&EPA
United States
EnviroimnU Protection
Agency
Review of the National Ambient Air Quality
Standards for Lead:
Policy Assessment of Scientific and Technical
Information
OAQPS Staff Paper
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EPA-452/R-07-013
November 2007
Review of the National Ambient Air Quality Standards
for Lead:
Policy Assessment of Scientific
and Technical Information
OAQPS Staff Paper
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina
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DISCLAIMER
This document has been reviewed by the Office of Air Quality Planning and Standards
(OAQPS), U.S. Environmental Protection Agency (EPA), and approved for publication. This
OAQPS Staff Paper contains the conclusions and recommendations of the staff of the OAQPS
and does not necessarily reflect the views of the Agency. Mention of trade names or commercial
products is not intended to constitute endorsement or recommendation for use.
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ACKNO WLEDGEMENTS
This Staff Paper is the product of the Office of Air Quality Planning and Standards
(OAQPS). For the chapters on lead-related health effects, exposure, risk, and primary standards,
the principal authors include Deirdre Murphy, and Zachary Pekar. For the chapter on lead-
related welfare effects and secondary standards, the principal author is Ginger Tennant. The
principal authors of the chapter on ambient lead include Mark Schmidt, Kevin Cavender, Tom
Pace, Joe Touma and Deirdre Murphy. Other staff from OAQPS and staff from other EPA
offices, including the Office of Research and Development, the Office of General Counsel, the
Office of Transportation and Air Quality and Region 7 also provided valuable comments.
An earlier draft of this document was formally reviewed by the Clean Air Scientific
Advisory Committee (CASAC) and made available for public comment. This document has
been informed by the expert advice and comments received from CASAC, as well as by public
comments submitted by independent scientists, officials from State and local air pollution
organizations, environmental groups, and industry groups and companies.
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TABLE OF CONTENTS
List Of Tables vi
List Of Figures vii
1 INTRODUCTION 1-1
1.1 PURPOSE 1-1
1.2 BACKGROUND 1-2
1.2.1 Legislative Requirements 1-2
1.2.2 History of Lead NAAQS Reviews 1-3
1.2.3 Current Lead NAAQS Review 1-4
1.3 GENERAL APPROACH AND ORGANIZATION OF THE DOCUMENT 1-6
REFERENCES 1-8
2 CHARACTERIZATION OF AMBIENT LEAD 2-1
2.1 INTRODUCTION/BACKGROUND 2-1
2.1.1 Properties of Ambient Lead 2-2
2.1.2 Fate and Transport of Pb Particles 2-3
2.2 SOURCES AND EMISSIONS TO THE ATMOSPHERE 2-5
2.2.1 Trends in National Emissions: 1980 to 2002 2-5
2.2.2 Types of Pb Sources 2-6
2.2.2.1 Stationary Sources 2-6
2.2.2.2 Mobile Sources 2-8
2.2.2.3 Resuspension of Previously Deposited Pb and other Sources 2-10
2.2.3 Number and Geographic Distribution of Sources 2-10
2.2.4 Largest Pb Point Sources in the 2002 NEI 2-13
2.2.5 Data Sources, Limitations and Confidence 2-15
2.3 AIR QUALITY MONITORING DAT A 2-17
2.3.1 Ambient Pb Measurement Methods 2-18
2.3.1.1 Inlet Design 2-18
2.3.1.2 Volume of Air Sampled 2-18
2.3.1.3 Sampling Frequency 2-19
2.3.1.4 Sample Analysis 2-19
2.3.2 Pb-TSP 2-19
2.3.2.1 Monitor Locations 2-20
2.3.2.2 Historical Trend 2-22
2.3.2.3 Data Analysis Details 2-23
2.3.2.3.1 Screening Criteria 2-24
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2.3.2.3.2 Urban Sites 2-24
2.3.2.3.3 Source-oriented Sites 2-25
2.3.2.3.4 Population Associations 2-26
2.3.2.3.5 Statistical Metrics 2-27
2.3.2.4 Current Concentrations 2-27
2.3.2.4.1 Source-oriented Sites 2-34
2.3.2.4.2 Urban Sites 2-44
2.3.2.5 Variability 2-49
2.3.3 Pb-PMio 2-53
2.3.3.1 Data Analysis Details 2-54
2.3.3.2 Current Concentrations 2-55
2.3.4 Pb-PM2.5 2-60
2.3.4.1 Data Analysis Details 2-62
2.3.4.2 Current Concentrations 2-63
2.3.5 Relationships among Different Particle-sized Pb Concentrations 2-65
2.3.6 Summary 2-69
2.4 AIR QUALITY MODELING 2-70
2.4.1 National Air Toxics Assessment 2-70
2.4.1.1 Methods 2-70
2.4.1.2 Findings and Limitations 2-71
2.4.1.3 Summary 2-72
2.4.2 Community Multiscale Air Quality Model 2-72
2.5 POLICY-RELEVANT BACKGROUND IN AIR 2-74
2.6 ATMOSPHERIC DEPOSITION 2-75
2.6.1 Temporal Trends 2-75
2.6.2 Deposition Flux Estimates since the Last Review 2-76
2.7 OUTDOOR DUST AND SOIL 2-76
2.7.1 Outdoor Dust 2-76
2.7.2 Soil 2-77
2.7.2.1 Temporal Trends 2-77
2.7.2.2 Current Surface Soil Concentrations 2-79
2.8 SURFACE WATER AND SEDIMENT 2-80
2.8.1 Temporal Trends 2-80
2.8.2 Current Concentrations 2-82
3 POLICY-RELEVANT ASSESSMENT OF HEALTH EFFECTS EVIDENCE 3-1
3.1 INTRODUCTION 3-1
3.2 INTERNAL DISPOSITION - BLOOD LEAD AS DOSE METRIC 3-2
3.3 NATURE OF EFFECTS 3-6
3.3.1 Developing Nervous System 3-10
3.3.2 Adult Nervous System 3-12
3.3.3 Cardiovascular System 3-12
11
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3.3.4 Renal System 3-13
3.3.5 Heme Synthesis 3-14
3.3.6 Immune System 3-15
3.4 LEAD-RELATED IMPACTS ON PUBLIC HEALTH 3-15
3.4.1 At-risk Subpopulations 3-16
3.4.2 Potential Public Health Impact 3-18
3.5 SUMMARY AND CONCLUSIONS 3-21
REFERENCES 3-24
4 CHARACTERIZATION OF HEALTH RISKS 4-1
4.1 INTRODUCTION 4-1
4.1.1 Overview of Risk Assessment from Last Review 4-2
4.1.2 CAS AC Advice on Pilot and Initial Risk Analyses in this Review 4-3
4.2 DESIGN OF EXPOSURE AND RISK ASSESSMENTS 4-4
4.2.1 Health Endpoint, Risk Metric and Concentration-response Functions 4-4
4.2.2 Case Studies 4-10
4.2.3 Air Quality Scenarios 4-10
4.2.4 Categorization of Policy-relevant Exposure Pathways 4-11
4.2.5 Overview of Analytical Steps 4-12
4.2.6 Generating Multiple Sets of Risk Results 4-14
4.2.7 Key Limitations and Uncertainties 4-16
4.3 EXPOSURE ASSESSMENT 4-21
4.4 RISK ASSESSMENT 4-28
REFERENCES 4-39
5 THE PRIMARY LEAD NAAQS 5-1
5.1 INTRODUCTION 5-1
5.2 BACKGROUND ON THE CURRENT STANDARD 5-2
5.2.1 Basis for Setting the Current Standard 5-2
5.2.1.1 Level 5-2
5.2.1.1.1 Sensitive Population 5-3
5.2.1.1.2 Maximum Safe Blood Level 5-3
5.2.1.1.3 Nonair Contribution 5-4
5.2.1.1.4 Air Pb Level 5-5
5.2.1.1.5 Margin of Safety 5-5
5.2.1.2 Averaging Time, Form, and Indicator 5-6
5.2.2 Policy Options Considered in the Last Review 5-7
5.3 APPROACH FOR CURRENT REVIEW 5-10
5.4 ADEQUACY OF THE CURRENT STANDARD 5-12
5.4.1 Evidence-based Considerations 5-14
in
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5.4.2 Exposure- and Risk-based Considerations 5-17
5.4.3 CASAC Advice and Recommendations 5-22
5.4.4 Staff Conclusions and Recommendations 5-24
5.5 ELEMENTS OF THE STANDARD 5-25
5.5.1 Indicator 5-25
5.5.2 Averaging Time and Form 5-27
5.5.3 Level 5-32
5.5.3.1 Evidence-based Considerations 5-32
5.5.3.2 Exposure- and Risk-based Considerations 5-34
5.5.3.3 CASAC Advice and Recommendations 5-41
5.5.3.4 Staff Conclusions and Recommendations 5-42
5.5.4 Summary of Staff Conclusions and Recommendations on the Primary
PbNAAQS 5-43
5.6 SUMMARY OF KEY UNCERTAINTIES AND RESEARCH
RECOMMENDATIONS RELATED TO SETTING PRIMARY
STANDARD 5-45
REFERENCES 5-47
6 ASSESSMENT OF THE SECONDARY STANDARD 6-1
6.1 INTRODUCTION 6-1
6.2 WELFARE EFFECTS 6-2
6.2.1 Effects in Terrestrial Ecosystems 6-2
6.2.1.1 Pathways of Exposure 6-3
6.2.1.2 Effects of Lead on Energy Flow and Biogeocycling 6-4
6.2.1.3 Tools for Identifying Ecotoxicity in Terrestrial Organisms 6-4
6.2.1.4 Effects on Plants 6-5
6.2.1.5 Effects on Birds and Mammals 6-6
6.2.1.6 Effects on Decomposers and Soil Invertebrates 6-8
6.2.1.7 Summary 6-8
6.2.2 Effects in Aquatic Ecosystems 6-9
6.2.2.1 Tools for Identifying Ecotoxicity in Aquatic Organisms 6-9
6.2.2.2 Effects in Marine/Estuarine Ecosystems 6-10
6.2.2.2.1 Pathways of Exposure 6-10
6.2.2.2.2 Effects on Organisms and Communities 6-10
6.2.2.3 Effects in Freshwater Ecosystems 6-11
6.2.2.3.1 Pathways of Exposure 6-11
6.2.2.3.2 Effects at an Ecosystem Level 6-11
6.2.2.3.3 Effects on Algae and Aquatic Plants 6-13
6.2.2.3.4 Effects on Invertebrates 6-14
6.2.2.3.5 Effects on Fish and Waterfowl 6-15
6.2.2.4 Summary 6-16
6.3 SCREENING LEVEL RISK ASSESSMENT 6-17
IV
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6.3.1 Overview of Analyses 6-17
6.3.2 Measures of Exposure and Effect 6-20
6.3.3 National-Scale Screen and Case Studies 6-21
6.3.3.1 National-Scale Screen 6-21
6.3.3.1.1 Fresh Surface Waters 6-21
6.3.3.1.2 Lead in Sediments 6-22
6.3.3.2 Ecologically Vulnerable Location 6-22
6.3.3.3 Primary Pb Smelter Case Study 6-23
6.3.3.4 Secondary Pb Smelter Case Study 6-24
6.3.3.5 Near Roadway Nonurban Case Study 6-25
6.3.4 Screening Values 6-25
6.3.4.1 Soil Screening Values 6-25
6.3.4.2 Surface Water Screening Values 6-26
6.3.4.3 Sediment Screening Values 6-26
6.3.5 Results for Case Studies and Comparison to Screening Value 6-27
6.3.5.1 National-scale Surface Water Screen 6-27
6.3.5.2 National-scale Sediment Screen 6-29
6.3.5.3 Primary Pb Smelter Case Study 6-31
6.3.5.4 Secondary Pb Smelter Case Study 6-33
6.3.5.5 Near Roadway Nonurb an Case Study 6-35
6.3.6 Discussion 6-35
6.3.7 Uncertainty and Variability 6-35
6.3.7.1 Primary Pb Smelter Case Study 6-37
6.3.7.2 Secondary Pb Smelter Case Study 6-37
6.3.7.3 Near Roadway Nonurb an Case Study 6-37
6.3.7.4 National-scale Surface Water Screen 6-38
6.3.7.5 National-scale Sediment Screen 6-39
6.4 THE SECONDARY LEAD NAAQS 6-40
6.4.1 Introduction 6-40
6.4.2 Background on the Current Standard 6-40
6.4.3 Approach for the Current Review 6-42
6.4.4 Adequacy of the Current Standard 6-42
6.4.4.1 Evidence-based Considerations 6-43
6.4.4.2 Risk-based Considerations 6-45
6.4.4.3 CASAC Advice and Recommendations 6-47
6.4.4.4 Staff Conclusions and Recommendations 6-48
6.4.5 Elements of the Standard 6-49
REFERENCES 6-51
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ATTACHMENT A: Clean Air Scientific Advisory Committee Letter (March 27, 2007)
ATTACHMENT B: Clean Air Scientific Advisory Committee Letter (September 27, 2007)
APPENDICES
Appendix 2A. Largest Stationary Source Categories for Pb in the 2002 NEI 2A-1
Appendix 2B. Additional 2B-1
Appendix 5 A. Predicted percent of counties with a monitor not likely to meet alternative
standards and associated percent populations 5A-1
Table of Tables
Table 2-1. Trend in Pb emissions (tpy) from 1980 to 2002 2-6
Table 2-2. Source categories emitting greater than 5 tpy of Pb in the 2002 NEI 2-7
Table 2-3. Lead emissions from leaded aviation gas use in the 2002 NEI version 3 2-9
Table 2-4. Size distribution of point sources within the 2002 NEI and associated estimated
emissions 2-13
Table 2-5. Point Sources with Pb emissions in 2002 NEI greater than or equal to 5 tpy 2-15
Table 2-6. Comparison of number of sites that exceed various Pb-TSP levels using different
averaging times or forms, 2003-2005 2-50
Table 2-7. Comparison of number of sites that exceed various Pb-TSP levels using different
averaging times or forms, 2003-2005 - continued 2-51
Table 2-8. Monitoring sites with collocated Pb data in different size fractions 2-68
Table 3-1. Summary of Lowest Observed Effect Levels for Key Lead-Induced Health Effects in
Children (reproduced from CD, Table 8-5) 3-8
Table 3-2. Summary of Lowest Observed Effect Levels for Key Lead-Induced Health Effects in
Adults (reproduced from CD, Table 8-6) 3-9
Table 3-3. Population subgroups with characteristics that may contribute to increased
susceptibility or vulnerability toPb health effects 3-19
Table 3-4. Population size in counties with Pb emissions, by total emissions (tpy) 3-20
Table 3-5. Population size in counties with Pb emissions, by emissions density 3-21
Table 4-1. Summary of blood Pb estimates for median total blood Pb 4-26
Table 4-2. Summary of blood Pb estimates for 95th percentile total blood Pb 4-27
Table 4-3. Summary of risk estimates for medians of total-exposure risk distributions 4-34
Table 4-4. Summary of risk estimates for 95th percentile of total-exposure risk
distributions 4-35
Table 4-5. Median IQ loss estimates for the current NAAQS scenario 4-36
Table 4-6. 95th percentile IQ loss estimates for the current NAAQS scenario 4-36
Table 4-7. Incidence of children with >1 point Pb-related IQ loss 4-37
Table 4-8. Incidence of children with >7 points Pb-related IQ loss 4-38
VI
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Table 6-1. Models and Measurements Used for Ecological Risk Screening Assessment 19
Table 6-2. Soil Screening Values for Pb for Ecological Receptors 26
Table 6-3. Results of Aquatic Risk Screen - Locations at which Dissolved Pb Measurements
Exceed AWQC, Excluding Mining Sites. A 28
Table 6-4. Concentrations of Total Pb in Sediments at Locations Near or Matching the 15 Sites
at which Dissolved Pb Concentrations Exceeded the AWQC, Excluding Mining Sites
30
Table 6-5. HQs for Soils for Primary Pb Smelter Case Study 32
Table 6-6. HQs Calculated for Surface Waters for Primary Pb Smelter Case Study 32
Table 6-7. HQs Calculated for Sediments in Surface Waters for Primary Pb Smelter Case Study.
33
Table 6-8. HQs Calculated for Soils for Secondary Pb Smelter Case Study.a 34
Table 6-9. HQs Calculated for Soils Near Roadway Nonurban Case Study 35
List of Figures
Figure 2-1. Principal pathways of human and ecological exposure to Pb. Among the policy-
relevant pathways, heavy arrows indicate the predominant human exposures 2-2
Figure 2-2. Emissions density from all Pb sources in the 2002 NEI 2-11
Figure 2-3. Emissions density from all stationary sources of Pb in 2002 NEI 2-12
Figure 2-4. Geographic distribution of point sources with >1 tpy Pb emissions in 2002 NEI. 2-14
Figure 2-5. Pb-TSP monitoring sites: 2003-2005 2-21
Figure 2-6. Change in the number of Pb-TSP monitoring sites from 1980 to 2005 2-22
Figure 2-7. Airborne Pb -TSP concentrations, averaged across continuously operating
monitoring sites: 1980-1989 and 1990-2006. (Sources: left plot - AQS data
extracted 10/10/07; right plot-EPA 2007.) 2-23
Figure 2-8. Distribution of Pb-TSP concentrations (represented by 6 different statistics) at the
189 Pb-TSP monitoring sites, 2003-2005 2-29
Figure 2-9. Percentages of Pb-TSP monitored populations residing in areas exceeding
various concentrations (for 4 different statistics), 2003-2005 2-30
Figure 2-10. Pb-TSP annual means (for all sites), 2003-2005 2-31
Figure 2-11. Pb-TSP maximum quarterly means (for all sites), 2003-2005 2-32
Figure 2-12. Maximum monthly Pb-TSP means (all sites), 2003-2005 2-33
Figure 2-13. Second maximum monthly Pb-TSP means (all sites), 2003-200 2-34
Figure 2-14. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the source-oriented monitoring sites, 2003-2005 2-36
Figure 2-15. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the non-source-oriented monitoring sites, 2003-2005 2-37
Figure 2-16. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the nine monitoring sites near previous large emission sources, 2003-2005... 2-38
Figure 2-17. Distribution of Pb-TSP annual mean concentrations at different categories
of sites, 2003-2005 2-39
Figure 2-18. Distribution of Pb-TSP maximum quarterly mean concentrations at different
categories of sites, 2003-2005 2-40
Figure 2-19. Distribution of Pb-TSP maximum monthly mean concentrations at different
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categories of sites, 2003-2005 2-41
Figure 2-20. Distribution of Pb-TSP second maximum monthly mean concentrations at
different categories of sites, 2003-2005 2-42
Figure 2-21. Medians, means, and population-weighted means for 4 site-level statistics 2-43
Figure 2-22. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the 140 urban monitoring sites, 2003-2005 2-45
Figure 2-23. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the 91 urban monitoring sites located in metropolitan areas (CBSAs) with
1 million or more population, 2003-2005 2-46
Figure 2-24. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the 49 urban monitoring sites located in CBSA's with less than 1 million
population, 2003-2005 2-47
Figure 2-25. Percentages of Pb-TSP urban monitored populations residing in areas (represented
by 4 different statistics) exceeding various levels 2-48
Figure 2-26. Pb-TSP monthly means at five sites located in the Dallas, TX metropolitan
area, 2003-2005 2-52
Figure 2-27. Pb-PMi0 (NATTS) monitoring sites network 2-54
Figure 2-28. Distribution of Pb-PMi0 concentrations (represented by 3 different statistics)
at all 28 monitoring sites, 2003-2005 2-56
Figure 2-29. Distribution of Pb-PMi0 concentrations (represented by 3 different statistics)
at the 25 urban monitoring sites, 2003-2005 2-57
Figure 2-30. Distribution of Pb-PMi0 concentrations (represented by 3 different statistics)
at the urban monitoring sites located in CBS As of >_1 million population,
2003-2005 2-58
Figure 2-31. Pb-PMi0 annual means (for all sites), 2003-2005 2-59
Figure 2-32. Pb-PMi0 maximum quarterly means (for all sites), 2003-2005 2-60
Figure 2-33. Pb-PM2.5 (CSN) monitoring sites 2-61
Figure 2-34. Pb-PM2.5 (IMPROVE) monitoring sites 2-62
Figure 2-35. Distribution of Pb-PM2.5 concentrations (represented by 3 different statistics)
at all 271 monitoring sites, 2003-2005 2-64
Figure 2-36. Pb-PM2.5 annual means (for all sites), 2003-2005 2-65
Figure 2-37. National mean and median monitor level Pb annual means for different size
cut PM networks, 2003-2005 2-67
Figure 2-38. Modeled soil concentrations of Pb in the South Coast Air Basin of
California based on four resuspension rates (A) 2-79
Figure 2-39. Pb concentrations in sediment samples in 12 Michigan lakes 2-81
Figure 2-40. Spatial distribution of dissolved lead in surface water (N = 3445).
