 – select an adjustment factor reflecting inter-individual 

variability in behavior and biokinetics related to Pb exposure for 

a single simulated child: A value is sampled randomly from the 

adjustment factor distribution described above. This single value 

represents the behavior and biokinetics (related to Pb exposure) for a 

single simulated child. 

Step 3 – generate a blood Pb level for a single simulated 

individual: The central-tendency blood Pb level selected in Step 1 is 

multiplied by the adjustment factor in Step 2 to produce an “adjusted”

blood Pb level for a single simulated child within the exposure zone 

selected in Step 1 (this value reflects both the central-tendency PbB

for that exposure zone as well as consideration for inter-individual 

variability in PbB levels). 

Repeat procedure 

50,000 times to 

generate set of 50,000 

simulated individuals

(Note, this set can be 

weighted down to 

reflect the actual 

population of children 

(0-7 years) within 

each study area in 

generating 

population-count 

related risk metrics). 

Input distributions and datasets used in the Monte Carlo-based population-exposure modeling: 

• Central-tendency blood Pb level dataset: Blood Pb modeling (described in Section 3.2.1) 

generates a central-tendency blood Pb level for each exposure zone (e.g., block, or blockgroup) 

within each study area (results dimensioned on blood metric and blood Pb model). 

• Demographic dataset: The child count (children <7 yrs of age) for each exposure zone is used 

to support population-weighted sampling of exposure zones.

• Adjustment factor distribution representing inter-individual variability in behavior and 

biokinetics related to Pb exposure: These adjustment factor distributions are derived by 

centering the GSDs (representing inter-individual variability in PbB levels) on a median value of 

1.0. This results in distributions that can be used to obtain adjustment factors reflecting the 

behavior and biokinetics (related to Pb exposure) for a single simulated child. 

Step 4 – place simulated child blood Pb level (output of Step 3) in 

pool of modeled blood Pb levels for that study area: This pool of 

simulated child blood Pb levels represents the distribution of blood Pb

levels across the study area and reflects (a) the demographic 

distribution of children across that study area (and their relation to Pb

levels in contact media) and (b) inter-individual variability in behavior 

and biokinetics related to Pb exposure.

Monte Carlo-Based Population Blood Pb Level Modeling Procedure:
 3-23

An additional point of clarification relates to the process used in differentiating specific 

percentile total blood Pb levels into pathway-specific fractions for the two point source case 

studies. All simulated individuals associated with a given exposure zone (i.e., U.S. Census block 

or block group), were assigned the same pathway-specific apportionment, which was derived 

from the blood Pb modeling performed for the exposure zone. The set of simulated individuals 

with a range of total blood Pb levels, produced subsequently via application of the GSD, are 

assumed to all have the same pathway-specific apportionment of those blood Pb levels (i.e., the 

same apportionment as that generated for the central tendency blood Pb level modeled for that 

exposure zone). In reality, it is likely that pathway apportionment would vary across children 

with different blood Pb levels located in the same exposure zone (e.g., the contribution of indoor 

dust exposure to total blood Pb might differ for kids living near each other who demonstrate 

different total blood Pb levels). The modeling approach used, however, does not provide 

pathway apportionment within an exposure zone, only across exposure zones (i.e., each exposure 

zone has a different pattern of pathway apportionment for its simulated children). 

The modeling approach presented in Figure 3-1 and described above, generates a 

population-level distribution of total blood Pb levels with pathway apportionment as described 

above. These distributions are used to generate several types of exposure metrics including: 

• Population-weighted exposure percentiles: total blood Pb levels (with pathway 

apportionment) for simulated individuals representing specific points along the population 

blood Pb distribution (e.g., 50, 90, and 95th percentile). 

• Incidence counts: number of children within a given study area projected to experience a 

specific degree of Pb exposure (total blood Pb level). 

3.2.3 GSD for Population Blood Pb Modeling Procedure 

A key aspect of the population-level blood Pb modeling for all three case studies is the 

application of the GSD reflecting interindividual variability in blood Pb levels. This GSD 

reflects a number of factors which operate together to produce interindividual variability in blood 

Pb levels, including: (a) biokinetic variability (differences in the uptake, distribution or clearance 

of Pb), (b) differences in behavior related to Pb exposure (e.g., varying hand-to-mouth activity, 

tap water ingestion rates, and time spent playing indoors) and (c) differences in environmental 

Pb exposure concentrations (e.g., spatial gradients in ambient Pb levels of a resolution beyond 

that simulated in each case study, differences in cleaning/vacuuming rates and air exchange 

rates). 

GSDs will tend to be larger for more diverse populations and/or larger study areas, 

reflecting the potential for greater variability in the factors listed above. Specifically, more 

diverse populations will tend to demonstrate greater diversity in behavioral and biokinetic factors 
 3-24

related to Pb exposure, thereby producing greater variation in blood Pb levels. Larger study area 

will tend to produce greater GSDs due both to more diverse populations, with their greater 

behavioral and biokinetic variability, as well as greater variation in Pb media concentrations 

across the study area. 

The dramatic reduction in blood Pb levels among children in the United States that was 

observed between the NHANES surveys of the late 1970s and those of the 1980s and later was 

accompanied by an increase in GSD (see Appendix H, Exhibit H-7). Possible reasons for this 

include the likelihood that, as blood Pb levels decrease, a wider variety of exposure pathways 

begin to play a role in determining overall blood Pb levels (at higher blood Pb levels, it likely 

that one, or a few related pathways dominate exposure). As more pathways come into play, the 

potential for interindividual variability in behavior and biokinetics related to these pathways 

increase, thereby producing greater variability in blood Pb levels (see Section 4.2.2 of EPA, 

1994b). Another possible explanation for the increase in GSDs is that, while overall Pb exposure 

levels have decreased, some fraction of children nationwide continue to be exposed to Pb paint 

and Pb in drinking water (associated with Pb solder used in older plumbing). These higher 

nonair-related exposures can produce elevated blood Pb level, especially when compared to 

average blood Pb levels in the current general population. Therefore, while the geometric mean 

blood Pb level may have decreased, the tail of the distribution may have remained anchored (for 

these paint and drinking water exposed children) resulting in a larger GSD. 

A number of studies have been conducted over the past three decades which provide 

insights into interindividual variability in Pb levels under various exposure conditions. Many of 

the studies from the 1970’s and 1980’s focused on populations living near smelters with fairly 

elevated blood Pb levels compared with levels modeled for our three case studies. For example, 

as seen in Appendix H, Exhibit H-7, geometric mean blood Pb levels for populations near active 

smelters in the past tended to exceed 8 μg/dL with GSDs on the order of 1.7. Note, that these 

earlier studies do not have readily available summaries of blood Pb levels for the metrics of 

interest in this analysis (concurrent and lifetime average metrics) and instead, provide more 

generalized summaries based on the individual measurements collected. 

Beginning in the 1980’s and extending through the 1990’s, a number of studies were 

conducted focusing on urban populations in specific U.S. cities (e.g., Lanphear et al 2005). These 

studies, which were intended to examine the link between Pb exposure and neurological effects 

in children, have readily available blood Pb summary data for both concurrent and lifetime 

average blood metrics. As can be seen in Appendix H, Exhibit H-9, geometric mean blood Pb 

levels vary significantly across these studies, with concurrent values ranging from 5.5 μg/dL to 

14.5 μg/dL and lifetime average values ranging from 4 μg/dL to 14.2 μg/dL. GSDs also vary 
 3-25

across the studies with GSDs of 1.4 to 1.7 reported for the lifetime average metric and 1.5 to 1.9 

for the concurrent metric. 

In addition to these smelter-related and city-specific studies, the CDC has also conducted 

several iterations of its NHANES national-scale survey over the past three decades which track 

changes in the national distribution of blood Pb levels among children in the U.S. (see CD, 

Section 4.3.1.3 and Appendix H). As mentioned above, between the earliest NHANES surveys 

and the later (post 1980) surveys average blood Pb levels in children in the U.S. decreased 

dramatically (following initiatives to remove Pb from gasoline and other products in the late 

1970’s and 1980’s), and the GSD increased significantly. This is most pronounced in the 

geometric mean blood Pb levels and associated GSDs reported for the first NHANES survey 

(1967-1980) (CDC, 2005) and the second NHANES survey (1988-1991) (CDC, 2005) with 

blood Pb levels decreasing from 14.9 to 3.6 and GSDs increasing from 1.4 to 2.1. 

GSDs were selected for each case study based on consideration for the study data 

summarized above. The selection of these GSDs reflected consideration for a number of factors 

including: (a) the type of study area and underlying population involved (e.g., point source 

versus more general urban area), (b) the fact that all three study areas use exposure zones with 

fairly uniform media concentrations and that the GSD selected will be used to represent blood Pb 

variability within each of those zones (c) age of the underlying survey population (the goal was 

to match the survey population to our study population to the extent possible), and (d) date of the 

survey (generally there is a desire, when possible, to use studies that are more contemporary to 

capture any underlying downward trends in blood Pb levels which have occurred). The GSDs 

for each of the case studies, as well as the rationale for their selection, is presented below. 

3.2.3.1 General Urban Case Study 

For this case study, two sets of population blood Pb estimates are developed for each 

blood Pb metric. This was done using GSDs intended to reflect: (a) a more uniform population 

living in a smaller urban area (represented by a smaller GSD for each metric) and (b) a more 

diverse urban population living in a larger urban area (represented by a larger GSD for each 

metric). Together, these two sets of estimates provide a range of results for each blood Pb metric 

intended to reflect uncertainty associated with this key component of the analysis (i.e., 

interindividual variability in blood Pb levels). The lower-bound GSDs (representing the more 

uniform, smaller urban population) were obtained from the Boston study (Bellinger, 1992) (i.e., 

concurrent GSD of 1.7 and a lifetime average value of 1.6). This study represents one of the 

more contemporary of the urban studies focusing on a smaller population. By contrast, the 

upper-bound GSDs (representing the larger, more diverse population) were obtained using 

NHANES IV (CDC, 2005). Here, the GSD from NHANES IV for 1-5 yr olds (2.1) is used for 
 3-26

the concurrent metric, and the GSD for the lifetime average metric (2.0) was derived by scaling 

the value for the concurrent metric using the relationship between concurrent and lifetime seen in 

the smaller urban-scale Boston study (Bellinger, 1992). 

3.2.3.2 Point Source Case Studies 

A critical consideration in identifying the GSD for use in the point source case studies, is 

the fact that each is modeled using a spatial template that divides the study area into exposure 

zones with relatively uniform media concentrations. As noted earlier, the GSDs are used to 

reflect interindividual variability in blood Pb levels for the group of modeled children located 

within each of those zones. Therefore, the GSD does not need to provide full coverage for media 

concentration variability and its impact on exposure, since this is covered to some extent by the 

spatial template. Therefore, a larger GSD such as that suggested by NHANES would likely 

overstate variability and a smaller GSD, possibly in line with values from smaller-scale studies 

such as the smelter or city-specific studies would seem to be more appropriate. Following this 

logic, a GSD of 1.7 was selected for the concurrent blood metric, based on consideration for the 

range of values from these smaller-scale studies. A matching lifetime average metric GSD value 

of 1.6 was selected, also based on the city-specific studies. 

3.3 ESTIMATED MEDIA CONCENTRATIONS 

This section summarizes the media concentration estimates for all air quality scenarios at 

all three case studies (Tables 3-3 to 3-6). The complete set of media concentration estimates for 

each air quality scenario is presented in Appendix C for the general urban case study, in 

Appendix D (Attachments D-7 through D-11) for the primary Pb smelter case study, and in 

Appendix E (Attachments E-3 through E-7) for the secondary Pb smelter case study. Estimates 

presented in this section are presented to three (for air) or zero (for dust and soil) decimal places, 

which results in various numbers of implied significant figures. This is not intended to convey 

greater precision for some estimates than others; it is simply an expedient and initial result of the 

software used for the calculation. 

For each air quality scenario for the two point source case studies, a range of percentile 

estimates derived from a population-weighted distribution of these media concentrations are 

presented for each exposure medium. For the general urban case study, however, only a single 

value is presented for each exposure medium. This reflects the fact that, while the point source 

case studies are modeled using spatial templates that include a large number of US Census 

blocks and/or block groups (allowing percentile media concentrations to be identified), the 

general urban case study is modeled using a single study area with uniform media 
 3-27

concentrations. Consequently, there is only one value presented for the general urban case study 

for each medium in each air quality scenario. 

As discussed in Section 2.3.3, Pb concentration in outdoor soil/dust is not changed with 

the alternate air quality scenarios. Rather, outdoor soil/dust concentration is held constant at the 

current conditions or current NAAQS level. This reflects the judgement that in most cases, a 

reduced air concentration would not yield a changed soil concentration over the near term (e.g., 

years to decades). In the case of an area such as the remediation zone of the primary Pb smelter 

case study, however, where soil dynamics have been changed by the substitution of 

contaminated soil with clean soil, or in areas where local sources may pose a more significant 

source to outdoor soil/dust than historic sources – and where there may be a currently increasing 

trend in surface Pb concentration - this may underestimate soil concentrations under some 

alternate NAAQS. 

As expected, the highest media concentrations for each case study are associated with the 

current NAAQS scenario. The relatively lower media concentrations for the alternate NAAQS 

scenarios vary in the following order of decreasing estimates: 1) the 0.5 μg/m3 maximum 

monthly average, 2) the 0.2 μg/m3 maximum quarterly, 3) 0.2 μg/m3 maximum monthly and 4) 

the 0.05 μg/m3 maximum monthly average. In the case of the general urban case study, both of 

the current conditions scenarios (mean and high-end) generate media concentrations below the 

current NAAQS, since both monitor-based values are below the current NAAQS. 

Table 3-3. Estimated annual ambient air concentrations. 

Average Annual Air Pb Concentration (μg/m3) 

Alternative NAAQS 

Statistic 

Current 

Conditions 

Current 

NAAQS 

0.2 μg/m3

, 

 max quarterly 

0.5 μg/m3

max monthly 

0.2 μg/m3

max monthly 

0.05 μg/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 0.740 0.161 0.326 0.130 0.033 

95th percentile 0.153 0.033 0.067 0.027 0.007 

Median 0.042 0.009 0.019 0.007 0.002 

5th percentile 0.015 0.003 0.007 0.003 0.001 

Minimum 

NA 

0.006 0.001 0.003 0.001 < 0.001 

Secondary Pb smelter case study 

Maximum 0.126 0.034 0.071 0.028 0.007 

95th percentile 0.015 0.004 0.008 0.003 0.001 

Median 0.003 0.001 0.002 0.001 < 0.001 

5th percentile 0.001 < 0.001 < 0.001 < 0.001 < 0.001 

Minimum < 0.001 

NAa

< 0.001 < 0.001 < 0.001 < 0.001 

a

The current conditions scenario for secondary Pb smelter case study met the current NAAQS. 
 3-28

Table 3-4. Estimated inhalation exposure concentrations. 

Average Annual Inhalation Exposure Concentration of Pb (μg/m3) 

Alternative NAAQS 

Statistic 

Current 

Conditions 

Current 

NAAQS 

0.2 μg/m3 max 

quarterly 

0.5 μg/m3

max monthly 

0.2 μg/m3

max monthly 

0.05 μg/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 0.310 0.067 0.136 0.055 0.014 

95th percentile 0.064 0.014 0.028 0.011 0.003 

Median 0.017 0.004 0.007 0.003 0.001 

5th percentile 0.006 0.001 0.003 0.001 < 0.001 

Minimum 

NA 

0.002 < 0.001 0.001 < 0.001 < 0.001 

Secondary Pb smelter case study 

Maximum 0.056 0.015 0.031 0.013 0.003 

95th percentile 0.007 0.002 0.004 0.002 < 0.001 

Median 0.001 < 0.001 0.001 < 0.001 < 0.001 

5th percentile < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 

Minimum < 0.001 

NAa 

< 0.001 < 0.001 < 0.001 < 0.001 

a

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

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

Statistic

Projected Average Outdoor Soil/Dust Pb 

Concentration (mg/kg) 

(Same for all air quality scenarios)a

General urban case study

NA - single 

study area 198

Primary Pb smelter case study

Maximum 958 

95th percentile 245 

Median 85 

5th percentile 30 

Minimum 17 

Secondary Pb smelter case study

Maximum 315 

95th percentile 66 

Median 12 

5th percentile 1 

Minimum <1 

a

 Estimates developed for current conditions (or current 

NAAQS) scenario were used for all alternate NAAQS 

scenarios. 
 3-29

Table 3-6. Estimated indoor dust concentrations. 

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

Alternative NAAQS 

Statistic 

Current 

Conditions 

Current 

NAAQS 

0.2 (μg/m3

) 

max quarterly 

0.5 (μg/m3) 

max monthly 

0.2 (μg/m3) 

max monthly 

0.05 (μg/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 1,944 648 1,077 557 383 

95th percentile 219 152 172 149 138 

Median 84 68 73 67 63 

5th percentile 53 45 47 44 43 

Minimum 

NA 

41 38 39 38 38 

Secondary Pb smelter case study 

Maximum 166 89 120 84 66 

95th percentile 73 63 67 63 61 

Median 63 61 61 61 60 

5th percentile 60 60 60 60 60 

Minimum 60 

NAa

60 60 60 60 

a

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

3.4 ESTIMATED BLOOD PB LEVELS 

Estimates of concurrent and average lifetime blood Pb level derived from outputs of the 

IEUBK model (Section 3.2) have been developed for each air quality scenario in each case study 

(see Appendix I). Further, multiple sets of blood Pb estimates were generated for each air 

quality scenario of each case study, reflecting an effort to consider key sources of uncertainty 

(e.g., indoor dust model, blood metric, GSD) and their impact on blood Pb estimates (see Section 

2.4.6.2). That is, eight separate blood Pb distributions were generated for each air quality 

scenario of the general urban case study (four for each of the two blood Pb metrics) and two 

distributions were generated for each air quality scenario of the point source case studies (one for 

each of the two blood Pb metrics) (see Table 2-3). The greater number of blood Pb distributions 

for the general urban case study reflects the larger number of modeling approaches implemented 

for this more conceptual case study, which differ by indoor dust model and GSD. 

Because general trends in blood Pb levels across both population percentiles and air 

quality scenarios (for a given case study) are similar for both the concurrent and lifetime average 

blood metrics, we have only presented estimates for the concurrent metric here. Full results are 

presented in Appendix I. Concurrent blood Pb estimates reflecting all pathways (total), the 

“recent air” pathways (see Section 3.2.2), and the recent plus past air pathways are presented in 

Table 3-7. The total estimates presented in Table 3-7 are those for the median in the distribution 

for each case study, and the estimates for the other two categories are the values for those 
 3-30

categories associated with the median for the total blood Pb estimate. The corresponding 

estimates for the 95th percentile in the distribution of total blood Pb estimates for each case study 

are presented in Table 3-8. In these tables, all values are rounded to one decimal place, and we 

have presented all values below 0.05 as <0.1. 

It is noted that given the various limitations of our modeling tools (Sections 2.4.3 and 

3.2.2), blood Pb levels associated with air-related exposure pathways and current levels of Pb 

emitted to the air (including via resuspension) are likely to fall between the estimates for “recent 

air” and those for “recent” plus “past air”. Additionally, with regard to the urban case study 

“recent air” and “recent” plus “past air” categories, an artifact of the hybrid dust Pb model tends 

to mask trends in the two components of dust Pb (recent air and other), which contribute to 

“recent air” and “past air” estimates, respectively (see Section 4.3.1 discussion of this 

uncertainty). For the primary Pb smelter case study, uncertainty in parsing out the "recent air" 

and "other" components of indoor dust (specifically for the site-specific regression model used in 

the remediation zone) have lead us to conclude that only "recent plus past air" exposures should 

be presented and "recent air" should not be separately presented, as is done for the other case 

studies (see Section 3.1.4.2). 

With regard to total blood Pb, estimates for the general urban and primary Pb smelter 

case studies indicate higher values for the current NAAQS scenario compared to any of the other 

scenarios. The difference is 1-2 μg/dL for the median estimates and up to 6 μg/dL difference for 

the 95th percentile estimates. Although no difference in median total blood Pb estimates 

(rounded to whole numbers) is observed between current conditions or alternative NAAQS 

scenarios for the general urban or primary Pb smelter case studies, differences among alternate 

NAAQS scenarios are observed for the primary Pb smelter case study subarea (Appendix P). 

Additionally, a 1 μg/dL difference in total blood Pb is observed at the 95th percentile between 

current conditions and lower alternative NAAQS scenarios for the general urban case study. 
 3-31

Table 3-7. Summary of blood Pb estimates for medians in total-exposure blood Pb 

distributions. 

Concurrent Blood Pb Levela

Case Study and Air Quality Scenario 

Recent airb Recent plus 

past airb Total Pb exposure 

General urban case study Low High Low High Low High 

Current NAAQS (1.5 μg/m3, max quarterly) 1.9 2.2 2.6 3.2 3.1 3.7 

Current conditions - 95th percentile (0.87 μg/m3, max 

quarterly) 0.4 0.8 1.4 1.6 2.0 2.1 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.5 0.9 1.5 1.6 2.0 2.2 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.3 0.7 1.3 1.5 1.9 2.0 

Current conditions - mean (0.14 μg/m3, max quarterly) 0.2 0.6 1.2 1.4 1.8 1.9 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.2 0.5 1.2 1.3 1.7 1.9 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.1 0.2 1.1 1.1 1.6 1.7 

 Primary Pb smelter - full study area

Current NAAQS (1.5 μg/m3, max quarterly) 0.8 1.5 

Alternative NAAQS (0.5 μg/m3, max monthly) 1.1 1.4 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.7 1.4 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.5 1.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 

NAc 

0.7 1.4 

 Primary Pb smelter - 1.5 km subarea

Current NAAQS (1.5 μg/m3, max quarterly) 4.0 4.6 

Alternative NAAQS (0.5 μg/m3, max monthly) 2.6 3.2 

Alternative NAAQS (0.2 μg/m3, max quarterly) 1.8 2.5 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.8 2.3 

Alternative NAAQS (0.05 μg/m3, max monthly) 

NAc 

1.1 1.7 

 Secondary Pb smelter - full study area

Current conditions <0.1 0.4 1.0 

Alternative NAAQS (0.5 μg/m3, max monthly) <0.1 0.1 1.0 

Alternative NAAQS (0.2 μg/m3, max quarterly) <0.1 0.4 1.0 

Alternative NAAQS (0.2 μg/m3, max monthly) <0.1 0.4 1.0 

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 0.4 1.0 

 Secondary Pb smelter - 1.5 km subarea 

Current conditions 0.1 0.7 1.3 

Alternative NAAQS (0.5 μg/m3, max monthly) <0.1 0.7 1.3 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.1 0.8 1.3 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.1 0.8 1.3 

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 0.6 1.2 

a - Estimates are rounded to one decimal place. 

b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air 

contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from inhalation 

of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with 

outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3). 

c – “Recent air” estimates were not developed for the primary Pb smelter case study (see Section 3.1.4.2). 
 3-32

Table 3-8. Summary of blood Pb estimates for 95th percentiles in total-exposure blood Pb 

distributions. 

Concurrent Blood Pb Levela 

Air Quality Scenario 

(and case study) Recent airb Recent plus 

past airb Total Pb exposure 

General urban case study Low High Low High Low High 

Current NAAQS (1.5 μg/m3, max quarterly) 4.6 7.5 6.3 9.9 7.6 11.5 

Current conditions - 95th% (0.87 μg/m3, max quarterly) 1.1 2.8 3.4 5.4 5.1 7.2 

Alternative NAAQS (0.5 μg/m3, max monthly) 1.2 2.6 3.5 5.1 4.8 6.7 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.8 2.3 3.1 5.1 4.4 6.9 

Current conditions - mean (0.14 μg/m3, max quarterly) 0.5 1.9 2.9 4.7 4.2 6.5 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.5 1.8 2.9 4.6 4.2 6.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.1 0.7 2.6 3.9 3.9 5.7 

Primary Pb smelter - full study area

Current NAAQS (1.5 μg/m3, max quarterly) 2.8 4.6 

Alternative NAAQS (0.5 μg/m3, max monthly) 3.1 4.2 

Alternative NAAQS (0.2 μg/m3, max quarterly) 2.4 4.0 

Alternative NAAQS (0.2 μg/m3, max monthly) 2.5 4.0 

Alternative NAAQS (0.05 μg/m3, max monthly) 

NAc 

1.9 3.8 

Primary Pb smelter - 1.5 km subarea

Current NAAQS (1.5 μg/m3, max quarterly) 10.3 12.3 

Alternative NAAQS (0.5 μg/m3, max monthly) 7.6 8.5 

Alternative NAAQS (0.2 μg/m3, max quarterly) 5.9 6.6 

Alternative NAAQS (0.2 μg/m3, max monthly) 4.9 6.1 

Alternative NAAQS (0.05 μg/m3, max monthly) 

NAc 

3.5 4.5 

Secondary Pb smelter - full study area

Current conditions <0.1 0.8 2.4 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.1 1.1 2.4 

Alternative NAAQS (0.2 μg/m3, max quarterly) <0.1 0.8 2.3 

Alternative NAAQS (0.2 μg/m3, max monthly) <0.1 1.1 2.4 

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 0.8 2.4 

Secondary Pb smelter - 1.5 km subarea 

Current conditions 0.5 2.2 3.3 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.4 2.5 3.3 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.2 2.3 3.1 

Alternative NAAQS (0.2 μg/m3, max monthly) <0.1 1.4 3.1 

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 1.6 3.1 

a - Estimates are rounded to one decimal place. 

b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air 

contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from 

inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air 

Pb levels, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see 

Section 2.4.3). 

c – “Recent air” estimates were not developed for the primary Pb smelter case study (see Section 3.1.4.2). 
 3-33

3.5 UNCERTAINTY CHARACTERIZATION AND SENSITIVITY ANALYSIS 

The characterization of uncertainty associated with exposure assessment included 

performance evaluation which is discussed in this section. In addition, uncertainty in exposure 

assessment is also considered as part of characterizing uncertainty in the risk estimates and is 

reflected in inclusions of multiple modeling approaches and the qualitative discussion of key 

sources of uncertainty which are discussed in Section 4.3. Elements of exposure assessment are 

also included in the sensitivity analysis described in Section 4.3.2. 