[CD, Figure AX7-2.2.7.] 2-83
Figure 2-41. Spatial distribution of total lead in bulk sediment <63 |im (N = 1466).
[CD, Figure AX7-2.2.9] 2-84
Figure 6-1. Overview of Ecological Screening Assessment 6-18
Vlll
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1 INTRODUCTION
1.1 PURPOSE
This OAQPS Staff Paper, prepared by staff in the U.S. Environmental Protection
Agency's (EPA) Office of Air Quality Planning and Standards (OAQPS), presents factors
relevant to EPA's current review of the primary (health-based) and secondary (welfare-based)
lead (Pb) national ambient air quality standards (NAAQS) that were originally established in
1978. In this document, OAQPS staff evaluates the policy implications of the key studies and
scientific information contained in the final document, Air Quality Criteria for Lead (USEPA,
2006a; henceforth referred to as the CD), prepared by EPA's National Center for Environmental
Assessment, and presents and interprets results from several quantitative analyses (e.g., human
exposure analyses, human health risk assessments and environmental assessments) that we
believe should also be considered in EPA's review of the Pb NAAQS. * Further, this document
presents OAQPS staff conclusions and recommendations on a range of policy options that we
believe are appropriate for the Administrator to consider concerning whether, and if so how, to
revise the primary and secondary Pb NAAQS.
The policy assessment presented in this Staff Paper is intended to help "bridge the gap"
between the scientific assessment contained in the CD and the judgments required of the EPA
Administrator in determining whether it is appropriate to retain or revise the NAAQS for Pb. In
evaluating the adequacy of the current standard and a range of policy alternatives, OAQPS staff
has considered the available scientific evidence and quantitative risk-based analyses, together
with related limitations and uncertainties, and has focused on the information that is most
pertinent to evaluating the basic elements of air quality standards: indicator2, averaging time,
form3, and level. These elements, which together serve to define each standard, must be
considered collectively in evaluating the health and welfare protection afforded by the Pb
standards. The information, conclusions, and staff recommendations presented in this Staff
Paper have been informed by comments and advice received from an independent scientific
review committee, the Clean Air Scientific Advisory Committee (CASAC), in their reviews of
an earlier draft of this document and drafts of related technical support documents, as well as
comments on these earlier draft documents submitted by public commenters.
1 The terms "staff and "we" throughout this document refer to OAQPS staff.
2 The "indicator" of a standard defines the chemical species or mixture that is to be measured in
determining whether an area attains the standard.
3 The "form" of a standard defines the air quality statistic that is to be compared to the level of the standard
in determining whether an area attains the standard.
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While this Staff Paper should be of use to all parties interested in the Pb NAAQS review,
it is written with an expectation that the reader has some familiarity with the technical
discussions contained in the CD. Further, we note that this document, which contains
conclusions and recommendations of OAQPS staff, does not necessarily reflect the views of the
Agency.
1.2 BACKGROUND
1.2.1 Legislative Requirements
Two sections of the Clean Air Act (Act) govern the establishment and revision of the
NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify and list each air
pollutant that "in his judgment, cause or contribute to air pollution which may reasonably be
anticipated to endanger public health and welfare" and whose "presence ... in the ambient air
results from numerous or diverse mobile or stationary sources" and to issue air quality criteria
for those that are listed. Air quality criteria are to "accurately reflect the latest scientific
knowledge useful in indicating the kind and extent of all identifiable effects on public health or
welfare which may be expected from the presence of [a] pollutant in ambient air . . .". Section
108 also states that the Administrator "shall, from time to time . . . revise a list" that includes
these pollutants, which provides the authority for a pollutant to removed from or added to the list
of criteria pollutants.
Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate
"primary" and "secondary" NAAQS for pollutants listed under section 108. Section 109(b)(l)
defines a primary standard as one "the attainment and maintenance of which in the judgment of
the Administrator, based on [air quality] criteria and allowing an adequate margin of safety, are
requisite to protect the public health."4 A secondary standard, as defined in Section 109(b)(2),
must "specify a level of air quality the attainment and maintenance of which, in the judgment of
the Administrator, based on criteria, is requisite to protect the public welfare from any known or
anticipated adverse effects associated with the presence of [the] pollutant in the ambient air."5
The requirement that primary standards include an adequate margin of safety was
intended to address uncertainties associated with inconclusive scientific and technical
4 The legislative history of section 109 indicates that a primary standard is to be set at "the maximum
permissible ambient air level. . . which will protect the health of any [sensitive] group of the population," and that
for this purpose "reference should be made to a representative sample of persons comprising the sensitive group
rather than to a single person in such a group." S. Rep. No. 91-1196, 91st Cong., 2d Sess. 10 (1970)
5 Welfare effects as defined in section 302(h) (42 U.S.C. 7602(h)) include, but are not limited to, "effects
on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and climate, damage to
and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal
comfort and well-being."
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information available at the time of standard setting. It was also intended to provide a reasonable
degree of protection against hazards that research has not yet identified. Lead Industries
Association v. EPA, 647 F.2d 1130, 1154 (D.C. Cir 1980), cert, denied, 449 U.S. 1042 (1980);
American Petroleum Institute v. Costle, 665 F.2d 1176, 1186 (D.C. Cir. 1981), cert, denied, 455
U.S. 1034 (1982). Both kinds of uncertainties are components of the risk associated with
pollution at levels below those at which human health effects can be said to occur with
reasonable scientific certainty. Thus, in selecting primary standards that include an adequate
margin of safety, the Administrator is seeking not only to prevent pollution levels that have been
demonstrated to be harmful but also to prevent lower pollutant levels that may pose an
unacceptable risk of harm, even if the risk is not precisely identified as to nature or degree.
In selecting a margin of safety, EPA considers such factors as the nature and severity of
the health effects involved, the size of the sensitive population(s) at risk, and the kind and degree
of the uncertainties that must be addressed. The selection of any particular approach to
providing an adequate margin of safety is a policy choice left specifically to the Administrator's
judgment. Lead Industries Association v. EPA, supra, 647 F.2d at 1161-62.
In setting standards that are "requisite" to protect public health and welfare, as provided
in section 109(b), EPA's task is to establish standards that are neither more nor less stringent
than necessary for these purposes. In so doing, EPA may not consider the costs of implementing
the standards. See generally Whitman v. American Trucking Associations, 531 U.S. 457, 471,
475-76 (2001).
Section 109(d)(l) of the Act requires that "not later than December 31, 1980, and at 5-
year intervals thereafter, the Administrator shall complete a thorough review of the criteria
published under section 108 and the national ambient air quality standards . . . and shall make
such revisions in such criteria and standards and promulgate such new standards as may be
appropriate . . . ." Section 109(d)(2) requires that an independent scientific review committee
"shall complete a review of the criteria . . . and the national primary and secondary ambient air
quality standards . . . and shall recommend to the Administrator any new . . . standards and
revisions of existing criteria and standards as may be appropriate . . . ." Since the early 1980's,
this independent review function has been performed by the Clean Air Scientific Advisory
Committee (CASAC) of EPA's Science Advisory Board.
1.2.2 History of Lead NAAQS Reviews
On October 5, 1978 EPA promulgated primary and secondary NAAQS for lead under
section 109 of the Act (43 FR 46246). Both primary and secondary standards were set at a level
of 1.5 micrograms per cubic meter (ug/m3), measured as Pb in total suspended particulate matter
(TSP), not to be exceeded by the maximum arithmetic mean concentration averaged over a
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calendar quarter. This standard was based on the 1977 Air Quality Criteria for Lead (USEPA,
1977).
A review of the Pb standards was initiated in the mid-1980s. The scientific assessment
for that review is described in the 1986 Air Quality Criteria for Lead (USEPA, 1986a), the
associated Addendum (USEPA, 1986b) and the 1990 Supplement (USEPA, 1990a). As part of
the review, the Agency designed and performed human exposure and health risk analyses
(USEPA, 1989), the results of which were presented in a 1990 Staff Paper (USEPA, 1990b).
Based on the scientific assessment and the human exposure and health risk analyses, the 1990
Staff Paper presented options for the Pb NAAQS level in the range of 0.5 to 1.5 |ig/m3, and
suggested the second highest monthly average in three years for the form and averaging time of
the standard (USEPA, 1990b). After consideration of the documents developed during the
review and the significantly changed circumstances since Pb was listed in 1976, as noted above,
the Agency did not propose any revisions to the 1978 Pb NAAQS. In a parallel effort, the
Agency developed the broad, multi-program, multimedia, integrated U.S. Strategy for Reducing
Lead Exposure (USEPA, 1991). As part of implementing this strategy, the Agency focused
efforts primarily on regulatory and remedial clean-up actions aimed at reducing Pb exposures
from a variety of nonair sources judged to pose more extensive public health risks to U.S.
populations, as well as on actions to reduce Pb emissions to air.
1.2.3 Current Lead NAAQS Review
EPA initiated the current review of the air quality criteria for Pb on November 9, 2004
with a general call for information (69 FR 64926). A project work plan (USEPA, 2005a) for the
preparation of the CD was released in January 2005 for CAS AC and public review. EPA held a
series of workshops in August 2005, with invited recognized scientific experts to discuss initial
draft materials that dealt with various lead-related issues being addressed in the Pb air quality
criteria document. These workshops helped to inform the preparation of the first draft CD
(USEPA, 2005b), which was released for CASAC and public review in December 2005 and
discussed at a CASAC meeting held on February 28-March 1, 2006.
A second draft CD (USEPA, 2006b) was released for CASAC and public review in May
2006, and discussed at the CASAC meeting on June 28, 2006. A subsequent draft of Chapter 7 -
Integrative Synthesis (Chapter 8 in the final CD), released on July 31, 2006, was discussed at an
August 15, 2006 CASAC teleconference. The final CD was released on September 30, 2006
(USEPA, 2006a). While the CD focuses on new scientific information available since the last
review, it appropriately integrates that information with scientific criteria from previous reviews.
In February 2006, EPA released the Plan for Review of the National Ambient Air Quality
Standards for Lead (USEPA 2006c) that described Agency plans and a timeline for reviewing
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the air quality criteria, developing human exposure and risk assessments and an ecological risk
assessment, preparing a policy assessment, and developing the proposed and final rulemakings.
In May 2006, EPA released for CASAC and public review a draft Analysis Plan for
Human Health and Ecological Risk Assessment for the Review of the Lead National Ambient Air
Quality Standards (USEPA, 2006d), which was discussed at a June 29, 2006 CASAC meeting.
CASAC panel members' views were received at and subsequent to the meeting (Henderson,
2006), and considered in the implementation of the human health and ecological risk
assessments. The May 2006 assessment plan discussed two assessment phases: a pilot phase
and a full-scale phase. The pilot phase of both the human health and ecological risk assessments
was presented in the draft Lead Human Exposure and Health Risk Assessments and Ecological
Risk Assessment for Selected Areas (ICF, 2006; henceforth referred to as the pilot phase or first
draft Risk Assessment Report) which was released for CASAC and public review in December
2006. The first draft Staff Paper, also released in December 2006, discussed the pilot
assessments and the most policy-relevant science from the CD. These documents were reviewed
by CASAC and the public at a public meeting on February 6-7, 2007.
Taking into consideration comments on the first draft Risk Assessment Report and the
first draft Staff Paper from CASAC (Henderson, 2007a) and the public, staff conducted full-scale
human exposure and health risk assessments, although no further work was done on the
environmental assessment due to resource limitations. The full-scale human exposure and health
risk assessments were presented in a second draft Risk Assessment Report (USEPA, 2007a)
which was released in July 2007 for review by CASAC at a meeting held on August 28-29,
2007. Taking into consideration CASAC comments (Henderson, 2007b) and public comments
on that document, staff conducted additional human exposure and health risk assessments, which
are presented in a final Risk Assessment Report (USEPA, 2007b) and discussed in this Staff
Paper.
The schedule for completion of this review is governed by a judicial order resolving a
lawsuit filed in May 2004, alleging that EPA had failed to complete the current review within the
period provided by statute. Missouri Coalition for the Environment, v. EPA (No. 4:04CV00660
ERW, Sept. 14, 2005). The order that now governs this review, entered by the court on
September 14, 2005, provides that EPA will finalize the Staff Paper no later than November 1,
2007. The order also specifies that EPA sign, for publication, notices of proposed and final
rulemaking concerning its review of the Pb NAAQS no later than May 1, 2008 and September 1,
2008, respectively. Since this review of the lead NAAQS was initiated, EPA has adopted a new
process for reviewing NAAQS that eliminates issuance of a Staff Paper and adds publication of
an advance notice of proposed rulemaking (ANPR). In applying this new process to this review
of the lead NAAQS, in addition to the issuance of this Staff Paper consistent with the judicial
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order, EPA also plans to publish an ANPR around the end of November 2007. To ensure that the
ordered final rulemaking deadline will be met, EPA has set an interim target date for a proposed
rulemaking of March 2008.
1.3 GENERAL APPROACH AND ORGANIZATION OF THE DOCUMENT
The policy assessment in this Staff Paper document is based on staffs evaluation of the
policy implications of the scientific evidence reviewed in the CD and results of quantitative
analyses based on that evidence, as well as the views presented by CASAC and various
stakeholders. Taken together, this information informs various conclusions and the identification
of a range of policy options to address public health and welfare effects associated with exposure
to ambient Pb resulting from emissions to the ambient air.
Following this introductory chapter, this Staff Paper is organized into three main parts:
the characterization of ambient Pb; Pb-related health effects and primary Pb NAAQS; and Pb-
related welfare effects and secondary Pb NAAQS. The content of these parts is discussed more
fully below.
The characterization of ambient Pb is presented in Chapter 2 and includes information on
Pb properties, current Pb air quality patterns, historic trends, and background levels. In
recognition of the multimedia nature of Pb and the distribution into other media of Pb emitted
into the air, Chapter 2 also includes information on Pb in media other than air including outdoor
dust, soil, surface water and sediment. This chapter provides a frame of reference for exposure
and risk analyses and subsequent discussion of the Pb NAAQS and alternative forms of Pb
standards.
Chapters 3 through 5 comprise the second main part of this document, dealing with
human health and primary standards. Chapter 3 presents an overview of key policy-relevant
health effects evidence, major health-related conclusions from the CD, and an examination of
issues related to the quantitative assessment of health risks. Chapter 4 describes the scope and
methods used in conducting human exposure and health risk assessments and presents key
results from those assessments together with a discussion of uncertainty and variability in the
results. Chapter 5 includes staff conclusions and policy recommendations on the adequacy of the
current primary standard and on an appropriate range of alternative primary standards for the
Administrator's consideration, together with a discussion of the science and public health policy
judgments underlying such standards.
Chapter 6 comprises the third main part of this document. Chapter 6 presents a policy-
relevant assessment of Pb welfare effects evidence and describes the scope and methods used in
conducting the environmental risk assessment, as well as results from the pilot environmental
assessment. This chapter includes staff conclusions and policy recommendations on the
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adequacy of the current secondary standard and on an appropriate range of alternative secondary
standards for the Administrator's consideration, together with a discussion of the science and
public welfare policy judgments underlying such standards.
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REFERENCES
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. 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. (1977) Air quality criteria for lead. Research Triangle Park, NC: Health
Effects Research Laboratory, Criteria and Special Studies Office; EPA report no. EPA-600/8-77-017.
Available from: NTIS, Springfield, VA; PB-280411.
U.S. Environmental Protection Agency. (1986a) Air quality criteria for lead. Research Triangle Park, NC: Office of
Health and Environmental Assessment, Environmental Criteria and Assessment Office; EPA report no.
EPA-600/8-83/028aF-dF. 4v. Available from: NTIS, Springfield, VA; PB87-142378.
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). In: Air quality criteria for lead, v.
1. Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria
and Assessment Office; pp. A1-A67; EPA report no. EPA-600/8-83/028aF. Available from: NTIS,
Springfield, VA; PB87-142378.
U.S. Environmental Protection Agency. (1989) Review of the national ambient air quality standards for lead:
Exposure analysis methodology and validation: OAQPS staff report. Research Triangle Park, NC: Office of
Air Quality Planning and Standards; report no. EPA-450/2-89/011. Available on the web:
http://www.epa.gov/ttn/naaqs/standards/pb/data/rnaaqsl eamv.pdf
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. (1991) U.S. EPA Strategy for Reducing Lead Exposure. Available from
U.S. EPA Headquarters Library/Washington, D.C. (Library Code EJBD; Item Call Number: EAP
100/1991.6; OCLC Number 2346675).
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U.S. Environmental Protection Agency. (2005a) Project Work Plan for Revised Air Quality Criteria for Lead.
Research Triangle Park, NC: National Center for Environmental Assessment-RTF Report no. NCEA-R-
1465. CASAC Review Draft.
U.S. Environmental Protection Agency. (2005b) Air Quality Criteria for Lead (First External Review Draft).
Washington, DC, EPA/600/R-05/144aA-bA. Available online at: www.epa.gov/ncea/
U.S. Environmental Protection Agency. (2006a) Air Quality Criteria for Lead. Washington, DC, EPA/600/R-
5/144aF. Available online at: www.epa.gov/ncea/
U.S. Environmental Protection Agency. (2006b) Air Quality Criteria for Lead (Second External Review Draft).
Washington, DC, EPA/600/R-05/144aB-bB. Available online at: www.epa.gov/ncea/
U.S. Environmental Protection Agency. (2006c) Plan for Review of the National Ambient Air Quality Standards for
Lead. Office of Air Quality Planning and 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. (2006d) 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
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) 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. EPA-
452/D-07-001a and EPA-452/D-07-001b.
U.S. Environmental Protection Agency. (2007b) Lead: Human Exposure and Health Risk Assessments for Selected
Case Studies, 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. EPA-452/R-07-
014a and EPA-452/R-07-014b.
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2 CHARACTERIZATION OF AMBIENT LEAD
2.1 INTRODUCTION/BACKGROUND
The focus for this Pb NAAQS review is on Pb derived from those sources emitting Pb to
ambient air. The multimedia and persistent nature of Pb, however, contributes several
complexities to the review.
First, exposures to Pb emitted into the air occur via multiple pathways. As described in
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, exposure to Pb is
multipathway, and we are considering both airborne Pb, as it contributes to 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 exposures through ingestion. Further, we
are considering that Pb, once deposited, may be resuspended in the air, contributing to inhalation
exposures or, upon redeposition, to ingestion exposures. Thus, as illustrated in Figure 2-1,
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".
Due to limited data, models, and time available, however, we are not able to fully and
completely characterize all of the various complexities associated with Pb exposure pathways.
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 (see CD, Sections 3.3 and
3.5). For purposes of this review, the Pb from these nonair sources is collectively referred to as
"policy-relevant background". Additionally Pb in diet and drinking water sources may derive
from Pb emitted into the ambient air (i.e., policy-relevant sources), however, we have not
explicitly described the current contribution from air pathways to these exposure pathways in
this chapter; these exposure pathways are described in the CD (Section 3.4).
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Policy-relevant
Background*
Policy-relevant Sources
Policy-relevant
Background*
Historically emitted Pb
Non-air Pb
releases
Outdoor Soil, Dusts
Ecological Exposures
Human Exposures
-Inhalation & ingestion
*Policy-relevant background sources and pathways are indicated by dashed lines.
"""Dietary exposure should not be considered to be limited to policy-relevant background, as it reflects a combination
of Pb introduced into food items during processing (policy-relevant background), as well as Pb associated with
atmospheric deposition (policy-relevant sources).
Figure 2-1. Principal pathways of human and ecological exposure to Pb. Among the
policy-relevant pathways, heavy arrows indicate the predominant human
exposures.
2.1.1 Properties of Ambient Lead
Due to its physicochemical properties, Pb exists in the environment predominantly in
solid form. Consequently upon emission into the air, Pb deposits onto surfaces or exists in the
atmosphere as a component of atmospheric aerosol (CD, Section 2.1). The various Pb
compounds that are naturally occurring in the environment or are introduced by anthropogenic
activities include oxides, chlorides (or other halides), sulfates, and sulfides (see CD, Table 2-5).
A more complete discussion of the physical and chemical properties of Pb and Pb compounds is
provided in the CD (Section 2.1). The Pb NAAQS pertains to the Pb content of all Pb
compounds that may be emitted to air (see Section 2.3.1 for discussion of collection and analysis
methods).
The relative presence of Pb among the various environmentally occurring compounds
influences its distribution within the environment, and the relative bioavailability of these
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compounds has implications for human and ecological exposures and risks (CD, Sections 4.2.1,
8.1.3 and 8.2.3). With regard to human exposures and risk, this is reflected in the exposure
modeling described in Chapter 4. Lead speciation and bioavailability are discussed further with
regard to environmental effects in Chapter 6.
2.1.2 Fate and Transport of Pb Particles
The atmosphere is the major environmental transport pathway for anthropogenic Pb (CD,
p 2-52). Lead can be transported in the atmosphere and undergo secondary dispersal via the
deposition and resuspension of particles containing Pb. As described in the CD (Section 2.3.1),
airborne Pb particles generally have a bimodal distribution with the greater mass of Pb found in
the fine fraction (CD, p. 2-52). Since small particles are much slower to deposit than larger
particles, Pb can be transported great distances in the atmosphere. Thus, Pb is widely dispersed,
as evidenced by detection of Pb even in the most remote places such as the arctic region (CD, pp.
2-52, 3-3).
Airborne concentrations of species emitted from a point source are frequently described
by a Gaussian distribution. Gaussian models are, in general, reasonably accurate for small
geographic scales, e.g., within -50 km of the source (CD, p. 2-53). The rate and direction of
dispersion are dependent both on pollutant characteristics and meteorological conditions.
Important meteorological factors influencing dispersion include wind speed, surface roughness,
inversion frequency, inversion duration, and temperature. Results are site specific. For long
range transport modeling, Lagrangian trajectory or Eulerian grid models are commonly
employed. These models determine how a parcel of air moves relative to the moving fluid and a
fixed coordinate system, respectively. Retrospective air mass trajectories based on hybrid
models are also used. Results of a study using such an approach have reported finding airborne
Pb in a less industrial country originating from emissions in several distant countries (CD, p. 2-
54).
Wet and dry deposition are the ultimate paths by which Pb particles are removed from the
atmosphere. Dry deposition is the process by which Pb particles are delivered from the
atmosphere onto surfaces in the absence of precipitation. Factors that govern dry deposition are
the level of atmospheric turbulence, especially in the layer nearest the ground, particle size
distributions and density, and the nature of the surface itself, such as smooth or rough. In the
commonly used model formulation for dry deposition, it is assumed that the dry deposition flux
is directly proportional to the local concentration of the pollutant species, at some reference
height above the surface (e.g., 10 m or less), multiplied by the deposition velocity (CD, p. 2-55).
The concentration is computed by the dispersion models mentioned above, depending on local
versus regional or global applications. Estimates of dry deposition velocity constitute the
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primary output of a large number of dry deposition models that have been developed during the
past ten years and most of these rely on so-called "resistance schemes". The advantage of this
deposition velocity representation is that all the complexities of the dry deposition process are
bundled in a single parameter, but the disadvantage is that because this parameter addresses a
variety of processes, it is difficult to specify properly. A large range of Pb deposition velocities
(0.05 to 1.3 cm/s) has been reported (CD, pp. 2-55 to 2-57 and Table 2-21).
Wet deposition, or the delivery of a pollutant to the ground in precipitation, is the process
by which airborne pollutants are scavenged by precipitation and removed from the atmosphere.
The flux of a depositing species can be defined as the product of the rate of precipitation and the
concentration of the chemical species in the precipitation (CD, pp. 2-57 to 2-59). Wet deposition
is affected by: 1) nucleation scavenging (removal by direct incorporation into new cloud
droplets); 2) in-cloud scavenging (removal by incorporation into existing cloud droplets); and 3)
precipitation washout (removal by rain as it is falling to the ground). The size of particles can
influence wet deposition rates. Large particles are scavenged by precipitation more efficiently
than smaller particles (CD, p. 2-59). Lead, beyond the influence of individual sources, is found
primarily in the submicron size range, and consequently does not undergo wet deposition as
easily as many of the crustal elements (CD, p. 2-59). Models vary in how they treat wet
deposition. Gaussian models focus solely on washout aspects, mainly because this process is
dominant within the 50 km limit of model applicability. Regional and global models have more
comprehensive treatment of wet deposition. Lead concentrations in precipitation have shown a
pronounced downward trend from the 1970s into the 1990s, presumed primarily due to the
phase-out of leaded fuel (CD, pp. 2-60 to 2-61 and Table 2-22).
The resuspension of soil-bound Pb particles and contaminated road dust can be a
significant source of airborne Pb (CD, Section 2.3.3, and p. 2-62). Studies of emissions in
southern California indicate that Pb in resuspended road dust may represent between 40% and
90% of Pb emissions in some areas (CD, p. 2-65). Lead concentrations in suspended soil and
dust, however, vary significantly (CD, p. 2-65). In general, the main drivers of particle
resuspension are typically mechanical stressors such as vehicular traffic, construction and
agricultural operations, and to a lesser extent, the wind. Understanding the physics of
resuspension from natural winds requires analyzing the wind stresses on individual particles and
although this analysis can be accurate on a small scale, predicting resuspension on a large scale
generally focuses on empirical data for soil movement due to three processes: saltation, surface
creep, and suspension (CD, pp. 2-62 to 2-63). Further, rather than a continuous process,
resuspension may occur as a series of events. Short episodes of high wind speed, dry conditions,
and other factors conducive to resuspension may dominate annual averages of upward flux (CD,
p. 2-65). These factors complicate emissions estimates.
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2.2 SOURCES AND EMISSIONS TO THE ATMOSPHERE
In this section we describe the available information on sources and emissions of Pb into
the ambient air. The section does not provide a comprehensive list of all sources of Pb, nor does
it provide estimates of emission rates or emission factors for all source categories. Rather, the
discussion here is intended to identify the larger source categories, either on a national or local
scale, and provide some characterization of their emissions and distribution within the U.S.
The primary data source for this discussion is the National Emissions Inventory (NEI) for
2002 (USEPA, 2007a). As a result of Clean Air Act requirements, emissions standards
implemented for a number of source categories since then are projected to result in considerably
lower emissions at the current time or in the near future.
It is noted that the Pb emissions estimates in the NEI, and presented in this chapter, are a
mixture of estimates specific to Pb (regardless of the compound in which it may have been
emitted) and estimates specific to the Pb compounds emitted. That is, emissions estimates for
some of the point sources are in terms of mass of Pb compounds, whereas the nonpoint source
and mobile source emissions estimates are in terms of mass of the Pb only. For the point
sources, approximately 80% are reported as mass of Pb and most of the other 20% are reported
as mass of Pb compounds. The high molecular weight of Pb (as compared to elements with
which it is associated in Pb compounds), however, reduces the impact of this reporting
inconsistency.
Historical trends in emissions are described in Section 2.2.1, information on the various
types of Pb sources is presented in Section 2.2.2, the number and geographic distribution of
sources is discussed in Section 2.2.3 and the larger Pb point sources are identified in Section
2.2.4. The data sources for, limitations of and our confidence in the information summarized
here are described in Section 2.2.5.
2.2.1 Trends in National Emissions: 1980 to 2002
Table 2-1 shows a downward trend in Pb emissions from the fuel combustion, industrial
process and solid waste sectors from 1980 through 2002, as well as the dramatic reduction in Pb
emissions in the transportation sector due to the removal of Pb from gasoline. While the most
dramatic reductions occurred prior to 1990, Pb emissions were further reduced by about 65%
(from approximately 5,000 to approximately 1,700 tpy) between 1990 and 2002 (USEPA 1990;
USEPA, 2007a). The greatest emission reductions were from mobile sources, primary and
secondary Pb and copper smelting, pulp and paper manufacturing, inorganic paint pigment
production and steel wire products. As discussed in the CD (Section 2.2.4), reductions in Pb
emissions from mobile sources include some associated with the latter period of the "phase-out"
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of leaded gasoline. From a national inventory perspective, the stationary source categories that
have the largest emissions in the 2002 NEI are summarized briefly in Appendix 2A.
Table 2-1. Trend in Pb emissions (tpy) from 1980 to 2002.
1980 1985
1990
1995 1999 2002
491a
377
719
110
Transportation 64,706 18,973 1,197 564
Fuel Combustion 4,299 515 500 490
Industrial Processes 3,938 2,531 2,474 2,271
Solid Waste 1,210 871 804 604 :
Total 74,153 22,890 4,975 3,929 3303 1,697
3 This value is not yet reflected in 2002 NEI (vers 3); it will be reflected in version
4, estimated for 2008 release.
Note: Estimates for 1980-1995 are from http://www.epa.gov/airtrends/econ-
emissions.html.
Detailed categorization of the 1999 NEI is not available.
Estimates for 2002 are from Version 3 of the 2002 National Emissions Inventory,
US EPA (USEPA, 2007a). The estimates for 2002 differ from those in Table 2-8
of the CD due to changes in the 2002 NEI subsequent to publication of the CD.
2.2.2 Types of Pb Sources
Lead is emitted from a wide variety of source types, some of which are small individually
but the cumulative emissions of which are large, and some for which the opposite is true. The
categories of Pb sources estimated in the 2002 NEI to emit -as a category- more than 5 tons per
year (tpy) of Pb are listed in Table 2-2. The main sources of emissions in the 2002 NEI are
comprised primarily of combustion-related emissions and industrial process-related emissions.
Point source emissions account for about 66% of the national Pb emissions in the 2002 NEI. The
point source emissions are roughly split between combustion and industrial processes, while
mobile, nonroad sources (emissions associated with general aviation aircraft leaded fuel) account
for 29%.
2.2.2.1 Stationary Sources
Table 2-2 presents emissions estimates for stationary sources grouped into descriptive
categories. Presence and relative position of a source category on this list does not necessarily
provide an indication of the significance of the emissions from individual sources within the
source category. A source category, for example, may be composed of many small (i.e., low-
emitting) sources, or of just a few very large (high-emitting) sources. Such aspects of a source
category, which may influence its potential for human and ecological impacts, are included in the
short descriptions of the largest stationary source categories presented in Appendix 2 A. The
relative sizes of stationary sources represented in the NEI and the geographic distribution of the
larger sources are presented in Sections 2.2.2.1 and 2.2.3, respectively.
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Table 2-2. Source categories emitting greater than 5 tpy of Pb in the 2002 NEI.
Source Category Description
ALL CATEGORIES b
Mobile sources
Industrial/Commercial/ Institutional Boilers & Process Heaters
Utility Boilers
Iron and Steel Foundries
Primary Lead Smelting
Hazardous Waste Incineration
Secondary Lead Smelting
Military Installations
Municipal Waste Combustors
Integrated Iron & Steel Manufacturing
Pressed and Blown Glass and Glassware Manufacturing
Stainless and Non Stainless Steel Manufacturing: EAF
Mining
Lead Acid Battery Manufacturing
Secondary Nonferrous Metals
Portland Cement Manufacturing
Primary Copper Smelting
Primary Metal Products Manufacturing
Industrial and Commercial Machinery Manufacturing
Fabricated Metal Products Manufacturing
Electrical and Electronics Equipment Manufacturing
Waste Disposal - Solid Waste Disposal
Industrial Inorganic Chemical Manufacturing
Pulp & Paper Production
Sewage Sludge Incineration
Mineral Products Manufacturing
Secondary Aluminum Production
Synthetic Rubber Manufacturing
Secondary Copper Smelting
Transportation Equipment Manufacturing
Ferroalloys Production
Nonferrous Foundries
Stationary Reciprocating Internal Combustion Engines
Commercial and Industrial Solid Waste Incineration
Primary Nonferrous Metals-Zinc, Cadmium and Beryllium
Residential Heating
Asphalt Processing and Asphalt Roofing Manufacturing
Total Emissions (tpy)a
1,697b
491C
190
168d
110
59
47
43
33
33
32
32
32
31
27
24
22
22
21
18
14
12
11
10
10
10
9
9
9
8
8
7
7
7
6
6
6
5
aSome values here differ from those in the CD (Table 2-8) due to changes in the 2002 NEI subsequent to CD
publication. Additionally, values just above 5 tpy have been rounded to 5.
Includes 91 tpy Pb emissions from 109 smaller categories (57 tpy in MACT categories and 34 tpy in non MACT).
c This value is not yet reflected in 2002 NEI (vers 3); it will be reflected in version 4, estimated for 2008 release.
d This estimate of 168 tons, which is based on the 2002 NEI, has uncertainties and differs from estimates in some
other studies and inventories. For example, the estimated lead emissions reported to the U.S. EPA's Toxic Release
Inventory for year 2004 is about 90 tons for this sector, and the projected estimate for year 2010 presented in the
1998 EPA Utility Air Toxics Study Report to Congress (U.S. EPA, 1998) is 92 tons.
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2.2.2.2 Mobile Sources
Thirty-five years ago, combustion of leaded gasoline was the main contributor of Pb to
the air. In the early 1970s, EPA set national regulations to gradually reduce the Pb content in
gasoline. In 1975, unleaded gasoline was introduced for motor vehicles equipped with catalytic
converters. EPA banned the use of leaded gasoline in highway vehicles after December 1995.
While Pb is not added to jet fuel that is used in commercial aircraft, military aircraft, or other
turbine engine aircraft, currently lead is still added to aviation gasoline (commonly referred to as
"avgas") used in most piston-engine aircraft and some types of race cars. Lead emissions from
the combustion of avgas are discussed below. Vehicles used in racing are not regulated by the
EPA under the Clean Air Act and can therefore use alkyl-Pb additives to boost octane. EPA has
formed a voluntary partnership with the National Association for Stock Car Auto Racing
(NASCAR) with the goal of permanently removing alkyl-Pb from racing fuels used in the Nextel
Cup, Busch and Craftsman Truck Series (CD, p. 2-50). In January of 2006, NASCAR agreed to
switch to unleaded fuel in its race cars and trucks beginning in 2008. NASCAR initiated this
switch in 2007.
Lead is also present as a trace contaminant in gasoline and diesel fuel and is a component
of lubricating oil (CD, pp. 2-45 to 2-48). Inventory estimates from these sources are not
currently available. Additional mobile sources of Pb include brake wear, tire wear, and loss of
Pb wheel weights (CD, pp. 2-48 to 2-50). Emission rates for Pb from brake wear have been
published but inventory estimates have not yet been developed from these data (Schauer et al.,
2006). Robust estimates of Pb from tire wear and wheel weights are not available. Currently, Pb
from combustion of leaded avgas is the only mobile source of Pb included in the 2002 NEI.
Currently, there are two main types of leaded avgas used, 100 Octane and 100 Octane
Low Lead (100 LL), which can contain up to 1.12 grams Pb per liter (g/L) (0.009347 pounds per
gallon, Ib/gal) and 0.56 g Pb/L (0.004673 Ib/gal), respectively (ASTM D 910). The vast majority
of leaded avgas used is 100LL. In 2002 approximately 280 million gallons of avgas were
supplied to the U.S. (DOE, 2006) contributing an estimated 491 tons of lead to the air and
comprising 29% of the national Pb inventory.1
Lead emission estimates from piston-engine aircraft in the 2002 NEI are allocated to
3,410 airports located throughout the United States (USEPA, 2007b). These Pb emissions are
1 Lead emissions from general aviation are calculated as the product of the fuel consumed, the
concentration of Pb in the fuel and the factor 0.75 to account for an estimated 25% of Pb being retained in the engine
and/or exhaust system of the aircraft. The estimate of 25% Pb retention was derived from estimates from light-duty
gas vehicles operating on leaded fuel and is an upper-bound estimate of the amount of Pb retained in a piston-engine
aircraft. Smaller retention values would proportionally increase the overall mobile source Pb inventory.
2-8
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allocated to each airport based on its percentage of piston-engine operations nationwide. These
operations for 2002 can be found in the Terminal Area Forecast (TAP) system, which is the
official forecast of aviation activity at FAA facilities. Airport-specific Pb emissions estimates in
the NEI include Pb emitted during the entire flight (i.e., not limited to the landing and take-off
cycle and local operations). EPA is using this allocation approach for Pb because it is important
to account for all of the Pb emitted by avgas use. There is currently not an alternative approach
for incorporating all the Pb emissions from aircraft into the NEI. EPA understands that
allocating lead emissions to airports from operations outside the landing-takeoff cycle and local
flying operations has a tendency to overstate the local emissions near airports because longer
duration (e.g., itinerant) flights emit lead at altitude as well as in the local area near the airport.
Airport-specific Pb emissions estimates in the 2002 NEI do not include the following
airport-related sources of Pb: evaporative losses of Pb from fuel storage and distribution,
military aircraft combustion emissions, and the small amounts of tetraethyl-lead (TEL) discarded
on the tarmac by pilots after their fuel check. Lead emissions from fuel storage and distribution
are estimated to total 0.3 tons nationally and are included in the NEI, but not assigned to specific
airports. Data regarding military piston engine aircraft emissions are supplied to EPA by states.
The 2002 version 3 inventory estimates for this category did not include state-submitted data, but
future updates to the NEI will include these estimates.
These current NEI estimates provide a valuable comparison with other ambient sources
of Pb. Future upgrades to these estimates and assessments specific to individual airports could
include more refined local data including characteristics of local operations (e.g., landings and
take-offs), Pb retention in piston engines, and fuel consumption rates.
Among the airports in the 2002 NEI where piston-engine aircraft operate, approximately
one percent of US airports listed have estimated Pb emissions of greater than one ton per year, a
greater percentage has estimated Pb emissions between one ton and 0.1 ton per year, while the
majority of airports are estimated to have Pb emissions less than 0.1 ton per year. Table 2-3
below demonstrates these estimated emission ranges.
Table 2-3. Lead emissions from leaded aviation gas use in the 2002 NEI version 3.
Emissions
Range
(tpy)
<0.1
0.1 to 1.0
> 1
Summary
Number of
Airports
2,104
1,270
36
3,410
Total
Emissions
(tpy)
76.7
367.5
47.1
491.3
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2.2.2.3 Resuspension of Previously Deposited Pb and other Sources
Resuspension of soil-bound Pb particles and contaminated road dust has been reported to
be a significant source of airborne Pb (CD, Section 2.3.3, and p. 2-62). Quantitative estimates of
resuspension-related emissions, however, are not included in the 2002 NEI. Studies of emissions
in southern California indicate that Pb in resuspended road dust may represent between 40% and
90% of Pb emissions in that area (CD, p. 2-65). Lead concentrations in suspended soil and dust,
however, vary significantly (CD, p. 2-65). In general, the main drivers of particle resuspension
are typically mechanical stressors such as vehicular traffic, construction and agricultural
operations, and to a lesser extent, the wind. Lead resuspended in soil near roadways that was in
place during the use of leaded gasoline may be a notable emissions source if or when such soil is
disturbed (e.g., road widening or building construction).
Understanding the physics of resuspension from natural winds requires analyzing the
wind stresses on individual particles and although this analysis can be accurate on a small scale,
predicting resuspension on a large scale generally focuses on empirical data for soil movement
due to three processes: saltation, surface creep, and suspension (CD, pp. 2-62 to 2-63). Rather
than a continuous process, resuspension may occur as a series of events. Short episodes of high
wind speed, dry conditions, and other factors conducive to resuspension may dominate annual
averages of upward flux (CD, p. 2-65). All of these factors complicate emissions estimates (CD,
Section 2.2.1) such that quantitative estimates for these processes remain an area of significant
uncertainty.
Other sources not currently included in the NEI are emissions of Pb from natural sources,
such as wind-driven resuspension of soil with naturally occurring Pb, sea salt spray, volcanoes,
wild forest fires, and biogenic sources (CD, Section 2.2.1). Estimates for these emissions, some
of which have significant variability (CD, p. 2-13) have not been developed for the NEI, as
quantitative estimates for these processes remain an area of significant uncertainty.
2.2.3 Number and Geographic Distribution of Sources
The geographic distribution and magnitude of Pb emissions in the U.S. from all sources
identified in the 2002 NEI is presented in Figure 2-2, in terms of emissions density (defined here
as tons per area, square mile, per county). This presentation indicates a broad distribution of Pb
emissions across the U.S., with the highest emitting counties scattered predominantly within a
broad swath from Minnesota to southern New England and southward.