Performance evaluation for the exposure assessment focused on evaluation of projections 

of Pb in exposure media (i.e., ambient air, outdoor soil, and indoor dust) (discussed in Section 

3.5.1) and projections of Pb in blood (covered in Section 3.5.2). Those case studies (or case 

study elements) for which media concentrations were estimated using empirical data as the basis 

are not considered here; only those estimates based directly on modeling were included. 

Comparing model output to monitoring data is often considered the most desirable form 

of performance evaluation. In addition to monitoring data, or in the absence of such data, 

outputs from other models and expert opinion about how outputs should look can be used as 

comparison benchmarks in performance evaluation. The performance evaluation activities 

reported here have focused on the comparison of model outputs to measurements. Favorable 

comparisons, including a lack of systematic trends in either over- or underestimation of modeled 

results relative to empirical data, provide confidence in the modeling estimates. 

3.5.1 Performance Evaluation Related to Exposure Media Modeling 

This section discusses performance evaluation conducted for modeling of Pb 

concentrations in exposure media including ambient air, outdoor soil and indoor dust. 

3.5.1.1 Evaluation of Modeled Ambient Air Pb Concentrations 

Performance evaluation for ambient air Pb predictions focused on the two point source 

case studies, in which air dispersion modeling was used. 

For the primary Pb smelter case study current NAAQS scenario, the ISCST3-Prime 

model was used with input files (e.g., source characterization, meteorological data) used in 

developing the 2007 proposed revision to the SIP for that location (Appendix D). The 

submission to EPA for the proposed SIP revision included a model performance evaluation, 

focused on the “actual value” modeling scenario (MDNR 2007a). The actual value modeling 

included three separate comparisons based on relating model predictions to measured Pb 

concentrations at five monitor sites in the study area. These comparisons included: 
 3-34

• Day-to-day evaluation of modeling output compared to monitor values: The review 

of the model performance evaluation conducted by the state of Missouri concluded 

that all sites demonstrated a pattern of overall accuracy for directional prediction (i.e., 

high modeled days being high monitored days and low modeled days being low 

monitored days), suggesting that the model was performing well in relating wind 

direction to Pb transport (MDNR, 2007a). 

• Comparison of source contribution analysis using chemical mass balance (CMB) with 

dispersion model predicted relative contributions: Source contribution analysis using 

CMB of monitor filter residue to identify significant sources of Pb for each monitor 

(e.g., roads inside facility boundaries and yard dust, blast furnace) were compared 

with relative contributions predicted by the dispersion model for individual modeled 

sources. The review of the model performance evaluation concluded that there was 

generally good agreement between the CMB results and the air dispersion results in 

terms of major sources contributing Pb at each monitor (MDNR, 2007a). 

• Comparison of overall average modeled results with monitored levels: This 

performance evaluation involved comparing modeled results (for 247 days simulated 

for 2005) at six monitor locations with actual measured values for that same period at 

those locations. Results of this evaluation suggested a slight overprediction bias 

(<10%) for those sites likely to have the greatest impacts from the facility. 

This evaluation of model performance for the actual value modeling scenario increases 

confidence in estimates developed for the current NAAQS scenario with this modeling setup and 

inputs particular to the 2007 proposed SIP revision. 

Performance evaluation of air dispersion modeling for the secondary Pb smelter is 

discussed in Appendix E (Section E.2.4), and involved comparing modeled ambient air Pb levels 

to measured levels at two monitoring locations within the study area. Results of that evaluation 

suggest that the model might be slightly underpredicting levels at the closest monitor, and 

slightly more underpredictive of levels at the more distant monitors. The use of meteorological 

data that is not site-specific and may not be fully representative of actual wind patterns in the 

area may be contributing to this. When modeled concentrations at distances matching those of 

the monitors, and for all directional points around the facility, are compared with monitor values, 

modeled values are identified which match or exceed the measured values. 

3.5.1.2 Evaluation of Modeled Outdoor Soil/Dust Pb Concentrations 

Modeling was used in estimating the spatial pattern of outdoor soil/dust Pb 

concentrations for the secondary Pb smelter case study (see Section 3.1.3.3). As the modeling 

did not use estimates of historical Pb emissions (presumed to be higher than current emissions), 

absolute values of the model predictions were not expected to be representative of current Pb 

concentrations in soil/dust in this case study. Although measurements were not available for the 
 3-35

case study location, comparison of model predicted soil Pb levels with Pb concentrations 

reported for a surrogate secondary Pb smelter location confirmed this expectation (see Appendix 

E, Section E.3). Model predictions were - on average - a factor of 3 lower than Pb 

concentrations at the surrogate location. Accordingly, we used a hybrid approach for 

characterizing outdoor soil Pb levels for this case study, scaling the model estimates of soil Pb 

concentration across the study area by factors derived from studies at other secondary Pb smelter 

locations (see Appendix E, Section E.3). 

3.5.1.3 Evaluation of Modeled Indoor Dust Pb Concentrations 

Performance evaluation completed in support of indoor dust Pb prediction involved two 

components. First, the hybrid model developed for the general urban case study was evaluated 

using available data from the literature (see Appendix G, Section G.3.6). Results of this 

evaluation help us assess the overall reasonableness of this model in supporting indoor dust 

prediction for the general urban case study in particular. Additionally, predicted indoor dust Pb 

levels for all three case studies were compared with data identified in the literature, including 

measurements from individual studies focusing on smaller areas and data from national-scale 

surveys. The general urban case study model and model projections are discussed here first 

The hybrid indoor dust model developed for the general urban case study (Section 3.1.4.1 

and Appendix G) is a combination of mechanistic model (to relate outdoor ambient air Pb to 

indoor dust Pb) and empirical data (to characterize the nonair related fraction of indoor dust Pb 

in the residential urban setting). Components of the mechanistic portion of the model were 

subjected to a range of evaluations based on available data in the literature. In addition, the 

conversion of model-generated indoor dust Pb loadings to concentrations (a key step required 

prior to blood Pb modeling) was evaluated. Finally, model predictions of indoor dust Pb levels 

were evaluated using data from several studies for specific cities. 

Evaluation of the mechanistic component of the hybrid dust model focused on (a) 

predicted deposition fluxes of Pb to indoor surfaces and (b) prediction of relationship between 

indoor air Pb and ambient outdoor air Pb (Appendix G, Section G.3.6). Generally, modeled 

indoor deposition fluxes for Pb appear to be in line with values reported for a location in 

Manhattan with closed windows (Caravanos et al., 2006). The model predicted ratio of indoor 

air Pb to outdoor ambient air Pb are lower (~50%) than ratios based on data collected from 

residences in the Midwest (Roy et al., 2003) but similar to ratios generated by a different hybrid 

model which combines empirical data with a mass balance modeling approach in predicting 

indoor ambient air concentrations (Riley et al., 2002). The underestimate of this ratio, when 

compared with the empirical data from Roy et al. (2003), may result from the fact that the hybrid 

model does not consider resuspension of indoor dust Pb. Generally, these findings with regard to 
 3-36

substeps of the mechanistic (and air-focused) component of the hybrid model (Appendix G, 

Section G.3.6) suggest a potential to underpredict the influence of outdoor ambient air Pb on 

indoor dust Pb. The conversion for predicted indoor dust loadings to concentrations, however, 

may overpredict concentrations based on a comparison data collected in residences in the 

Midwest (Roy et al., 2003; Appendix G, Section G.3.6). This finding may counterbalance any 

potential for the mechanistic component of the model to underestimate the influence of ambient 

air Pb on indoor dust Pb. 

Indoor dust Pb concentrations (in terms of mass per mass) were identified from a few 

studies in the literature (e.g., CD, Table 3-8; Tang et al., 2004), as well as the HUD National 

Survey of Lead-based Paint in Housing (USEPA, 1995) described in Section 3.1.4.1. These 

studies are identified in Table 3-9. Although the HUD survey was used for the empirical 

component of the hybrid model, it is considered here with regard to the total dust Pb 

concentrations predicted by both the hybrid and air-only models used in the general urban case 

study (use of these models is described in Section 3.1.4.1).6

 Comparison of the model-predicted 

indoor dust concentrations for the current conditions scenarios (146 and 198 ppm for the hybrid 

model and 107-157 ppm for the air-only model) to empirical data collected in Jersey City, NJ, 

and Ottawa Canada (Table 3-9) suggest that the model may be underpredicting indoor dust Pb, 

although the housing stock in these studies was much older than housing generally in U.S., 

which may mean that the measurements are impacted to a greater extent by indoor Pb paint than 

would be the general case in the U.S. Thus, it would be expected that those reported values 

would be higher than the model predictions. Additionally, the model-predicted values fall 

between the medians for the youngest and oldest houses sampled in the HUD national survey 

(Table 3-9). Given that indoor dust Pb modeling completed for the general urban case study was 

aimed at capturing central tendency indoor dust Pb levels (and is not expected to, for example, 

capture variability related to cleaning rates or indoor paint Pb levels), this finding provides 

confidence in the estimates. 

 

6

 The use of HUD indoor dust Pb data for performance evaluation is considered reasonable, even in light of 

its use in deriving the air-related portion of the hybrid model, since total indoor dust Pb levels generated by the 

hybrid model (reflecting both the air-related and non air-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 for the evaluation. 
 3-37

Table 3-9. Evaluation of model-predicted indoor dust Pb levels against empirical data 

obtained from the literature. 

Modeled indoor dust Pb levels (ppm) 

Case study 

Air quality 

scenario Median 

5th to 95th

Percentile 

(min-max) 

Indoor Dust Pb Observations reported in 

the literature 

General urban 

case study 

Current 

conditions 

(air-only 

regression 

and hybrid) 

Mean current conditions: 

(107 to 146) 

95th percentile current 

conditions: (157-198)a

Primary Pb smelter 

case study 

Current 

NAAQS 84 53 - 219 

(41 - 1,944) 

Secondary Pb 

smelter 

case study Current 

conditions 

63 60 – 73 

(60 – 166) 

- Residences near smelters: 1283-4140 ppm 

(CD, Table 3-8) 

- Jersey City, NJ housing (floor): 857 ppm 

(CD, Table 3-8) 

- Residences in the Midwest (windowsill): 

954ppm (CD, Table 3-8) 

- Ottawa Canada 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). 

a - because a single study area is used for the general urban case study, a single dust estimate was generated for each 

combination of current conditions air quality scenario and indoor dust model (i.e., four separate estimates were generated for the 

general urban case study). 

For the two point source case studies, the model-predicted indoor dust Pb concentrations 

were compared to observations from the literature (see Table 3-9). An important factor to keep 

in mind when reviewing the modeled results presented in Table 3-10 is that the current NAAQS 

scenario modeled for the primary Pb smelter involved regions of the study area with ambient air 

Pb levels significantly higher than either of the other case studies. 

In consideration of indoor dust Pb levels predicted for the primary Pb smelter, we have 

focused more on the central tendency values. This reflects the fact that the higher-end modelgenerated values for this case study likely reflect significant ambient air impacts which are not 

captured in any of the empirical data identified in the literature, thereby reducing the utility of 

performance evaluations for these higher-end predictions. The median indoor dust Pb 

concentration generated for the primary Pb smelter case study (84 ppm) falls near the lower end 

of the range in the HUD dataset (i.e., near the median of 87 ppm reported for newer housing in 

that study). This seems reasonable since a significant fraction of the study area for this case study 

has ambient air Pb levels not significantly different from ambient air Pb levels seen across the 

U.S. (see Table 3-4). 

Regarding the secondary Pb smelter case study, the median value for this case study (63 

ppm) is below the range of values reported in the past for smelters (CD, Table 3-8), and also just 
 3-38

below median value reported for the youngest housing in the HUD national data set (USEPA, 

1995), suggesting that indoor dust Pb levels may be underpredicted for this case study. 

3.5.2 Performance Evaluation Related to Blood Pb Modeling 

Performance evaluation completed in support of blood Pb modeling involved three steps: 

(a) evaluation of candidate blood Pb models for the analysis, (b) comparison of ambient outdoor 

air Pb-to-blood Pb ratios generated for the three case studies against ratios obtained from the 

literature and (c) comparison of modeled blood Pb levels for these three case studies against 

NHANES IV data. Each of these evaluation steps is discussed below. 

3.5.2.1 Evaluation of Candidate Blood Pb Models 

Evaluation of candidate blood Pb models (IEUBK and Leggett) involved three separate 

stages: (a) application of the candidate models to three hypothetical individual child exposure 

scenarios used previously by EPA and others in evaluations of blood Pb models, (b) comparison 

of candidate model predictions for a general U.S. childhood exposure scenario (using typical Pb 

exposures for key pathways) to NHANES IV empirical data, and (c) evaluation of candidate 

model performance in replicating measurements of urban child blood Pb levels obtained in 

Rochester. Detailed results of the evaluation of candidate blood Pb models are presented in 

Appendix J (and summarized in Section J.4). 

The first stage (focusing on reproducing results of previous performance evaluations) 

demonstrated that we were applying the candidate models correctly. Tests of the models against 

specific individual exposure scenarios (Section J.1.3) reproduced, to a high degree, the results of 

previous model comparison. 

The second stage of the model evaluation (focusing on reproducing general US child 

blood Pb levels presented in NHANES IV) demonstrated that, depending on assumptions 

regarding typical outdoor soil/dust and indoor dust Pb concentrations, the IEUBK model either 

moderately overpredicted GM blood Pb levels (by two-fold or less) or generated predictions 

close to NHANES summary statistics (see Section J.3.1). By contrast, predictions from the 

Leggett model were more than three to six times higher than the age-specific NHANES IV GM 

values. 

The third stage of the model evaluation focused on evaluating the candidate models in 

predicting blood Pb levels for an urban child cohort (Appendix J, Section J.3.2). The dataset, 

which included matched Pb media concentrations (outdoor soil/dust and indoor dust levels) and 

blood Pb levels, was collected as part of an epidemiological study focusing on the effects of Pb 

exposure in children living in Rochester, NY (Lanphear et al., 1995; Lanphear and Roghmann, 

1997). Blood Pb levels for each child sampled in the study were predicted using IEUBK and 

Leggett, with the measured media Pb levels collected as part of the study as inputs. These 
 3-39

predicted blood Pb levels were then compared to the measured blood Pb levels for each child. 

The results of this stage of the model evaluation were similar to those from the NHANES 

evaluation in that IEUBK results suggested a moderate overprediction (~70%) while Leggett 

results indicated a much greater overprediction (a factor of 2 to 6). 

Results of the model evaluation for IEUBK and Leggett suggest that IEUBK generates 

more representative blood Pb estimates relative to Leggett in the context of the evaluations 

conducted here. Based on the results of the model evaluation, we decided to use IEUBK in 

generating the primary set of exposure and risk results for this analysis, and include Leggett as 

part of the sensitivity analysis but not in the primary analysis (see Section 4.3.2). 

3.5.2.2 Evaluation of model-derived outdoor air Pb-to-blood Pb ratios 

In deriving the current NAAQS in 1978 (43 FR 46246), USEPA used an estimate of the 

relationship between ambient air Pb concentration and associated blood Pb concentration (i.e., 

1:2, μg/m3

 to μg/dL). In this assessment, we rely on several distinct modeling steps which, when 

taken together, translate ambient air Pb into blood Pb. As part of the blood Pb model 

performance evaluation we have extracted air-to-blood Pb ratios from the modeling completed 

for the three case studies for comparison to estimates reported in the literature. 

Ratios were developed that relate ambient air Pb to blood Pb contributed from the 

following different exposure pathways or pathway combinations: (a) inhalation of ambient air, 

(b) inhalation of ambient air plus ingestion of the Pb in indoor dust that is predicted to be 

associated with ambient air Pb levels (i.e., “recent air” per Section 2.4.3), and (c) inhalation of 

ambient air plus ingestion of indoor dust plus ingestion of outdoor soil/dust (i.e., not including 

the diet and drinking water ingestion pathways). The limitations of our modeling tools precluded 

us from parsing air-related blood contributions any more finely. The ratios (actually, the blood 

Pb side of the ratio) will be larger as you move from (a) to (c) because there is a progressively 

larger fraction of overall blood Pb (exposure) being associated with air. With regard to the 

potential impact of ambient air Pb on blood Pb, the first ratio (inhalation pathway) is an 

underestimate because it excludes the important ingestion pathways to which ambient air Pb can 

contribute. Conversely, the third ratio, although not including any impact of air Pb on diet (and 

blood), potentially includes some contributions to blood Pb that are not influenced by air (e.g., 

indoor paint). For the purposes of this model performance discussion, we have focused on the 

second type of ratio (those for “recent air”) derived using the concurrent blood Pb metric. The 

full set of ratios are presented for each case study in Appendix I 

In this evaluation, we have considered the ratios derived from the blood Pb estimate prior 

to application of the GSD reflecting interindividual variability in Blood Pb levels (i.e., the central 

tendency estimate of blood Pb derived for each US Census block or blockgroup for the point 
 3-40

source case studies and for the entire study area for the general urban case study). Although airto-blood Pb ratios can be derived for individual simulated children after application of the GSD, 

these would be less relevant to empirical ratios reported in the literature, which tend to capture 

central tendency or typical ratios for a study population (through statistical regression modeling, 

for example). While this interindividual 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 3-10. Air-to-blood Pb ratios for “recent air” contribution to concurrent blood Pb 

level. 

Case study 

Annual average 

ambient air 

Pb concentrations 

(μg/m3

) 

Air-to-Blood 

Pb 

Ratio 

Air-to-Blood Pb Ratios 

Identified from the 

Literature 

General urban – current conditions 

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

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

Primary Pb smelter – current NAAQS 

Median air concentration 0.093 1:3 

95th percentile air concentration 0.458 1:7 

Secondary Pb smelter- current conditions 

Median air concentration 0.005 1:5 

95th percentile air concentration 0.011 1:4 

- Review of studies published 

before 1984 reports air-toblood Pb ratios for children 

generally ranging from 1:3 to 

1:5 (Brunekreef, 1984). 

- Pooled analysis of air-toblood Pb relationship (log-log 

regression) based on above 

studies yielded ratios of 1:3 to 

1:6 (Brunekreef, 1984). 

- An air-to-blood ratio 

developed based on more 

recent study data (Hilts, 2003) 

is 1:7 (for children in the 

vicinity of an operating 

smelter which experienced a 

modification in facility 

operations, leading to a 

marked decrease in air Pb 

emissions). 

a

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

Several observations can be made regarding higher ratios generated for two of the case 

studies presented in Table 3-10: 

• 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 
 3-41

concentration per unit ambient outdoor air Pb relative to the air-only regression 

model. 

• For the primary Pb smelter case study, the higher ratio (1:7) is also the result of a 

more potent dust model (the site-specific regression model developed for the 

remediation zone of this study area). That indoor dust model was used for the higherimpact (95th percentile) block, while the regression (air plus soil) model was used for 

the majority of the study area, including the median block (producing the 1:3 ratio). 

The air-to-blood Pb ratios from the literature presented in Table 3-10 all come from a 

combination of older studies summarized in a single review from 1984 (Brunekreef, 1984) and 

more recent data (Hilts, 2003). The Brunekreef, 1984 review presents both (a) the ratios 

identified from the reviewed studies, which included surveys focused on smelter and urban study 

areas (generally range from 1:3 to 1:5) and (b) the results of a pooled analysis where a log-log 

regression model was developed relating ambient air Pb directly to blood Pb levels based on the 

underlying study data (this yields the range of 1:3 to 1:6 presented in Table 3-10). 

Data presented in Hilts, 2003, track Pb exposure levels (e.g., outdoor ambient air Pb, 

outdoor soil Pb and indoor dust Pb) and measured blood Pb levels (annual geometric mean blood 

Pb levels for children 6 to 60 months of age), for a population of children living in the vicinity of 

smelting operations in Trail, British Columbia for the period 1996 to 2001. The facility 

experienced a change in operations in 1997 which resulted in a significant decrease in Pb 

emissions from the facility and a resulting decrease in ambient air Pb levels in the vicinity of the 

facility. In deriving an air-to-blood Pb ratio from these data, we compared ambient air Pb levels 

for 1996 and 1999 to geometric mean blood Pb levels for the same years (all data were obtained 

from Table 3 in Hilts, 2003). An air-to-blood Pb ratio of 1:7 was generated based on this 

comparison. Note, also, that a second air-to-blood Pb ratio could have been generated by 

comparing data for 1996 and 2001 (which would have tracked the continued decrease in both 

ambient air Pb levels and geometric blood Pb levels). However, blood Pb measurements in 2001 

focused on children 6-36 months of age, while blood Pb measurements for 1996 and 1999, 

involved children 6-60 months of age. This disconnect in the age groups would have prevented a 

direct comparison of the ratios generated using the two pairings of years. Therefore, we focused 

on the ratio developed by comparing 1996 and 1999 since the blood Pb measurements for these 

two years focused on an age group (6-60 month old children) closer to that used in our modeling 

of risks for the case studies (children <7 years of age). 

The air-to-blood Pb ratio of 1:7 generated using the Hilts, 2003 data is slightly higher 

than the range presented in Brunekreef, 1984 (1:3 to 1:6). This might reflect the fact that overall 

exposures associated with the Hilts, 2003 study are lower than those reflected in the Brunekreef, 
 3-42

1984 study and, as noted in Brunekreef, 1984, there appears to be greater efficiency in translating 

air Pb to blood Pb at lower exposure levels, compared with higher levels. 

Although the blood Pb levels associated with Hilts, 2003 (geometric mean blood Pb 

levels of 4 to >10 μg/dL) are closer to those associated with our modeled case studies than are 

the levels in Brunekreef (1984), the levels in Hilts are still higher. In the case of the blood Pb 

levels reflected in the studies summarized in Brunekreef (1984), these measured blood Pb levels 

(typically > 10ug/dL) are significantly higher than those modeled for our case studies. This 

disconnect between the studies in the literature and the modeled case studies (regarding blood Pb 

ranges) introduces uncertainty into performance evaluation of air-to-blood Pb ratios completed 

here. That is, it is possible that ratios seen at blood Pb levels significantly above 5 to 10 μg/dL 

(levels seen in the literature) are different from those below 5 μg/dL (those associated with the 

modeled case studies), although this can not be verified without updated characterizations of airto-blood Pb ratios based on contemporary trends in Pb exposure and blood Pb levels. Despite 

this uncertainty, however, comparison of the air-to-blood Pb ratios developed for the three case 

studies against those found in the literature is considered useful. 

The lower air-to-blood ratios generated for the three case studies (1:4 for the general 

urban case study, 1:3 for the primary Pb smelter case study and 1:4 to 1:5 for the secondary Pb 

smelter case study) fall within the range of ratios identified in the literature (1:3 to 1:7). 

However, the higher ratios, ranging from 1:7 to 1:10, are higher than the bulk of Brunekreff 

(1984) reported ratios and just include the 1:7 ratio developed from the Hilts (2003) dataset, 

although a subset of the individual studies reviewed by Brunekreef (1984) presented air-to-blood 

Pb ratios that were 8.5 and higher. In several instances, such higher ratios are associated with 

lower blood Pb levels and lower ambient air Pb levels (both factors that would seem to be more 

relevant to exposure conditions found in the three case studies modeled for this analysis). 

However, the studies reporting these higher ratios are complicated by a number of factors (e.g., 

involving older children, use of ambient air measurement techniques which may underestimate 

results, thereby inflating ratios, relatively small sample sizes, etc.). 

3.5.2.3 Comparison of modeled blood Pb levels to nationally representative data 

Evaluation of modeled blood Pb levels using empirical data depends on the availability 

of empirical datasets for populations with exposure similar to those in the case studies. While 

there are many blood Pb studies reported in the literature, they are largely composed of 

populations experiencing exposures from all pathways that are higher than exposures reflected in 

our case studies. No datasets were identified for specific subpopulations with exposures 

corresponding to current exposures that would be appropriate for performance evaluation here. 
 3-43

In the case of the general urban case study, while it is not location-specific, we have not 

identified any contemporary datasets for urban locations with ambient air Pb levels matching 

those modeled in the current scenario that are not heavily influenced by Pb-based paint. For the 

primary Pb smelter case study location, the most generally available blood Pb monitoring study 

was completed in 2001 and 2002, and given the changes in exposures since that time and the use 

of a future (current NAAQS) scenario these older blood Pb surveys do not correspond to the 

exposures modeled for this case study. In the case of the secondary Pb smelter, the blood Pb 

data available for the county containing the secondary Pb smelter does not indicate residence 

location handicapping efforts to consider blood Pb levels for children in the secondary Pb 

smelter case study location. 