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Total Lead Emissions
(tpy/1000 square i
0-0.099
0.1 -0.99
1-9.9
Figure 2-2. Emissions density from all Pb sources in the 2002 NEL
Within the NEI, emissions from stationary sources may be associated with specific
"points" (i.e., point sources) or with activities estimated to occur with some frequency within an
"area" such as a county (area sources) or with mobile sources (see Section 2.2.2.2). Emissions
from all stationary sources represented in the NEI are presented in Figure 2-3, in terms of
emissions density (tons per area, square mile, per county).
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stationary Lead Emissions
(tpy/1000 square miles)
Figure 2-3. Emissions density from all stationary sources of Pb in 2002 NEI.
There are some 13,067 point sources (industrial, commercial or institutional) in the 2002
NEI, each with one or more processes that emit Pb to the atmosphere (Table 2-4). Most of these
sources emit less than 0.1 tpy Pb. There are approximately 1,300 point sources of Pb in the NEI
with estimates of emissions greater than or equal to 0.1 tpy and these point sources, combined,
emit 1058 tpy, or 94% of the Pb point source emissions. In other words, 94% of Pb point source
emissions are emitted by the largest 10% of these sources.
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Table 2-4. Size distribution of point sources within the 2002 NEI and associated estimated
emissions.
Emissions
Range
(tpy)
<0.1
0.1 to 1.0
1.0 to 5
>5
Summary
Number
of Sources
11,800
1,028
210
29
13,067
Total
Emissions
(tpy)
73
326
421
301
1121
Average
Emissions
per Source
(tpy)
<0.01
0.3
2
10
2.2.4 Largest Pb Point Sources in the 2002 NEI
While Section 2.2.2 described source categories that rank highest due to cumulative
national Pb emissions, this section is intended to consider Pb emissions on the individual source
level. The geographic distribution of point sources estimated to emit greater than 1 tpy is
presented in Figure 2-4.
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Figure 2-4. Geographic distribution of point sources with >1 tpy Pb emissions in 2002
NEI.
As mentioned in Section 2.2.3, the 2002 NEI includes 30 facilities with emissions
estimated to be greater than or equal to 5 tons per year (see Table 2-4). Most of these sources
(Table 2-5) are metallurgical industries, followed by waste disposal facilities and manufacturing
processes.
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Table 2-5. Point Sources with Pb emissions in 2002 NEI greater than or equal to 5 tpy.
Source Category Name
Primary Lead Smelting
Military Installation
Mining
Copper Refiningb
Primary Copper Smelting
Electric Arc Furnaces
Secondary Lead Smelting
Integrated Iron & Steel Manufacturing
Pressed and Blown Glass and Glassware Manufacturing
Military Installation
Hazardous Waste Incineration
Lead Acid Battery Manufacturing
Industrial and Commercial Machinery Manufacturing
Synthetic Rubber Products Manufacturing - Fabric
Coating
Commercial and Industrial Solid Waste Incineration
Iron and Steel Foundries
Integrated Iron & Steel Manufacturing
Integrated Iron & Steel Manufacturing
Mineral Products Manufacturing
Commercial and Industrial Solid Waste Incineration
Ferroalloys Production
Nonferrous Foundries
Portland Cement Manufacturing
Hazardous Waste Incineration
Coke Oven
Iron and Steel Foundries
Mining
a (USEPA, 2007)
b This entry is included in the total provided for "secondary
c Following compliance with the MACT standards in 2008,
State
MO
OK
MO
TX
AZ
IL
MO
IN
TN
PA
AR
KY
KS
IN
AR
OH
IN
IN
NM
CT
OH
NE
MD
OH
VA
IA
MO
nonferrous
2002 Point
County Name Emissions
Jefferson County
Pittsburg County
Reynolds County
Potter County
Gila County
Peoria County
Iron County
Lake County
Madison County
Franklin County
Union County
Madison County
Marshall County
Cass County
Clark County
Cuyahoga County
Porter County
Lake County
Socorro County
Windham County
Washington
County
Nemaha County
Frederick County
Lorain County
Buchanan County
Jefferson County
Reynolds County
metals" in Table 2-2.
(TPY)a
58.8
17.2
15.4
13.9
12.8
12.5
12.4
11.3
10.9
10.4
10.2C
9.9
8.2
7.4
7.3
7.3
7.2
6.1
6.1
5.8
5.7
5.5
5.4
5.4
5.1
5.1
5
Pb emissions are estimated to be 0.7 tpy.
2.2.5 Data Sources, Limitations and Confidence
The Pb emissions information presented in the previous sections is drawn largely from
EPA's NEI for 2002 (USEPA, 2007a). The NEI is based on information submitted from State,
Tribal and local air pollution agencies and data obtained during the preparation of technical
support information for EPA's hazardous air pollutant regulatory programs. EPA has recently
developed version 3 of the NEI for 2002 and that version is anticipated to be posted on the
EPA's CHIEF website soon at (http://www.epa.gov/ttn/chief/net/2002inventory.html). The
information presented in this document is based on version 3.
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The process of identifying sources that emit Pb into the air has been ongoing since before
the Clean Air Act of 1970. The comprehensiveness of emission inventories generally, and the
NEI, specifically, depends upon knowledge of source types emit Pb, their locations and their
operating characteristics, as well as the reporting of this information to the inventory. As noted
above, the NEI relies on information that is available from a variety of sources for this
information. There are numerous steps, each with its own uncertainties, associated with the
development of this information for use in the emissions inventory. First, the categories emitting
Pb must be identified. Second, the sources' processes and control devices must be known.
Third, the activity throughputs and operating schedules of these sources must be known. Finally,
we must have emission factors to relate emissions to the operating throughputs, process
conditions and control devices. The process, control device, throughputs and operating
schedules are generally available for each source. However, the emission factors represent
average emissions for a source type and average emissions may differ significantly from source
to source. In some cases, emissions testing provides source-specific information. In others,
emissions factors must be estimated from similar sources or source categories or other
information. More information on emission factors and the estimation of emissions is found in
the introduction to EPA's Compilation of Air Pollutant Emissions Factors (USEPA, 2006a).
Further information on emission factors is available at: http://www.epa.gov/ttn/chief/ap42/.
The NEI is limited with regard to Pb emissions estimates for some sources such as
resuspended road dust (Section 2.2.2.3), biomass burning and trace levels of Pb in motor fuel and
lubricating oil (Section 2.2.2.2), and others. We have not yet developed estimates for the NEI of
Pb emissions associated with resuspension of Pb residing in roadway dust and nearby surface
soil. Emissions estimates are also not yet in the NEI for the miscellaneous categories of on-road
emissions (e.g., combustion of fuel with Pb traces, lubricating oil, mechanical wear of vehicle
components, etc.) and Pb that may be emitted from wildfires.
The 2002 NEI underwent extensive 3-month external review, including a review of the
process for developing the inventory which includes extensive quality assurance and quality
control steps (QA/QC). For example, we created a QA/QC process and tracking database to
provide feedback reports to point source data providers at regular intervals during the QA of the
data. The feedback reports included the following 4 QC reports: data integrity,
latitude/longitudes QC, stack parameters QC, and emissions QC. Further, there was additional
QA/QC conducted for emission inventory information for facilities that are included in the Risk
and Technology Review (RTR) source categories (60FR14734). As a result we have strong
confidence in the quality of the data for these facilities. Version 3 of the 2002 NEI used in RTR
has undergone additional peer review and QA/QC based on comments received to Docket #
EPA-HQ-OAR-2006-0859.
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In summary, generic limitations to the 2002 NEI include the following:
1 Consistency: The 2002 NEI for Pb is a composite of emissions estimates generated by
state and local regulatory agencies, industry, and EPA. Because the estimates
originated from a variety of sources, as well as for differing purposes, they will in turn
vary in quality, whether Pb is reported for particular source types, method of reporting
compound classes, level of detail, and geographic coverage.
1 Variability in Quality and Accuracy of Emission Estimation Methods: The accuracy of
emission estimation techniques varies with pollutants and source categories. In some
cases, an estimate may be based on a few or only one emission measurement at a
similar source. The techniques used and quality of the estimates will vary between
source categories and between area, major, and mobile source sectors. Generally, the
more review and scrutiny given to emissions data by states and other agencies, the
more certainty and accuracy there is in that data.
2.3 AIR QUALITY MONITORING DATA
The EPA has been measuring Pb in the atmosphere since the 1970s. For the most part,
Pb concentrations have decreased dramatically over that period. This decrease is primarily
attributed to the removal of Pb from gasoline; however, some individual locations still have Pb
concentrations above the level of the NAAQS. The following sections describe the ambient Pb
measurement methods, the sites and networks where these measurements are made, as well as
how the ambient Pb concentrations vary geographically and temporally.
Ambient air Pb concentrations are measured by four monitoring networks in the United
States, all funded in whole or in part by EPA. These networks provide Pb measurements for
three different size classes of airborne particulate matter (PM): total suspended PM (TSP), PM
less than or equal to 2.5 um in diameter (PM^.s), and PM less than or equal to 10 um in diameter
(PMio). The networks include the Pb TSP network, the PM2 5 Chemical Speciation Network
(CSN), the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, and
the National Air Toxics Trends Stations (NATTS) network. The subsections below describe
each network and the Pb measurements made at these sites.
In addition to these four networks, various organizations have operated other sampling
sites yielding data on ambient air concentrations of Pb, often for limited periods and/or for
primary purposes other than quantification of Pb itself. Most of these data are accessible via
EPA's Air Quality System (AQS): http://www.epa.gov/ttn/airs/airsaqs/. In an effort to gather as
much air toxics data, including Pb, into one database, the EPA and State and Territorial Air
Pollution Program Administrators and the Association of Local Air Pollution Control Officials
(STAPPA/ALAPCO) created the Air Toxics Data Archive. The Air Toxics Data Archive can be
accessed at: http://vista.cira.colostate.edu/atda/.
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2.3.1 Ambient Pb Measurement Methods
A number of methods are used to collect Pb and measure Pb concentrations in the
atmosphere. Most methods use similar sample collection approaches. Ambient air is drawn
through an inlet for a predetermined amount of time (typically 24 hours) and the PM is collected
on a suitable filter media. After the sample has been collected, the filter may be used to
determine the mass of PM collected prior to then being used for determination of Pb. The filter
is chemically extracted and analyzed to determine the Pb concentration in the particulate
material. The concentration of Pb found in the atmosphere, in |ig/m3, is calculated based on the
concentration of Pb in the volume extracted, the size of the collection filter, and the volume of
air drawn through the filter.
The primary factors affecting the measurements made are the sampling frequency,
duration of sampling, type of inlet used, volume of air sampled, and the method of analyzing the
filter for Pb content. The following paragraphs describe how these factors affect the Pb
measurements.
2.3.1.1 Inlet Design
In ambient air monitors, a number of inlet designs have been developed that allow certain
particle size ranges to be sampled. The inlets use either impaction or cyclone techniques to
remove particles larger than a certain size (the size cutpoint) from the sample stream. Three
particle size cutpoints are used in ambient Pb measurements including TSP, PM2.s, PMi0. The
TSP inlet is designed to allow as much suspended particulate into the sampling device as
possible while protecting against precipitation and direct deposition on to the filter (nominally 25
to 45 micrometers) (USEPA, 2004c).
Sampling systems employing inlets other than the TSP inlet will not collect Pb contained
in the PM larger than the size cutpoint. Therefore, they do not provide an estimate of the total Pb
in the ambient air. This is particularly important near sources which may emit Pb in the larger
PM size fractions (e.g., fugitive dust from materials handling and storage).
2.3.1.2 Volume of Air Sampled
The amount of Pb collected is directly proportional to the volume of air sampled. Two
different sampler types have evolved for PM and Pb sampling - a high-volume and a low-
volume sampler. High-volume samplers draw between 70 and 100 mVhr of air through an 8 inch
by 10 inch filter (0.05 m2 filter area). Low-volume samplers typically draw 1 m3/hr through a 47
mm diameter filter (0.002 m2 filter area). Currently all Federal Reference Method (FRM) and
Federal Equivalence Method (FEM) for Pb-TSP are based on high-volume samplers.
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2.3.1.3 Sampling Frequency
The frequency of Pb sampling used in the U.S. varies between one sample every day (1 in
1 sampling) to the more common frequency of one sample every 6 days (1 in 6 sampling). Semi-
continuous methods for the measurement of ambient metals (including Pb) are currently being
explored which would allow for more frequent sampling (as frequent as 1 sample per hour), but
much more work is needed on these methods before they can be deployed in a network setting.
More frequent sampling reduces the uncertainty in estimates of quarterly or annual
averages associated with temporal variations in ambient concentrations. However, the costs of
sampling and analysis are directly tied to sample frequency. As such, it is necessary to evaluate
the reduction in measurement error versus the increase in sampling and analysis costs when
selecting the required sampling frequency. A discussion of the observed temporal variation of
Pb measurements is given later in this section.
2.3.1.4 Sample Analysis
After the samples have been collected on filters and the filters have been weighed, the
filters are analyzed for Pb content. A number of analytical methods can be used to analyze the
filters for Pb content including x-ray fluorescence analysis (XRF), proton-induced x-ray
emission (PIXE), neutron activation analysis (NAA), atomic absorption (AA), or inductively-
coupled plasma mass spectrometry (ICP/MS) (CD, pp. 2-80 to 2-81). A detailed discussion of
these methods was given in the 1986 CD (USEPA, 1986), and the reader is referred to that
document for more information on these analytical methods. A search conducted on the AQS
database2 shows that the method detection limits for all of these analytical methods (coupled
with the sampling methods) are very low, ranging from 0.01 ug/m3 to as low as 0.00001 |ig/m3,
and are more than adequate for determining compliance with the current NAAQS.
2.3.2 Pb-TSP
This network is comprised of state and locally managed Pb monitoring stations which
measure Pb in TSP, i.e., particles up to 25 to 45 microns. These stations use samplers and
laboratory analysis methods which have either FRM or FEM status. The FRM and FEM method
descriptions can be found in the U.S. Code of Federal Regulations, Section 40 part 50, Appendix
G. Sampling is conducted for 24-hour periods, with a typical sampling schedule of 1 in 6 days.
Some monitoring agencies "composite" samples by analyzing several consecutive samples
together to save costs and/or increase detection limits.
2 EPA's AQS can be accessed at http://www.epa.gov/ttn/airs/airsaqs/
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2.3.2.1 Monitor Locations
The locations of Pb-TSP sites in operation between 2003 and 2005 are shown in Figure
2-5. State and local agencies are required to operate two Pb-TSP monitors in any area which has
exceeded the NAAQS in the last two years (40 CFR 58 Appendix D). State and local agencies
have the latitude to operate more monitors beyond the minimum requirement. Agencies which
operate these sites report the data to EPA's AQS where they are accessible via several web-based
tools. EPA's series of annual air quality trends reports have used data from this network to
quantify trends in ambient air Pb concentrations. The most recent Trends report for Pb-TSP can
be found at http://www.epa.gov/airtrends/lead.html.
A review of the Pb-TSP network's coverage of the highest Pb emitting sources (as
identified in the current version of the 2002 NEI) was conducted as part of preparing this
document. This review indicates that many of the highest Pb emitting sources in the 2002 NEI
do not have nearby Pb-TSP monitors. This review indicates that only 2 of 27 facilities (both Pb
smelters3) identified as emitting greater than 5 tpy have a Pb-TSP monitor within 1 mile. The
lack of monitors near large sources indicates we are likely currently underestimating the extent
of occurrences of relatively higher Pb concentrations. Additionally, none of the 189 Pb-TSP
sites included in the 2003-2005 analysis described in Sections 2.3.2.3 and 2.3.2.4 are located
within a mile of airports identified in the NEI as an airport where piston-engine aircraft operate
(i.e., aircraft that still use leaded aviation fuel).4
3 Primary and secondary smelters were the source types given particular priority at the time of the last Pb
NAAQS review (USEPA, 1990; USEPA, 1991).
4 While there are limited historical data (going back to 1993) in AQS for 12 Pb-TSP monitoring sites
operating within one mile of 11 of these airports, time constraints have limited the extent of our analysis here of
these data or of other such data that may be available elsewhere.
2-20
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Figure 2-5. Pb-TSP monitoring sites: 2003-2005.
The number of sites in the Pb-TSP network has decreased significantly since the 1980s
(see Figure 2-6). The number of sites in the network reached its highest point in 1981 (946
sites). About 250 sampling sites operated during 2005. This decline in the number of Pb-TSP
sites is attributable to the dramatic decrease in Pb concentrations observed since the 1980s and
the need to fund new monitoring objectives (e.g., PM2.5 and ozone monitoring). Lead-TSP sites
in lower concentration areas were shut down to free up resources needed for monitoring of other
pollutants such as PM2.5 and ozone.
2-27
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Number of Pb-TSP Monitoring Sites
IUUU
Qfm
Rrm
firm -
cnn _
Arm
-inn
orm
irm
n -
^
\
- - \
\
\ /\
" ^^""^-x
\
Year
Figure 2-6. Change in the number of Pb-TSP monitoring sites from 1980 to 2005.
2.3.2.2 Historical Trend
Airborne concentrations of Pb in the United States have fallen dramatically over the last
30 years due largely to the phase-out of leaded gasoline additives. Figure 2-7 shows national
trends in airborne TSP Pb concentrations for two subsets of NAAQS FRM monitoring sites not
considered to be "source-oriented" (see Section 2.3.2.3.3), one for 1980 through 1989
(representing 168 sites), and the second for 1990 through 2006 (representing 44 sites). Two
separate graphs were used to characterize the overall (1980 through 2006) long-term trend due to
data limitations associated with a single graph; only 15 sites met the inclusion criterion for the
entire 27 year period. The data in both graphs are plotted in terms of the site-level maximum
quarterly arithmetic mean for each year (the form of the current NAAQS) and are shown in
relation to the current NAAQS of 1.5 ug/m3 (maximum quarterly average). The monitors used in
this analysis are typically population-oriented urban monitors that are not source-oriented. The
left plot shows an 86 percent decrease in national average maximum quarterly means from 1980
to 1989 and the right plot shows a 54 percent decrease in the same statistic from 1990 to 2006.
The combination of these equates to an overall decline of about 94 percent from 1980 to 2006.
The single, much smaller subset of sites that cover the full 27-year period (n=15) showed a 95
2-22
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percent decrease in the same metric from 1980 through 2006. Since the early 1980's, major
declines over several orders of magnitude have been observed not only in urban areas, but also in
rural regions and remote locations. The sharp decline through the 1980s has also been observed
in Pb associated with fine particles (less than or equal to 2.5 microns) at remote and rural sites
throughout the United States and have been attributed to the phase out of leaded gasoline (Eldred
and Cahill, 1994).
g
5
O
l.O
1.4
1.2
1
0.8
0.6
0.4
0.2
8
National Standard 168 sites
90% of sites
^^y below this line
^**v Average
N _./^ 1 0% of sites
^/ *Sw below this line
Jxd
^^r ** • — __
^~~~
0 81 82 83 84 85 86 87 88 8
1.0 -
,-,1-4
i, 1-2-
^ 1 "
|0.8-
1 0-6-
O
c 0 4 -
o
o «* •
0.2-
9 9
National Standard 44 sites
Averaae ^ percent of sites are below this line.
~~ — ^^^ L— -^_ — — -
•
0 92 94 96 98 00 02 04 Of
1980 to 1989: 86% decrease
1990 to 2006: 54% decrease
Figure 2-7. Airborne Pb -TSP concentrations, averaged across continuously operating
monitoring sites: 1980-1989 and 1990-2006. (Sources: left plot - AQS data
extracted 10/10/07; right plot - EPA 2007.)
2.3.2.3 Data Analysis Details
Lead-TSP data collected in 2003-2005 (parameter code 12128, durations '7' and 'C')
were extracted from EPA's AQS on May 22, 2007. Most of the monitors reporting data for that
timeframe utilized FRM or FEM, and therefore, are candidates for comparisons to the NAAQS.
Some of the Pb-TSP monitors, however, were placed for nonregulatory purposes (e.g., for toxics
monitoring initiatives) and utilize methods other than a FRM or FEM. Although measurements
from these monitors cannot be compared to the NAAQS for purposes of non-attainment
decisions, they were considered worthy for inclusion in this national Pb-TSP characterization.
The non-FRM/FEM Pb-TSP methods typically have lower reported uncertainties and detection
limits than the FRM/FEM. Detection limits vary somewhat even for the data generated using
FRM or FEM. In summary aggregations, the AQS generally substitutes one half the method
detection level (MDL) for reported concentration readings less than or equal MDL. That
protocol was not utilized in this national aggregation; data were used 'as reported' to AQS. Only
a small number of Pb-TSP measurements for 2003-2005 were flagged for exceptional events
(e.g., structural fires, chemical spills, sandblasting); none of the exceptional event flag-flagged
data, however, were concurred (i.e., approved) by the associated EPA Regional Office. Data
flags were ignored in this analysis.
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2.3.2.3.1 Screening Criteria
Measurements of Pb-TSP with 24-hour sample collection duration were reported to AQS
for more than 350 monitors for the years 2003 to 2005. 189 of those monitors met the following
screening criteria and were used in this national characterization. The completeness criteria
employed for this national characterization were: 1) a minimum of 10 observations per quarter,
2) for at least one full year (all 4 quarters), and 3) at least 9 months with 4 observations each5; all
three criteria had to be met for inclusion. 209 monitors met the three-pronged criteria; of these
209 monitors, 20 were collocated with another complete monitor. Only one monitor from each
collocated pair (i.e., from each site location) was kept in the analysis, specifically the one with
highest 3-year maximum quarterly mean. Thus, data from 189 monitors at 189 distinct locations
were actually used; 109 of these monitors/sites had 3 complete years, 36 monitors/sites had two
complete years, and 44 monitors/sites had only one compete year. Complete quarters that were
not part of a complete year were used. Likewise, all complete months were used, even if they
did not correspond to the complete years. The 189 sites have an average of about 10 complete
quarters and 28 complete months. The 189 utilized monitors are listed along with various
summary and demographic data in Appendix 2B, Tables 2B-1 and 2B-2.
2.3.2.3.2 Urban Sites
The 189 monitors are located in 86 counties, in 23 states. 140 of the 189 sites were
deemed 'urban' and aggregated as such. Sites were labeled 'urban' if they located within a
defined urbanized area or urban cluster (per 2000 Census geographic definitions). All of the
'urban' designated sites were located in a Core Based Statistical Area (CBSA) per 2003 CBSA
geographic definitions. CBSA is a collective term for both metropolitan and micropolitan
statistical areas. A metro area contains a core urban area of 50,000 or more population, and a
micro area contains an urban core of at least 10,000 (but less than 50,000) population. Each
metro or micro area consists of one or more whole counties and includes the counties containing
the core urban area, as well as any adjacent counties that have a high degree of social and
economic integration with the urban core. The monitors in the analysis map to 65 unique
CBSA's. Only 10 of the 189 monitors are not located within a CBSA. CBSA's do not always
exclusively encompass wholes or parts of urbanized areas and/or urbanized clusters. 39 of the
189 Pb monitoring sites are located in a CBSA but are not classified as 'urban'. Although
'urban' locations (i.e., parts of urbanized areas or urban clusters) are found in counties not
defined as (or part of) a CBSA, all of the 140 urban sites in this characterization are located in a
5 Quarterly means calculated with less than ten observations, annual means calculated with only three
quarters, and monthly means derived with less than four observations were also considered valid if that mean value
exceeded the level of the current standard (i.e., 1.5 ug/m3for quarterly mean).
2-24
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CBS A. 91 of the 140 urban sites are located in CBSA's with one million or greater population.
Note that the 65 CBSA's containing the Pb-TSP monitoring sites are generally among the largest
in the nation (with respect to total population). Almost 75 percent of the Pb-TSP CBSA's are
larger (in population) than the highest-population 75 percent of all U.S. CBSA's. With respect to
total CBS A population, the five overall largest CBSA's and 18 of the largest 25 contain at least
one Pb-TSP monitor.
2.3.2.3.3 Source-oriented Sites
Monitoring sites were classified as being "source oriented" with regard to sources of Pb
emissions if: 1) they met a graduated (or sliding scale of) cumulative emission ton per year by
distance criterion, or 2) they were classified as source oriented in previous EPA analysis. Sixty
of the 189 Pb-TSP sites met at least one of these criteria. Of the 60 total source-oriented sites, 40
met the first criterion and 51 met the second.
The graduated cumulative emission ton per year to distance criterion (criterion #1)
utilized the 2002 (version 3) national emission inventory (NEI) for Pb point sources and Pb area
nonpoint sources. The Pb point source emissions were assigned to the specific facility point
locations (longitude/latitude coordinates), and the area nonpoint inventory was allocated to
Census tracts and based on an assumed uniform distribution across those extents. To meet the
graduated 'source-oriented' criterion, a Pb monitoring site had to be within at least one multiple
of 0.1 miles (checking up to 1 mile away) for a corresponding multiple of 0.1 tpy of total point
and nonpoint emissions (e.g., Within 0.1 mile of a cumulative 0.1 tpy, within 0.2 miles of a
cumulative 0.2 tpy, within 0.3 miles of a cumulative 0.3 tpy, ..., or within 1.0 miles of a
cumulative 1.0 tpy). The area nonpoint contribution to the comparison cumulative inventory was
based on the composite emission densities of the Census tract in which a site was located and all
other tracts with population centroids within a mile of the monitoring site.
The sites 'classified as source oriented in previous EPA analysis' (criterion #2) were
identified via a reference list that was last updated in 2003; this list has been utilized in recent
EPA Trends Report analysis. The list encompasses 114 sites. Many of the monitoring sites on
this list did not have data that met the data completeness criteria for 2003-2005 because they
have permanently discontinued Pb monitoring, most ostensibly because the associated nearby Pb
emission source(s) has implemented controls, closed operations, and/or reduced production.
Some ambient monitoring sites continue monitoring even after significant reported reductions in
nearby new Pb emissions. Sites were not screened out of the source-oriented classification in
those instances. In addition to including such sites in the source-oriented category, these sites
were separately reviewed to see if they still had higher concentrations than nonsource sites
because of previously emitted Pb becoming resuspended into the air and/or possible emission
2-25
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estimate errors. These sites are termed, "previous source-oriented sites" in relevant figures and
tables.
There are only nine sites that were categorized as "previous" source-oriented in this
national analysis. The particular circumstances related to the emission sources associated with
these nine monitoring sites vary considerably. In some instances the emission sources have been
closed for more than a decade and the facility locations have undergone remediation. For other
sources, production and clean-up status was not fully ascertained. In the case of one emission
source (that has numerous nearby monitoring sites), production was reportedly halted at the end
of 2003 and no significant clean-up activity has yet been undertaken. For the monitoring sites
associated with this source, two sets of statistics were generated (or attempted). Statistics
representing the entire 3-year period were calculated and used everywhere applicable except for
the "previous" category, and statistics representing the post-production period (2004-2005) were
generated and used for the "previous" classification. Note that some of these monitoring sites
met the data completeness criteria for the 3-year period (2003-2005) but not for the 2-year period
(2004-2005). Because of the small number of sites included in the "previous" source oriented
classification and the uncertainty in the emission source status, results for this category should be
viewed with caution.
2.3.2.3.4 Population Associations
Two population statistics were summarized with the Pb concentration data, the "total
population" within 1 mile of the site (a.k.a., a "radial mile") and the "under age 5 population"
within 1 mile of the site. Populations assigned to sites were based on Census block group
population densities, specifically the density of the block group in which the site was located and
(if relevant) the density of other block groups with population centroids within 1 mile of the site.
The average population density across these blocks (expressed in square miles) was multiplied
by pi (3.142) to obtain a radial mile population (i.e., the number of people living within a one
mile radius of the monitoring site). Population data and block group definitions utilized are from
the 2000 Census.
The median size of populations associated with the Pb-TSP monitors in this analysis is
about 6,200 and the corresponding under age 5 median population is around 420. These median
populations are slightly smaller than the overall U.S. block group median radial mile populations
(19 percent smaller for total and 7 percent smaller for under age 5). Appendix 2B, Table 2B-1
shows the assigned site-level populations; CBSA information for each site is also shown. Based
on the radial mile population association (described above) approximately 1.73 million people
(0.125 million under the age of 5) are in proximity of a 2003-2005 Pb-TSP monitor included in
this analysis.
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2.3.2.3.5 Statistical Metrics
Four basic statistics were computed for the 2003-2005 Pb-TSP concentration data: annual
means, maximum quarterly means, maximum monthly means, and second maximum monthly
means. These metrics were calculated at the site level. They were calculated both for the overall
3-year period (2003-2005) and for each of the three individual years (2003, 2004, and 2005).
The former set of statistics (representing the overall 3-year period) were the general focus of the
analysis, and unless otherwise stated, figures, maps, and text should be assumed to be that type.
Note that the 3-year annual mean statistic is actually the average of the annual means for the
complete years; thus it is the average of three annual means, the average of two annual means, or
the only available single complete annual mean. Annual means were computed from quarterly
means. The 3-year maximum quarterly mean statistic represents the highest quarterly mean of
the complete ones (sites have from four to 12 complete quarters), and the 3-year maximum
monthly mean represents the highest monthly mean of the complete ones (each site has from
nine to 36 complete months). The 3-year second maximum monthly mean represents the highest
second highest monthly mean of the complete ones. Two additional 3-year metrics were also
calculated but, like the individual year statistics for the four basic metrics, utilized sparingly.
These two metrics are 1) the average of the three overall highest monthly means for the 3-year
period (year nonspecific), and 2) the average of the annual maximum monthly means.
Population weighted means were also calculated for the four basic metrics for various
aggregation levels. The site-level means were weighted by total population. To compute the
population weighted measures, 1) the mean for each site in a specific category was multiplied by
its associated population (i.e., within a mile radius), 2) these products (of #1) and the associated
populations were summed, and 3) the sum of the products of #1 were divided by the population
sums. Theoretically, these population weighted means show the average outdoor concentration
exposure for each individual within a mile of a monitoring site. That supposition, of course,
assumes that concentrations reported at the monitor are uniform over the entire radial mile.
2.3.2.4 Current Concentrations
In the following subsections, analyses are presented for the different categorizations of
Pb-TSP monitoring sites described above. These categories include "all Pb-TSP sites meeting
screening criteria", and the following subsets: sites in urban areas, sites in urban areas of
population greater than 1 million, sites that are source-oriented, sites that are not known to be
source-oriented, and sites that were previously source-oriented.
The site-level Pb-TSP concentrations for all computed statistics are shown in Appendix
2B, Tables 2B-1 and 2B-2. The distributions of sites for the four basic (3-year) statistics (annual
mean, maximum quarterly mean, maximum monthly mean, and second maximum monthly
2-27
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mean) and the two additional 3-year statistics (average of three overall highest monthly means
and average of 3 annual maximum monthly means) are shown in Figure 2-8; the boxes depict
inter-quartile ranges and medians, whiskers depict the 5th and 95th percentiles, and asterisks
identify composite averages. Additional points on the distributions for these statistics are given
in Appendix 2B, Table 2B-3. For example, the national composite average annual mean was
0.09 ug/m3, and the corresponding median annual mean was 0.02 ug/m3. The national composite
average maximum quarterly mean was 0.17 ug/m3 and the corresponding median maximum
quarterly mean was 0.03 ug/m3. The national composite average maximum monthly mean was
0.31 ug/m3 and the median maximum monthly mean was 0.04 ug/m3. The national composite
average second maximum monthly mean was 0.21 ug/m3 and the median value was 0.03 ug/m3.
The national composite average of the mean of the three overall highest monthly averages was
0.31 ug/m3 and the median value was 0.04 ug/m3. The national composite average of the mean
of the annual highest monthly means was 0.21 ug/m3 and the median value was 0.03 ug/m3.
2-25
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o
.
s
o
g
o
1 .4
1 .3
1 ,2
1 .1
1 ,(H
Annual mean Max quarterly Max monthly 2nd max monthly Average of 3 Average of annual
mean mean mean overall max max monthly
monthly means means
Figure 2-8. Distribution of Pb-TSP concentrations (represented by 6 different statistics)
at the 189 Pb-TSP monitoring sites, 2003-2005.
Figure 2-9 shows cumulative percentages of total monitored populations associated with
each of the four Pb metrics for various levels [> 0.02 |ig/m3, > 0.05 |ig/m3, > 0.20 |ig/m3, > 0.50
|ig/m3, and > 1.54 jig/m3]. Note that site statistics were rounded to 2 decimal places before
comparing to stated levels. The phrase "monitored populations" refers to the number of people
residing in proximity to these 189 monitors as described in Section 2.3.2.3.4. The site-level
values for the four statistical metrics (annual average, maximum quarterly mean, maximum
quarterly mean, and second maximum monthly mean) are mapped in Figures 2-19 through 2-13.
As seen when comparing these figures, the geographic locations of the high (and low)
concentration values for all three metrics are generally the same. In fact, there are significant
correlations among all four 3-year (2003-2005) summary metrics; see Appendix 2B, Table 2B-4.
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70%
e
.X oUyo
_g
§• 50%
e.
•d
J; 40%
5
o 30% -
«w
^ 20%
1
3 lUyo
D annual mean • max quarterly mean D max monthly mean D 2nd max monthly mean
j|\!
i
^
(r\-
,V
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9
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#-1 '
JTV^
A^B A^L4!!
H ^ ^^- o,,^V
0% i i ii
>0.02 >0.05 >0.20 >0.50 > 1.54
jig/m3
Figure 2-9. Percentages of Pb-TSP monitored populations residing in areas exceeding
various concentrations (for 4 different statistics), 2003-2005.
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Concentration range
(Hg/m3)
O
•
>1.
0.50-
0.20-
0.05-
<0.
55
1.55
0.50
0.20
05
Figure 2-10. Pb-TSP annual means (for all sites), 2003-2005.
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Concentration range
(ug/m3)
>1.55
* 0.50-1.55
0.20-0.50
0.05-0.20
< 0.05
Figure 2-11. Pb-TSP maximum quarterly means (for all sites), 2003-2005.
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Concentration range
(ug/m3)
> 1.55
0 0.50-1.55
« 0.20-0.50
0.05-0.20
* <0.05
o
Figure 2-12. Maximum monthly Pb-TSP means (all sites), 2003-2005.
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Concentration range
(ug/m3)
> 1.55
• 0.50-1.55
• 0.20-0.50
0.05-0.20
* <0.05
Figure 2-13. Second maximum monthly Pb-TSP means (all sites), 2003-200
The site-level ratios of 1) maximum quarterly mean to annual mean, 2) maximum
monthly mean to annual mean, and second maximum monthly mean to annual mean are
presented in Appendix 2B, Table 2B-5. For all TSP-Pb sites included in the analysis, the
national median for the ratio of site-level maximum quarterly average to site-level annual mean
was about 1.8; the national median for the ratio of site-level maximum monthly mean to site-
level annual mean was about 2.8; and the national median for the ratio of site-level second
maximum monthly mean to site-level annual mean was about 2.1.
2.3.2.4.1 Source-oriented Sites
As seen in the previously discussed Figure 2-8, the national ("all sites") means are
substantially higher than the national medians for all four statistical metrics (annual mean,
maximum quarterly mean, maximum monthly mean, and second maximum monthly mean).
This is due to a small number of monitors with significantly higher levels. These monitors with
higher concentrations are almost exclusively associated with industrial point sources.
2-34
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Eliminating the source-oriented monitors from the national aggregations lowers most of the
corresponding distribution statistics and makes the means more comparable to the medians.
The distributions of the site-level metrics for the source-oriented sites, the non-source-
oriented sites, and the "previous" source-oriented sites, are presented in Figures 2-14, 2-15, and
2-16, respectively. For comparison purposes, Figures 2-17 through 2-20 present the categorical
data distributions for each of the four statistical metrics on the same scales. In all of these
figures, the boxes depict inter-quartile ranges and medians, whiskers depict the 5th and 95th
percentiles, and asterisks identify composite averages. Additional points on the distributions of
these statistical metrics for these three categories of monitoring sites are given in Appendix 2B,
Table 2B-3. The medians, means, and population-weighted means of the site-level values of the
three statistical metrics are presented in Figure 2-21 for the source-oriented and other groupings
of monitoring sites.
Per Figure 2-18, the median maximum quarterly mean for source-oriented sites (0.25
|ig/m3) is about 14 times greater than the same statistic for non-source-oriented sites (0.02
|ig/m3); in fact, the median (50th percentile) maximum quarterly mean for non-source-oriented
sites is approximately the same value as the 5th percentile for source-oriented sites. Almost 95
percent of all monitors identified as being source-oriented had a maximum quarterly average of
0.02 ug/m3 or more, and over 25 percent had maximum quarterly average of 0.50 ug/m3 or more.
2-35
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4"
3-
S
o
o
O
Annual mean
Max quarterly
mean
Max monthly
mean
2nd max monthly
mean
Figure 2-14. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the source-oriented monitoring sites, 2003-2005.
2-36
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o
(L)
O
O
0,26^
0.25-
0.24-
0.23-
0.22-
0.21-
0.20 =
0.19-
0.18-
0.17-
0.16-
O!H-
0.13-
0.12-
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n nn-
*
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Annual mean
Max quarterly
mean
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mean
2nd max monthly
mean
Figure 2-15. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the non-source-oriented monitoring sites, 2003-2005.
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0
0.7
0.
0.5-
0.4-
0.3
0.2
0.
o.
Annual mean Max quarterly
mean
Max monthly 2nd max monthly
mean mean
Figure 2-16. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the nine monitoring sites near previous large emission sources, 2003-2005.
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1 .0-
0.9-
0 .8-
0 .7-
m
1, 0.6-
J 0.5-
2
8 0.4-
o
O
0.3-
0.2-
o.i-
o .0-
*
i
, J , , [ | l^^^^l i * »
f [ I 1 1 1 1
All sites Source- Not source Previous Urban Urban CBSA Urban CBSA
(189 sites) oriented oriented source- (140) pop > 1M pop < 1M
(60) (129) oriented (9) (91) (49)
Figure 2-17. Distribution of Pb-TSP annual mean concentrations at different categories of sites, 2003-2005.
2-39
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1 .8-
1 .7-
1.6-
1 .5-
1 .4-
1 .3-
^ 1.2-
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-1 1.1-
3 1.0-
Concentration
o o o o
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, 1 ,
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All sites Source- Not source Previous Urban Urban CBSA Urban CBSA
(189 sites) oriented oriented source- (140) pOp > 1M pop < 1M
(60) (129) oriented (9) (91) (49)
Figure 2-18. Distribution of Pb-TSP maximum quarterly mean concentrations at different categories of sites, 2003-2005.
2-40
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^i
o
1
"H
o
o
O
4 .0
3.5-
3.0-
2.5
2 .0
1 .5
1 .0
0.5
0.0
All sites
(189 sites)
Source- Not source
oriented oriented
(60) (129)
Previous
source-
oriented (9)
Urban
(140)
Urban CBSA
pop. > 1M
(91)
Urban CBSA
pop. < 1M
(49)
Figure 2-19. Distribution of Pb-TSP maximum monthly mean concentrations at different categories of sites, 2003-2005.
2-41
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.1
^i
o
1
"H
o
o
O
2 .5
2 .0-
1 ,5
1 .0-
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All sites
(189 sites)
Source-
oriented
(60)
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oriented
(129)
Previous
source-
oriented (9)
Urban
(140)
Urban CBSA
pop. > 1M
(91)
Urban CBSA
pop. < 1M
(49)
Figure 2-20. Distribution of Pb-TSP second maximum monthly mean concentrations at different categories of sites, 2003-
2005.
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Annual Mean
•
i
al sites
noun -
U.oUU "
0.700 -
0600-
ii ^iiii -
(i/inn
0300 -
,-
dmean
• median
D population weighted mean
-
r If lr[h
source- non source- previous urban sites urban sites urban sites
oriented oriented source- in CBSAs > inCBSAs
sites sites oriented IMpop. inCBSAs
sites sites oriented IMpop. in CBSAs
sites sites oriented 1M pop. <1M pop.
n find
n son
0.400 -
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2nd Max Monthly Mean
r
i
~
D mean
B median
n population weighted mean
]
rn i n
rfl rtl r 1 Jl
all sites source- non source- previous urban sites urban sites urban sites
oriented oriented source- in CBSAs > inCBSAs
sites sites oriented 1M pop. <1M pop.
Figure 2-21. Medians, means, and population-weighted means for 4 site-level statistics. (All
y-axes are in concentration units of jig/m3).