While blood Pb datasets were not identified for populations representative of those 

modeled for the three case studies, we have compared the modeled blood Pb levels against 

general national-scale blood Pb levels (for a child age group matching that modeled for the case 

studies). While each of the case studies involves a population that is different than the national 

population (reflecting the nature of the exposure scenario being considered), the background 

exposures for the case studies and the national population are similar. Accordingly, the 

relationship between the modeled blood Pb levels for each study area and the national 

distribution are evaluated. For this evaluation, we have used measured values interpolated from 

the NHANES IV dataset. Specifically, we have interpolated a series of percentile estimates for 7 

yr olds based on summary data presented for NHANES IV (CDC, 2005). The process of 

interpolating these values involved the following steps: (a) use summary statistics (GM and 

associated confidence intervals) for 1-5 yr olds and 6-19 yr olds (for years 1999-2002) to 

establish lognormal distributions for each age range, (b) identify population percentiles of 

interest (e.g., median, 90th, 95th percentiles) for each age group using these lognormal 

distributions, and (c) interpolate a series of percentiles for a 7yr age cohort using the percentiles 

for the two age ranges (the 1-5 yr old and 6-19 yr old). This interpolation procedure resulted in 

the population percentile estimates for a 7 yr old cohort (for the years 1999-2002) presented in 

Table 3-11. 
 3-44

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

1999-2002). 

Population 

percentile 

Blood Pb level 

(μg/dL) 

median 1.5 

75th 2.55 

90th 4.1 

95th 5.25 

Figure 3-2 compares modeled blood Pb levels for the three case studies with the 

interpolated NHANES IV blood Pb levels presented in Table 3-11. 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 scenarios for both 

the primary and secondary Pb smelter case studies. 

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

0

2

4

6

8

10

12

14

median 75th 90th 95th

Population Percentile

Blood Pb level (ug/dL)

NHANES-IV (interpolated 1999-

2002, 7yr old)

General urban case study (mean

current conditions - low er)

General urban case study (mean

current conditions - higher)

Primary Pb smelter (current

NAAQS)

Primary PB smelter (smaller

1.5km study area - current

NAAQS)

Secondary Pb smelter (current

conditions)

Secondary Pb smelter (smaller

1.5km study area - current

conditions)

The following observations are made regarding the blood Pb modeling results for the 

three case studies (Figure 3-2): 

• General urban case study: The low- and high-end estimates for the mean current 

conditions scenario bracket the NHANES IV data. Of the three case studies, it is 

reasonable to assume that results for the current conditions (mean) scenario for this 
 3-45

case study might be the closest to matching the NHANES. Interestingly, of the 

results for the low- and high-end modeling approaches for this scenario, the high-end 

results appear to be closer to the NHANES data, which would indicate that modeling 

for this case study may not be biased high with regard to total blood Pb levels. 

Additionally, unlike the point source case studies, the general urban case study 

included consideration of variability in background exposure pathways through 

application of the larger GSD in the high-end modeling approach for that case study. 

• Primary Pb smelter case study: It would be expected that higher percentile exposure 

levels for this case study might exceed the NHANES data, however, that is not seen 

either at the 90th or 95th percentiles in Figure 3-2. There are three factors which 

may account for this observation. First, we have not generated higher population 

percentile estimates (i.e., greater than 95th percentile) and it is possible that 

exposures for those higher percentiles at the primary Pb smelter might begin to 

exceed levels seen with the NHANES-interpolated data. Second, it is important to 

reiterate that modeling for this case study did not account fully for exposure 

variability in background Pb exposures. It is likely that if modeling for this case 

study had included consideration of background exposure variability (e.g., relatively 

high paint Pb and drinking water exposures), there would be a greater difference 

between high-end blood Pb levels modeled for this case study and those interpolated 

form NHANES IV, with the potential for the primary Pb smelter percentiles to 

exceed those derived from the NHANES IV data. Note, however, this lack of full 

accounting for blood Pb variability from background sources is not considered a 

major limitation in this analysis because the focus is on ambient air-related 

exposures. Finally, as discussed in Section 4.3.1, modeling for the point source case 

studies did not consider other sources of ambient air Pb besides the smelter. This 

could produce low bias in predictions of exposure, especially for portions of the 

study area further from the facility. 

• Secondary Pb smelter case study: Modeled blood Pb levels for this case study are 

significantly lower than NHANES IV levels, especially for higher population 

percentiles. As with the primary Pb smelter case study, this likely reflects three 

factors: (a) absence of predictions (due to technical limitations) of higher population 

percentiles above the 95th percentile, where exposure related to facility emissions is 

likely more dominant, (b) exclusion of consideration for higher background 

exposures to Pb in paint and drinking water, and (c) failure to consider ambient air Pb 

sources impacting the study area, other than the smelter facility. Note, also that, for 

the secondary Pb smelter case study, overall uncertainty associated with modeling of 

exposure and risk is considered sufficiently high to significantly reduce overall 

confidence in results for this case study (see Section 4.3.1). 
 3-46

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 4-1

4 RISK ASSESSMENT 

This chapter describes the approach used to characterize risk for the initial analyses of the 

full-scale assessment, including discussion of the methodology (Section 4.1), presentation of risk 

estimates (Section 4.2), and uncertainty characterization (Section 4.3). Additional analyses 

completed after the August 2007 CASAC meeting (Henderson, 2007a), involving a core 

modeling approach, are presented in Chapter 5. 

4.1 METHODS FOR DERIVING RISK ESTIMATES 

 Risk characterization for this assessment focuses on IQ loss in children. IQ loss is 

derived using a set of concentration-response functions developed based on results from a pooled 

analysis of epidemiology studies (Lanphear et al., 2005). These concentration-response 

functions are combined with the population-level blood Pb distributions generated for the case 

studies to produce distributions of IQ loss estimates for each study population. IQ loss is also 

apportioned among different exposure pathways using the pathway apportionment information 

generated as part of the exposure analysis. 

Two key elements of the risk methodology are described in greater detail below: (a) the 

concentration-response functions used in the analysis (Section 4.1.1) and (b) the stepwise 

analytical procedure used to generate the IQ loss (risk) distributions (Section 4.1.2). 

4.1.1 Concentration-Response Functions 

As discussed in Section 2.1.5, log-linear concentration-response functions for IQ loss for 

the concurrent and lifetime average blood Pb metrics were obtained from a large pooled study 

(Lanphear et al., 2005) and used as the basis for estimating IQ changes in children in this 

analysis. Three types of concentration-response functions were drawn from this study, and two 

equations of each type are used in this assessment: one for the concurrent blood Pb metric, and a 

second for the lifetime average blood Pb metric. The three types include: (a) a log-linear 

function with cutpoint (Section 4.1.1.1), (b) a log-linear function with low-exposure linearization 

(Section 4.1.1.2) and (c) a two-piece linear function (Section 4.1.1.3). Plots of the three types of 

IQ loss functions developed for this assessment are presented in Figure 4-1 for the concurrent 

blood Pb metric. Table 4-1 presents a comparison of total IQ loss and incremental IQ loss (IQ 

loss/μg/dL) for the three functions across a range of concurrent blood Pb levels. As can be seen 

by comparing the plots of the three functions together with the results presented in Table 4-1, the 

log-linear function with low-exposure linearization will generate the greatest IQ change across 

the exposure range 0 to 10 μg/dL followed by the log-linear function with cutpoint and then the 

two-piece linear function. 
 4-2

 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 μg/dL. 

0

2

4

6

8

10

0.1

1

1.92.83.74.65.56.47.38.29.1

10

Concurrent PbB (ug/dL)

IQ loss

log-linear with cutpoint

log-linear with low-exposure

linearization

two-piece linear

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

three concentration-response functions. 

Concentration-Response Function 

Log-linear 

with 

cutpoint 

Log-linear 

with lowexposure 

linearization 

Two-piece 

linear 

Performance Metric Points, IQ loss 

at 2 μg/dL 1.9 4.6 0.9 

at 5 μg/dL 4.3 7.0 2.3 

at 7.5 μg/dL 5.4 8.1 3.4 

Total IQ loss 

at 10 μg/dL 6.2 8.9 4.5 

<2 μg/dL 0.94 2.29 0.45 

<5 μg/dL 0.87 1.41 0.45 

<7.5 μg/dL 0.73 1.09 0.45 

Incremental IQ loss 

 (points per μg/dL) 

<10 μg/dL 0.62 0.89 0.45 
 4-3

4.1.1.1 Log-Linear Function with Cutpoint 

This concentration-response function is the log-linear function for IQ change from 

Lanphear et al. (2005), with incorporation of a cutpoint at the blood Pb level corresponding to 

the lowest levels represented by measurements in the underlying pooled analysis. The values 

used for cutpoints, for both the concurrent and lifetime average blood Pb metrics, are based on 

the corresponding low end of the range of values for these two indices in the pooled analysis 

(Hornung, 2007a, b). The values are 1.0 μg/dL and 1.47 μg/dL, for the concurrent and lifetime 

average blood Pb metrics, respectively. Slopes for the two log-linear functions were obtained 

directly from the study (Lanphear et al., 2005). Parameterization for these two log-linear 

functions with cutpoints is presented below, along with the mathematical form of the function: 

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

• Log-linear function with cutpoint (concurrent metric): 

 - beta (slope): -2.70 

 - cutpoint: 1.0 μg/dL 

• Log-linear function with cutpoint (lifetime average metric): 

 - beta (slope): -3.04 

 - cutpoint: 1.47 μg/dL 

4.1.1.2 Log-Linear Function with Low-Exposure Linearization 

We also developed risk estimates that reflect the possibility that IQ loss is associated with 

the entire range of Pb exposure all the way down to "zero" exposure, as recommended by 

CASAC (Henderson, 2007b). The risk assessment included IQ loss prediction based on a loglinear IQ change function with a linearization of the log-linear function taking over at lower 

exposure levels. The transition point from the log-linear function to the linearized slope was 

selected with the same basis as the cutpoint described in Section 4.1.1.1, the lower end of the 

range of the pertinent blood Pb index values in the pooled analysis (Lanphear et al., 2005). The 

linearized slope is obtained by taking the tangent to the log-linear function at the point of 

departure, with different slopes being identified for the concurrent and lifetime average blood Pb 

metrics (per Lanphear et al, 2005). Parameterization for the two log-linear functions with lowexposure linearization is presented below, with the mathematical form of the function: 

• Form of function: 

- For blood Pb level ≥ cutpoint: 

 IQ loss = beta * ln (concurrent blood Pb/cutpoint) + linear slope * cutpoint 

- For blood Pb level < cutpoint: 

 IQ loss = linear slope * concurrent blood Pb 
 4-4

• Log-linear function with low-exposure linearization (concurrent metric): 

 - beta (slope): -2.70 

 - linear slope to be applied below 1.0 μg/dL: -2.70 

• Log-linear function with low-exposure linearization (lifetime average metric): 

 - beta (slope): -3.04 

 - linear slope to be applied below 1.47 μg/dL: -2.1 

4.1.1.3 Two-piece Linear Function 

This category of concentration-response function was developed by fitting a two-piece 

linear model to the log-linear IQ loss function obtained from the pooled analysis (Lanphear et al., 

2005). Inclusion of this function in the risk assessment is intended to address the practical 

problem of the shape associated with application of the log-linear model at the low end of the 

blood Pb value range. In this case, we consider an alternate functional form that allows ready 

prediction of IQ loss across the full range of modeled Pb exposure. The procedure involved first 

generating blood Pb values for each of the two blood Pb metrics, concurrent and lifetime 

average, for a set of N = 1,333 simulated children representative of those included in the pooled 

analysis (Lanphear et al., 2005). This was accomplished by sampling from a blood Pb 

distribution constructed from the median and 95th percentile of the concurrent and lifetime 

average blood Pb indices, respectively, reported in Lanphear et al. (2005). IQ values for each of 

the 1,333 simulated children were then estimated using the reported log-linear models that relate 

blood Pb to absolute IQ (Lanphear, et al., 2005). Nonlinear regression techniques were then 

used to fit two piece linear models to these two sets of simulated children with their matched 

pairs of blood Pb and IQ values. The regressions provided parameter estimates (slopes and 

"hinge" point) of the best fitting two piece linear segment function for each blood Pb metric, 

concurrent and lifetime average. As discussed in Section 4.3.1, the use of a constructed dataset 

introduces uncertainty related to fitting of a model to a model. Parameterization for the two twopiece linear functions is presented below, with the mathematical form of the function: 

• Form of function: 

- For blood Pb level > hinge: 

 IQ loss = beta 2 * concurrent blood Pb 

- For blood Pb level ≤ hinge: 

 IQ loss = beta 1 * concurrent blood Pb 

• Two-piece linear function (concurrent metric): 

 - "hinge" linking two segments: 10.82 μg/dL 

 - beta 1 (slope at ≤ 10.82 μg/dL): -0.4539 

 - beta 2 (slope at >10.82 μg/dL: -0.1130 
 4-5

• Two-piece linear function (lifetime average metric): 

 - "hinge" linking two segments: 13.39 μg/dL 

 - beta 1 (slope at ≤ 13.39 μg/dL): -0.3790 

 - beta 2 (slope at >13.39 μg/dL: -0.1187 

4.1.2 Projection of Population Risk 

Risk characterization completed for this assessment involved converting the populationlevel blood Pb distributions generated for the three case studies into population-level 

distributions of IQ loss using the three types of concentration-response functions described in the 

last section. This procedure is described below for each of the functions. 

• Log-linear function with cutpoint: Each modeled blood Pb level is compared against 

the cutpoint. If the blood Pb level is lower than the cutpoint, then no IQ loss is 

estimated because the simulated individual’s blood Pb level is below the level for 

predicting IQ loss with this function. If the blood Pb level is greater than the 

cutpoint, then the log-linear function is used to predict IQ loss for the portion of the 

estimated blood Pb level extending above the cutpoint. 

• Log-linear function with low-exposure linearization: Each modeled blood Pb level is 

compared against the point of linearization. If the blood Pb level is below the point 

where the function becomes linear, than the linear slope is used to predict IQ loss. If 

the modeled Pb level is above the point where the function becomes linear, than IQ 

loss is calculated as the sum of IQ loss across the linear portion of the curve plus the 

additional contribution from the log-linear portion of the function extending up to the 

total blood Pb level. 

• Two-piece linear function: Similar to the last function, the modeled blood Pb level is 

compared against the blood Pb level of the “hinge”. If the blood Pb level falls below 

the hinge, as is the case for most simulated individuals at the three case studies, then 

the steeper, low-exposure slope is used to estimate IQ loss. If the simulated blood Pb 

level falls above the hinge, then IQ loss associated with the low-exposure (steeper 

slope) piece of the function (for the portion of the blood Pb level up to the hinge) is 

combined with IQ loss estimated using the shallower and higher exposure slope, for 

that portion of the blood Pb level extending above the hinge. 

The IQ loss estimates generated using this approach are pooled to form a population-level 

distribution of IQ loss for a given study area. Each of these IQ loss estimates are pathway 

apportioned among policy-relevant pathways and policy-relevant background based on pathway 

contribution to the underlying blood Pb levels (see Sections 2.4.3 and 3.2.2). As with pathway 

apportionment for blood Pb levels (Section 3.2.2), pathway apportionment of risk estimates is 

also at the level of exposure zone. All simulated individuals from a given zone are assigned the 
 4-6

same pathway apportionment. As noted with regard to blood Pb levels (Section 3.2.2), there is 

increased uncertainty associated with pathway apportioned IQ loss estimates for higher-end risk 

percentiles, since these reflect an assumption that relative pathway contributions at central 

tendency exposure levels hold at the high-end percentiles of the blood Pb level distribution. 

Just as with the population-level exposure estimates discussed in Section 3.2.2, risk 

estimates generated using the approach outlined above are used to generate several types of risk 

metrics, depending on the case study. For the two point source case studies, because they are 

location-specific and include a defined and enumerated receptor population, two categories of 

risk metrics are generated: 

• Population-weighted risk (IQ loss) percentiles: IQ loss (with pathway apportionment) 

for simulated individuals representing specific points along the population risk 

distribution (e.g., 50, 90, 95 percentile simulated individuals). 

• Incidence counts: Number of children within a given study area projected to experience a 

magnitude of IQ loss. 

As for the exposure estimates, the general urban case study risk metrics are restricted to 

population-weighted risk (IQ loss) percentiles. 

4.2 RISK ESTIMATES 

Estimates of IQ loss resulting from Pb exposure have been developed for each air quality 

scenario in each case study (see Appendix K). In addition, IQ loss results have been developed 

for subareas associated with each of the point-source case studies (see Appendix P). Further, 

multiple sets of risk results were generated for each combination of case study and air quality 

scenario, in an effort to consider key sources of uncertainty and their impact on blood Pb 

estimates (see Section 2.4.6.2). That is, twenty four separate risk distributions were generated 

for each air quality scenario of the general urban case study and six distributions were generated 

for each air quality scenario of the point source case studies (see Table 2-3). 

As discussed in Section 2.4.6.2, generating multiple sets of risk results for each 

combination of case study and air quality scenario provides a range of results reflecting the 

impact of key sources of uncertainty on risk results. However, because we could not assign 

specific confidence levels to each modeling approach, these multiple sets of results are not 

translated into single uncertainty distributions of risk for each air quality scenario in each case 

study. Therefore, we consider the multiple sets of risk results to span the best estimate risk 

distribution. In response to CASAC comments on the July 2007 draft risk assessment report 

(Henderson, 2007a), however, we have omitted results from the two-piece linear (with hinge at 
 4-7

10.82 μg/dL for concurrent blood metric) from the summary of results presented in this chapter 

(these results are available in Appendix K). 

Three types of risk estimates are summarized in Tables 4-2 and 4-3: 

• Risk estimates for recent air: This estimate is the portion of total IQ loss derived 

from inhalation of ambient air Pb and ingestion of Pb in indoor dust that is predicted 

to be associated with ambient air Pb concentrations based on the indoor dust model 

(i.e., “recent air” risk per Sections 2.4.3 and 3.2.2) Given the modeling approach 

used in this analysis and its limitations, this set of risk estimates is expected to be 

most responsive to alternative NAAQS. 

• Risk estimates for recent air plus past air (other indoor dust and outdoor soil 

contribution): This estimate is the portion of total IQ loss derived from inhalation of 

ambient air Pb, ingestion of indoor dust and ingestion of outdoor soil/dust (Sections 

2.4.3 and 3.2.2). That is, this estimate includes the recent air pathways in addition to 

other indoor dust and outdoor soil pathways. This estimate include some contribution 

from indoor paint because the indoor dust models handicapped our ability to parse out 

this contribution. Otherwise, these estimates reflect contributions from all except the 

policy-relevant background pathways of diet and drinking water. 

• Risk estimates for all pathway contributions: This estimate is the IQ loss associated 

with the total blood Pb concentration.

As noted previously (Sections 2.4.3 and 3.2.2), policy-relevant pathways are represented 

in the first two bullets above, with the true values for the policy-relevant pathways considered to 

fall between the estimates for “recent air” and those for “recent” plus “past air”. Additionally, 

with regard to the urban case study “recent air” and “recent” plus “past air” categories, an artifact 

of the hybrid dust Pb model tends to mask trends in the two components of dust Pb (recent air 

and other), which contribute to “recent air” and “past air” estimates, respectively (see Section 

4.3.1 discussion of this uncertainty). For the primary Pb smelter case study, uncertainty in 

parsing out the "recent air" and "other" components of indoor dust (specifically for the sitespecific regression model used in the remediation zone) have led us to conclude that only "recent 

plus past air" IQ loss estimates should be presented and "recent air" results should not be 

separately presented for the primary Pb smelter, as is done for the other case studies (see Section 

3.1.4.2). 

IQ loss estimates reflecting all exposure pathways (total), the “recent air” pathways (see 

Section 3.2.2), and the recent plus past air pathways are presented in Tables 4-2 and 4-3. The 

total estimates presented in Table 4-2 are those for the median in the distribution of total risk, 

and the estimates for the other two categories in that table (i.e., recent air and recent plus past air) 

are the values for those categories associated with the median for the total exposure pathway 
 4-8

estimate. The corresponding estimates for the 95th percentile in the distribution of IQ loss 

estimates for total blood Pb for each case study are presented in Table 4-3. This presentation 

may lead to some seeming inconsistencies in trends for recent air and recent air plus past air risk 

estimates across air quality scenarios. This is because the recent air or recent plus past air 

estimates associated with the median and 95th percentile total blood Pb estimate may not 

necessarily be the median and 95th percentiles of the distribution of estimates for those specific 

categories. This is because the blood Pb level (and associated total IQ loss) for a simulated child 

reflects not only the total Pb uptake (from both background and ambient air-related pathways), 

but also the GSD-based adjustment factor. This means that two simulated children could have 

identical blood Pb levels (and IQ loss estimates), but one child could have a higher recent air 

exposure, with the other child having their lower recent air exposure compensated for by a higher 

GSD-based adjustment factor, thereby resulting in both children having the same total blood Pb 

level. 

In presenting IQ loss estimates, all values are rounded to one decimal place. The 

complete set of risk estimates are presented in Appendix K. The risk estimates in Tables 4-2 and 

4-3 indicate the following: 

• IQ loss estimates were uniformly higher for the current NAAQS air quality scenario 

compared with any of the alternative NAAQS or current conditions air quality 

scenarios for the primary Pb smelter and general urban case studies. This trend held 

for both the median and 95th percentile risk results. Risk estimates for the secondary 

Pb smelter case study (both the full study area and sub-area) differed little across any 

of the air quality scenarios. 

• IQ loss estimates decreased with increasingly lower alternative NAAQS, as a general 

observation across case studies. However, this trend varied in consistency and 

magnitude. For example, the trend was more pronounced with the recent air estimates 

(both median and 95th percentile) for the general urban case study, and was less 

obvious with the risk estimates for total blood Pb (in this case, it is likely that 

background exposures overwhelmed the recent air related exposures). The trend was 

also stronger for the subarea of the primary Pb smelter, compared with the full study 

area, reflecting the fact that the subarea has higher ambient air Pb impacts and 

therefore, is likely to demonstrate greater variation in IQ impacts across alternative 

NAAQS. 

• As expected, risks associated with recent plus past air contributions are larger, across 

all population percentiles for all case studies and air quality scenarios evaluated, 

compared with the recent air risk estimates. This reflects that the “past air” category 

includes contributions from the outdoor soil/dust contribution to 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, including resuspended, previously deposited Pb 
 4-9

(see Section 2.4.3). As noted previously (Sections 2.4.3 and 3.2.2), policy-relevant 

pathways are represented in both of these categories, with the true values for the 

policy-relevant pathways considered to fall between the estimates for “recent air” and 

those for “recent” plus “past air”. 

• Recent air and recent plus past air IQ loss estimates were higher for the subareas of the 

point source case studies. This reflects the fact, mentioned above, that these subareas 

experience significantly greater ambient air-related impacts compared with the full 

study areas. While the full study areas include a large number of children in areas with 

ambient air Pb levels well below the alternative NAAQS considered, whose exposures 

consequently are less affected under alternative NAAQS, these areas are omitted from 

the subarea, thus increasing the percentage of simulated children whose exposures and 

associated risks are notably reduced under alternate NAAQS. 

• Risk estimates generated for both of the point source case studies (full study areas) are 

lower across all population percentiles (for the same air quality scenario) compared 

with the general urban case study. This reflects two factors. First, the point source 

case studies include spatial gradients, such that areas near the facility may be at or near 

a given ambient air Pb level (associated with an air quality scenario), while the 

majority of the study area (and consequently study population) experiences ambient air 

Pb levels considerably lower than the level associated with the air quality scenario. By 

contrast, the general urban case study assumed a uniform ambient air Pb concentration 

at the level of the air quality scenario being evaluated. Consequently, the entire 

hypothetical study population would experience that uniform air concentration. The 

second reason for the point source case studies having risk estimates lower than those 

for the general urban case study, is that they only consider Pb emitted from the 

industrial facility of interest and do not consider emissions from any other sources 

within the study area, or near the study area that could impact populations within the 

study area. By contrast, current conditions for the general urban case study is based on 

monitoring data for urban locations, which will by default reflect the actual mix of Pb 

sources influencing air Pb concentrations. Consequently, the general urban case study 

reflects the combined impact of different Pb sources, while the point source case 

studies only consider emissions from the smelter under consideration and the influence 

of these emissions lessens (and consequently air Pb concentrations decline) with 

distance from the facilities. 

• For the general urban case study, the large difference seen in the recent air risk 

estimates between the three higher and the lowest alternate NAAQS scenario for the 

95th percentile is not seen for the risk estimates for recent plus past air contributions. 

This reflects an artifact of the approach used in the hybrid indoor dust model to 

characterize "other" (non-“recent air”-associated) contributions to indoor dust (see 

Appendix G). Because of the method used to convert dust loadings to dust 

concentrations within the hybrid dust model and the approach used to apportion total 

dust concentrations between other and recent air components, the "other" indoor dust 

concentration predicted by this model varies with air quality scenario, with that value 

increasing as the ambient air Pb level decreases (see Appendix C). This means that, as 

the recent air contribution to exposure through indoor dust ingestion decreases (as the 

lower alternative NAAQS levels are considered), the estimate of contribution of 
 4-10

"other" indoor dust actually increases. This partially accounts for the trend noted 

above. That is, even though recent air risk estimates drop, they are partially offset by 

risk resulting from an increase in exposure to the "other" Pb contributions to indoor 

dust. The impact of this aspect of the hybrid dust model is identified in Section 4.3.1 

and evaluated in a sensitivity analysis presented in Chapter 5. 