Although 60 Pb-TSP monitoring sites met the source oriented classification criteria, that
number does not correspond to the number of represented or "covered" sources of significant
emissions. Recall that the emissions sliding scale was based on the aggregate emissions within
one mile of the site (see Section 2.3.2.3.3). Thus, instead of having only one significant source
within a specified range, a site tagged as source oriented could actually have several nearby
moderate-sized emission sources and/or many nearby small sources. However, the majority of
the source-oriented sites in this national analysis do have just one nearby significant emission
source. Furthermore, many of these significant emission sources have multiple Pb-TSP monitors
2-43
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in the vicinity. For example, the Herculaneum primary Pb smelter has 7 nearby Pb-TSP
monitoring sites that are included in this national characterization (as well as others that operated
during 2003-2005 but that did not meet the screening criteria). Thus, the 60 source-oriented sites
really represent fewer than 60 significant emission sources. For the 60 source-oriented sites,
there are only 37 unique closest emission sources (i.e., NEI site ID's). The 60 source-oriented
sites are located in 29 different counties.
Although the "previous" source-oriented category contains only a limited number of sites
(nine) with varied and undetermined circumstances, the distribution statistics for that category
(for all three metrics) are generally much higher than the non-source-oriented levels; for
example, the "previous" median maximum quarterly mean of 0.10 |ig/m3 is more than five times
higher than the comparable non-source-oriented level of 0.02 |ig/m3.
2.3.2.4.2 Urban Sites
The distributions of the site-level values for the four statistical metrics for the set of 140
sites classified as "urban" are presented in Figure 2-22. The distributions for the subset of sites
(n = 91) located in a CBS A with one million or more population are presented in Figure 2-23,
and for the subset of sites (n=49) located in a CBSA with less than a million population, in
Figure 2-24. In these figures, the boxes depict inter-quartile ranges and medians, whiskers depict
the 5th and 95th percentiles, and asterisks identify composite averages. Additional points on the
distributions for these statistics for these three groupings of monitoring sites are given in
Appendix 2B, Table 2B-3.
Previously mentioned Figures 2-17 through 2-20 plot on uniform scales the four
statistical metrics for these three categories of urban sites. The median and mean values for all
three concentration metrics are lower for sites in less populated CBSA's than they are for sites in
high population CBSA's. Figure 2-25 shows cumulative percentages of urban monitored
populations ("total) associated with each of the three Pb metrics for various concentration ranges
[> 0.02 |ig/m3, > 0.05 |ig/m3, > 0.20 |ig/m3, > 0.50 |ig/m3, and > 1.54 |ig/m3]. The phrase
"monitored populations" refers to the number of people residing in proximity to monitors as
described in Section 2.3.2.3.4. Figure 2-25, for urban monitored populations, resembles Figure
2-9 (for all monitored populations) because the large majority of the monitored population
resides in urban areas.
2-44
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o
o
O
O.1
0.81
0.7
0.5
0.4
0.3
0.2-1
0.1
0.0
Annual mean Max quarterly Max monthly 2nd max monthly
mean mean mean
Figure 2-22. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the 140 urban monitoring sites, 2003-2005.
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2.0-
1.9-
1.8-
1.7-
1.6-
1 .5-
1 ,4-
mg 1.3-
^ 1 .2-
^~^ 1 1 '
s l.l
1 1.0-
1 0-9-
1 0.8-
0 0.7-
0.6-
0.5-
0.4-
0.3-
0.2-
0.1-
0.0^
s
^=
;= d
^
E
b
Annual mean Max quarterly Max monthly 2nd max monthly
mean
mean
mean
Figure 2-23. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the 91 urban monitoring sites located in metropolitan areas (CBSAs) with 1
million or more population, 2003-2005.
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0.71
s
-t—»
o
o
0.5
0.4
0.3-
0.2
0.
0.
Annual mean
Max quarterly
mean
Max monthly
mean
2nd max monthly
mean
Figure 2-24. Distribution of Pb-TSP concentrations (represented by 4 different statistics)
at the 49 urban monitoring sites located in CBSA's with less than 1 million
population, 2003-2005.
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D annual mean • max quarterly mean D max monthly mean 0 2nd max monthly mean
>0.02
>0.05
>0.20
jig/m
>0.50
> 1.54
Figure 2-25. Percentages of Pb-TSP urban monitored populations residing in areas (represented by 4 different statistics)
exceeding various levels. (Note: Site statistics were rounded to 2 decimal places before comparing to stated levels.)
2-48
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2.3.2.5 Variability
Some seasonal variability related to meteorology is common for air Pb concentrations.
The extent to which seasonal variability associated with meteorological conditions is present for
a given area depends on precipitation trends, wind direction patterns, and mixing height
fluctuations. For monitors situated near Pb point sources, aspects related to the facilities'
operations also contribute to temporal variability. These same factors, weather and emissions
(location and magnitude), also contribute to spatial variability, as do other features such as
topography. Many monitors that are not source oriented exhibit no discernable seasonal pattern
because their concentration levels are so low; over 57 percent of the 129 non-source-oriented
sites had over 50 percent of their 2003-2005 raw concentration data at levels equal to or less than
the associated MDL. Note that some data reporting agencies erroneously submit substituted
values (e.g., zero or half the minimum detection limit) for data less than or equal the MDL
instead of the requested actual quantifiable value.
Temporal variability in Pb-TSP concentrations, especially near source locations, is better
characterized by short-term averaging times (e.g., monthly) than longer-term averaging times
(e.g., yearly or quarterly). This is demonstrated in Tables 2-6 and 2-7. These tables show the
number of TSP monitors, in the "all sites" database and the urban site subset, that exceeded
various concentration levels between 0.02 and 1.5 |ig/m3 with averaging times or forms of
maximum quarterly, maximum monthly, and second maximum monthly For example, with a
stated level equal to the current standard of 1.5 |ig/m3 (actually 1.54 |ig/m3 per rounding
protocol) and a 3-year evaluation window (i.e., the first table subset), 3 sites in 3 counties (1
urban site) exceeded on a quarterly averaging basis and 11 sites in 6 counties (5 urban sites in 2
counties) exceeded on a maximum monthly basis. At the lowest level examined, 0.02 |ig/m3,
107 sites in 47 counties (75 urban sites in 39 counties) exceeded that level on a maximum
quarterly average basis and 127 sites in 59 counties (94 urban sites in 50 counties) exceeded that
level on a maximum monthly average basis.
The four additional table subsets in Tables 2-6 and 2-7 are results for 1-year evaluations;
each of the three component years (2003, 2004, 2005) are shown individually plus an average of
the three years is provided. Almost all of the 3-years results are greater than the 1-year results
(mathematically they could not be less), indicating year-to-year variability. For example, at the
1.55 |ig/m3 concentration level with a maximum quarterly average statistic, 3 sites exceeded
using the 3-year window but only one site per year (actual and on average) exceeded with the 1-
year window. At the 0.02 |ig/m3 concentration level and same statistic, 107 sites exceeded using
the 3-year window but only 84 sites on average exceeded with a 1-year window.
2-49
-------
Table 2-6. Comparison of number of sites that exceed various Pb-TSP levels using
different averaging times or forms, 2003-2005
3-year statistics, 2003-2005
Level
0.02
0.05
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.54
Maximum Quarterly Mean
All Sites
(189 in 86 counties)*
Sites
107
74
49
35
25
19
18
17
13
11
9
7
3
Counties
47
35
21
15
11
8
8
7
6
6
6
5
3
Urban Sites
(140 in 73 counties) *
Sites
75
47
26
17
10
7
6
6
5
5
4
4
1
Counties
39
29
15
10
6
3
2
2
2
2
2
2
i
Maximum Monthly Mean
All Sites
(189 in 86 counties)*
Sites
127
85
64
49
37
30
27
26
22
19
19
15
11
Counties
59
40
31
21
16
12
12
11
10
9
9
7
6
Urban Sites
(140 in 73 counties)*
Sites
94
55
38
26
17
14
11
10
9
7
7
6
5
Counties
50
33
25
16
11
8
7
6
5
3
3
2
2
2nd Maximum Monthly Mean
All Sites
(189 in 86 counties)*
Sites
110
71
54
42
29
26
20
16
15
15
13
9
4
Counties
50
34
24
19
12
11
9
7
7
7
5
5
3
Urban Sites
(140 in 73 counties) *
Sites
78
44
28
22
12
11
9
6
5
5
5
4
2
Counties
42
28
17
13
7
6
5
2
2
2
2
2
1
1-year statistics, 2003
Level
0.02
0.05
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.54
Maximum Quarterly Mean
All Sites
(175 in 83 counties) *
Sites
90
60
39
28
20
15
15
12
8
6
4
2
1
Counties
41
27
15
12
10
7
7
7
6
5
3
2
1
Urban Sites
(131 in 70 counties)*
Sites
63
35
19
12
7
4
4
2
2
1
1
1
0
Counties
35
21
9
7
5
2
2
2
2
1
1
1
0
Maximum Monthly Mean
All Sites
(177 in 83 counties) *
Sites
103
73
50
36
29
25
20
18
16
15
14
11
5
Counties
46
34
23
15
12
11
10
8
8
8
7
6
4
Urban Sites
(133 in 71 counties) *
Sites
76
48
28
17
13
11
7
5
5
4
4
3
1
Counties
40
28
17
10
8
6
5
3
3
3
3
2
1
2nd Maximum Monthly Mean
All Sites
(176 in 83 counties) *
Sites
89
56
40
29
24
20
15
14
12
9
7
4
2
Counties
42
26
15
11
9
9
7
7
6
5
4
3
2
Urban Sites
(132 in 71 counties)*
Sites
62
31
19
13
9
7
4
4
3
1
1
1
1
Counties
36
20
9
8
5
4
2
2
2
1
1
1
1
1-year statistics, 2004
Level
0.02
0.05
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.54
Maximum Quarterly Mean
All Sites
(166 in 77 counties) *
Sites
82
53
37
21
14
13
12
11
10
8
6
6
1
Counties
39
26
18
11
7
6
6
5
5
4
4
4
1
Urban Sites
(127 in 68 counties) *
Sites
56
31
22
12
8
7
6
6
5
5
4
4
0
Counties
32
20
12
6
4
3
2
2
2
2
2
2
0
Maximum Monthly Mean
All Sites
(169 in 77 counties)*
Sites
100
63
46
30
21
15
15
14
13
11
11
10
7
Counties
50
30
23
16
11
8
8
7
6
5
5
5
4
Urban Sites
(130 in 6 8 counties)*
Sites
73
39
27
18
11
8
8
7
7
6
6
6
5
Counties
42
24
17
10
6
4
4
3
3
2
2
2
2
2nd Maximum Monthly Mean
All Sites
(168 in 77 counties) *
Sites
90
54
37
22
14
14
12
12
10
10
10
6
3
Counties
44
26
17
10
7
7
6
6
5
5
5
4
2
Urban Sites
(129 in 68 counties) *
Sites
64
32
21
13
8
8
6
6
5
5
5
4
2
Counties
37
20
11
6
4
4
2
2
2
2
2
2
1
2-50
-------
Table 2-7. Comparison of number of sites that exceed various Pb-TSP levels using
different averaging times or forms, 2003-2005 - continued.
1-year statistics, 2005
Level
0.02
0.05
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.54
Maximum Quarterly Mean
All Sites
(157 in 76 counties)*
Sites
80
52
35
19
15
10
8
7
5
5
5
5
1
Counties
39
27
17
10
8
6
6
5
4
4
4
4
1
Urban Sites
(122 in 67 counties) *
Sites
57
35
23
12
8
5
3
3
3
3
3
3
1
Counties
33
23
13
7
4
3
2
2
2
2
2
2
i
Maximum Monthly Mean
All Sites
(157 in 76 counties)*
Sites
93
60
46
32
22
18
16
14
11
8
8
6
3
Counties
45
31
23
16
12
9
9
9
7
6
6
5
3
Urban Sites
(122 in 67 counties)*
Sites
70
40
30
21
14
11
9
7
5
3
3
3
1
Counties
39
26
19
13
9
6
5
4
3
2
2
2
1
2nd Maximum Monthly Mean
All Sites
(157 in 76 counties)*
Sites
83
53
34
26
16
13
8
8
8
6
5
5
1
Counties
42
27
17
15
8
7
5
5
5
4
4
4
1
Urban Sites
(122 in 67 counties) *
Sites
60
36
22
17
8
7
4
4
4
3
3
3
1
Counties
36
22
12
11
4
4
2
2
2
2
2
2
1
Average of 3-year statistics, 2003-2005
Level
0.02
0.05
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.54
Maximum Quarterly Mean
All Sites
Sites
84.0
55.0
37.0
22.7
16.3
12.7
11.7
10.0
7.7
6.3
5.0
4.3
1.0
Counties
39.7
26.7
16.7
11.0
8.3
6.3
6.3
5.7
5.0
4.3
3.7
3.3
1.0
Urban Sites
Sites
58.7
33.7
21.3
12.0
7.7
5.3
4.3
3.7
3.3
3.0
2.7
2.7
0.3
Counties
33.3
21.3
11.3
6.7
4.3
2.7
2.0
2.0
2.0
1.7
1.7
1.7
0.3
Maximum Monthly Mean
All Sites
Sites
98.7
65.3
47.3
32.7
24.0
19.3
17.0
15.3
13.3
11.3
11.0
9.0
5.0
Counties
47.0
31.7
23.0
15.7
11.7
9.3
9.0
8.0
7.0
6.3
6.0
5.3
3.7
Urban Sites
Sites
73.0
42.3
28.3
18.7
12.7
10.0
8.0
6.3
5.7
4.3
4.3
4.0
2 3
Counties
40.3
26.0
17.7
11.0
7.7
5.3
4.7
3.3
3.0
2.3
2.3
2.0
1.3
2nd Maximum Monthly Mean
All Sites
Sites
87.3
54.3
37.0
25.7
18.0
15.7
11.7
11.3
10.0
8.3
7.3
5.0
2.0
Counties
42.7
26.3
16.3
12.0
8.0
7.7
6.0
6.0
5.3
4.7
4.3
3.7
1.7
Urban Sites
Sites
62.0
33.0
20.7
14.3
8.3
7.3
4.7
4.7
4.0
3.0
3.0
2.7
1.3
Counties
36.3
20.7
10.7
8.3
4.3
4.0
2.0
2.0
2.0
1.7
1.7
1.7
1.0
* Note that the total site counts and total county counts vary for the 3-year period and 1-periods. Only one valid ('complete') year was required for inclusion in the 3-year dataset but additional
complete quarter and months (not part of the valid year(s)) were kept and considered for the max statists!cs. The 1-year (max) data statistics were computed from available quarters and months with
no additional completeness criteria imposed.. Thus, some sites have valid max monthly statistics but not valid max quarterly and/or max 2nd monthly statistic; because of this, the 1-year site and
county counts varied slightly for the different metrics. However, most sites did have 75%+ data capture for all years represented.. [Percent of sites with 75%+ data capture for all sites: 2003=95%,
2004=91%, 20005=90%.. Oprecent of sites with 75% data capture for urban sites: 2003=95%, 2004=93%, 2005=91%.]
To further illustrate temporal variability and also spatial variability, Figure 2-26 graphs
2003-2005 Pb-TSP monthly averages for several sites in the Dallas, TX metropolitan area
(CBSA). Three of the plotted sites (the ones using circle, square, and diamonds symbols) were
classified as source-oriented; they are all located within one mile (generally north) of a facility
that emits three tons of Pb per year. The other two sites (using the triangle symbols) are located
about 25 miles south of the facility. The 3-year maximum monthly averages for the three
source-oriented sites were 0.97, 0.80, and 0.48 |ig/m3; the 3-year maximum monthly averages for
the two sites that were not source oriented sites were 0.23 and 0.10 |ig/m3. The 3-year maximum
quarterly averages for the three source-oriented sites were 0.70, 0.35, and 0.21 |ig/m3; the 3-year
quarterly averages for the two non-source-oriented sites were 0.08 and 0.06 |ig/m3. Thus, the
2-51
-------
highest source-related values and lowest non-source- related values for those two statistical
metrics varied by about a factor often to 12 (ten for maximum monthly average and 12 for
maximum quarterly average). This situation is common to metropolitan areas with both source
and nonsource sites; the ratios of highest area value to lowest area value for the maximum
quarterly and maximum monthly statistics ranged from two to over 300 in the 14 CBSA's with
Pb-TSP sites in both categories. The two non-source-oriented Dallas sites shown in Figure 2-26
recorded a large number of 24-hour Pb-TSP concentration values less than or equal to the
corresponding minimum detection limit; these low values accounted for about 18 percent of
their combined total number of values in 2003-2005. Even the high site, though, had some of
these low reported values; about 3 percent of its 2003-2005 data were at or below detection
limits.
3.
'B
o
O
O
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
jan feb
mar apr may jun
aug
sep oct nov dec
2005
jan
aug sep oct nov dec
Figure 2-26. Pb-TSP monthly means at five sites located in the Dallas, TX metropolitan
area, 2003-2005.
The data in Figure 2-26 also illustrate some of the points made with Tables 2-6 and 2-7.
For example, two sites exceed a monthly comparison level of 0.50 |ig/m3 using a 3-year
2-52
-------
evaluation window, but only one site exceeds that level using a 1 -year timeframe with year 2003
or 2004. And, although two sites exceeded a maximum monthly average level of 0.50 |ig/m3
using the 3-year timeframe only one site exceeded that level using a maximum quarterly metric
with the same 3-year period. The high site (circle symbols) in the top plot provides good
examples of the inconsistencies between maximum monthly averages and maximum quarterly
averages. The highest concentration month for that site in 2003 was April with an average of
0.89 |ig/m3; however, the corresponding quarterly average is 0.48 |ig/m3 which is the only third
highest of the year. Note that the quarter (second) with the highest monthly average (April) also
contains a month with the lowest average (June). Thus, the low month offset the high month in
the quarterly average and the high month was not within the high quarter. These situations are
not atypical in the Pb-TSP data. In the 189 site 2003-2005 Pb-TSP database, the site level
maximum month for the 3-year period occurred in the maximum quarter 66 percent of the time.
All three of the largest months were synonymous with the largest quarter at only six percent of
the sites.
2.3.3 Pb-PM10
The NATTS network operated in 2003-2005 included 23 sites in mostly urban, but some
rural, areas (Figure 2-27). These sites are operated by 21 state or local host agencies. All collect
particulate matter as PMio for toxic metals analysis, typically on a 1 in 6 day sampling schedule.
Lead in the collected sample is generally quantified via the ICP/MS method. The standard
operating procedure for metals by ICP/MS is available at:
http://www.epa.gov/ttn/amtic/airtox.html. These NATTS sites are relatively new, with 2004
being the first year in which all were operating. The AQS can be accessed at
http://www.epa.gov/ttn/airs/airsaq s/.
2-53
-------
Figure 2-27. Pb-PMi0 (NATTS) monitoring sites network.
2.3.3.1 Data Analysis Details
Lead-PMio data collected in 2003-2005 (parameter code 82128, duration '7') were
extracted from EPA's AQS on May 22, 2007. Most of the monitors reporting such data are in
the NATTS network. The same screening criteria utilized for Pb-TSP were implemented for Pb-
PMio with one variation; because of the limited amount of available data, only three valid
quarters were required (instead of all four) to make a valid year. Thus the criteria used were: 1)
a minimum of 10 observations per quarter, 2) for at least three quarters of one calendar year, and
3) at least 9 months with 4 observations each; all three criteria had to be met for inclusion. Forty
monitors met the three-part criteria. Of these 40 monitors, two were collocated with another
complete monitor. Only one monitor from each collocated pair (i.e., from each site location) was
kept in the analysis, specifically the one with highest maximum quarterly mean. Thus, data from
38 monitors at 38 distinct site locations were actually used. Seven of the 38 sites had complete
data (i.e., 3 or 4 valid quarters) for each of the three years (2003-2005), 10 sites had only two
years of complete data; and 21 sites had only one complete year of data. Complete quarters that
were not part of a complete year were used. Likewise, all complete months were used, even if
they did not correspond to the complete years. The 38 sites have an average of about 7 complete
quarters and 19 complete months.
2-54
-------
As with the Pb-TSP data processing, the PMio data were used "as reported"; that is, 1A
MDL substitutions were not made for reported concentrations less than or equal to MDL. Pb-
PMio sites were categorized similarly to the Pb-TSP sites. However, no Pb-PMi0 sites fell into
the source-oriented classification. 25 of the 38 Pb-PMi0 sites were classified as urban; 20 of
those 25 sites are located in CBSA's of 1 million or more population and the other 5 are located
in smaller CBSA's. The 38 Pb-PMio monitors are listed with various summary and demographic
data in Appendix 2B, Table 2B-6.