• Risk estimates for the secondary Pb smelter case study are considerably lower than risk 

estimates for the other case studies (for all air quality scenarios considered). 

Limitations in the design for the secondary Pb smelter case study (described in Section 

4.3.1) contribute significant uncertainty to these estimates. Accordingly these results 

should not be used to draw conclusion regarding similar facilities risks experienced by 

populations living in the vicinity of other similar point sources. 
 4-11

Table 4-2. Summary of risk estimates for medians of total-exposure risk distributions. 

Points IQ lossa

Recent airb 

Recent plus 

past airb 

Total Pb 

Case Study and Air Quality Scenario exposure 

Low High Low High Low High 

General urban case study 

Current NAAQS (1.5 μg/m3, max quarterly) 2.1 4.3 2.9 6.0 3.5 7.0 

Current conditions - 95th percentile 

 (0.87 μg/m3, max quarterly) 0.4 2.0 1.3 4.0 1.8 5.4 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.5 2.1 1.4 4.1 1.9 5.4 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.3 1.7 1.2 3.8 1.7 5.2 

Current conditions – mean 

 (0.14 μg/m3, max quarterly) 0.2 1.4 1.1 3.6 1.5 5.0 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.2 1.4 1.0 3.6 1.5 5.0 

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 0.6 0.9 3.1 1.3 4.6 

Primary Pb smelter - full study area

Current NAAQS (1.5 μg/m3, max quarterly) 0.6 2.5 1.1 3.8 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.8 2.2 1.0 3.7 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.6 2.7 0.9 3.6 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.4 2.1 0.9 3.6 

Alternative NAAQS (0.05 μg/m3, max monthly) 

NAc 

0.4 2.4 0.9 3.5 

Primary Pb smelter - 1.5 km subarea

Current NAAQS (1.5 μg/m3, max quarterly) 3.2 6.3 3.7 6.8 

Alternative NAAQS (0.5 μg/m3, max monthly) 2.1 4.9 2.6 5.8 

Alternative NAAQS (0.2 μg/m3, max quarterly) 1.5 4.3 2.0 5.2 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.2 4.0 1.9 2.0 

Alternative NAAQS (0.05 μg/m3, max monthly) 

NAc 

0.9 2.9 1.4 4.6 

Secondary Pb smelter - full study area

Current conditions 0d <0.1 0d 1.0 0d 2.8 

Alternative NAAQS (0.5 μg/m3, max monthly) 0 <0.1 0 1.0 0 2.7 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0 <0.1 0 1.0 0 2.7 

Alternative NAAQS (0.2 μg/m3, max monthly) 0 <0.1 0 0.9 0 2.7 

Alternative NAAQS (0.05 μg/m3, max monthly) 0 <0.1 0 1.0 0 2.7 

Secondary Pb smelter - 1.5 km subarea

Current conditions <0.1 0.4 0.4 2.7 0.8 3.8 

Alternative NAAQS (0.5 μg/m3, max monthly) <0.1 0.1 0.4 1.9 0.7 3.7 

Alternative NAAQS (0.2 μg/m3, max quarterly) <0.1 <0.1 0.3 1.7 0.6 3.6 

Alternative NAAQS (0.2 μg/m3, max monthly) <0.1 0.1 0.4 1.9 0.6 3.6 

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 <0.1 0.3 2.1 0.6 3.6 

a - With the exception of "true zero values" (see note d below), all estimates are rounded to one decimal place. 

b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air 

contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from 

inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air 

Pb levels, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see 

Section 2.4.3). 

c – “Recent air” estimates were not developed for the primary Pb smelter case study (see Section 3.1.4.2). 

d – Table entries of “0” (in the “low” column) are truly zero values and reflect application of log-linear 

concentration-response function with cutpoint to blood Pb estimates below 1 μg/dL. 
 4-12

Table 4-3. Summary of risk estimates for 95th percentiles of total exposure risk 

distributions. 

Points IQ lossa

Recent airb 

Recent plus 

past airb 

Total Pb 

exposure 

Case Study and Air Quality Scenario Low High Low High Low High 

General urban case study 

Current NAAQS (1.5 μg/m3, max quarterly) 3.3 6.0 4.6 8.3 5.5 10.5 

Current conditions - 95th percentile 

 (0.87 μg/m3

, max quarterly) 1.0 3.4 3.1 6.6 4.2 8.8 

Alternative NAAQS (0.5 μg/m3, max monthly) 1.0 3.6 3.1 6.9 4.2 9.1 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.7 2.9 2.8 6.4 4.0 8.7 

Current conditions – mean 

 (0.14 μg/m3, max quarterly) 0.5 2.5 2.7 6.1 3.9 8.5 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.4 2.3 2.7 6.0 3.9 8.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.1 1.0 2.4 5.5 3.7 8.1 

Primary Pb smelter - full study area

Current NAAQS (1.5 μg/m3, max quarterly) 2.3 4.2 3.7 6.8 

Alternative NAAQS (0.5 μg/m3, max monthly) 2.5 4.5 3.4 6.6 

Alternative NAAQS (0.2 μg/m3, max quarterly) 1.9 5.2 3.2 6.5 

Alternative NAAQS (0.2 μg/m3, max monthly) 2.0 4.0 3.2 6.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 

NAc 

1.5 4.9 3.1 6.3 

Primary Pb smelter - 1.5 km subarea

Current NAAQS (1.5 μg/m3, max quarterly) 5.7 8.8 6.8 9.5 

Alternative NAAQS (0.5 μg/m3, max monthly) 5.1 7.5 5.8 8.5 

Alternative NAAQS (0.2 μg/m3, max quarterly) 4.7 6.9 5.3 7.8 

Alternative NAAQS (0.2 μg/m3, max monthly) 3.9 6.7 4.9 7.6 

Alternative NAAQS (0.05 μg/m3, max monthly) 

NAc

2.8 4.7 3.6 6.5 

Secondary Pb smelter - full study area

Current conditions <0.1 <0.1 0.8 1.8 2.4 5.2 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.1 0.1 1.0 2.0 2.3 5.1 

Alternative NAAQS (0.2 μg/m3, max quarterly) <0.1 <0.1 0.8 2.1 2.3 5.1 

Alternative NAAQS (0.2 μg/m3, max monthly) <0.1 <0.1 1.1 1.8 2.3 5.1 

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 <0.1 0.8 1.7 2.3 5.1 

Secondary Pb smelter - 1.5 km subarea

Current conditions 0.5 1.4 2.1 5.0 3.2 6.3 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.4 0.5 2.4 3.9 3.2 6.3 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.2 0.1 2.3 3.2 3.1 6.1 

Alternative NAAQS (0.2 μg/m3, max monthly) <0.1 0.2 1.4 4.3 3.1 6.1 

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 <0.1 1.6 4.2 3.0 6.1 

a - Estimates are rounded to one decimal place. 

b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air 

contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from 

inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air 

Pb levels, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see 

Section 2.4.3). 

c – “Recent air” estimates were not developed for the primary Pb smelter case study (see Section 3.1.4.2). 
 4-13

4.3 UNCERTAINTY CHARACTERIZATION AND SENSITIVITY ANALYSIS 

This section discusses uncertainty related to exposure and risk estimates generated for 

this analysis. Several methods have been used to examine uncertainty in our modeling approach 

and its potential impact on exposure and risk estimates. These include: 

• qualitative discussion of key sources of uncertainty and their potential impact on 

exposure and risk estimates (Section 4.3.1), 

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

4.3.3), and, 

• development of multiple sets of exposure and risk estimates for each assessment 

scenario that illustrate the combined impact of different models and input data on risk 

results and the associated uncertainty (Section 4.3.4). 

 In addition to these methods for considering uncertainty, a sensitivity analysis was also 

conducted to characterize the potential impact of individual modeling elements on risk results 

(see Section 4.3.2). Each of these elements of the uncertainty characterization, along with the 

sensitivity analysis, is briefly summarized below. 

4.3.1 Qualitative Discussion of Key Sources of Uncertainty 

Given the complexity of this assessment and the range of models and input data used in 

completing it, there is a wide variety of sources of uncertainty potentially impacting the exposure 

and risk results generated (Appendix M, Table M-1). This section identifies those sources of 

uncertainty with the potential to have a significant impact on risk results (see Appendix M, Table 

M-1 bold text). When it was feasible with the available methods and data, these key sources of 

uncertainty have been quantitatively assessed for their potential impact on risk results either as 

part of the multiple modeling approaches implemented (see Section 4.3.4). In addition, some of 

these modeling elements have been included in the sensitivity analysis (see Section 4.3.2). Key 

sources of uncertainty include: 

• Temporal aspects: As described in Section 2.4.1, exposure for the simulated child 

population begins at birth and continues for 7 years, with Pb concentrations in all 

exposure media remaining constant throughout the period, and children residing in the 

same exposure zone throughout the period. In characterizing exposure media 

concentrations, annual averages are derived and held constant through the seven year 

period. Exposure factors and physiological parameters vary with age of the cohort 

through the seven year exposure period, several exposure factors and physiological 

parameters are varied on an annual basis within the blood Pb modeling step (see 

Section 3.2). These aspects are a simplification of population exposures that 

contributes uncertainty to our exposure and risk estimates. 
 4-14

• General urban case study: This case study differs from the others in several ways 

(described in more detail in Section 2.2.1). It is by definition a general case study and 

not based on a specific location. There is a single exposure zone for the case study 

within which all media concentrations of Pb are assumed to be spatially uniform; that 

is, no spatial variation within the area is simulated (see Sections 2.4.2, and 3.1.1). 

Additionally, the case study does not rely on any specific demographic values. Within 

the single exposure zone a theoretical population of unspecified size is assumed to be 

uniformly distributed. Thus this case study is a simplified representation of urban 

areas intended to inform our assessment of the impact of changes in ambient Pb 

concentrations on risk, but which carries with it attendant uncertainties in our 

interpretation of the associated exposure and risk estimates. For example, the risk 

estimates for this case study, while generally representative of an urban residential 

population exposed to the specified ambient air Pb levels, cannot be readily related to a 

specific urban population. Specific urban populations are spatially distributed in a 

nonuniform pattern and experience ambient air Pb levels that vary through time and 

space. Consequently, interpretations of the associated blood Pb and risk estimates with 

regard to their relevance to specific urban residential exposures carry uncertainty and 

presumably an upward bias in risk, particularly for large areas, across which air 

concentrations may vary substantially. 

• Point source case studies: Dispersion modeling was used to characterize ambient air 

Pb levels in the point source case studies. This approach simulates spatial gradients 

related to dispersion and deposition of Pb from emitting sources. In the case of the 

point sources modeled, sources were limited to those associated with the smelter 

operations, and did not include other sources such as resuspension of roadside Pb not 

immediately related to facility operations, and other stationary sources of Pb within or 

near the study area. This means that, with distance from the facility, there is likely 

underestimation of ambient air-related Pb exposure because with increased distance 

from the facility there would be increasing influence of other sources relative to that of 

the facility. We believe this limitation to have more significant impact on risk 

estimates associated with the full study than on those for the subareas, and to perhaps 

have a more significant impact on risk estimates associated with the smaller secondary 

Pb smelter (see below). 

• Secondary Pb smelter case study: Air Pb concentration estimates derived from the air 

dispersion modeling completed for the secondary Pb smelter case study are subject to 

appreciably greater uncertainty than that for those for the primary Pb smelter case 

study due to a number of factors, including: (a) a more limited and less detailed 

accounting of emissions and emissions sources associated with the facility (particularly 

fugitives), (b) a lack of prior air quality modeling analyses and performance analyses, 

and (c) a substantially smaller number of Pb-TSP monitors in the area that could be 

used to evaluate and provide confidence in model performance1

. Further, as mentioned 

in the previous bullet, no air sources of Pb other than those associated with the facility 

were accounted for in the modeling. Given the relatively smaller magnitude of 

 

1

 The information supporting the air dispersion modeling for the primary Pb smelter case study (see Section 

3.5.1.1) provides substantially greater confidence in estimates for that case study. 
 4-15

emissions from the secondary Pb smelter, the underestimating potential of this 

limitation with regard to air concentrations with distance from the facility has a greater 

relative impact on risk estimates for this case study than for the primary Pb smelter 

case study. The aggregate uncertainty of all of these factors has left us with low 

confidence in estimates for this case study. We note that exposure and risk estimates 

are lower than those for the other case studies. Although we had initially intended to 

use this case study as an example of areas near stationary sources of intermediate size 

(smaller than the primary Pb smelter), our experience with this analysis indicates that 

substantially more data and multiple case studies differing in several aspects would be 

needed to broadly characterize risks for such a category of Pb exposure scenarios. 

• Indoor dust Pb modeling for the general urban case study: The hybrid indoor dust Pb 

model was developed for the general urban case study due to a lack of an existing 

urban-focused dust model, and this hybrid model is subject to particular uncertainties. 

Key among these uncertainties is failure to consider house-to-house variability in 

factors related to the infiltration of ambient air Pb indoors and subsequent buildup of 

Pb on indoor surfaces. This handicaps our ability to predict variation in indoor dust Pb 

levels for non-typical residential conditions. In addition, a lack of comprehensive data 

in the literature on rates and efficiency of indoor cleaning, introduces uncertainty into 

the model. The method used to convert Pb loadings generated by the hybrid model to 

Pb concentrations is also subject to uncertainty, partially due to the age of the 

underlying dataset. Because the underlying dataset is an older dataset (see Section 

3.1.4.1), there is potential for bias toward greater representation of housing with Pbbased paint. Additionally, the component of the hybrid model that converts dust Pb 

loadings to concentrations (Appendix G, Section G.3.4) introduces an uncertainty into 

the estimates of percent contribution from recent air compared to other pathways. This 

is related to a nonlinearity in this conversion and results in the "other" indoor dust 

concentration predicted by this model varying with air quality scenario, with that value 

increasing as the ambient air Pb level decreases (see Appendix C). This means that, as 

the recent air contribution to exposure through indoor dust ingestion decreases for the 

lower alternative NAAQS levels, the estimate of contribution of "other" indoor dust 

actually increases. This issue of the uncertainty in estimating the "other" component of 

indoor dust (and relating it to the "recent air" component) has been examined further as 

a sensitivity analysis (see Section 5.3.3.4). 

• Estimates of indoor dust Pb concentrations for the primary Pb smelter case study 

(application of the site-specific regression model): There is uncertainty associated 

with the site-specific regression model applied in the remediation zone (see Section 

3.1.4.2), and relatively greater uncertainty associated with its application to air quality 

scenarios that simulate notably lower air Pb levels. Limitations in the dataset from 

which the model was derived limited its form to that of a simple regression that 

predicts dust Pb concentration as a function of air Pb concentration plus a constant 

(intercept). We recognize, however, that there may be variables in addition to air that 

influence dust Pb concentrations and their absence in the regression contributes 

uncertainty to the resulting estimates. To the extent that these unaccounted for 

variables are spatially related to the smelter facility Pb sources, our estimates could be 

biased, not with regard to the absolute dust Pb concentration, but with regard to 
 4-16

differences in dust Pb concentration estimate between different air quality scenarios. 

Those differences may be overestimated because of potential overestimation of the air 

coefficient and underestimation of the intercept in the regression model. Examples of 

such not-accounted-for variables are roadside dust Pb and historically contributions to 

current levels of indoor dust Pb (e.g., dust Pb contributed to a house in the past that 

continues to contribute to current dust Pb levels). See Sections 3.1.4.2 and 3.5.1.3 for 

additional discussion. 

• Estimates of outdoor soil/dust Pb concentrations: Outdoor soil/dust Pb concentrations 

in all air quality scenarios have been set equal to the values for the current conditions 

scenarios. That is, we are not simulating an impact of changes in air Pb concentrations 

on soil concentrations, or the associated impact on dust concentrations, blood Pb and 

risk estimates. In areas where air concentrations have been greater in the past, 

however, implementation of a reduced NAAQS might be expected to yield reduced soil 

Pb levels over the long term. As described in Section 2.3.3, however, there is 

potentially uncertainty associated with this specification, particularly with regard to 

implications for areas in which a Pb source may locate where one of comparable size 

had not been previously. Additionally, we note that control measures implemented to 

meet alternative NAAQS may result in changes to soil Pb concentrations; these are not 

reflected in the assessment 

• Recontamination of residential yard soil near the primary Pb smelter: Although data 

collected in residential yards within 3⁄4 mile of the primary Pb smelter indicate a trend 

of increasing surface soil Pb concentrations, soil Pb estimates in this area of the 

primary Pb smelter case study were not adjusted using this information. Therefore, 

these estimates likely were underestimates of soil Pb levels associated with the current 

ambient air Pb levels. Because the indoor dust Pb model used in this part of this case 

study did not rely on outdoor soil/dust concentrations this did not affect indoor dust Pb 

estimates, although as described in a previous bullet the lack of soil data to consider in 

developing the indoor dust Pb model increased uncertainty in that model’s results. 

Additionally, the outdoor soil/dust ingestion pathway is a small contributor to blood Pb 

levels, which also reduced the impact of this factor on blood Pb estimates . 

• Interindividual variability GSD: There is uncertainty associated with the GSD 

specified for each case study. In the case of the general urban case study, the use of a 

uniform ambient air Pb concentration across the hypothetical study area complicates 

selection of a GSD. This is because any urban area for which GSDs can be developed 

would be subject to spatial variation in ambient air Pb levels. To the extent that these 

ambient air Pb levels influence children’s blood Pb levels, this influence would be 

reflected in the associated GSD. In the case of the two point source case studies, the 

use of the spatial templates complicates selection of a GSD for a different reason. 

Because the spatial templates are intended to contribute variability in risk results 

related to spatial gradients in air-related Pb media concentrations, the GSDs used for 

these case studies need to reflect the remaining sources of variability in blood Pb levels 

(e.g., interindividual variability in behavior and biokinetics related to Pb exposure as 

well as variability in non-air related Pb exposures). The extent to which the specified 

GSDs reflect the sources of variability at play in each of these case studies is unknown. 
 4-17

• Exposure pathway apportionment for higher percentile blood Pb level and IQ loss 

estimates: As discussed in Section 3.2.2, pathway apportionment of blood Pb levels 

for higher population percentiles is specified to be the same as that estimated using the 

central tendency estimate of blood Pb in an exposure zone. This introduces uncertainty 

into projections of pathway apportionment for higher population percentiles of blood 

Pb and IQ loss. In reality, pathway apportionment may shift as you consider higher 

exposure percentiles. For example, paint and/or drinking water exposures may 

increase in importance, with air-related contributions decreasing as an overall 

percentage of blood Pb levels and associated risk. 

• Projection of IQ loss at lower exposure levels: Because available epidemiological data 

are limited in their description of the relationship of IQ decrement to blood Pb at lower 

blood Pb levels (e.g., < 5 μg/dL), there is uncertainty associated with projecting IQ loss 

at these blood Pb levels which are particularly relevant to the current population of 

children in the U.S. (see discussion in Section 2.1.5). For additional discussion of 

uncertainty associated with predicting IQ loss resulting from Pb exposure, see Sections 

5.3.3.1. 

4.3.2 Sensitivity Analysis 

Sensitivity analysis techniques were used to evaluate the impact of individual modeling 

elements on exposure and risk estimates. Specifically, we used a "one element at a time 

elasticity analysis" approach, in which the full risk model was run with one of the selected 

modeling elements adjusted to reflect an alternate input value or modeling choice. The results of 

that run with the modified modeling element was then compared to those for the "baseline risk" 

run to determine the magnitude of the impact on risk results of selections for that one modeling 

element. 

The sensitivity analysis described in Appendix L focused on the general urban case study, 

reflecting the fact that this case study has potential relevance for a larger number of Pb-exposed 

children compared with the two point source case studies. Additionally, we recognized that 

exposure patterns for urban children can be highly variable compared with exposures near Pb 

smelters and the availability of data sets for specific urban areas that reflect current blood Pb 

levels is limited. The modeling elements examined in the sensitivity analysis included those 

inputs and modeling steps believed to have a significant potential for impacting exposure and 

risk results (e.g., oral uptake factor, interindividual blood Pb variability GSD, biokinetic model, 

concentration-response function for IQ loss). The one-element-at-a-time sensitivity analysis 

indicates which of the modeling elements included in the sensitivity analysis has the greatest 

impact on risk results, which can be used to guide future efforts to refine the overall risk model. 

Note, that an additional sensitivity analysis involving the hybrid indoor dust model and 

specifically the derivation of the "other" component of indoor dust was conducted as part of the 

core analysis (see Section 5.3.3.4). 
 4-18

The results of the sensitivity analysis (Appendix L) can be considered both in terms of 

the impact of individual modeling element choices on (a) overall risk results and (b) recent air 

risk results, i.e., IQ loss estimates for inhalation plus ingestion of indoor dust Pb predicted to be 

associated with ambient outdoor air Pb levels, where ambient outdoor air potentially includes 

resuspended, previously deposited Pb (see Section 2.4.3). Given the relevance of the recent air 

exposure pathways to the NAAQS review, the results of the sensitivity analysis are summarized 

here in terms of their impact on recent air risk results, rather than total risk results. Results of the 

sensitivity analysis for total risk estimates, however, are similar (Appendix L). 

Results of the sensitivity analysis showed the following modeling elements to have the 

greatest impact on recent air risk estimates for the general urban case study (i.e., >40% impact on 

the 95th percentile recent air risk estimates and an even greater impact, in terms of percent 

change, on median estimates). These elements are presented in order of decreasing magnitude of 

impact on risk estimates (Note, results presented here reflect the impact on the 95th percentile 

risk results - see Appendix L for sensitivity analysis results reflecting impact on the median 

population percentile): 

• IQ loss function: Use of the log-linear with linearization IQ loss model (the model 

producing the greatest IQ loss) resulted in 226% increase in recent air risk results over 

results for baseline which used the 2-piece linear model. These results suggest that 

characterization of the relationship between Pb exposure and IQ loss has the greatest 

impact on high-end risk results. 

• Indoor dust Pb modeling: Use of a combination of high-end modeling parameters for 

the hybrid dust model results in a 139% increase in recent air risk estimates over results 

using the hybrid model with those input values used in the primary analysis. In 

addition, use of the air-only regression model in place of the hybrid model results in a 

60% decrease in the recent air risk results. These results together suggest that 

predicting the relationship between outdoor ambient air Pb and indoor dust Pb is an 

important factor impacting risk results. 

• Blood Pb modeling: Use of the Leggett model as compared to the IEUBK model, 

results in a 170% increase in recent air risk estimates. This indicates the importance of 

the blood Pb modeling step in the analysis. However, these results need to be 

considered in the context of the performance evaluation for blood Pb which suggested 

that the Leggett model may be significantly overpredicting blood Pb levels, and the 

fact that the Legget model is designed to provide results over shorter time frames (e.g., 

with daily time step), while the IEUBK model provides longer-term quasi steady-state 

estimates. 

• Monitor-based ratio of maximum quarterly to annual average Pb-TSP concentrations: 

A ratio of monitored maximum quarterly average to annual average air Pb 

concentrations is used in the general urban case study to translate maximum quarterly 

average air concentrations into the annual average values used in the risk assessment. 
 4-19

Use of the 95th percentile of urban Pb-TSP monitor ratios as compared to the mean 

value, results in a 50% reduction in recent air risk estimates. 

• GSD reflecting interindividual variability in blood Pb levels: Use of the larger GSD 

(2.1) in place of the smaller value (1.7) resulted in a 40% increase in the recent air risk 

estimates. 

 Alternate selections for the other modeling elements included in the sensitivity analysis 

yielded changes to the 95th percentile risk results of less than 40% from baseline. 

4.3.3 Performance Analyses 

Performance evaluation for the exposure assessment (Section 3.5) focused on evaluation 

of estimates of Pb in ambient air, outdoor soil, and indoor dust (discussed in Section 3.5.1) and 

estimates of Pb in blood (covered in Section 3.5.2). Consideration of the results of performance 

evaluation can provide insights into potential sources of uncertainty in an analysis, by identifying 

those elements of the analysis that appear inconsistent with available empirical data. This can, in 

turn, point to underlying bias or other errors associated with that particular modeling step, 

reflecting either parameter or model uncertainty. This section identifies key findings of the 

performance analysis describing the nature of associated uncertainty, including results which 

either supported modeling elements or suggested increased uncertainty. 

• Modeled ambient air Pb levels: The evaluation of the air model performance for the 

primary Pb smelter case study (Sect 3.5.1.1) indicated performance generally 

consistent with empirical data, increasing our confidence in air-related results 

generated for this case study. Evaluation of air dispersion model performance for the 

secondary Pb smelter suggested the potential for low bias in predictions of ambient air 

Pb concentrations. 

• Estimates of outdoor soil/dust Pb concentration for secondary Pb smelter case study: 

The use of a combination of dispersion and soil mixing models to generate a spatial 

pattern of concentrations combined with a scaling factor based on a surrogate location 

contributes significant uncertainty to the soil Pb characterization for this case study. 

Specifically, while the approach used is believed unlikely to significantly 

underestimate soil Pb levels at this type of facility, the exact extent to which it is 

representative of conditions at this specific location is not known. 

• Modeled indoor dust Pb concentrations: Evaluation of the hybrid indoor dust model 

used in the general urban case study suggested the potential for both under- and 

overestimation. The mechanistic ambient air-related portion of the model may 

underestimate that component of dust Pb, while the indoor dust loading-toconcentration conversion algorithm may contribute to overestimate of dust Pb. It is not 

know to what extent these two biases cancel out each other. Overall comparison of 

indoor dust Pb concentrations generated for the three case studies against available 

empirical data suggest that: (a) for the general urban case study, estimates fall within 

the range of measured values from a national-scale study, adding confidence to the 

estimates, (b) central tendency estimates for the primary Pb smelter are close to the 
 4-20

lower end of the range for the national-scale dataset referenced above for the general 

urban case study and high-end estimates seem to fit with available data near smelters, 

and (c) comparison of estimates for the secondary Pb smelter against empirical data 

suggest that these estimates may be biased low. 

• Evaluation of candidate blood Pb models: A number of performance evaluations were 

completed on the two candidate blood Pb models considered for this analysis (IEUBK 

and Leggett), as well as the Lanphear empirical model. The results of these 

performance evaluations, which included application of both models in replicating 

national-scale child blood Pb levels (NHANES IV results) and blood Pb levels for an 

urban child cohort, suggested that the Leggett model consistently overpredicted blood 

Pb levels by a factor of 3 to 6, while IEUBK estimates were usually within a factor of 

2. These findings, in addition to CASAC recommendations, resulted in our selecting 

the IEUBK model as primary blood Pb model for this assessment, with the Leggett 

model being reserved for application in the sensitivity analysis. 

• Outdoor air Pb-to-blood Pb ratios: Three sets of outdoor air Pb-to-blood Pb ratios 

were derived. These related outdoor ambient air Pb to blood Pb resulting from: (1) the 

inhalation pathway only; (2) all recent air pathways (inhalation plus ingestion of indoor 

dust Pb predicted to be associated with ambient air Pb levels, with ambient air 

potentially including resuspended, previously deposited Pb); and (3) all recent and past 

air pathways (see Section 2.4.3). All ratios were derived prior to application of the 

GSD reflecting interindividual variability in blood Pb levels and therefore reflect 

central tendency blood Pb levels and not high-end population percentiles. The 

modeled ratios were compared to both empirical data and statistically derived ratios 

based on a pooled analysis (Section 3.5.2.2). With the exception of the primary Pb 

smelter case study recent air ratio using 95th percentile air concentration and the 

general urban case study recent air ratios for the hybrid dust model, the ratios for recent 

air contribution to concurrent blood Pb level (Table 3-10) generated for the three case 

studies were reasonable and supported by available empirical data. As described in 

Section 3.5.2.2, the exceptions relate to use in those case studies of the dust models 

predicting the greatest influence of air concentration on indoor dust Pb among the set 

of dust models used. These were the general urban case study hybrid dust model and 

the site-specific regression dust model used in the remediation zone for the primary Pb 

smelter case study. In both cases, the ratios based on these models were higher than 

empirically derived ratios obtained from the literature. However, the literature 

indicated a potential for higher ratios for locations with either lower ambient air Pb 

levels or lower blood Pb levels, both conditions of which are present at the two case 

studies. Therefore, the higher modeled ratios obtained using the site-specific indoor 

dust models for the general urban and primary Pb smelter case studies do not 

necessarily point to potential high bias in predicting blood Pb levels for the study 

populations. 

• Comparison of modeled blood Pb levels to nationally representative data: Our ability 

to compare modeled blood Pb levels to empirical data was handicapped by a lack of 

studies for populations of children directly comparable to those in the three case studies 

(see Section 3.5.2.3). Therefore, we focused most of our evaluation of modeled blood 

Pb levels on consideration of the national-level data obtained from NHANES IV. Note 
 4-21

that while higher-end population percentiles for the three case studies might not be 

reflected in a national-level dataset, blood Pb levels associated with background 

exposures for all three case studies are similar to blood Pb levels in the national 

dataset. Thus, we considered a comparison of modeled blood Pb levels for the three 

case studies against a national-scale dataset for central tendency population percentiles 

appropriate. In this comparison, we included the highest and lowest of the multiple 

sets of blood Pb estimates for the general urban case study generated using the multiple 

modeling approaches employed used for this case study. These two blood Pb 

distributions bracketed the NHANES IV based distribution, which adds confidence to 

blood Pb modeling conducted for this case study. Results for the primary Pb smelter 

(full study area) were similar to the NHANES IV distribution across population 

percentiles up to the 95th. This result is not surprising given the fact that exposure 

modeling for the primary Pb smelter did not fully consider variability in background Pb 

exposure (e.g., paint, diet, drinking water). Had background exposures been fully 

considered, the degree of divergence between the case study and national-level 

percentiles would likely have been much greater (with the primary Pb smelter 

distribution exceeding the NHANES IV distribution at higher population percentiles). 

Results for the subarea of the primary Pb smelter case study were consistently higher 

than the NHANES IV distribution across all population percentiles, as is expected 

given that the entire study area experiences elevated facility-related ambient air Pb 

levels. Blood Pb levels for all population percentiles for the secondary Pb smelter are 

lower than those for the national-scale dataset. As with the primary Pb smelter case 

study, this likely reflects the fact that variability in background Pb exposures was not 

fully considered in modeling this case study. 

4.3.4 Uncertainty in Modeling Approaches – Multiple Sets of Results 

For those more highly influential analytical steps for which it is not clear which model or 

input would generate “best estimate” results, we have implemented multiple modeling 

approaches (see Section 2.4.6.2). Because each of the case studies uses different modeling 

approaches for some of the analytical steps such as the indoor dust modeling step, and these 

approaches are associated with differing uncertainty, the identity and size of the areas of 

uncertainty associated with each case study differs. The specific modeling approaches for each 

case study and their elements are presented in Figure 2-3. For the general urban case study, two 

different dust models and two GSDs were used as compared to one model and GSD for these 

analytical steps in the two point source case studies. However, the same number of blood Pb 

metrics and IQ loss functions are used for all three case studies. 

Consideration of the range of risk results generated using the multiple modeling 

approaches for each case study provides perspective on the combined effect of key sources of 

uncertainty on risk results. The median and 95th percentile estimates associated with the 

modeling approaches yielding the highest and lowest risk results for specific scenarios of the 

three case studies are presented in Table 4-4. 
 4-22

Table 4-4. Impact of multiple sources of uncertainty on risk results. 

IQ loss 

(population 

percentile) 

Percent difference 

between highest 

and lowest 

approaches 

Case study Modeling approach Median 95th Median 95th 

Lowest risk: Dust Model (Air-only Regression-based), 

GSD (1.7), blood Pb Metric (Concurrent), IQ Function 

(Two-piece Linear) 

General 2 4 

urban 

(mean current 

conditions) 

Highest risk: Dust Model (Hybrid), GSD (2.0), blood Pb 

Metric (Lifetime), IQ Function (Log-linear with 

Linearization) 

5 9 

230% 120% 

Lowest risk: blood Pb Metric (Concurrent), IQ Function 

(Two-piece Linear) 1 4 Primary Pb 

smelter 

(current 

NAAQS) 

Highest risk: blood Pb Metric (Lifetime), IQ Function 

(Log-linear with Linearization) 4 7 

280% 80% 

Lowest risk: blood Pb Metric (Concurrent), IQ Function 

(low-linear with cutpoint) 0 2 Secondary Pb 

smelter 

(current 

conditions)

Highest risk: blood Pb Metric (Lifetime), IQ Function 

(Log-linear with Linearization) 3 5 

>300% 120% 

The lack of a consistent pattern regarding the magnitude of differences between the 

modeling approaches generating the lowest and highest risk results either across case studies, or 

across population percentiles (Table 4-4), is due to different influences of various factors specific 

to the modeling approaches for each case study. For example, the finding with regard to the 95th

percentile results that the greatest difference across approaches occurs for the secondary Pb 

smelter case study (>300%) reflects the fact that the lowest predicted median blood Pb level for 

this case study is below the cutpoint associated with one of the three concentration-response 

functions (thereby resulting in zero predicted IQ loss). The finding for the 95th percentile results 

that there is a greater difference for the general urban case study than the primary Pb smelter 

case study is because of the larger range of GSDs applied in this case study (2.1 and 1.6). 

In summary, results presented in Table 4-4 suggest that these key sources of uncertainty, 

when acting in concert, can produce an impact on overall risk results on the order of a factor of 2 

to 3. 
 4-23

REFERENCES 

Henderson, R. (2007a) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to 

Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of 

the 2nd Draft Lead Human Exposure and Health Risk Assessments Document. September 27, 2007. 

Henderson, R. (2007b) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to 

Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee’s (CASAC) Review of 

the 1st Draft Lead Staff Paper and Draft Lead Exposure and Risk Assessments. March 27, 2007. 

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

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

Lanphear, B.P., Hornung, R., Khoury, J., Yolton, K., Baghurst, P., Bellinger, D.C., Canfield, R.L., Dietrich, K.N., 

Bornschein, R., Greene, T., Rothenberg, S.J., Needleman, H.L., Schnaas, L., Wasserman, G., Graziano, J., 

and Robe, R. (2005) Low-level environmental Pb exposure and children's intellectual function: An 

international pooled analysis. Environmental Health Perspectives. 113(7):894-899. 
 5-1

5 ADDITIONAL ANALYSES 

This chapter presents the additional analyses performed for the Pb NAAQS risk 

assessment subsequent to the public meeting of the CASAC Pb Panel on August 28-29, 2007 

(Henderson, 2007b). These analyses include reanalysis of the general urban and primary Pb 

smelter case studies and new analyses for three location-specific urban case studies located in 

Cleveland, Chicago and Los Angeles.1 The methods used in conducting these additional 

analyses, as well as the resultant exposure and risk estimates, sensitivity analyses and uncertainty 

characterization are presented in this chapter. 

5.1 DESIGN OF EXPOSURE AND RISK ASSESSMENTS 

This section presents an overview of the design for the additional analyses. Additional 

details regarding the methods used in estimating exposure for both analyses can be found in 

Section 5.2. Details on the concentration-response functions for IQ loss and methods used to 

generate population risk estimates for these additional analyses can be found in Section 5.3. 

5.1.1 Assessment Scenarios 

The design of the assessment scenarios for the additional analyses is generally the same 

as that for the initial analyses (see Section 2.3 and subsections). The main difference is the 

addition of an alternate NAAQS scenario of 0.02 μg/m3, as a maximum monthly average; 

otherwise, the air quality scenarios for the additional analyses are the same as those included in 

the initial analyses (Section 2.3.1). Specification of the air concentrations for the current 

conditions location-specific urban case studies is summarized in Section 5.1.3 and described 

more fully in Section 5.2.2.1 and Appendix O. 

5.1.2 Analytical Approach 

The general analytical approach employed for the initial analyses, described in Section 

2.4, was also employed for the additional analyses, including the temporal aspects (Section 

2.4.1), categorization of policy-relevant exposure pathways (Section 2.4.3), and the analytical 

steps (Section 2.4.4). Additionally, the spatial scale and resolution aspects of the reanalyses for 

the general urban and primary Pb smelter case studies were the same as for the initial analyses 

(see Section 2.4.2). The spatial scale and resolution aspects of the location-specific urban case 

studies are summarized in Section 5.1.3. 

 

1

 Due to limitations with the design of the secondary Pb smelter case study and the associated high level of 

uncertainty noted in Sections 4.2 and 4.3.1,this case study was not part of the additional analyses described in this 

chapter. 
 5-2

A notable difference in the approach between the initial analyses and the additional 

analyses is that the multiple modeling approaches described in Section 2.4.6.2 and implemented 

for the initial analyses were narrowed down to a core modeling approach for the additional 

analyses. This was done in consideration of CASAC comments on the initial analyses 

(Henderson, 2007). Consistent with CASAC comments, the core modeling approach includes a 

single indoor dust model for the general and location-specific urban case studies, focuses on the 

concurrent blood Pb metric, and applies a single blood Pb GSD for the general urban case study. 

Additionally, CASAC recommended use of a linear concentration-response function derived 

from a subpopulation with peak blood Pb levels below 7.5 μg/dL, and indicated less favor for the 

two-piece linear function employed in the initial analyses of the full-scale assessment (Section 

4.1.1.3). Accordingly, a different set of concentration-response functions was employed in the 

additional analyses (see Section 5.3.1.1). Figure 5-1 identifies the core modeling approach for 

each case study. 

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

Elements of modeling approaches 

Case Study 

Indoor dust 

modeling Blood Pb metric GSD 

Concentrationresponse function 

Number of 

sets of results 

General Urban 

 Case Study 

And 

Locationspecific Urban 

Case Studies 

1 model: 

hybrid 

mechanisticempirical 

1 GSD, 

representing 

larger, regional 

scale 

1 * 1 * 1 * 4 = 4 

Primary Pb 

Smelter Case 

Study 

1 model: 

statistical 

(regression) 

approach 

1 metric: 

(a) concurrent 

1 GSD 

4 functions: 

(a) log-linear with 

low-exposure 

linearization, 

 (b) log-linear with 

cutpoint, and 

 (c) dual linear, 

stratified at 7.5 

μg/dL 

(d) dual linear, 

stratified at 10 μg/dL 

1 * 1 * 1 * 4 =4 

5.1.3 Location-Specific Urban Case Studies 

The primary design difference between the location-specific case studies and the general 

urban case study, is that, unlike the spatially uniform general urban case study in which ambient 

air Pb levels are uniform and fixed at the air concentration considered (see Section 2.2.1) both 

the number of children and the ambient air Pb levels vary within the location-specific urban 

study areas. As a result, although the data on which these location-specific urban case studies 

are based are quite limited, exposure and risk estimates for these location-specific urban case 

studies provide a better representation of some urban areas in the U.S. than do those for the 

general urban case study.
 5-3

Locations were selected for these study areas based on consideration of the number and 

spatial distribution of Pb-TSP monitors, the relative representation of source-oriented versus 

other monitors and the relative Pb concentrations reported for those monitors (Appendix A, 

Attachment A-2). Due to time constraints, the 2003-2005 dataset of Pb-TSP monitors analyzed 

in Appendix A (Attachment A-2) was the focus for this selection process and for subsequent 

consideration of air quality for the selected areas. Among all metropolitan areas represented in 

this dataset, preference was given to urban areas with relatively more monitors having a more 

uniform distribution and relatively higher Pb concentrations, and monitors that were not 

considered source oriented (see Appendix A, Section A.2.2.2.3). This led to selection of areas in 

Cleveland (6 monitors), Chicago (11 monitors) and Los Angeles (7 monitors). 

The three study areas differ in several respects. First, they differ in Pb air levels, with the 

highest reported for Cleveland and the lowest for Los Angeles. Further, while monitors in 

Cleveland and Chicago included a subset classified as source oriented, this was not the case in 

Los Angeles. Last, the source oriented monitors were segregated from the other monitors in 

Chicago, while they were relatively more dispersed among the other monitors in Chicago. 

The size of each study area is based on a polygon encompassing the area of each city in 

which the monitors occur. That polygon is defined initially using those monitors as apices and 

then a one mile-wide buffer is included beyond them in defining the final study area polygon 

(Appendix O, Exhibits O-2 through O-4). The U.S. Census blocks whose centroids fall within 

this polygon comprise the study area for each location-specific case study (Appendix O). Using 

centroids as the basis for determining whether a Census block is inside or outside of the study 

area provides a simple way for determining membership for those blocks that straddle the study 

area boundary. The target population for each study area is those children less than 7 years of 

age located within the study area blocks. The three study areas range in size from 67 to 1091 

square km, and in target population size from 13,990 to 396,511 children (less than 7 years of 

age). 

Exposure zones within each study area were defined based on assignment of census 

blocks to monitor sites, such that the number of exposure zones for a study area was equal to the 

number of monitors in a study area. The total child count for each exposure zone reflects the 

sum of children less than seven years old in the U.S. census blocks assigned to it, and the 

ambient air Pb concentrations for that zone reflect characteristics of data for that monitor. 

Consistent with the approach for all case studies, Pb concentrations in air (as well as other 

media) are homogenous within each exposure zone. 

Within each study area, Census blocks were assigned to exposure zones differently 

depending on whether or not they were near a source oriented monitor. All blocks whose 

centroids fall within a mile of a source oriented TSP monitor were assigned to a source oriented 
 5-4

exposure zone defined by that monitor. All other blocks (i.e., those for which the centroid was 

farther than a mile from a source oriented monitor) were assigned to the nearest non source 

oriented monitor. This approach reflects the recognition that air Pb concentrations associated 

with sizeable stationary sources exhibit sharper gradients with distance than air Pb 

concentrations less influenced by such sources. Accordingly, source influenced Pb 

concentrations are constrained to smaller exposure zones than non source influenced 

concentrations. 

Lead concentrations in indoor dust and outdoor soil/dust were estimated for the locationspecific urban case studies using specific elements of the core modeling approach applied in the 

general urban case study (e.g., the indoor dust Pb model and Pb level in outdoor dust/soil). 

Once the media concentrations are assigned to the exposure zones, the design of the 

exposure and risk analyses for each of these location-specific urban case studies is similar to that 

employed in the initial analyses to model the point source case studies (see Section 2.4.4.1 and 

2.4.4.2), with a population-weighted simulation of exposure and risk conducted at the U.S. 

Census block level. 

5.2 EXPOSURE ASSESSMENT 

5.2.1 Methods for General Urban and Primary Pb Smelter Case Studies 

For estimating ambient air concentration, inhalation exposure concentration and Pb 

concentration in outdoor soil/dust, the methods described in Section 3.1.1, 3.1.2 and 3.1.3 for the 

general urban and primary Pb smelter case studies also apply to the additional analyses for those 

case studies. 

For the general urban case study, indoor dust Pb concentrations were estimated using the 

hybrid model. The primary rationale for deemphasizing the air only regression model and 

favoring the hybrid model in the additional analyses is that there is reduced confidence in using 

the air only regression model in the urban residential context, since this statistical model was 

developed with datasets that favored areas near Pb smelter sites (see Section 3.1.4.1 and 

Appendix G.1.1). By contrast, the hybrid indoor dust model was developed specifically for 

application in the urban residential context. 

For the primary Pb smelter case study, indoor dust Pb concentrations are estimated as 

described in Section 3.1.4.2. 

Blood Pb concentrations for the additional analyses for the general urban and primary Pb 

smelter case studies were estimated using methods previously described in Section 3.2, with two 

distinctions. First, for these additional analyses, the core modeling approach employed included 

only the concurrent blood Pb metric, consistent with CASAC recommendations (Henderson, 

2007b). Secondly, for the general urban case study core approach we used the GSD of 2.1 which 
 5-5

represents the GSD for the concurrent blood Pb metric identified from NHANES IV (Section 

3.2.3.1). This GSD is consistent with the CASAC recommendation to use a GSD more 

descriptive of the variability in blood Pb levels reported in NHANES-IV, as compared with the 

smaller GSDs observed for more localized populations near specific Pb emitting industries (e.g., 

see Section 3.2.3). The rationale for selecting the larger GSD, is that larger urban areas are 

likely to include more heterogeneous populations with regard to Pb exposure and blood Pb levels 

that more closely resemble the national NHANES population than a smaller more homogeneous 

population. For the primary Pb smelter case study, which includes a more localized study 

population, we have retained the smaller GSD of 1.7 used in the initial analyses (see Section 

3.2.3.2). 

The remaining elements of the core analysis, related to blood Pb modeling, including the 

blood Pb model, characterization of background Pb exposures, apportionment of exposure 

pathway contributions and probabilistic population modeling are those described in Section 3.2. 

5.2.2 Methods for Location-specific Urban Case Studies 

The methods used to estimate media and blood Pb concentrations in the location-specific 

urban case studies are described in the following subsections. The primary difference in 

methods between the location-specific case studies and the general urban case study is the 

inclusion of multiple exposure zones per study area across which numbers of children and 

ambient air Pb levels vary. 

5.2.2.1 Ambient Air and Inhalation Exposure Concentrations 

Air quality scenarios evaluated for these case studies included: (a) a current conditions 

scenario based on monitor-specific Pb-TSP statistics for the 2003-2005 dataset (Appendix A, 

Attachment A-2), (b) a current NAAQS scenario based on a roll-up of Pb-TSP values to the 

current NAAQS level, and (c) alternative NAAQS scenarios for levels falling below the current 

conditions levels for each case study. 

For each scenario, the annual average ambient air Pb concentration estimate for each 

exposure zone that is the input to the blood Pb model for that exposure zone is based on the 

statistics for the monitor associated with that exposure zone, using the monitor-specific statistics 

presented in Appendix A, Attachment A-2. How this is derived varies with the air quality 

scenario as described below. 

• Current conditions scenario: Each exposure zone is assigned the Pb-TSP values 

derived from the analysis of the 2003-2005 dataset in Appendix A (Attachment A-2) 

for the monitor assigned to that zone. Specifically, the annual average ambient Pb 

concentration estimate that is the input to the blood Pb model for a given exposure 
 5-6

zone is the three-year annual average concentration associated with the Pb-TSP 

monitor assigned to that zone. 

• Current NAAQS scenario: A proportional roll-up procedure is used to derive air 

concentrations for all exposure zones such that the highest maximum quarterly average 

concentration among all of the study area monitors just meets the current NAAQS 

(maximum quarterly average of 1.5 μg/m3). 

⎯ For the Cleveland and Los Angeles study areas, this procedure involves adjusting 

up the maximum quarterly average estimates for all monitors by the same factor as that 

used to set the highest monitor equal to the current NAAQS (max quarterly value of 1.5 

μg/m3

). Then those adjusted maximum quarterly values are converted into annual 

averages using the ratio of annual average to maximum quarterly average specific to 

each monitor (as presented in Appendix A, Attachment A-2). 

⎯ For Chicago, given the spacing of the monitors, we have defined two independent 

roll-up subareas, each represented by a separate set of monitors. For this scenario, the 

highest maximum quarterly average value in each of the two subareas is rolled up to 

equal the current NAAQS and the other monitors in each subarea are increased by the 

same proportion. Then the annual average values are calculated using monitor-specific 

ratios as for the other study areas. 

• Alternative NAAQS scenarios: The procedure for rolling back ambient air Pb levels to 

reach alternative NAAQS is similar to the roll-up procedure used for the current 

NAAQS scenario, except the values are reduced rather than increased. Only those 

alternative NAAQS that are exceeded by the current conditions for a given case study 

were evaluated for that case study. Consequently, several of the higher alternative 

NAAQS were not evaluated at all for the location-specific urban case studies. As for 

the roll-up for the current NAAQS scenario, roll-backs for the Chicago case study are 

implemented separately for each of the two independent subareas, based on the highest 

Pb-TSP monitor in each subarea. 

Inhalation exposure air concentrations were defined for both source oriented and non 

source oriented exposure zones using the same procedure employed for the general urban case 

study (see Section 3.1.2). 

5.2.2.2 Other media Concentrations 

Characterization of Pb exposure from incidental outdoor soil/dust ingestion used the 

values used for the general urban case study (Section 3.1.3.1), with the same values being used to 

characterize conditions in all exposure zones (source oriented or not) in each of the three case 

studies. 

Indoor dust Pb concentrations are estimated for the location-specific urban case studies 

using the hybrid indoor dust model (see Section 3.1.4.1). Specifically, the hybrid indoor dust 

model is combined with the annual average ambient air Pb level for a particular exposure zone 

and air quality scenario to generate an indoor dust Pb level for that zone. Therefore, while 

background Pb exposure levels and outdoor soil/dust Pb levels are fixed across each of the urban 
 5-7

case studies, both ambient air Pb levels and indoor dust Pb levels are allowed to vary across 

source and non source exposure zones. 

5.2.2.3 Blood Pb levels 

Estimates of concurrent blood Pb are derived from application of the IEUBK model as 

described in Appendix H, with characterization of Pb exposure from background sources (i.e., 

drinking water and diet) relying on the same values as for all case studies (Appendix H). The 

apportionment of the blood Pb levels among the various exposure pathways is described in 

Section 3.2.2. 

Population-level exposure modeling for the location-specific urban case studies utilizes 

essentially the same population-weighted sampling procedure as was used for the point source 

case studies (Section 3.2.2.2, including Figure 3-1). Use of this more sophisticated, populationweighted, multizone approach is required for the location-specific urban case studies since they 

utilize multiple exposure zones with differing demographic data and ambient air Pb levels, 

negating the use of the simpler, single zone approach used in the general urban case study. The 

remaining elements of the blood Pb modeling approach used for the location-specific urban case 

studies are identical to those used for the core approach for the general urban case study (Section 

5.2.1). These include: (a) use of the concurrent blood Pb metric, (b) use of the IEUBK blood Pb 

model and (c) use of the GSD of 2.1 to represent interindividual variability in blood Pb levels. 

5.2.3 Media concentrations 

The following subsections summarize the media concentration estimate for all case 

studies and air quality scenarios. Estimates presented in these subsections are presented to three 

(for air) or zero (for dust and soil) decimal places, which results in various numbers of implied 

significant figures. This is not intended to convey greater precision for some estimates than 

others; it is simply an expedient and initial result of the software used for the calculation. 