Three statistical metrics were computed for the Pb-PMio data: annual means, maximum
quarterly means, and maximum monthly means. These metrics were calculated at the site level.
They were calculated only for the overall 3-year period (2003-2005). Note that the 3-year annual
mean statistic is actually the average of the annual means for the complete years; thus it is the
average of three annual means, the average of two annual means, or the only available single
complete annual mean. The 3-year maximum quarterly mean statistic represents the highest
quarterly mean of the complete quarters (sites have from three to 12 complete quarters), and the
3-year maximum monthly mean represents the highest monthly mean of the complete months
(each site has from nine to 36 complete months).
2.3.3.2 Current Concentrations
Monitoring site-level concentrations for each of the 3 statistical metrics (annual mean,
maximum quarterly mean, and maximum monthly mean) are provided in Appendix 2B, Table
2B-6. Figure 2-28 shows the distributions of the annual means, maximum quarterly averages,
and maximum monthly means for the 38 Pb-PMio sites. The national composite average annual
mean for Pb-PMio was 0.006 ug/m3 for the 3-year period, 2003-2005; the corresponding median
annual mean was also 0.006 ug/m3. The national composite average maximum quarterly mean
was 0.012 ug/m3 for 2003-2005 and the corresponding median maximum quarterly mean was
0.009 ug/m3. The national composite average maximum monthly mean was 0.021 ug/m3 and the
median maximum monthly mean was 0.014 ug/m3. Figure 2-29 shows distribution boxplots for
the 25 urban sites and Figure 2-30 shows distribution boxplots for the 20 urban sites located in
CBSA's with one million or more population. In these three figures (Figures 2-28 through 2-30),
the boxes depict inter-quartile ranges and medians, whiskers depict the 5th and 95th percentiles,
and asterisks identify composite averages. Additional points on the distribution for these
statistics are given in Appendix 2B, Table 2B-7.
2-55
-------
0.081
0,07
0.06-
m
I, 0.05"
£H
| 0.041
o
§ 0.05"
0.02-
0,01
o.oo-i
Annual mean Max quarterly Max monthly
mean mean
Figure 2-28. Distribution of Pb-PMi0 concentrations (represented by 3 different statistics)
at all 28 monitoring sites, 2003-2005.
2-56
-------
0.081
0,07
0.06-
m
I, 0.05"
£H
| 0.041
o
§ 0.05"
0.02-
0,01
o.oo-i
Annual mean Max quarterly Max monthly
mean mean
Figure 2-29. Distribution of Pb-PMi0 concentrations (represented by 3 different statistics)
at the 25 urban monitoring sites, 2003-2005.
2-57
-------
0.13-
0, 12-
0. 11-
o.io-
0.09-
5 0.08-
M
^ 0,07-
o
| 0.06-
§ 0.05-
o
0.04-
0.03-
0.02-
o.oi-
o oo-
j 3
S
~~~~~~~~J
1
_____
— ^
V p V W | |
Annual mean Max quarterly Max monthly
mean mean
Figure 2-30. Distribution of Pb-PMio concentrations (represented by 3 different statistics)
at the urban monitoring sites located in CBSAs of >_1 million population,
2003-2005.
Site-level annual means are mapped in Figure 2-31 and the corresponding maximum
quarterly means are mapped in Figure 2-32.
2-58
-------
> 0.015
0.010-0.015
0.005-0.010
< 0.005
Figure 2-31. Pb-PMi0 annual means (for all sites), 2003-2005.
2-59
-------
Concentration range
(ug/m3)
> 0.015
0.010-0.015
0.005-0.010
< 0.005
Figure 2-32. Pb-PMio maximum quarterly means (for all sites), 2003-2005
2.3.4 Pb-PM2.5
Two networks measure Pb in PM2.5, the EPA CSN and the IMPROVE network. The
CSN consists of 54 long-term trends sites (commonly referred to as the Speciation Trends
Network or STN sites) and about 150 supplemental sites, all operated by state and local
monitoring agencies. Most STN sites operate on a 1 in 3 day sampling schedule, while most
supplemental sites operate on a 1 in 6 day sampling schedule. All sites in the CSN network
determine the Pb concentrations in PM2.5 samples and, as such, do not measure Pb in the size
fraction >2.5 jim in diameter. Lead is quantified via the XRF method. The standard operating
procedure for metals by XRF is available at:
http://www.epa.gov/ttnamtil/files/ambient/pm25/spec/xrfsop.pdf Data are managed through the
AQS.
2-60
-------
The IMPROVE network is administered by the National Park Service, largely with
funding by EPA, on behalf of federal land management agencies and state air agencies that use
the data to track trends in rural visibility. Lead in the PM2 5 is quantified via the XRF method, as
in the CSN. Data are managed and made accessible mainly through the VIEWS website
(http://vista.cira.colostate.edu/views/), but also are available via the AQS. Samplers are operated
by several different federal, state, and tribal host agencies on the same 1 in 3 day schedule as the
STN.
The locations of the CSN are shown in Figure 2-33. Nearly all of the CSN sites are in
urban areas, often at the location of highest known PM2.5 concentrations. The first CSN sites
generally began operation around 2000.
Trends
Supplemental
Tribal
Figure 2-33. Pb-PM2.5 (CSN) monitoring sites.
In the IMPROVE network, PM2.5 monitors are placed in "Class I" areas (including
National Parks and wilderness areas) and are mostly in rural locations (Figure 2-34). The oldest
of these sites began operation in 1988, while many others began in the mid 1990s. There are 110
formally designated IMPROVE sites, which are located in or near national parks and other Class
I visibility areas, virtually all of these being rural. Approximately 80 additional sites at various
urban and rural locations, requested and funded by various parties, are also informally treated as
part of the network.
2-61
-------
« *
0 •.
«
» /
O, *
li •'
.- €! :
* •. (,.-.-•''
^ @
o ,,, *p t>
./*
Figure 2-34. Pb-PM2.5 (IMPROVE) monitoring sites.
2.3.4.1 Data Analysis Details
2003-2005 Pb-PM2.5 data (parameter code 88128, duration '7') were extracted from
EPA's AQS on May 22, 2007. Data generated with IMPROVE collection/analysis methods
were excluded from the central focus of this national characterization on the basis that most of
the monitors utilizing those methods are located in rural or remote areas distant from both Pb
sources and large populations. Most remaining data are associated with EPA's CSN program.
The same screening criteria utilized for Pb-PMi0 were also implemented for Pb-PM2 5: 1)
a minimum of 10 observations per quarter, 2) for at least 3 quarters of one calendar year, and 3)
at least 9 months with 4 observations each; all three criteria had to be met for inclusion. 278
monitors met the data completeness criteria. Of these 278 monitors, 7 were collocated with
another complete monitor. Only one monitor from each collocated pair (i.e., from each site
location) was kept in the analysis, specifically the one with highest maximum quarterly mean.
Thus, data from 271 monitors at 271 distinct locations were actually used. 192 of the 271 sites
had complete data (i.e., 3 or 4 valid quarters) for each of the three years (2003-2005), 40 sites
had only two years of complete data; and 39 sites had only one complete year of data. Complete
2-62
-------
quarters that were not part of a complete year were used. Likewise, all complete months were
used, even if they did not correspond to the complete years. The 38 sites have an average of
about 10 complete quarters and 29 complete months Pb-PM2 5 data were used "as reported"; /^
MDL substitutions were not made for reported concentrations less than or equal MDL.
PM2.5 sites were categorized similarly to the sites in the other size cuts. Only eight Pb-
PM2.5 sites were classified as source-oriented. 216 of the 271 Pb-PM2.5 sites were classified as
urban; 99 of those 216 sites are located in CBS As of 1 million or more population and the other
117 are located in smaller CBS As. The 271 Pb-PM2.s monitors are listed with various summary
and demographic data in Appendix 2B, Table 2B-8.
2.3.4.2 Current Concentrations
The site-level Pb-PM2.s concentrations for each of the three statistics (annual mean,
maximum quarterly mean, and maximum monthly mean) during the three-year period, 2003-
2005, are shown in Appendix 2B, Table 2B-8. Figure 2-35 shows the distributions of the three
statistical metrics for the 271 Pb-PM2.s sites; the boxes depict inter-quartile ranges and medians,
whiskers depict the 5th and 95th percentiles, and asterisks identify composite averages.
Additional points on the distribution for these statistics are given in Appendix 2B, Table 2B-9.
The national composite average annual mean was 0.004 ug/m3 for the 3-year period, 2003-2005;
the corresponding median annual mean was 0.003 ug/m3. The national composite average
maximum quarterly mean was 0.008 ug/m3 for 2003-2005 and the corresponding median
maximum quarterly mean was 0.005 ug/m3. The national composite average maximum monthly
mean was 0.013 ug/m3 and the median maximum monthly mean was 0.007 ug/m3. As also
shown in Appendix 2B, Table 2B-9, the median and mean site-level annual mean and maximum
quarterly mean levels for source oriented sites were approximately double those for the non-
source-oriented sites. Figure 2-36 maps the annual means for Pb-PM2.5 sites.
2-63
-------
o
0.034-
0.032-
0.030-
0.028-
0.026-
0,024-
0.022-
0,020-
0,018-
0.016-
0,014-
0,012-
o.oio-
0,008-
0,006-
0.004-
0.002-
o.ooo-
*
,
L_
( i 1
Annual mean Max quarterly Max monthly
mean mean
Figure 2-35. Distribution of Pb-PM2.s concentrations (represented by 3 different statistics)
at all 271 monitoring sites, 2003-2005.
2-64
-------
Concentration range
(ug/m3)
> 0.015
0.010-0.015
0.005-0.010
< 0.005
Figure 2-36. Pb-PM2.5 annual means (for all sites), 2003-2005.
2.3.5 Relationships among Different Particle-sized Pb Concentrations
There are not many sites where Pb measurements are made in different PM size fractions
at the same location and the same day (and where Pb values exceed minimum detection limits).
Very few locations in the United States have measured all three PM size fractions at the same
time. Table 2-8 shows information for sites with collocated Pb data size fraction data. The first
table section shows sites with collocated Pb-TSP and Pb-PMi0 measurements. For this particular
analysis, data prior to 2003 (and in one case, data not from AQS) were utilized. In general, there
is typically good correlation between Pb measurements in TSP and PMi0; the average site level
correlation coefficient (r) is 0.79 for the 23 listed sites. Although site-level correlations ranged
from 0.05 to 1.00, about two thirds of the sites (15) had correlations of 0.80 or better. All but
one of the 23 sites was categorized as not source-oriented, and the low concentrations at those 22
sites seem to corroborate this classification. The lone known source-influenced site has much
higher concentrations; it also has a high r (0.95). For the 23 sites listed, on the days collocated
measurements were made, most of the measured Pb-TSP appears to fall in the PMio fraction. On
2-65
-------
average, daily Pb-PMi0 concentrations were about 83 percent of the level of the Pb-TSP
concentrations. All sites had an average daily ratio (Pb-PMio / Pb-TSP) of 0.60 or greater; 19 of
the 23 sites (83 percent) had an average ratio of 0.75 or more. The lone source site had an
average ratio of 0.65. The next table section lists sites with collocated Pb-TSP and Pb-PM2.5
during 2003-2005. The relationship between these two size fractions is not nearly as strong as
for Pb-TSP and Pb-PMio. At the 31 sites the average r was 0.49; only four of the 31 sites (-13
percent) have correlations of 0.80 or higher. Site-level correlations ranged from 0.04 to 0.96.
On average, daily Pb-PM2 5 concentrations were about 55 percent of the level of Pb-TSP
concentrations; however, the percentage varied significantly by site. The last section of the table
lists the sites with co-located Pb-PMio and Pb-PM2.5 during 2003-2005. It appears that the
relationship between Pb-PMi0 and Pb-PM2.5 is stronger than that for Pb-TSP and Pb-PM2.5 but
not as strong as between Pb-TSP and Pb-PMi0. The average r for the 28 sites is 0.69; ten of the
26 sites (38 percent) have r's (correlation coefficients) of 0.80 or above. Site-level correlations
ranged from 0.31 to 1.00. On average, daily Pb-PM2.5 concentrations were about 79 percent of
the level of Pb-PMi0 concentrations; all but 3 sites had an average daily ratio (Pb-PM2.5 / Pb-
PMio) of 0.50 or more. All of the above results should be viewed with some caution based on
the limited number of sites with collocated data. Further, the relationships described can only be
presumed to exist at sites with little influence from significant Pb sources. Lack of source-
oriented PMio data is a significant data gap in understanding size relationships where Pb
exposures are of most potential concern.
Figure 2-37 summarizes (for all sites, not just the ones with collocated data) the annual
means and medians for Pb in the various PM size fractions collected by different monitoring
networks. Additionally, means and medians are presented for different site classifications. The
top chart uses a scale that fits all shown categories, up to the maximum 95th percentile. The Pb-
TSP monitor averages for the "source-oriented" subset (and other subsets that include those
monitors; e.g., "TSP - all sites") dwarf the other categories. The bottom chart replots the data on
a smaller concentration scale for enhanced resolution. Using the national averages (left bars), the
Pb-TSP non-source-oriented annual means are about 2.6 times larger than the Pb-PMio "all sites"
averages (recall that no Pb-PMio sites were classified as source-oriented); using the national
medians (right bars), the ratio was closer to 1.8. Restated as a PMio/TSP ratio it is 0.55, (i.e., 55
percent of TSP Pb is in the PMio fraction); the collocated sites analysis discussed above has a
median PMio/TSP ratio of 0.85) The Pb-PMio "all sites" averages are about 1.4 (using means) to
1.6 (using medians) times the Pb-PM2.5 CSN urban averages. The Pb-PM2.5 CSN urban averages
are about 3.3 times the Pb-PM2.5 IMPROVE averages.
2-66
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0.3000 i
0.2800
0.2600
0.2400
0.2200
0.2000
0.1800
0.1600
0.1400
0.1200
0.1000
0.0800
0.0600
0.0400
0.0200
0.0000
TSP-all TSP source- TSPnot
TSP
TSPurban TSP urban TSPurban PMlO-all PM10 urban PM10 urban PM10 urban PM2.5 CSN PM2.5 CSN PM2.5 CSN PM2.5 CSN PM2.5 CSN PM2.5 CSN PM2.5 CSN
oriented
sites
source- previous
oriented source-
sites oriented
sites
sites in sites in
CBSA's > CBSA's <
1M 1M
population population
sites in sites in
CBSA's > CBSA's <
1M 1M
population population
source- not source- urban sites urban sites urban sites IMPROVE
oriented oriented in CBSA's > inCBSA's< (National
sites sites 1M 1M parks, rural)
population population
mean • median
0.0000
TSP-all
sites
TSP source-
oriented
TSPnot
source-
oriented
sites
TSP
previous
source-
oriented
sites
TSPurban
sites
TSPurban
sites in
CBSA's >
1M
TSPurban
sites in
CBSA's <
1M
PMlO-all PM10 urban PMlOurb;
sites sites sites in
CBSA's >
1M
PM10 urban PM2.5 CSN PM2.5 CSN PM2.5 CSN PM2.5 CSN PM2.5 CSN PM2.5 CSN PM2.5
sites in - all sites source- not source- urban sites urban sites in urban sites in IMPROVE
CBSA's < oriented oriented CBSA's > CBSA's < (National
1M sites sites 1M 1M parks,
population population
population population
population population mostly rural)
Figure 2-37. National mean and median monitor level Pb annual means for different size cut PM networks, 2003-2005.
2-67
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Table 2-8. Monitoring sites with collocated Pb data in different size fractions
Site
Collocated P
060658001
060374002
270530053
Unknown
060850004
261390009
201730009
060771002
202090015
201730007
202090020
060990002
260770905
201770007
060250005
295100085
060290014
261630033
060190008
201290003
201730008
490110001
201731012
Collocated P
010730023
010731009
060250005
060371103
060658001
080010006
080410011
100032004
120571075
130690002
150032004
170314201
180970078
260810020
261130001
261610008
261630001
261630033
270530963
295100085
350010023
410390060
410510246
420450002
450790019
470370023
482011034
490110001
490110004
550270007
261630001 *
Collocated P
490110001
250250042
482011039
080770017
261630033
490110004
440070022
330150014
530330080
550270007
330110020
410610119
482011039*
410390060
530330080 *
410510246
110010043
450250001
530630016
295100085
170314201
130890002
130890002 *
120573002
211930003
121030026
Urban?
-TSP and 1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
-TSP and 1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
-PMlOam
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
source-
orintecl?
b-PMlO
1
D-PM2.5
1
PD-PM2.5
years
1995 - 1996
1995 - 2000
1996 - 2001
1988
1994 - 1999
2000 - 2001
1993 - 1997
1995 - 2000
1993 - 1997
1993 - 1997
1993 - 1997
1995 - 1998
1993 - 1996
1993 - 1997
1996 - 2001
2004 - 2004
1995 - 2000
2003 - 2006
1995 - 2001
1993 - 1998
1993 - 1997
2003 - 2003
1993 - 1997
2005
2005
2003-2005
2003-2005
2003-2005
2003-2005
2003-2005
2003-2005
2003-2005
2003-2005
2003-2005
2003-2005
2003-2005
2005
2003-2005
2003-2005
2003-2005
2003-2005
2003
2004
2004
2003-2004
2003
2003-2005
2003-2005
2003-2004
2003-2005
2003-2005
2003
2004-2005
2003-2004
CSN1
2003
2003-2005
2003-2005
2004-2005
2003-2005
2003
2003-2005
2003-2005
2003-2005
2005
2003-2005
2004
2004
2004
2003-2004
2003
2004-2005
2004
2005
2003-2005
2005
2003-2005
2004
2004-2005
2003-2005
2004-2005
number of
collocated
days
54
129
13
22
23
26
18
53
118
18
107
17
78
19
205
26
32
167
32
14
16
19
22
14
15
129
165
148
125
132
50
57
73
151
115
155
29
157
69
172
152
54
53
15
56
57
167
54
48
147
23
13
42
67
20
108
161
108
148
134
151
55
159
48
59
95
35
75
99
120
85
111
54
148
56
158
34
113
165
70
average
(larger size
cut)
0.031
0.041
0.017
2.245
0.025
0.013
0.019
0.021
0.028
0.018
0.092
0.024
0.017
0.017
0.031
0.021
0.020
0.031
0.020
0.020
0.015
0.024
0.022
0.0224
0.0042
0.0159
0.0228
0.0138
0.0323
0.0176
0.0100
0.0038
0.0021
0.0015
0.0113
0.0097
0.0076
0.0032
0.0079
0.0086
0.0239
0.0106
0.0135
0.0029
0.0027
0.0082
0.0374
0.0154
0.0052
0.0087
0.0291
0.0107
0.0067
0.0083
0.0341
0.0063
0.0057
0.0047
0.0208
0.0060
0.0110
0.0033
0.0047
0.0057
0.0046
0.0017
0.0025
0.0024
0.0046
0.0096
0.0049
0.0029
0.0058
0.0130
0.0059
0.0026
0.0038
0.0036
0.0039
0.0025
average
(smaller
size cut)
0.018
0.022
0.008
1.121
0.018
0.009
0.015
0.017
0.021
0.013
0.059
0.015
0.014
0.014
0.027
0.017
0.019
0.028
0.018
0.017
0.015
0.025
0.022
0.0154
0.0023
0.0122
0.0048
0.0062
0.0080
0.0020
0.0045
0.0020
0.0015
0.0010
0.0036
0.0050
0.0042
0.0023
0.0042
0.0042
0.0118
0.0048
0.0077
0.0003
0.0018
0.0058
0.0042
0.0096
0.0041
0.0027
0.0036
0.0017
0.0044
0.0040
0.0041
0.0026
0.0020
0.0023
0.0113
0.0032
0.0098
0.0022
0.0033
0.0044
0.0033
0.0011
0.0017
0.0014
0.0034
0.0084
0.0039
0.0022
0.0037
0.0096
0.0047
0.0028
0.0049
0.0025
0.0042
0.0023
minimum
ratio of
smaller size
cut /larger
size cut
0.24
0.14
0.33
0.17
0.38
0.46
0.38
0.39
0.29
0.33
0.04
0.40
0.55
0.50
0.27
0.59
0.65
0.33
0.60
0.55
0.50
0.96
0.63
0.00
0.00
0.12
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.11
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.27
0.00
0.25
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.49
0.00
0.00
0.00
maximum
ratio of
smaller size
cut / larger
size cut
1.17
1.64
0.89
1.86
1.57
1.07
1.53
1.84
2.79
1.27
11.92
1.19
1.55
1.33
1.55
1.13
1.44
2.69
1.33
1.45
2.06
1.10
3.33
2.04
1.84
1.50
1.22
1.39
3.52
1.17
1.94
8.78
17.06
14.88
1.40
3.04
1.43
6.54
1.82
1.48
2.20
1.83
7.40
0.37
9.32
3.11
0.75
1.81
8.12
1.73
0.29
0.66
1.45
0.74
0.28
2.15
2.34
3.24
1.92
2.96
3.65
2.76
3.75
2.43
2.79
6.86
2.23
7.31
3.60
4.04
10.43
4.63
4.23
7.30
13.13
3.86
1.81
19.63
33.45
13.13
average
ratio of
smaller size
cut /larger
size cut
0.60
0.60
0.61
0.65
0.77
0.78
0.81
0.81
0.82
0.82
0.85
0.85
0.85
0.87
0.87
0.89
0.92
0.92
0.92
0.93
0.98
1.03
1.05
0.77
0.57
0.71
0.27
0.42
0.43
0.24
0.44
1.12
1.01
0.92
0.35
0.66
0.53
0.78
0.52
0.46
0.48
0.45
0.94
0.10
1.07
0.74
0.11
0.58
0.79
0.32
0.11
0.22
0.58
0.47
0.11
0.46
0.49
0.51
0.54
0.56
0.58
0.60
0.72
0.74
0.77
0.77
0.78
0.78
0.78
0.83
0.86
0.88
0.88
0.89
1.00
1.02
1.09
1.16
1.42
1.42
correlation
(r)
0.44
0.76
0.93
0.97
0.72
0.94
0.50
0.82
0.65
0.08
0.99
0.99
0.99
0.67
0.95
0.98
0.98
0.90
0.94
0.91
0.05
1.00
0.96
0.59
0.12
0.96
0.11
0.74
0.12
0.07
0.79
0.79
0.55
0.18
0.08
0.52
0.59
0.59
0.51
0.59
0.71
0.48
0.32
0.50
0.26
0.69
0.04
0.79
0.40
0.42
0.95
0.06
0.93
0.83
0.95
0.52
0.43
0.31
0.89
0.62
1.00
0.79
0.86
0.93
0.54
0.46
0.81
0.63
0.87
0.99
0.39
0.52
0.77
0.55
0.63
0.95
0.99
0.48
0.58
0.61
2-68
-------
As described in the CD, several special time-limited studies have investigated Pb
concentrations in different PM size fractions (CD, p. 3-13). For example, average Pb
concentrations reported in a rural area in the southeastern U.S. were 6.11 ng/m3 in PM2 5 and
15.04 ng/m3 in TSP samples, with the average total mass concentration of 9.5 |ig/m3 and 19.1
|ig/m3 for PM2.5 and TSP, respectively (Goforth and Christoforou, 2006); thus, Pb constituted a
similar very small proportion of particles in each size fraction. Another study included two areas
in the Los Angeles basin (Singh et al, 2002). In Downey, a site where refineries and traffic
contribute heavily to particle concentrations, Pb was proportionally greater in the fine and
ultrafme fractions of PMi0. In Riverside, which is considered a receptor site for particles
transported from the Los Angeles basin and also has agricultural sources, Pb was proportionally
greater in the coarse fraction of PMi0. In Boston, MA, Pb concentrations of 326 ng/m3 and 75.6
ng/m3 were reported from PM2.5 and PMio-2.5 (Thurston and Spengler, 1985). Overall, these
findings suggest that for locations primarily impacted by combustion sources, Pb concentrations
appear to be higher in the fine fraction of particles. However, at locations impacted by
noncombustion sources (e.g., agriculture), Pb contained in the larger particles can be of
significantly higher concentrations than those for the fine particles.