5.2.3.1 Location-specific Urban Case Studies 

This section summarizes the media concentration estimates for the core modeling 

approach for each air quality scenario for the location-specific urban case studies. The complete 

set of media concentration estimates for these case studies is presented in Appendix O. 

As discussed in Section 2.3.3, Pb concentration in outdoor soil/dust is not changed with 

the alternate air quality scenarios. Rather, outdoor soil/dust concentration is held constant at the 

current conditions or current NAAQS level. 

Media concentrations generated for the three location-specific urban case studies are 

summarized in Tables 5-1 through 5-4, for annual ambient air Pb concentrations, inhalation 

exposure concentrations, outdoor soil/dust Pb and indoor dust Pb concentrations, respectively. 
 5-8

5.2.3.2 General Urban and Primary Pb Smelter Case Studies 

This section summarizes the media concentration estimates for the core modeling 

approach for each air quality scenario for the general urban and primary Pb smelter case studies. 

The complete set of media concentration estimates for these case studies are presented in 

Appendix N. 

For each air quality scenario for the primary Pb smelter case study, a range of percentile 

estimates are presented for each exposure medium. For the general urban case study, however, 

only a single value is presented for each exposure medium. This reflects the fact that, while the 

primary Pb smelter case study is modeled using spatial templates that include a large number of 

U.S. Census blocks and/or block groups (allowing percentile media concentrations to be 

identified), the general urban case study is modeled using a single study area with uniform media 

concentrations. Consequently, there is only one value presented for the general urban case study 

for each medium in each air quality scenario. 

As mentioned above, it is assumed that outdoor soil/dust Pb concentrations are not 

effected by changes in ambient air Pb levels. In the case of an area such as the remediation zone 

of the primary Pb smelter case study, however, where soil dynamics have been changed by the 

substitution of contaminated soil with clean soil, or in areas where local sources may pose a 

more significant source to outdoor soil/dust than historic sources – and where there may be a 

currently increasing trend in surface Pb concentration - this may underestimate soil 

concentrations under some alternate NAAQS. 

Media concentrations generated for the primary Pb smelter and general urban case studies 

using the core modeling approach are summarized in Tables 5-1 through 5-4, for annual ambient 

air Pb concentrations, inhalation exposure concentrations, outdoor soil/dust Pb and indoor dust 

Pb concentrations, respectively. As with similar presentations in Chapter 3, the percentiles 

presented here are from population weighted distributions of the media concentrations (see 

description in Appendices N, O and P). 
 5-9

Table 5-1. Estimated annual average ambient air concentrations. 

Average Annual Air Pb Concentration (μg/m3) 

Alternative NAAQS 

1 2 3 4 5 

Statistic* 

Current 

Conditions 

Current 

NAAQS 

0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3, 

Max Monthly 

0.2 μg/m3, 

Max 

Monthly 

0.05 μg/m3, 

Max 

Monthly 

0.02 μg/m3, 

Max 

Monthly 

Location Specific Case Study(Chicago) 

Maximum 0.040 0.809 0.040 0.040 0.040 0.010 0.004 

95th percentile 0.040 0.809 0.040 0.040 0.040 0.010 0.004 

Median 0.027 0.428 0.027 0.027 0.021 0.005 0.002 

5th percentile 0.014 0.286 0.014 0.014 0.014 0.004 0.001 

Minimum 0.011 0.225 0.011 0.011 0.011 0.003 0.001 

Location Specific Case Study(Cleveland) 

Maximum 0.121 0.506 0.067 0.108 0.043 0.011 0.004 

95th percentile 0.121 0.506 0.067 0.108 0.043 0.011 0.004 

Median 0.021 0.085 0.011 0.018 0.007 0.002 0.001 

5th percentile 0.017 0.071 0.009 0.015 0.006 0.002 0.001 

Minimum 0.017 0.071 0.009 0.015 0.006 0.002 0.001 

Location Specific Case Study(Los Angeles) 

Maximum 0.022 0.360 0.022 0.022 0.022 0.009 0.003 

95th percentile 0.022 0.360 0.022 0.022 0.022 0.009 0.003 

Median 0.019 0.300 0.019 0.019 0.019 0.007 0.002 

5th percentile 0.015 0.239 0.015 0.015 0.015 0.006 0.002 

Minimum 0.006 0.091 0.006 0.006 0.006 0.002 0.001 

General Urban Case Study 

High-end: 

NA – Single 0.114 

Study Area Mean: 

0.056 

0.600 0.080 0.125 0.050 0.013 0.005 

Primary Pb SmelterCase Study- full study area 

Maximum 0.740 0.161 0.326 0.130 0.033 0.013 

95th percentile 0.153 0.033 0.067 0.027 0.007 0.003 

Median 0.042 0.009 0.019 0.007 0.002 0.001 

5th percentile 0.015 0.003 0.007 0.003 0.001 < 0.001 

Minimum 

NA 

0.006 0.001 0.003 0.001 < 0.001 < 0.001 

Primary Pb SmelterCase Study- 1.5 km sub area 

Maximum 0.740 0.161 0.326 0.130 0.033 0.013 

95th percentile 0.675 0.147 0.297 0.119 0.030 0.012 

Median 0.238 0.052 0.105 0.042 0.011 0.004 

5th percentile 0.137 0.030 0.060 0.024 0.006 0.002 

Minimum 

NA 

0.098 0.021 0.043 0.017 0.004 0.002 

*- The percentiles presented here are from population weighted distributions of the media concentrations. 
 5-10

Table 5-2. Estimated inhalation exposure concentrations. 

Average Annual Inhalation Exposure Concentration of Pb (g/m3) 

Alternative NAAQS 

1 2 3 4 5 

Statistic* 

Current 

Conditions 

Current 

NAAQS 

0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3, 

Max 

Monthly 

0.2 μg/m3, 

Max 

Monthly 

0.05 μg/m3, 

Max Monthly 

0.02 μg/m3, 

Max Monthly 

Location Specific (Chicago) 

Maximum 0.017 0.347 0.017 0.017 0.017 0.004 0.002 

95th 

percentile 0.017 0.347 0.017 0.017 0.017 0.004 0.002 

Median 0.012 0.184 0.012 0.012 0.009 0.002 0.001 

5th percentile 0.006 0.123 0.006 0.006 0.006 0.002 0.001 

Minimum 0.005 0.097 0.005 0.005 0.005 0.001 < 0.001 

Location Specific (Cleveland) 

Maximum 0.052 0.217 0.029 0.047 0.019 0.005 0.002 

95th 

percentile 0.052 0.217 0.029 0.047 0.019 0.005 0.002 

Median 0.009 0.037 0.005 0.008 0.003 0.001 < 0.001 

5th percentile 0.007 0.030 0.004 0.006 0.003 0.001 < 0.001 

Minimum 0.007 0.030 0.004 0.006 0.003 0.001 < 0.001 

Location Specific (Los Angeles) 

Maximum 0.010 0.154 0.010 0.010 0.010 0.004 0.001 

95th 

percentile 0.010 0.154 0.010 0.010 0.010 0.004 0.001 

Median 0.008 0.129 0.008 0.008 0.008 0.003 0.001 

5th percentile 0.006 0.103 0.006 0.006 0.006 0.002 0.001 

Minimum 0.002 0.039 0.002 0.002 0.002 0.001 < 0.001 

General Urban 

NA – Single High-end: 0.049 

Study Area Mean: 0.024 

0.258 0.034 0.054 0.021 0.005 0.002 

Primary Pb Smelter- full study area 

Maximum 0.310 0.067 0.136 0.055 0.014 0.005 

95th 

percentile 0.064 0.014 0.028 0.011 0.003 0.001 

Median 0.017 0.004 0.007 0.003 0.001 < 0.001 

5th percentile 0.006 0.001 0.003 0.001 < 0.001 < 0.001 

Minimum 

NA 

0.002 < 0.001 0.001 < 0.001 < 0.001 < 0.001 

Primary Pb Smelter- 1.5 km subarea 

Maximum 0.310 0.067 0.136 0.055 0.014 0.005 

95th 

percentile 0.282 0.061 0.124 0.050 0.012 0.005 

Median 0.100 0.022 0.044 0.018 0.004 0.002 

5th percentile 0.057 0.012 0.025 0.010 0.003 0.001 

Minimum 

NA 

0.041 0.009 0.018 0.007 0.002 0.001 

*- The percentiles presented here are from population weighted distributions of the media concentrations. 
 5-11

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

Projected Average Outdoor Soil/Dust Pb 

Concentration (mg/kg) 

Statistic* (Same for all air quality scenarios) 

Location Specific (Chicago) 

NA - Full Study Area 198 

Location Specific (Cleveland) 

NA - Full Study Area 198 

Location Specific (Los Angeles) 

NA - Full Study Area 198 

General Urban 

NA – Single Study Area 198 

Primary Pb Smelter- full study area 

Maximum 958 

95th percentile 245 

Median 85 

5th percentile 30 

Minimum 17 

Primary Pb Smelter- 1.5km sub area 

Maximum 294 

95th percentile 223 

Median 150 

5th percentile 42 

Minimum 42 

*-The percentiles presented here are from population weighted distributions of the 

media concentrations. 
 5-12

Table 5-4. Estimated indoor dust concentrations. 

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

Alternative NAAQS 

1 2 3 4 5 

Statistic* 

Current 

Conditions 

Current 

NAAQS 

0.2 μg/m3, Max 

Quarterly 

0.5 μg/m3, 

Max 

Monthly 

0.2 μg/m3, 

Max 

Monthly 

0.05 μg/m3, 

Max Monthly 

0.02 μg/m3, 

Max Monthly 

Location Specific (Chicago) 

Maximum 128 491 128 128 128 84 71 

95th 

percentile 128 491 128 128 128 84 71 

Median 111 363 111 111 103 74 67 

5th percentile 91 300 91 91 91 70 65 

Minimum 86 269 86 86 86 68 64 

Location Specific (Cleveland) 

Maximum 203 392 158 193 132 85 72 

95th 

percentile 203 392 158 193 132 85 72 

Median 101 174 86 98 78 66 63 

5th percentile 96 161 82 93 75 65 63 

Minimum 96 161 82 93 75 65 63 

Location Specific (Los Angeles) 

Maximum 104 334 104 104 104 81 68 

95th 

percentile 104 334 104 104 104 81 68 

Median 99 307 99 99 99 78 67 

5th percentile 92 276 92 92 92 75 65 

Minimum 75 179 75 75 75 67 63 

General Urban 

NA – Single High-end: 198 

Study Area Mean: 146 

426 169 206 140 88 73 

Primary Pb Smelter- full study area 

Maximum 1944 648 1077 557 383 381 

95th 

percentile 219 152 172 149 138 120 

Median 84 68 73 67 63 62 

5th percentile 53 45 47 44 43 42 

Minimum 

NA 

41 38 39 38 38 38 

Primary Pb Smelter- 1.5km subarea 

Maximum 1944 648 1077 557 205 106 

95th 

percentile 1819 606 1008 521 192 99 

Median 860 287 477 246 91 60 

5th percentile 578 193 320 166 61 60 

Minimum 

NA 

453 151 251 130 60 60 

*- The percentiles presented here are from population weighted distributions of the media concentrations. 

 
 5-13

5.2.4 Blood Pb levels 

Estimates of concurrent blood Pb level derived from outputs of the IEUBK model 

(Section 3.2) have been developed for each air quality scenario in each case study (see 

Appendices N and O). Table 5-5 presents median concurrent blood Pb estimates for the 

location-specific urban case studies (Chicago, Cleveland and Los Angeles) and the core analysis 

of the primary Pb smelter (and sub-area) and the general urban case study. Table 5-6 presents 

95th percentile concurrent blood Pb estimates for the same set of case studies. As with blood Pb 

level results presented in Section 3.4, these tables present the median and 95th percentile total Pb 

levels (i.e., simulated children for each case study area ranked based on total Pb level in order to 

identify population percentiles), along with the blood Pb contributions to those total blood 

estimates from policy-relevant pathways.2 While some of the blood Pb levels presented in 

Section 3.4 (those for the primary Pb smelter) included a range reflecting multiple modeling 

options related to blood Pb estimation, the results presented in Tables 5-5 and 5-6 are single 

values, reflecting the fact that the core analysis, while including multiple concentration-response 

functions for IQ loss, did not include multiple modeling options related to blood Pb estimation 

(see Section 5.1.2). 

It is noted that given the various limitations of our modeling tools (Sections 2.4.3 and 

3.2.2), blood Pb levels associated with air-related exposure pathways and current levels of Pb 

emitted to the air (including via resuspension) are likely to fall between the estimates for “recent 

air” and those for “recent” plus “past air”. Additionally, with regard to the location-specific 

urban case studies “recent air” and “recent” plus “past air” categories, an artifact of the hybrid 

dust Pb model tends to mask trends in the two components of dust Pb (recent air and other), 

which contribute to “recent air” and “past air” estimates, respectively (see Section 4.3.1 

discussion of this uncertainty). For the primary Pb smelter case study, uncertainty in parsing out 

the “recent air” and “other” components of indoor dust (specifically for the site-specific 

regression model used in the remediation zone) have led us to conclude that only “recent plus 

past air” exposures should be presented and “recent air” results should not be separately 

presented for the primary Pb smelter, as is done for the other case studies (see Section 3.1.4.2). 

 

2

 Seemingly inconsistent trends across air quality scenarios seen in these two categories (recent air and 

recent plus past air) may be a result of presenting results for these policy-relevant categories based on populationweighted ranking of total blood Pb. 
 5-14

Table 5-5. Summary of blood Pb level estimates for median total blood Pb. 

Concurrent Blood Pb Levela

Air Quality Scenario 

(and case study) Recentb

Recent plus 

Pastb Total Pb 

Exposure 

Location-specific (Chicago)

Current NAAQS (1.5 μg/m3, max quarterly) 1.9 2.5 3.0 

Current conditions 

 (0.14 μg/m3

 max quarterly; 0.31 μg/m3 max monthly) 0.4 1.2 1.8 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.3 1.2 1.8 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.1 1.1 1.6 

Alternative NAAQS (0.02 μg/m3, max monthly) <0.1 1.0 1.6 

Location-specific (Cleveland)

Current NAAQS (1.5 μg/m3, max quarterly) 1.2 1.8 2.1 

Current conditions 

(0.36 μg/m3

 max quarterly; 0.56 μg/m3 max monthly) 0.3 1.2 1.8 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.7 1.3 1.8 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.2 1.1 1.7 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.1 1.1 1.7 

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 1.0 1.6 

Alternative NAAQS (0.02 μg/m3, max monthly) <0.1 1.0 1.6 

 Location-specific (Los Angeles)

Current NAAQS (1.5 μg/m3, max quarterly) 1.3 2.1 2.6 

Current conditions 

(0.09 μg/m3

 max quarterly; 0.17 μg/m3 max monthly) 0.3 1.2 1.7 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.2 1.1 1.6 

Alternative NAAQS (0.02 μg/m3, max monthly) 0.1 1.0 1.6 

General urban

Current NAAQS (1.5 μg/m3, max quarterly) 1.9 2.6 3.1 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.9 1.6 2.2 

Current conditions –high-end (0.87 μg/m3, max quarterly) 0.8 1.6 2.1 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.7 1.5 2.0 

Current conditions - mean (0.14 μg/m3, max quarterly) 0.6 1.4 1.9 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.5 1.4 1.9 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.2 1.1 1.7 

Alternative NAAQS (0.02 μg/m3, max monthly) 0.1 1.1 1.6 

 Primary Pb smelter - full study area

Current NAAQS (1.5 μg/m3, max quarterly) 0.8 1.5 

Alternative NAAQS (0.5 μg/m3, max monthly) 1.1 1.4 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.7 1.4 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.5 1.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.7 1.4 

Alternative NAAQS (0.02 μg/m3, max monthly) 

NAc

0.9 1.4 

Primary Pb smelter - 1.5km study area

Current NAAQS (1.5 μg/m3, max quarterly) 4.0 4.6 

Alternative NAAQS (0.5 μg/m3, max monthly) 2.6 3.2 

Alternative NAAQS (0.2 μg/m3, max quarterly) 1.8 2.5 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.8 2.3 

Alternative NAAQS (0.05 μg/m3, max monthly) 1.1 1.7 

Alternative NAAQS (0.02 μg/m3, max monthly) 

NAc

1.1 1.6 

a - Estimates are rounded to one decimal place. 

b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air 

contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from inhalation of 

ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with 

outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3). 

c – “Recent air” estimates were not developed for the primary Pb smelter case study (see Section 3.1.4.2).
 5-15

Table 5-6. Summary of blood Pb level estimates for high-end total blood Pb. 

Concurrent Blood Pb Levela

Air Quality Scenario 

(and case study) Recentb

Recent plus 

Pastb Total Pb 

Exposure 

Location-specific (Chicago)

Current NAAQS (1.5 μg/m3, max quarterly) 6.5 8.7 10.2 

Current conditions 

 (0.14 μg/m3

 max quarterly; 0.31 μg/m3 max monthly) 1.1 4.1 6.0 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.5 4.2 6.0 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.3 3.6 5.5 

Alternative NAAQS (0.02 μg/m3, max monthly) 0.2 3.6 5.4 

Location-specific (Cleveland) 

Current NAAQS (1.5 μg/m3, max quarterly) 2.3 5.4 7.4 

Current conditions 

(0.36 μg/m3

 max quarterly; 0.56 μg/m3 max monthly) 0.9 4.1 6.1 

Alternative NAAQS (0.5 μg/m3, max monthly) 1.5 4.2 6.0 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.7 3.9 5.8 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.4 3.8 5.7 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.1 3.5 5.4 

Alternative NAAQS (0.02 μg/m3, max monthly) 0.1 3.5 5.3 

 Location-specific (Los Angeles) 

Current NAAQS (1.5 μg/m3, max quarterly) 4.6 7.1 8.9 

Current conditions 

(0.09 μg/m3

 max quarterly; 0.17 μg/m3 max monthly) 1.1 4.0 5.9 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.5 3.7 5.5 

Alternative NAAQS (0.02 μg/m3, max monthly) 0.2 3.5 5.4 

General urban 

Current NAAQS (1.5 μg/m3, max quarterly) 6.4 8.8 10.6 

Alternative NAAQS (0.5 μg/m3, max monthly) 2.9 5.6 7.4 

Current conditions –high-end (0.87 μg/m3, max quarterly) 2.7 5.4 7.2 

Alternative NAAQS (0.2 μg/m3, max quarterly) 2.3 5.0 6.8 

Current conditions - mean (0.14 μg/m3, max quarterly) 1.9 4.7 6.5 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.7 4.6 6.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.7 3.9 5.7 

Alternative NAAQS (0.02 μg/m3, max monthly) 0.4 3.7 5.5 

 Primary Pb smelter - full study area 

Current NAAQS (1.5 μg/m3, max quarterly) 2.8 4.6 

Alternative NAAQS (0.5 μg/m3, max monthly) 3.1 4.2 

Alternative NAAQS (0.2 μg/m3, max quarterly) 2.4 4.0 

Alternative NAAQS (0.2 μg/m3, max monthly) 2.5 4.0 

Alternative NAAQS (0.05 μg/m3, max monthly) 1.9 3.8 

Alternative NAAQS (0.02 μg/m3, max monthly) 

 

 

NAc

 

 

 3.2 3.8 

Primary Pb smelter - 1.5km study area 

Current NAAQS (1.5 μg/m3, max quarterly) 10.3 12.3 

Alternative NAAQS (0.5 μg/m3, max monthly) 7.6 8.5 

Alternative NAAQS (0.2 μg/m3, max quarterly) 5.9 6.6 

Alternative NAAQS (0.2 μg/m3, max monthly) 4.9 6.1 

Alternative NAAQS (0.05 μg/m3, max monthly) 3.5 4.5 

Alternative NAAQS (0.02 μg/m3, max monthly) 

 

 

NAc

 

 

 3.0 4.2 

a - Estimates are rounded to one decimal place. 

b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air 

contribution to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from inhalation of 

ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor ambient air Pb levels, with 

outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3). 

c – “Recent air” estimates were not developed for the primary Pb smelter case study (see Section 3.1.4.2).
 5-16

5.2.5 Uncertainty Characterization 

As was done for the initial analyses (see Section 3.5), characterization of uncertainty 

related to the exposure assessment for the additional analyses focused on performance evaluation 

of both modeled media concentrations and modeled blood Pb levels. The sensitivity analysis 

described in Section 4.3.2 included several modeling elements related to exposure assessment 

which have bearing on blood Pb levels generated for the analyses described in this chapter for 

the primary Pb smelter and general urban case study and the location-specific urban case studies 

(e.g., interindividual variability GSD, blood lead model selection). Additional key sources of 

uncertainty related to exposure assessment specifically for the location-specific urban case 

studies (e.g., relating child study populations within the study areas to Pb-TSP monitor values in 

order to assign ambient air Pb levels) have not been investigated quantitatively, but are addressed 

as part of the qualitative discussion of uncertainty for the overall risk assessment (Section 

5.3.3.1). 

Performance evaluation completed for both the modeled media concentrations and 

modeled blood Pb levels is discussed below. 

5.2.5.1 Performance Evaluation of Modeled Media Concentrations 

As discussed in Section 3.5.1 for the previous analyses, performance evaluation for 

modeled media concentrations focuses on those estimates that were generated primarily through 

modeling. Media concentrations characterized using empirical data, such as air concentrations 

estimated using monitor measurements, are not subjected to performance evaluation. The 

following observations can be made regarding performance evaluation of model-estimate media 

concentrations for the analysis using the core modeling approach of the primary Pb smelter, 

general urban and location-specific urban case studies. 

• All media concentrations for the primary Pb smelter and general urban case study are 

presented in Chapter 3, and the reader is referred back to Section 3.5.1 for performance 

evaluation of those estimates. 

• Ambient air Pb levels (and, indirectly, inhalation exposure concentrations) for the 

location-specific urban case studies are based on Pb-TSP monitoring data and therefore 

will not be addressed quantitatively in this performance evaluation. Note, however, 

that the method for assigning Pb-TSP monitors to subsets of the child study 

populations in each study area is a key step in this analysis and one which is subject to 

uncertainty. However, characterizing ambient air Pb levels for the study populations at 

the location-specific urban case studies is addressed as part of the qualitative 

discussion of key sources of uncertainty (see Section 5.3.3.1). 

• Outdoor soil/dust Pb levels for the location-specific urban case studies utilize the same 

uniform value as was used for the general urban case study (see Section 3.5.1.2). 
 5-17

• Indoor dust Pb levels for the location-specific urban case studies are generally similar 

to those generated for the general urban case study. This is expected since the locationspecific urban case studies utilize the same hybrid indoor dust Pb model as was used 

for the general urban case study. Because of the similarity of the indoor dust Pb levels 

generated for the location-specific urban case studies and the general urban case study, 

the reader is referred to the previous performance evaluation discussion for indoor dust 

Pb addressing the general urban case study (see Section 3.5.1.3). 

5.2.5.2 Performance Evaluation of Modeled Blood Pb Levels 

In this section, we discuss air-to-blood ratios derived by comparing changes (deltas) in 

median total blood Pb level (concurrent) to associated changes in annual average air Pb levels as 

you step to the next lowest air quality scenario (Table 5-7). This analysis is only available for 

the general urban and primary Pb smelter case studies (including both the full study area and 1.5 

km subarea for the latter). Note that these air-to-blood ratios are different than those presented in 

Section 3.5.2.2 for the general urban, primary Pb smelter and secondary Pb smelter case studies. 

The ratios derived in Section 3.5.2.2 are based on comparing annual average ambient air Pb 

levels (used in modeling these case studies) to the recent air portions of blood Pb levels. 

Therefore, these ratios reflect a generalization of the relationship between air Pb and blood Pb 

across the entire exposure spectrum, reflecting that relationship as an average of the two absolute 

values. By contrast, the air-to-blood ratios presented in this section look at the incremental 

change in blood Pb as ambient air Pb changes incrementally with lower alternate NAAQS levels 

(Table 5-7). 

The model-derived air-to-blood ratios presented in Table 5-7 can be compared to ratios 

identified from the literature (see last column of Table 3-24). In fact, conceptually, it may be 

more appropriate to compare the air-to-blood ratios presented in Table 5-7 (than those in Section 

3.5.2.2) to ratios obtained from the review by Brunekreef (1984), since the individual ratios 

summarized in that analysis included a large number of studies that compared blood Pb levels of 

populations living in different locations with that associated different ambient air Pb levels to 

derive air-to-blood ratios. Conceptually, this is fairly similar to the approach used in deriving the 

air-to-blood ratios presented in this section. Similarly, Hilts (2003) considered exposure levels 

(ambient air Pb) and associated blood Pb levels for the same population at two different points in 

time, straddling a change in operation of a nearby smelter. This is conceptually similar to the 

way the air-to-blood ratios are calculated here. Comparison of the air-to-blood ratios presented 

in Table 5-7 with the ratios identified in Brunekreef (1984) and calculated based on data 

presented in Hilts (2003), as summarized in Table 3-24, results in the following observations: 

• Air-to-blood ratios for the general urban case study for air quality scenarios ranging 

from current NAAQS down to the alternative NAAQS of 0.2 μg/m3

, maximum 

monthly average, are similar to ratios reported in the literature (i.e., between 1:3 and 
 5-18

1:5). Ratios for the two lowest alternative NAAQS are larger, ranging from 1:6 to 1:9. 

However, even these larger ratios are within the range of some of the studies reported 

in Brunekreef (1984) and within the range of ratios calculated based on data presented 

in Hilts (2003). As noted in Section 3.5.2.2, there are a number of factors which may 

produce higher air-to-blood ratios, which may pertain to urban locations, such as lower 

blood Pb levels relative to those reflected in the meta analysis by Brunekreef (1984). 

• Air-to-blood ratios for the primary Pb smelter case study (full study area) are similar in 

magnitude to the general urban case study and the points made in the previous bullet 

apply to this case study. 

• Air-to-blood ratios for the 1.5 km subarea of the primary Pb smelter are significantly 

higher than ratios for the other two study areas noted above, with values ranging from 

1:10 to 1:19. These ratios, and particularly the higher values associated with the lower 

NAAQS levels, are significantly higher than values reported in Brunekreef (1984) and 

those based on data in Hilts (2003). Many of the studies cited in Brunekreef (1984) use 

differences in average ambient air Pb levels and blood Pb levels between clusters of 

individuals living in different areas to derive their ratios. It is possible that the indoor 

dust Pb model developed for the remediation zone of the primary Pb smelter case study 

captures a relatively stronger relationship between ambient air Pb and indoor dust Pb 

(and hence blood Pb) which exists in the vicinity of industrial facilities. It is also 

possible that reservoirs of Pb associated with past airborne Pb are still within these 

houses near the facility and correlated with current ambient air Pb levels are reflected 

in the site-specific regression model established between ambient air Pb levels and 

indoor dust Pb. Thus, a portion of indoor dust Pb that results from contributions from 

indoor reservoirs may be being attributed through our application of the model to 

current ambient air Pb. 

The air-to-blood ratios presented earlier in Section 3.5.2.2 for the general urban case 

study and primary Pb smelter case study are also pertinent to the core modeling approach results 

for these two case studies presented in this chapter and the reader is referenced to that earlier 

section for discussion of these alternative ratios and implications in the context of performance 

evaluation for blood Pb modeling. Other elements of performance evaluation for blood Pb 

modeling presented earlier in Section 3.5.2 (e.g., evaluation of candidate blood Pb models - see 

Section 3.5.2.1) also pertain to the analyses described in this section. 
 5-19

Table 5-7. Air-to-blood ratios derived by comparing air quality scenario air and blood Pb 

estimates. 

Air Scenario 

Median 

Total Blood 

Pb (μg/dL)

Annual 

Average 

Ambient Air 

Concentration 

(μg/m3)

a Ratiob

General urban case study 

Current NAAQS (1.5 μg/m3, max quarterly average) 3.12 0.600 

Alternative NAAQS 2 (0.5 μg/m3, max monthly average) 2.16 0.125 1:2 

Current Conditions (95th percentile) 2.13 0.114 1:3 

Alternative NAAQS 1 (0.2 μg/m3, max quarterly average) 2.01 0.080 1:4 

Current Conditions (mean) 1.91 0.056 1:4 

Alternative NAAQS 3 (0.2 μg/m3, max monthly average) 1.88 0.050 1:5 

Alternative NAAQS 4 (0.05 μg/m3, max monthly average) 1.67 0.013 1:6 

Alternative NAAQS 5 (0.02 μg/m3, max monthly average) 1.61 0.005 1:9 

Primary Pb smelter case study (full study area) 

Current NAAQS (1.5 μg/m3, max quarterly average) 1.50 0.042 

Alternative NAAQS 2 (0.5 μg/m3, max monthly average) 1.44 0.019 1:3 

Alternative NAAQS 1 (0.2 μg/m3, max quarterly average) 1.40 0.009 1:3 

Alternative NAAQS 3 (0.2 μg/m3, max monthly average) 1.40 0.007 1:4 

Alternative NAAQS 4 (0.05 μg/m3, max monthly average) 1.38 0.002 1:4 

Alternative NAAQS 5 (0.02 μg/m3, max monthly average) 1.37 0.001 1:7 

Primary Pb smelter case study (1.5 km subarea) 

Current NAAQS (1.5 μg/m3, max quarterly average) 4.58 0.238 

Alternative NAAQS 2 (0.5 μg/m3, max monthly average) 3.20 0.105 1:10 

Alternative NAAQS 1 (0.2 μg/m3, max quarterly average) 2.50 0.052 1:13 

Alternative NAAQS 3 (0.2 μg/m3, max monthly average) 2.35 0.042 1:15 

Alternative NAAQS 4 (0.05 μg/m3, max monthly average) 1.75 0.011 1:19 

Alternative NAAQS 5 (0.02 μg/m3, max monthly average) 1.63 0.004 1:19 a

 For the primary Pb smelter case study entries, these are the median air concentrations in a 

population weighted distribution of air concentration estimates for each scenario. 

b

 These are air-to-blood ratios derived by comparing changes (deltas) in median total blood Pb 

levels (concurrent) to associated changes in annual average air Pb levels as you step to the next 

lowest air quality scenario. Accordingly, a ratio is not presented adjacent to the current NAAQS 

air quality scenario (for any of the case studies). In this case, the first ratio presented for any of 

the case studies is generated by comparing median blood Pb levels at the current NAAQS level 

to the median blood Pb level at the highest of the alternative NAAQS levels (the 0.05 ug/m3

maximum monthly value). 
 5-20

5.3 RISK ASSESSMENT 

This chapter describes the approach used to characterize risk using the core modeling 

approach, including discussion of the methodology (Section 5.3.1), presentation of risk estimates 

(Section 5.3.2), and uncertainty characterization (Section 5.3.3). 

5.3.1 Methods for Deriving Risk Estimates 

 Risk characterization for this assessment focuses on IQ loss in children. IQ loss is 

derived using a set of concentration-response functions developed based on results from a pooled 

analysis of epidemiology studies (Lanphear et al., 2005). These concentration-response 

functions are combined with the population-level blood Pb distributions to produce distributions 

of IQ loss estimates for each case study population. IQ loss is also apportioned among different 

exposure pathways using the pathway apportionment information generated as part of the 

exposure analysis. 

Two key elements of the risk methodology are described in greater detail below: (a) the 

concentration-response functions used in the analysis (Section 5.3.1.1) and (b) the stepwise 

analytical procedure used to generate the IQ loss (risk) distributions (Section 5.3.1.2). 

5.3.1.1 Concentration-response Functions 

For the risk analyses described in this chapter, performed subsequent to the August 2007 

public meeting of the CASAC Pb Panel, four types of concentration-response functions for IQ 

loss were used. Some of the functions in this set differ from those used in the initial analyses 

(Section 4.1.1), reflecting CASAC recommendations to use a two-piece or dual linear function 

that recognizes a change in slope (to a notably higher value) at blood Pb levels of 7.5 μg/dL and 

give less prominence to the two-piece linear function with hinge at 10.82 μg/dL derived for the 

initial analyses of the full-scale assessment (Henderson, 2007b). The set of four concentrationresponse functions used in the analyses presented in this chapter include the following: 

• The log-linear function with a cutpoint at 1.0 μg/dL (for the concurrent blood Pb 

metric): This function is described in Section 4.1.1.1. 

• The log-linear function with low-exposure linearization (for the concurrent blood Pb 

metric): This function is described in Section 4.1.1.2. 

• Dual linear function, stratified at 10 μg/dL peak: This represents two linear functions 

reported in Lanphear et al., 2005. To derive these, the full study population was 

stratified into two groups - children with peak blood Pb levels above and below 10 

μg/dL - and separate linear functions were fit to the IQ and concurrent blood Pb values 

for these two groups (Lanphear et al., 2005). In order to utilize this model, we 

considered the relationship between peak and concurrent blood Pb levels in the pooled 

dataset, as well as the relationship in the cohort comprising the bulk of the low blood 

Pb subsets. For the full pooled dataset, the average peak blood Pb is 18.0 μg/dL, while 
 5-21

the average concurrent blood Pb is 9.7 μg/dL, approximately a factor of two difference 

(Lanphear et al., 2005). For the Rochester cohort, which comprised the majority of the 

subset of children with peak blood Pb values below 10 μg/dL, the average peak blood 

Pb is 9.0 μg/dL, while the average concurrent blood Pb is 4.0 μg/dL, approximately a 

factor of two difference (Lanphear et al., 2005). Accordingly, in specifying the 

concurrent blood Pb level at which this function would change slope, we assumed that 

the peak blood Pb of 10 μg/dL corresponded to a concurrent blood Pb of 5 μg/dL. The 

form of the function as applied in this risk assessment is: 

- For concurrent blood Pb level ≤ 5 μg/dL: 

 IQ loss = beta 1 * concurrent blood Pb 

- For concurrent blood Pb level > 5 μg/dL: 

 IQ loss = beta 2 * concurrent blood Pb 

Where: 

 beta 1 = -0.80 

 beta 2 = -0.13 

• Dual Linear function, stratified at 7.5 μg/dL peak: This represents two linear functions, 

and like the previous ones, these were separately derived from subgroups of the full 

study population (Lanphear et al., 2005). In this case, the population was stratified into 

groups of children with peak blood Pb levels above and below 7.5 μg/dL. For the same 

considerations of the relationship between peak and concurrent blood Pb levels stated 

for previously described function in specifying the concurrent blood Pb level at which 

this function would change slope, we assumed that the peak blood Pb of 7.5 μg/dL 

corresponded to a concurrent blood Pb of 3.75 μg/dL. The form of this function as 

applied in this risk assessment is: 

- For concurrent blood Pb level ≤ 3.75 μg/dL: 

 IQ loss = beta 1 * concurrent blood Pb 

- For concurrent blood Pb level > 3.75 μg/dL: 

 IQ loss = beta 2 * concurrent blood Pb 

Where: 

 beta 1 = -2.94 

 beta 2 = -0.16 

The four functions are presented in Figure 5-2 and compared in Table 5-8 with regard to 

total IQ loss and incremental IQ loss (IQ loss per μg/dL) across a range of concurrent blood Pb 

levels. A number of observations can be made by considering the plots of the four functions 

together with the results presented in Table 5-8. This discussion presents IQ loss estimates 

rounded to the nearest integer, although they are presented with greater precision in Table 5-8. 

At blood Pb levels less than or equal to about 2 μg/dL, with 2 μg/dL being near the average 

blood Pb level for young children in the U.S., based on NHANES-IV data and near the average 
 5-22

blood Pb estimated in the risk assessment for the three location-specific urban case studies, the 

log-linear with cutpoint model and the dual linear - stratified at 10 μg/dL peak generate similar 

IQ loss estimates. Both models predict a total of 2 points IQ loss associated with a blood Pb 

level of 2 μg/dL. By contrast, the remaining two models (log-linear with low-exposure 

linearization and dual linear - stratified at 7.5 μg/dL peak) generate substantially larger IQ loss 

estimates, ranging from 5 to 6 points IQ loss. 

At blood Pb levels above 2 μg/dL, the pattern of predicted IQ loss shifts substantially 

across the four models, with the dual linear - stratified at 7.5 μg/dL peak maintaining a constant 

slope (up to 3.75 μg/dL) which is significantly higher than the other models, all of which either 

have slopes which decrease (for the log-linear models) or have a lower slope to begin with (the 

dual linear - stratified at 10 μg/dL peak). At higher exposure levels ranging from 2 and 10 

μg/dL, the dual linear - stratified at 10 μg/dL peak and the log-linear with cutpoint perform 

similarly, with the latter predicting somewhat higher total IQ loss at 10 μg/dL (6 points, versus 5 

for the dual linear-stratified at 10 μg/dL). Within this higher exposure range, the log-linear with 

linearization model generates IQ loss estimates which are higher than the two models just 

discussed, but lower than the dual linear - stratified at 7.5 peak. Specifically, at 10 μg/dL, the 

log-linear with linearization model predicts 9 points IQ loss, while the dual linear - stratified at 

7.5 peak model predicts 12 points IQ loss. As discussed in Section 2.1.5, the dataset from which 

the lower blood Pb portion of the dual linear – stratified at 7.5 peak function is derived is much 

smaller than those from which the other functions are derived and includes variable 

representation of six of the seven cohorts in the pooled analysis with a dominance (~65% of 

total) of one cohort. 

Both of the dual linear models with stratification have lower slopes above 5 μg/dL than 

either of the log-linear models. This means that reductions in blood Pb levels for modeled 

children with relatively high exposures (> 5 μg/dL total blood Pb) will result in relatively lower 

IQ benefits using the two dual-linear models, compared with the two log-linear models (even if, 

in the case of the dual linear - stratified at 7.5 μg/dL model, the overall IQ loss is higher for that 

simulated child than with either of the log-linear models). 
 5-23

Figure 5-2. Comparison of four concentration-response functions for concurrent blood 

Pb levels < 10 μg/dL. 

0

2

4

6

8

10

12

14

0 1 2 3 4 5 6 7 8 9 10

Concurrent blood Pb (ug/dL)

IQ loss

log-linear w ith cutpoint

dual linear - stratified at

10 ug/dL, peak

dual linear - stratified at

7.5 ug/dL, peak

log-linear w ith low -

exposure linearization

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

four concentration-response functions. 

Concentration-Response Function 

Loglinear 

with 

cutpoint

Log-linear 

with lowexposure 

linearization

Dual linear - 

stratified at 

10 μg/dL 

peak 

Dual linear - 

stratified at 

7.5 μg/dL 

peak 

Performance Metric Points, IQ loss 

at 2 μg/dL 1.9 4.6 1.6 5.9 

at 5 μg/dL 4.3 7.0 3.9 11.1 

at 7.5 μg/dL 5.4 8.1 4.3 11.5 Total IQ loss 

at 10 μg/dL 6.2 8.9 4.6 11.9 

<2 μg/dL 0.94 2.29 0.80 2.94 

<5 μg/dL 0.87 1.41 0.80 2.24 

<7.5 μg/dL 0.73 1.09 0.58 1.55 

Incremental IQ loss 

(average # points 

 per μg/dL) <10 μg/dL 0.62 0.89 0.47 1.20 
 5-24

5.3.1.2 Projection of Population Risk 

The methods used to project population-level risk for the additional analyses are those 

described in Section 4.1.2. 

With regard to population-level risk metrics, risk estimates for different population 

percentiles are generated for all case studies. For the location-specific urban case studies, an 

additional type of population risk metric is also presented. Specifically, we have estimated the 

number of children associated with different magnitudes of IQ loss resulting from total Pb 

exposure under different ambient air quality scenarios (i.e., IQ loss incidence estimates). This 

metric illustrates (a) the overall number of children within a given urban case study location 

projected to experience various levels of IQ loss due to Pb exposure and (b) how that distribution 

of incidence changes with alternate NAAQS levels. 

5.3.2 Risk Estimates 

Estimates of IQ loss resulting from Pb exposure have been developed for each air quality 

scenario in each case study (see Appendices N and O). Further, multiple sets of risk results were 

generated for each combination of case study and air quality scenario, in an effort to consider 

uncertainty specifically related to specifying the relationship between Pb exposure and IQ loss 

(see Section 5.1.2). That is, four separate risk distributions were generated for each air quality 

scenario for each of the case studies presented here (see Table 5-1). These risk estimates include 

(a) population-risk distribution estimates (in the form of estimated IQ loss for specific population 

percentiles) and (b) IQ loss incidence estimates, as described below. 

As noted earlier in presenting exposure results (see Section 5.2.4), IQ loss estimates 

associated with air pathways and current levels of Pb emitted to the air (including via 

resuspension) are likely to fall between the estimates for “recent air” and those for “recent” plus 

“past air”. Additionally, with regard to the location-specific urban case studies “recent air” and 

“recent” plus “past air” categories, an artifact of the hybrid dust Pb model tends to mask trends in 

the two components of dust Pb (recent air and other), which contribute to “recent air” and “past 

air” estimates, respectively (see Section 4.3.1 discussion of this uncertainty). For the primary Pb 

smelter case study, uncertainty in parsing out the "recent air" and "other" components of indoor 

dust (specifically for the site-specific regression model used in the remediation zone) have led us 

to conclude that only "recent plus past air" IQ loss estimates should be presented and "recent air" 

results should not be separately presented for the primary Pb smelter, as is done for the other 

case studies (see Section 3.1.4.2). 
 5-25

5.3.2.1 Population Risk Distribution Estimates 

Risk estimates for specific population percentiles of the risk distribution associated with 

total blood Pb levels are presented in Tables 5-9 and 5-10. These include the median (Table 5-9) 

and 95th percentile (Table 5-10) IQ loss estimates based on total blood Pb. Two additional types 

of risk estimates are summarized in Tables 5-9 and 5-10 in addition to IQ loss based on total 

blood Pb. Also presented are the contributions to IQ loss from the recent air and recent plus past 

air exposure pathways (see Section 3.2.2), which as noted previously (Sections 2.4.3 and 3.2.2) 

represent the policy-relevant pathways, with the true values for the policy-relevant pathways 

considered to fall between the estimates for “recent air” and those for “recent” plus “past air”. 

More detailed pathway apportionment estimates (including percentile contributions to total IQ 

loss from individual exposure pathways) are presented in the detailed risk results tables in 

Appendices N, O and P. These more detailed risk results are, however, subject to the same 

limitations related to presentation of pathway apportionment, as noted above for individual case 

studies (e.g., reduced confidence in parsing out recent air-related risk from recent plus past air 

risk for the primary Pb smelter case study). 

The total Pb exposure risk estimates presented in Table 5-9 are those for the median in 

the distribution of total risk, and the estimates for the other two categories in that table (i.e., 

recent air and recent plus past air) are the values for those categories associated with the median 

for the total exposure pathway estimate. The corresponding estimates for the 95th percentile in 

the distribution of IQ loss estimates for total blood Pb for each case study are presented in Table 

5-10. This presentation may lead to some seeming inconsistencies in trends for recent air and 

recent air plus past air risk estimates across air quality scenarios. This is because the recent air 

or recent plus past air estimates associated with the median and 95th percentile total blood Pb 

estimate may not necessarily be the median and 95th percentiles of the distribution of estimates 

for those specific categories. This is because the blood Pb level (and associated total IQ loss) for 

a simulated child reflects not only the total Pb uptake (from both background and ambient airrelated pathways), but also the GSD-based adjustment factor. This means that two simulated 

children could have identical blood Pb levels (and IQ loss estimates), but one child could have a 

higher recent air exposure, with the other child having their lower recent air exposure 

compensated for by a higher GSD-based adjustment factor, thereby resulting in both children 

having the same total blood Pb level. In presenting IQ loss estimates in tables 5-9 and 5-10, all 

values are rounded to one decimal place. 

As mentioned in Chapter 1, risk results are provided here without substantial 

interpretation. Rather, interpretative discussion of these results is provided in the Staff Paper. 

 
 5-26

Table 5-9. Summary of risk estimates for medians of total-exposure risk distributions. 

Points IQ Lossa

Recent Airb 

(low, LLL and high C-R 

function estimates) 

Recent plus Past Airb

(low, LLL and high C-R 

function estimates)

Total Pb Exposure 

(low, LLL and high C-R 

function estimates)

Case Study and Air Quality Scenario

Low LLLc High Low LLL High Low LLL High 

Location-specific (Chicago)

Current NAAQS (1.5 μg/m3, max quarterly) 1.4 3.4 5.6 2.0 4.7 7.4 2.4 5.6 8.8

Current conditions 

(0.14 μg/m3 max quarterly; 0.31 μg/m3 max monthly) 0.3 0.6 0.7 1.0 2.9 3.5 1.4 4.2 5.2 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.3 0.6 0.9 1.0 2.9 3.6 1.4 4.2 5.2

Alternative NAAQS (0.05 μg/m3, max monthly) 0.1 0.2 0.2 0.9 2.6 3.2 1.3 4.0 4.8

Alternative NAAQS (0.02 μg/m3, max monthly) <0.1 0.1 0.1 0.8 2.6 3.1 1.3 4.0 4.7

Location-specific (Cleveland)

Current NAAQS (1.5 μg/m3, max quarterly) 0.6 2.8 2.1 1.3 3.9 4.6 1.7 4.7 6.3 

Current conditions 

(0.36 μg/m3 max quarterly; 0.56 μg/m3 max monthly) 0.2 0.7 0.9 1.0 2.9 3.6 1.4 4.2 5.2 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.2 0.6 1.9 1.0 2.9 3.9 1.4 4.2 5.2

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.1 0.4 0.5 0.9 2.8 3.3 1.4 4.1 5.0

Alternative NAAQS (0.2 μg/m3, max monthly) 0.1 0.6 0.4 0.9 2.8 3.2 1.3 4.1 4.9

Alternative NAAQS (0.05 μg/m3, max monthly) <0.1 0.1 0.1 0.8 2.6 3.1 1.3 4.0 4.7

Alternative NAAQS (0.02 μg/m3, max monthly) <0.1 <0.1 0.1 0.8 2.6 3.0 1.2 3.9 4.6

Location-specific (Los Angeles)

Current NAAQS (1.5 μg/m3, max quarterly) 1.1 2.7 4.0 1.7 4.2 6.2 2.1 5.3 7.7

Current conditions 

(0.09 μg/m3 max quarterly; 0.17 μg/m3 max monthly) 0.2 0.7 0.9 0.9 2.9 3.5 1.4 4.2 5.1 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.1 0.3 0.4 0.9 2.7 3.2 1.3 4.0 4.8

Alternative NAAQS (0.02 μg/m3, max monthly) <0.1 0.1 0.2 0.8 2.6 3.1 1.3 4.0 4.7

General urban

Current NAAQS (1.5 μg/m3, max quarterly) 1.5 3.5 5.6 2.1 4.8 7.7 2.5 5.8 9.2 

Alternative NAAQS (0.5 μg/m3, max monthly) 0.7 1.9 2.5 1.3 3.6 4.8 1.7 4.8 6.4 

Current conditions (0.87 μg/m3 max quarterly) 0.6 1.8 2.4 1.3 3.6 4.7 1.7 4.7 6.3 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.5 1.5 2.0 1.2 3.4 4.3 1.6 4.6 5.9 

Current conditions (0.14 μg/m3 max quarterly) 0.4 1.3 1.6 1.1 3.2 4.1 1.5 4.5 5.6 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.4 1.2 1.5 1.1 3.2 4.0 1.5 4.4 5.6 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.2 0.5 0.6 0.9 2.8 3.3 1.3 4.1 5.0 

Alternative NAAQS (0.02 μg/m3, max monthly) 0.1 0.3 0.3 0.9 2.6 3.1 1.3 4.0 4.8 

Primary Pb smelter - full study area

Current NAAQS (1.5 μg/m3, max quarterly) 0.6 1.9 2.3 1.2 3.8 4.4

Alternative NAAQS (0.5 μg/m3, max monthly) 0.8 2.9 2.7 1.0 3.7 4.2

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.6 2.3 2.6 0.9 3.6 4.2

Alternative NAAQS (0.2 μg/m3, max monthly) 0.4 2.7 3.0 0.9 3.6 4.1

Alternative NAAQS (0.05 μg/m3, max monthly) 0.4 1.7 1.9 0.9 3.6 4.0

Alternative NAAQS (0.02 μg/m3, max monthly) 

NAd

0.6 2.6 3.0 0.9 3.6 4.1

Primary Pb smelter - 1.5 km subarea

Current NAAQS (1.5 μg/m3, max quarterly) 3.2 6.0 9.4 3.7 6.8 11.2

Alternative NAAQS (0.5 μg/m3, max monthly) 2.1 4.5 7.7 2.6 5.8 9.4

Alternative NAAQS (0.2 μg/m3, max quarterly) 1.5 3.8 5.6 2.0 5.2 7.4

Alternative NAAQS (0.2 μg/m3, max monthly) 1.2 3.7 5.1 1.9 5.0 6.9

Alternative NAAQS (0.05 μg/m3, max monthly) 0.9 2.8 3.4 1.4 4.2 5.1

Alternative NAAQS (0.02 μg/m3, max monthly) 

NAd

0.9 2.9 3.3 1.3 4.0 4.8

a - Estimates are rounded to one decimal place. 

b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air contribution to indoor dust, and outdoor soil/dust 

pathways, while “recent air” refers to contributions from inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor 

ambient air Pb levels, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3). 

c - Log-linear with low-exposure linearization concentration-response function. 

d – “Recent air” estimates were not developed for the primary Pb smelter case study (see Section 3.1.4.2).
 5-27

Table 5-10. Summary of risk estimates for 95th percentiles of total exposure risk 

distributions. 

Points IQ Lossa

Case Study and Air Quality Scenario Recent Airb

(low, LLL and high C-R 

function estimates)

Recent plus Past Airb

(low, LLL and high C-R 

function estimates)

Total Pb Exposure 

(low, LLL and high C-R 

function estimates)

Low LLLc High Low LLL High Low LLL High 

Location-specific (Chicago)

Current NAAQS (1.5 μg/m3, max quarterly) 3.0 5.8 7.7 4.0 7.6 10.3 4.7 9.0 12.1

Current conditions 

(0.14 μg/m3 max quarterly; 0.31 μg/m3 max monthly) 

0.7 1.3 2.0 2.8 5.2 7.8 4.1 7.5 11.4 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.0 1.9 2.8 2.9 5.3 8.1 4.1 7.5 11.4

Alternative NAAQS (0.05 μg/m3, max monthly) 0.2 0.4 0.7 2.7 4.8 7.5 4.1 7.3 11.3

Alternative NAAQS (0.02 μg/m3, max monthly) 0.2 0.3 0.5 2.7 4.8 7.4 4.1 7.3 11.3

Location-specific (Cleveland)

Current NAAQS (1.5 μg/m3, max quarterly) 1.4 2.6 3.7 3.1 5.9 8.5 4.3 8.1 11.6

Current conditions 

(0.36 μg/m3 max quarterly; 0.56 μg/m3 max monthly) 

0.6 1.2 1.8 2.8 5.2 7.8 4.1 7.6 11.4 

Alternative NAAQS (0.5 μg/m3, max monthly) 1.1 1.9 2.9 2.9 5.3 8.1 4.1 7.5 11.4

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.5 0.9 1.4 2.8 5.0 7.6 4.1 7.4 11.3

Alternative NAAQS (0.2 μg/m3, max monthly) 0.3 0.6 0.9 2.7 4.9 7.5 4.1 7.4 11.3

Alternative NAAQS (0.05 μg/m3, max monthly) 0.1 0.2 0.3 2.6 4.7 7.4 4.0 7.2 11.3

Alternative NAAQS (0.02 μg/m3, max monthly) <0.1 0.1 0.1 2.6 4.7 7.2 4.0 7.2 11.3

Location-specific (Los Angeles)

Current NAAQS (1.5 μg/m3, max quarterly) 2.3 4.4 6.1 3.6 6.9 9.5 4.5 8.6 11.8

Current conditions 

(0.09 μg/m3 max quarterly; 0.17 μg/m3 max monthly) 

0.8 1.4 2.1 2.8 5.2 7.8 4.1 7.5 11.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.4 0.7 1.1 2.7 4.9 7.6 4.1 7.3 11.3

Alternative NAAQS (0.02 μg/m3, max monthly) 0.1 0.2 0.4 2.7 4.7 7.4 4.0 7.2 11.3

General urban

Current NAAQS (1.5 μg/m3, max quarterly) 2.9 5.5 7.3 3.9 7.6 10.1 4.7 9.1 12.1

Alternative NAAQS (0.5 μg/m3, max monthly) 1.7 3.2 4.6 3.3 6.1 8.8 4.3 8.1 11.6

Current conditions – high-end (0.87 μg/m3 max quarterly) 1.6 3.1 4.4 3.2 6.0 8.7 4.3 8.0 11.6

Alternative NAAQS (0.2 μg/m3, max quarterly) 1.4 2.6 3.8 3.1 5.8 8.5 4.2 7.9 11.5

Current conditions - mean (0.14 μg/m3 max quarterly) 1.2 2.2 3.3 3.0 5.6 8.3 4.2 7.7 11.5

Alternative NAAQS (0.2 μg/m3, max monthly) 1.1 2.1 3.1 3.0 5.6 8.2 4.2 7.7 11.4

Alternative NAAQS (0.05 μg/m3, max monthly) 0.5 0.9 1.4 2.8 5.0 7.7 4.1 7.4 11.3

Alternative NAAQS (0.02 μg/m3, max monthly) 0.3 0.5 0.7 2.7 4.8 7.5 4.1 7.3 11.3

 Primary Pb smelter - full study area

Current NAAQS (1.5 μg/m3, max quarterly) 2.3 4.2 6.8 3.7 6.8 11.2

Alternative NAAQS (0.5 μg/m3, max monthly) 2.5 4.9 8.3 3.4 6.6 11.1

Alternative NAAQS (0.2 μg/m3, max quarterly) 1.9 3.9 6.6 3.2 6.5 11.1

Alternative NAAQS (0.2 μg/m3, max monthly) 2.0 4.0 7.0 3.2 6.4 11.1

Alternative NAAQS (0.05 μg/m3, max monthly) 1.5 3.1 5.4 3.1 6.3 11.0

Alternative NAAQS (0.02 μg/m3, max monthly) 

NAd

2.5 5.3 9.2 3.1 6.3 11.0

Primary Pb smelter - 1.5 km subarea

Current NAAQS (1.5 μg/m3, max quarterly) 4.2 8.0 10.4 5.0 9.5 12.4

Alternative NAAQS (0.5 μg/m3, max monthly) 4.0 7.5 10.5 4.5 8.5 11.8

Alternative NAAQS (0.2 μg/m3, max quarterly) 3.7 6.9 10.2 4.2 7.8 11.5

Alternative NAAQS (0.2 μg/m3, max monthly) 3.4 6.1 9.2 4.1 7.6 11.4

Alternative NAAQS (0.05 μg/m3, max monthly) 2.8 5.3 8.7 3.6 6.8 11.1

Alternative NAAQS (0.02 μg/m3, max monthly) 

NAd

2.4 4.7 7.9 3.3 6.5 11.1

a - Estimates are rounded to one decimal place. 

b -The term “past air” includes contributions from the outdoor soil/dust contribution to indoor dust, historical air contribution to indoor dust, and outdoor soil/dust 

pathways, while “recent air” refers to contributions from inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoor 

ambient air Pb levels, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3). 

c - Log-linear with low-exposure linearization concentration-response function. 

d – “Recent air” estimates were not developed for the primary Pb smelter case study (see Section 3.1.4.2). 
 5-28

5.3.2.2 IQ Loss Incidence Estimates 

Incidence of different levels of IQ loss has been estimated for the three location-specific 

urban case studies (Appendix O). These estimates are summarized in Tables 5-11 and 5-12. 

Specifically, Table 5-11 shows the number of children projected to have IQ loss greater than one 

point under the current conditions scenario and the change in that metric under the current 

NAAQS and the alternate NAAQS considered for each location-specific urban case study. The 

value of one IQ point was selected as consistent with CASAC recommendations as to the 

magnitude of IQ loss they considered to be significant from a public health perspective 

(Henderson, 2007b). Similarly, Table 5-12 presents incidence estimates associated with a 

greater magnitude of cognitive impact, the number of children exceeding 7 points IQ loss. The 

value of 7 IQ points is the magnitude of IQ loss projected for the 95th population percentile 

(using the log-linear with low-exposure linearization function) under current conditions at the 

case studies evaluated in this analysis (See Table 5-10). As mentioned in Chapter 1, risk results 

are provided here without substantial interpretation. Rather, interpretative discussion of these 

results is provided in the Staff Paper. 
5-29

1 Table 5-11. Incidence of children with >1 point IQ loss. 

dual linear - stratified at 

7.5 μg/dL peak 

log-linear with 

linearization 

dual linear - stratified 

at 10 μg/dL peak log-linea

Air Quality Scenario 

(for location-specific urban case studies) 

Incidence 

of 

>1 point 

IQ loss 

Delta 

(change in 

incidence 

compared to 

current 

conditions) 

Incidence 

of 

>1 point, 

IQ loss 

Delta 

(change in 

incidence 

compared 

to current 

conditions) 

Incidence of 

>1 point, 

IQ loss 

Delta 

(change in 

incidence 

compared 

to current 

conditions) 

Incidence 

of 

>1 point

IQ loss 

Chicago (total modeled population: 396,511) Chicago Current Conditions 391,602 389,754 271,031 236,257 Current NAAQS (1.5 μg/m3 Maximum Quarterly) 395,797 4,195 395,528 5,773 347,415 76,384 314,053 Alternative NAAQS (0.2 μg/m3 Maximum Monthly) 391,158 -444 389,461 -293 271,444 412 235,559 Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 389,572 -2,030 387,407 -2,347 253,775 -17,256 224,394 Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 389,176 -2,427 386,630 -3,125 249,865 -21,166 219,294 Cleveland (total modeled population: 13,990)Cleveland Current Conditions 13,809 13,745 9,526 8,515 Current NAAQS (1.5 μg/m3 Maximum Quarterly) 13,893 84 13,857 112 10,664 1,137 9,769 Alternative NAAQS (0.2 μg/m3 Maximum Quarterly) 13,770 -38 13,703 -42 9,221 -305 8,160 Alternative NAAQS (0.5 μg/m3 Maximum Monthly) 13,789 -20 13,720 -25 9,497 -29 8,464 Alternative NAAQS (0.2 μg/m3 Maximum Monthly) 13,759 -50 13,694 -51 9,083 -443 8,010 Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 13,729 -80 13,642 -103 8,785 -741 7,720 Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 13,720 -88 13,628 -117 8,736 -790 7,668 Los Angeles (total modeled population: 372,252)Los Angeles Current Conditions 282,216 280,711 191,675 170,474 Current NAAQS (1.5 μg/m3 Maximum Quarterly) 285,272 3,056 284,945 4,234 240,988 49,313 226,608 Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 281,112 -1,104 279,658 -1,053 183,395 -8,280 161,914 Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 280,740 -1,476 279,057 -1,654 180,745 -10,929 158,234 

2 
5-30

1 Table 5-12. Incidence of children with >7 points IQ loss. 

dual linear - stratified at 7.5 

μg/dL peak 

log-linear with 

linearization 

dual linear - stratified at 

10 μg/dL peak 

Air Quality Scenario 

(location-specific urban case studies) 

Incidence of 

> 7 points 

 IQ loss 

Delta 

(change in 

incidence 

compared to 

current 

conditions)

Incidence 

of 

> 7 points 

 IQ loss 

Delta 

(change in 

incidence 

compared 

to current 

conditions)

Incidence of 

> 7 points 

 IQ loss 

Delta 

(change in 

incidence 

compared to 

current 

conditions)

Chicago (total modeled population: 396,511)Chicago Current Conditions 136,709 33,664 63 Current NAAQS (1.5 μg/m3 Maximum Quarterly) 244,401 107,692 100,159 66,495 555 492 Alternative NAAQS (0.2 μg/m3 Maximum Monthly) 136,067 -642 32,546 -1,118 48 -16 Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 120,706 -16,003 27,367 -6,297 16 -48 Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 117,819 -18,890 26,027 -7,637 8 -56 Cleveland (total modeled population: 13,990)Cleveland Current Conditions 4,834 1,212 3 Current NAAQS (1.5 μg/m3 Maximum Quarterly) 6,139 1,305 1,858 647 4 2 Alternative NAAQS (0.2 μg/m3 Maximum Quarterly) 4,525 -309 1,073 -139 1 -2 Alternative NAAQS (0.5 μg/m3 Maximum Monthly) 4,806 -28 1,180 -31 1 -2 Alternative NAAQS (0.2 μg/m3 Maximum Monthly) 4,424 -410 1,026 -186 1 -2 Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 4,106 -728 886 -326 0 -3 Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 4,051 -783 866 -345 0 -3 Los Angeles (total modeled population: 372,252)Los Angeles Current Conditions 94,684 22,665 23 Current NAAQS (1.5 μg/m3 Maximum Quarterly) 158,171 63,487 57,834 35,168 183 160 Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 87,303 -7,382 19,781 -2884 11 -11 Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 83,909 -10,775 17,939 -4726 17 -6 
 5-31

5.3.3 Uncertainty Characterization and Sensitivity Analysis 

This section discusses uncertainty related to exposure and risk estimates generated for the 

results for the core modeling approach applied to the primary Pb smelter, general urban and 

location-specific urban case studies. It also discusses sensitivity analyses completed to inform 

application of the core modeling approach to these case studies. Several methods have been used 

to examine uncertainty in our modeling approach and its potential impact on exposure risk 

estimates. These methods for uncertainty evaluation include the following: 

• Qualitative discussion of key sources of uncertainty and their potential impact on 

exposure and risk estimates (Section 5.3.3.1). This qualitative discussion focuses on 

factors particular to the new analyses presented in this section - those sources of 

uncertainty that these new analyses share with the analyses presented in Chapters 3 and 

4 are not repeated here (see Section 4.3.1 for that discussion). 

• Evaluation of model performance, including comparison with empirical data (Section 

5.3.3.2). As with the qualitative discussion of uncertainty, only those performance 

evaluation elements particular to the analyses in this chapter (i.e., performance 

evaluation of modeled blood Pb levels - see Section 5.2.5.2) are discussed here. The 

reader is referred back to Section 4.3.3 for a more complete discussion of performance 

evaluation related to modeled media concentrations and blood Pb modeling for this risk 

assessment. 

• Development of multiple sets of risk estimates for each assessment scenario that 

illustrate the impact of different concentration-response models relating Pb exposure to 

IQ loss and the associated uncertainty (see Section 5.3.3.3). 

 

In addition to these methods for evaluating uncertainty in the modeling approach, an 

additional sensitivity analysis has been completed for this portion of the analysis, specifically 

focused on the hybrid indoor dust Pb model used in the general urban case study and the 

location-specific urban case studies. This sensitivity analysis is discussed in Section 5.3.3.4. 

The results of the sensitivity analysis described in Section 4.3.2 for the general urban case study 

are broadly applicable to that case study, as well as the location-specific urban case studies. 

5.3.3.1 Qualitative Discussion of Key Sources of Uncertainty 

This section provides qualitative discussion of key sources of uncertainty related to the 

analyses presented in this section. Specifically, it addresses sources of uncertainty specific to the 

location-specific urban case studies; sources of uncertainty related to the primary Pb smelter and 

general urban case studies have already been discussed (see Section 4.3.1). In addition, many of 

the sources of uncertainty that impact the general urban case study also impact the locationspecific urban case studies, since both share similar modeling elements (e.g., hybrid indoor dust 

Pb model, IQ loss functions, blood Pb GSD). The reader is referred back to Section 4.3.1 for 
 5-32

discussion of those sources of uncertainty. Sources of uncertainty particular to the locationspecific urban case studies include: 

• Location-specific urban case studies: As recognized in Appendix A, the Pb-TSP 

monitoring network is currently quite limited. The number of monitors available to 

represent air concentrations in these case studies ranged from six for Cleveland to 11 

for Chicago. Accordingly, our estimates of the magnitude of and spatial variation of 

air Pb concentrations are subject to uncertainty associated with the limited data. In 

applying the available data to each of these case studies, exposure zones, one 

corresponding to each monitor, were created and each U.S. Census block (and the 

children within that demographic unit) were distributed among the exposure zones. 

The details of the approach used are described in Section 5.1.3. Although this 

approach provides a spatial gradient across the study area due to differences in 

monitor values for each exposure zone, this approach assumes a constant 

concentration within each exposure zone (i.e., no spatial gradient within a zone). 

Additionally, the nearest neighbor approach to assign blocks to exposure zones 

assumes that a monitor pertains to all locations that are closer to that monitor than to 

any of the others in the study area. In reality, there may be different and more 

variable spatial gradients in a study area than those reflected in the approach used 

here. This introduces uncertainty into the characterization of risk for the urban case 

studies. 

• Current NAAQS air quality scenarios: For the location-specific urban case studies, 

proportional roll-up procedures were used to adjust ambient air Pb concentrations up 

to just meet the current NAAQS (see Sections 2.3.1 and 5.2.2.1 for detailed 

discussion). Staff recognizes that it is extremely unlikely that Pb concentrations in 

urban areas would rise to meet the current NAAQS and that there is uncertainty with 

our simulation of such conditions. In these case studies we have simulated a 

proportional roll-up, such that it is assumed that the current spatial distribution of air 

concentrations (as characterized by the current data) is maintained and increased Pb 

emissions contribute to increased Pb concentrations, the highest of which just meets 

the current standard. There are many other types of changes within a study area that 

could result in a similar result such as increases in emissions from just one specific 

industrial operation that led to air concentrations in a part of the study area that just 

meet the current NAAQS, while the remainder of the study area remained largely 

unchanged (at current conditions). For the primary Pb smelter case study, where 

current conditions exceed the current NAAQS, reaching the current NAAQS was 

simulated using air quality modeling, emissions and source parameters used in 

developing the 2007 proposed revision to the State Implementation Plan for the area 

(see Section 3.1.1.2 for details). 

• Alternative NAAQS air quality scenarios: In all case studies, proportional roll-down 

procedures were used to adjust ambient air Pb concentrations downward to reach 

alternative NAAQS (see Sections 2.3.1 and 5.2.2.1). We recognize that there is 

uncertainty in simulating conditions associated with the implementation of emissions 

reduction actions to meet a lower standard. There are a variety of changes other than 

that represented by a proportional roll-down that could result in air concentrations 
 5-33

that just meet lower alternative standards. For example, control measures might be 

targeted only at the specific area exceeding standard, resulting in a reduction of air Pb 

concentrations to the alternate standard while concentrations in the rest of the study 

area remain unchanged (at current conditions). Consequently, there is uncertainty 

associated with estimates for the alternate NAAQS scenarios. 

• Relating blood Pb levels to IQ loss: Specification of the quantitative relationship 

between blood Pb level (exposure) and IQ loss is subject to uncertainty, especially in 

the projection of IQ loss at lower blood Pb levels (below 5 μg/dL concurrent blood 

Pb). As discussed in Section 2.1.5, this reflects limitations in the data available for 

characterizing the concentration-response relationship. For example, the pooled 

international dataset analyzed by Lanphear and others (2005) includes relatively few 

children with blood Pb levels below 5 μg/dL and no children with levels below 1 

μg/dL (see Section 2.1.5). As presented in Section 5.2.4, blood Pb levels in this 

region are a particular focus in this review. For example, as is the case for mean 

blood Pb levels nationally in the U.S. (CD, Section 4.3.1.3), concurrent blood Pb 

estimates for the median of the populations simulated in this assessment fall below 5 

μg/dL. In recognition of the uncertainty in specifying a quantitative concentrationresponse relationship at such levels, our core modeling approach involves the 

application of four different functions to generate a range of risk estimates (see 

Sections 4.2.6 and 5.3.1). The range of absolute IQ loss seen for a given case 

study/air quality scenario combination when modeled using the four concentrationresponse functions is typically close to a factor of 3. However, it is important to point 

out that the relative (proportional) change in IQ loss across air quality scenarios (i.e., 

the pattern of risk reduction across air quality scenarios) is fairly consistent across all 

four models. Note, however, that the function producing higher overall risk estimates 

(the dual linear function, stratified at 7.5 μg/dL, peak) will also produce larger 

absolute reductions in IQ loss compared with the other three functions. 

5.3.3.2 Performance Analyses 

This section discusses the performance evaluation completed for blood Pb modeling for 

the core analysis of the general urban and primary Pb smelter case studies. As mentioned above, 

the performance evaluation of media concentrations for these case studies is discussed in Section 

4.3.3. 

The additional performance evaluation discussed in this chapter focused on a set of airto-blood ratios developed using a different approach than that used in Chapter 3– (see Section 

5.2.5.2). The results of this analysis suggested that ratios generated for the general urban case 

study and for the primary Pb smelter (full study area) were generally similar to those identified in 

the literature, with the exception of ratios associated with the lowest alternative NAAQS, which 

were larger than the typical range of ratios seen cited in the literature. By contrast, the set of 

ratios generated for all air quality scenarios for the 1.5 km subarea of the primary Pb smelter 

were noticeably larger than the values cited in the literature. A number of plausible explanations 

for this discrepancy are presented in Section 5.2.5.2. 
 5-34

5.3.3.3 Uncertainty in Modeling Approaches - Multiples Sets of Results 

For the case studies included in the additional analyses presented in this chapter, four sets 

of risk estimates were generated for each air quality scenario, reflecting consideration of four 

concentration-response functions relating Pb exposure to IQ loss. The resulting median and 95th

percentile risk estimates for each case study and air quality scenario combination are presented in 

Tables 5-9 and 5-10. As can be seen from these tables, estimates based on these four functions 

result in a risk range spanning a factor of 4 for a particular case study and air quality scenario, 

although the range among results for three of the four functions is much tighter. 

5.3.3.4 Sensitivity Analysis – Indoor Dust Pb Modeling 

One additional sensitivity analysis was completed as part of the analyses based on the 

core modeling approach. Specifically, an alternate version of the hybrid indoor dust Pb model 

used for the general urban and location-specific urban case studies was considered. This 

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

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

alternative concentration-response functions for IQ loss is not discussed here as part of the 

sensitivity analysis, since all four concentration-response functions were used to evaluate the full 

set of case studies and those results can be reviewed to identify differences in the performance of 

these alternate models. The sensitivity analysis focusing on the hybrid indoor dust Pb model is 

presented here. 

Comments received from a member of the public regarding the full-scale analysis 

methodology suggested a modification to the hybrid indoor dust Pb model used in evaluating the 

general urban case study (see Section 3.1.4.1 for details on the hybrid indoor dust Pb model). 

Specifically, rather than establishing the nonair fraction of indoor dust Pb as part of overall Pb 

loading, as is currently done in the hybrid model, the recommendation was made that EPA take 

the final version of the hybrid model, which predicts indoor dust Pb concentration, set the air 

term to zero and solve for nonair indoor dust Pb (this providing an estimate of nonair indoor dust 

Pb). This recommendation was made, in part, to address concerns raised by the commenter that 

the nonair indoor dust Pb concentrations generated by the hybrid model, were not constant across 

air quality scenarios. EPA recognizes that the hybrid indoor dust Pb model, as used in the fullscale analysis, does produce nonair indoor dust Pb concentrations which vary by air quality 

scenarios. This results from nonlinearity in the loading to concentration conversion algorithm 
 5-35

within the model, which generates a greater per unit concentration at lower overall loading 

levels. 

To inform our understanding of model uncertainty related to predicting indoor dust Pb 

levels for the urban scenarios, we have included this alternative hybrid model in this sensitivity 

analysis. However, it is important to point out a key limitation in this formulation of the hybrid 

dust model. By setting ambient air Pb levels to zero and then solving for indoor dust Pb, one is 

using the steepest part of the loading-to-concentration curve to conduct the key step of 

translating indoor dust Pb loading to equivalent concentration. In reality, we would expect to 

always have a mixture of indoor dust Pb loadings from nonair and air sources, thereby resulting 

in a larger total loading value, which would in turn be translated into an indoor dust Pb 

concentration at a flatter portion of the curve. 

Solving of the hybrid indoor dust Pb model for an ambient air Pb level of zero, yields the 

fixed nonair indoor dust Pb level of 61 ppm. For the alternative hybrid model, this value has 

been used as the nonair indoor dust Pb level. The ambient air-related portion of indoor dust Pb 

concentration was then generated for each simulated individual by subtracting this value of 61 

ppm from the total indoor dust Pb level generated by the hybrid model. If the total indoor dust 

Pb concentration was lower than 61 ppm, then we fixed the concentration at 61 ppm and 

assigned all of it to nonair sources. 

Table 5-13 presents the median and 95th percentile IQ estimates associated with total 

blood Pb estimates associated with applying the two versions of the hybrid indoor dust Pb model 

to the general urban case study. The table also includes the IQ loss apportioned to recent air (i.e., 

inhalation plus ingestion of indoor dust loaded from air) for the median and 95th percentile IQ 

estimates associated with total blood Pb estimates. All IQ loss estimates presented in Table 5-13 

were generated using the log-linear with low-exposure linearization concentration-response 

model. 
 5-36

Table 5-13. Comparison of hybrid indoor dust model with a modified form of the model. 

Points, IQ loss 

Hybrid indoor dust model 

(from full-scale analysis)

Alternate hybrid indoor dust 

model (with fixed "other" dust 

value) 

Median 95th percentile Median 95th percentile 

Air quality scenario 

(General urban case study) Total 

Recent 

air Total 

Recent 

air Total 

Recent 

air Total 

Recent 

air 

Current NAAQS 

 (1.5 μg/m3, max quarterly) 5.8 3.5 9.1 5.5 5.8 3.1 9.1 4.8 

Current conditions–high-end 

(0.87 μg/m3

, max quarterly) 4.7 1.8 8.0 3.1 4.7 1.4 8.0 2.3 

Current conditions – mean 

(0.14 μg/m3

, max quarterly) 4.5 1.3 7.7 2.2 4.4 0.9 7.7 1.6 

Alternative NAAQS 

 (0.5 μg/m3

, max monthly) 4.8 1.9 8.1 3.2 4.8 1.5 8.1 2.4 

Alternative NAAQS 

 (0.2 μg/m3, max quarterly) 4.6 1.5 7.9 2.6 4.6 1.1 7.9 1.9 

Alternative NAAQS 

 (0.2 μg/m3

, max monthly) 4.4 1.2 7.7 2.1 4.4 0.8 7.7 1.5 

Alternative NAAQS 

 (0.05 μg/m3

, max monthly) 4.1 0.5 7.4 0.9 4.1 0.3 7.4 0.5 

Alternative NAAQS 

 (0.02 μg/m3, max monthly) 4.0 0.3 7.3 0.5 4.0 0.1 7.3 0.3 

Results presented in Table 5-13 suggest that the alternate hybrid indoor dust model (with 

the fixed "other indoor dust value") does produce indoor dust Pb concentrations for recent air 

that are noticeably lower than values generated using the original hybrid indoor dust model used 

in the full-scale analysis. However, it is important to point out that because both versions of the 

hybrid model generate the same total indoor dust Pb value (and only differ in the way that value 

is apportioned between other and recent air), the trend in total IQ loss due to Pb exposure across 

the air quality scenarios (moving from current NAAQS to the lowest alternative NAAQS 

evaluated) is the same for both versions of the hybrid model. 
 5-37

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Henderson, R. (2007a) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to 

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Agency Research Triangle Park, NC 

Environmental Protection Health and Environmental Impacts Division October 2007 United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-07-014a