2.3.6 Summary
Ambient air Pb concentrations are measured by four monitoring networks in the United
States, all funded in whole or in part by EPA. These networks - the Pb NAAQS compliance
network, the PM2.5 STN, the PM2.5 IMPROVE network, and the PMi0 network - provide Pb
measurements for 3 different sizes of PM, and the PM2.5 size is measured separately in urban and
remote locations.
Airborne concentrations of Pb-TSP in the United States have fallen dramatically over the
last 30 years due largely to the phase-out of leaded gasoline additives. Despite this decline, there
have still been a small number of areas that have not met the current Pb NAAQS over the past
few years. The sources of Pb in these areas are stationary sources (e.g. primary and/or secondary
smelters). There appears to be significant 'under-monitoring' near large Pb emission point
sources. Except for the monitors in a limited number of areas, Pb-TSP averages are quite low
with respect to the current NAAQS; the median monitor level maximum quarterly average for
2003-2005 is about fifty times lower than the 1.5 |ig/m3 NAAQS level. However, when current
concentrations are compared to alternative NAAQS thresholds (e.g., 0.05 and 0.20 jig/m3), the
number of locations that exceed those levels, and their associated populations, are much higher.
For example, over 25 percent of the monitored Pb-TSP population exceeded the 0.05 |ig/m3 max
quarterly level in 2003-2005 and 5 percent exceeded the 0.20 level.
2-69
-------
Some monthly variability is common for ambient Pb concentrations. The current form of
the standard (quarterly average) attempts to account for seasonal variability. As suggested
during the last review, a shorter averaging period (monthly) would better capture short-term
increases in Pb concentrations (USEPA 1990). Although there have only been 3 sites that
violated the 1.5 |ig/m3 max quarterly average NAAQS during the 2003 - 2005 period, 11 sites
violated that level with respect to a maximum monthly average.
There are not many sites that collect ambient Pb data in all three size ranges. Analyses of
the available limited collocated Pb data for different size particles indicate that TSP-sized Pb and
PMio-sized Pb are fairly well correlated, however, almost all of the study sites were presumably
not source-oriented. Additional source-oriented Pb-PMio monitoring, collocated with Pb-TSP,
would benefit additional evaluations. If further analyses also corroborate a fairly strong TSP-
PMio Pb relationship for source-oriented sites, Pb-PMio measurements may be useful as a Pb-
TSP surrogate.
2.4 AIR QUALITY MODELING
2.4.1 National Air Toxics Assessment
As part of the Agency's national air toxics assessment (NAT A) activities, a national scale
assessment of hazardous air pollutants including Pb compounds has been performed twice over
the past few years (USEPA 2006b, 2002c, 200la). These two assessments included the use of
the NEI for the years 1996 and 1999, respectively, with atmospheric dispersion modeling to
predict associated annual average Pb air concentrations across the country. A national scale
assessment is not yet available based on the 2002 NEI. A number of limitations are associated
with the 1996 and 1999 ambient concentration estimates (see Section 2.4.1.2) and the underlying
emissions estimates (e.g., see Section 2.2.5). While the associated limitations handicap a
reliance on the absolute magnitude of these estimates, they may prove informative with regard to
relative patterns of concentrations across the country, and are presented in that light.
2.4.1.1 Methods
To develop national-scale estimates of annual average ambient Pb concentrations, EPA
used the Assessment System for Population Exposure Nationwide (ASPEN) model. ASPEN
uses a Gaussian model formulation and climatological data to estimate long-term average
pollutant concentrations. The ASPEN model takes into account important determinants of
pollutant concentrations, such as: rate of release, location of release, the height from which the
pollutants are released, wind speeds and directions from the meteorological stations nearest to
the release, breakdown of the pollutants in the atmosphere after being released (i.e., reactive
decay), settling of pollutants out of the atmosphere (i.e., deposition), and transformation of one
2-70
-------
pollutant into another (i.e., secondary formation). ASPEN concentration estimates do not
account for day-of-week or seasonal variations in emissions (USEPA, 200la).
For each source, the model calculates ground-level concentrations as a function of radial
distance and direction from the source at a set of receptors laid out in a radial grid pattern. For
each grid receptor, concentrations are calculated for each of a standard set of stability class/wind
speed/wind direction combinations. These concentrations are averaged together using the annual
frequency of occurrence of each combination (i.e., the climatology) as weightings to obtain
annual average concentrations (USEPA, 200la). For the 1999 NATA assessment,
meteorological data for 1999 were used and the frequency distributions were also stratified by
time of day into eight 3-hour time blocks. This along with similar emission rate stratification
helps to preserve any characteristic diurnal patterns that might be important in subsequent
estimation of population exposure. The resulting output of ASPEN is a grid of annual average
concentration estimates for each source/pollutant combination by time block (USEPA, 200la).
Annual average concentration estimates for grid receptors surrounding each emission
source are spatially interpolated to the census tract centroids within the 50 kilometers impact
zone, and contributions from all modeled sources are summed to give cumulative ambient
concentrations in each census tract. By accounting for all identified source categories (including
background concentrations, which are added to the ASPEN-calculated concentrations), the sum
of the concentration increments yields an estimate of the overall Pb concentration within each
census tract. For many pollutants modeled, total concentrations include a "background"
component which includes concentrations due to natural sources, sources not in the emissions
inventory, and long-range transport (USEPA, 200la). In the case of Pb, however, a background
concentration value of zero was used.
2.4.1.2 Findings and Limitations
Historical studies show that Gaussian dispersion models, such as ASPEN, typically agree
with monitoring data within a factor of 2 most of the time. In the case of Pb in the NATA
assessment, model estimates at monitor locations were generally lower than the monitor averages
for Pb, suggesting that the modeling system (i.e., emissions estimates, spatial allocation
estimates, dispersion modeling) may be systematically underestimating ambient concentrations.
This may be particularly true for Pb as metals tend to deposit rapidly with distance from the
source according to their particle size and weight. The model-to-monitor analysis is described in
detail at http://www.epa.gov/ttn/atw/nata 1999/99compare.html. The modeling system
underestimation may also be due in part to a lack of accounting for resuspension of previously
emitted and deposited particles (these resuspended particles may be observed by the monitors,
but they are not accounted for in the emissions inventory, and thus would not contribute to the
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model estimate). For more details on the limitations of the 1999 NATA national scale
assessment, see http://www.epa.gov/ttn/atw/natal999/limitations.html.
Because higher Pb concentrations are associated with localized sources, which are not
well-characterized by this modeling approach, national scale assessments such as this can only
provide answers to questions about emissions, ambient air concentrations, exposures and risks
across broad geographic areas (such as counties, states and the country) for that period. They are
also based on assumptions and methods that limit the range of questions that can be answered
reliably such as identifying Pb exposures and risks for specific individuals, or identifying
exposures and risks in small geographic regions such as a specific census tract.
Given the limitations of this analysis with regard to estimating Pb concentrations
nationally (see above), specific absolute ambient concentration estimates for Pb compounds
generated by this analysis are not presented here. The general pattern of results, presented
elsewhere (USEPA, 2006b), is consistent with the following conclusions: 1) there are Pb
concentrations projected in remote areas; 2) there are distinct geographical variations in ambient
Pb concentrations; concentrations in rural areas are generally much lower than in urban areas;
and, 3) there are areas with high Pb concentrations associated with localized sources with high
emissions. These results also support the general conclusion that more detailed source and site
specific analyses are needed when addressing Pb impacts.
2.4.1.3 Summary
The NATA national scale assessment estimates based on 1999 NEI reflect the quantity
and distribution of Pb emissions, with the highest estimates associated with point sources. For
example, the census tract with the highest estimated Pb concentration is located in the county
with the highest Pb emissions estimate in the 1999 NEI, and the second highest census tract is
located in a county with a now-closed major Pb smelter. Limitations of the assessment,
however, contribute to uncertainty and potential underestimation of Pb concentrations.
2.4.2 Community Multiscale Air Quality Model
The Community Multiscale Air Quality (CMAQ) model is a three-dimensional grid-
based Eulerian air quality model designed to estimate the formation and fate of oxidant
precursors, primary and secondary particulate matter (including Pb) concentrations and
deposition over regional and urban spatial scales, such as over the contiguous U.S. (Byun and
Ching, 1999; Byun and Schere, 2006).
The key inputs to the CMAQ model include emissions from anthropogenic and biogenic
sources, meteorological data, and initial and boundary conditions. The CMAQ meteorological
input files were derived from simulations of the Pennsylvania State University / National Center
for Atmospheric Research Mesoscale Model (Grell et al., 1994). This model, commonly referred
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to as MM5, is a limited-area, nonhydrostatic, terrain-following system that solves for the full set
of physical and thermodynamic equations which govern atmospheric motions. The lateral
boundary and initial species concentrations were obtained from a three-dimensional global
atmospheric chemistry model, GEOS-CHEM (Yantosca, 2004; Moon and Byun, 2004).
To assess the potential usefulness of CMAQ simulations for this review, a preliminary
national scale air quality modeling analysis was performed using version 4.6 of the CMAQ
modeling system that simulates urban and regional air quality. Aerosol phase HAPs track toxic
components within particulate matter and are treated as chemically inert but undergo the same
microphysical processes and deposition rates determined within the aerosol module (Binkowski
and Roselle, 2003). Lead is treated as aerosol phase HAP within CMAQ. Simulations were
performed for the period January 1 through December 31, 2002. The computational grid used
148 by 112 grid cells, with horizontal dimensions equal of 36 km on each side, to cover the
continental United States. Vertically, the model domain spanned from the surface to about 15
km and divided the distance into 14 layers based on sigma pressure coordinates.
The 2002 CMAQ model simulation used meteorological data for that year produced by
the MM5 model that were processed through the Meteorology-Chemical Interface Processor
(MCIP), version 3.1. The Sparse Matrix Operator Kernal Emissions (SMOKE) version 2.0
(http://cf.unc.edu/cep/empd/products/smoke/index.cfm) was used to produce model-ready
emissions inputs for the CMAQ simulation based on the 2002 National Emission Inventory
(NEI) version 3 (http://www.epa.gov/ttn/chief/net/2002inventory.html) For CMAQ, the
biogenic emissions were computed based on the 2002 meteorology data using the Biogenic
Emission Inventory System (BEIS) version 3.13 model from SMOKE. The BEIS3.13 model
computes gridded, hourly, model-species emissions for combination with the anthropogenic
emissions to put into CMAQ. Emissions are calculated for the U.S., Mexico, and Canada and
accounts for CO, VOC, and NOx emissions from vegetation and soils. The meteorology data on
which the biogenic emissions depend are the same as the meteorology data input to the CMAQ
model.
CMAQ outputs include Pb-PM2.5, Pb-PMio and atmospheric deposition. To evaluate
predictions, we obtained observations of metals in PM2.5 from the US EPA's Air Quality System
database (AQS). Observations are on a national scale and have an averaging period equal to 24
hours. Sampling frequencies range from several days to a week (see Section 2.3 for more
discussion on Pb monitoring). Model performance varies by season but in general, predicted
concentrations of lead underestimate observed concentrations (negative biases) and have a weak
ability to match the time dependency of observations (low correlation coefficients). Lead
predictions match observations within 50% at all locations. Comparison between predictions
and observations is difficult when the observation equals zero and the prediction is above or near
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the detection limit. In this case, the comparison gives an incorrectly high value of model biases
because the actual observation lies below the detection limit by an uncertain amount especially at
rural locations such as desert, forests and agricultural areas.
There are several reasons for the differences between modeled and monitored values
including emissions and meteorological factors. One reason is that sources of emissions from
Canada and Mexico were not included in the modeling. Lead associated with resuspension of
historic Pb emissions (CD, Section 2.3.3) is also not included. Additionally, Pb emissions arise
from both fuel combustion and aerosol suspension driven by mechanical action such as winds
and disturbance of lead-bearing soil by construction activities. These types of sources driven by
wind speed will have significant effects in areas that had large historic emissions yet are not
included in the current emission inventory and thus are not input into the model. Issues related
to aerosol suspension of Pb by mechanical action are equally important for both size classes, Pb-
PM2.5 and Pb-PMi0.
Model resolution at 36 km2 grids affects emission processes because grid cells use rates
that are composites of many sources. This method removes how individual sources affect
concentrations based on their time dependent emissions and location relative to monitoring sites
which are influenced by nearby sources. Composite sources produce larger errors in grid cells
where individual sources have large changes in emissions. The error often likely occurs over
populated areas such as residential areas in urban locations. Observed concentrations reflect
unpredictable activity in adjacent automotive traffic, construction and businesses.
Besides the errors in the meteorological and emissions inputs, the CMAQ model does not
include an aerosol mode that represents ultrafme particles (diameters < 50 nm). The aerosol
mode is emitted by combustion and industrial sources such as diesel engines, boilers, metal
foundries and plating or produced by gas to particle conversion near emission sources. Thus
CMAQ can underpredict at urban and some suburban locations where sources of ultrafme
particles are more numerous because coagulation is too slow to grow ultrafme particles into fine
particles. Issues related to ultrafme lead emissions pertain to the PM2.5 fraction since ultrafmes
would not be expected to grow into particles larger than PM2.5. PMio emissions are usually
dominated by particles formed by mechanical action and not from combustion processes. These
comparisons are expected to improve in the future from better Pb emissions inventories
(suspension, biomass burning, and anthropogenic fuel composition) and better CMAQ science
(smaller grid resolution, better boundary conditions, and better algorithms).
2.5 POLICY-RELEVANT BACKGROUND IN AIR
Some amount of Pb in the air derives from background sources, such as volcanoes, sea
salt, and windborne soil particles from areas free of anthropogenic activity (CD, Section 2.2.1).
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The impact of these sources on current air concentrations is expected to be quite low (relative to
3
current concentrations) and has been estimated to fall within the range from 0.00002 |ig/m and
0.00007 |ig/m3 based on mass balance calculations (CD, Section 3.1 and USEPA 1986, Section
7.2.1.1.3). The midpoint in this range, 0.00005 |ig/m3, has been used in the past to represent the
contribution of naturally occurring air Pb to total human exposure (USEPA 1986, Section
7.2.1.1.3). The data available to derive such an estimate are limited and such a value might be
expected to vary geographically with the natural distribution of Pb. Comparing this to reported
air Pb measurements is complicated by limitations of the common analytical methods and by
inconsistent reporting practices. This value is one half the lowest reported nonzero value in
AQS. For the purposes of the risk assessment described in Chapter 4, the value of 0.00005
|ig/m3 was selected as representative of policy-relevant background Pb in air. Unlike for other
criteria pollutants, the role of this value for Pb is limited. In considering risk contributions from
policy-relevant background, the contributions from exposures to nonair media are such that any
credible estimate of policy-relevant background in air is likely insignificant in comparison.
2.6 ATMOSPHERIC DEPOSITION
As described in Section 2.1.2, deposition is the path by which Pb particles are removed
from the atmosphere and transferred to other environmental media through deposition, and, as
recognized in Chapters 4 and 6, deposited Pb, plays a major role in human and ecological
exposures. There are several approaches described in the literature for estimating atmospheric
deposition, or transfer of Pb from the atmosphere to soil or water bodies. These include
measurements of Pb in rainfall (wet deposition) and on collection surfaces during dry periods
(dry deposition); dry deposition has also been estimated via measurements of airborne Pb
particles coupled with estimates of deposition velocity (see CD, Section 2.3.2). Studies that
measure Pb in sediment or soil cores, coupled with isotope dating methods (see CD, Sections
2.2.1 and 8.1.2), provide observations informative of atmospheric deposition rates and trends.
As there are currently no nationwide Pb atmospheric deposition monitoring programs, the
information in this section is drawn from a variety of sources as discussed in the CD (CD,
Sections 2.3, 8.2.2 and AX7.1.2.3 ).
2.6.1 Temporal Trends
The available atmospheric studies of dry, wet and bulk deposition of Pb indicate a
pronounced downward trend in Pb deposition in the U.S. during the 1980s to early 1990s, likely
reflecting the reduction in atmospheric levels during that time period (CD, Section 2.3.2). As an
example, Pirrone and others (1995) estimated an order of magnitude reduction in dry deposition
from 1982 to 1991 in Detroit, Michigan (CD, Section 2.3.2). Measurements of Pb in rainfall in
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Lewes, Delaware (a small town at mouth of Delaware Bay) have fallen from approximately 3
ug/L in the early 1980s to less than 1 ug/L by 1989 (CD, pp. 2-60 and AX7-35; Scudlark et al.,
1994). Sediment core studies provide evidence of the larger historical pattern (CD, Section
2.3.1). For example, Jackson and others (2004) reported that deposition to the Okefenokee
Swamp, Georgia, USA peaked during the period from 1940s through 1970s, followed by a
period of steady decline into the 1990s (CD, Section 2.3.1).
2.6.2 Deposition Flux Estimates since the Last Review
Contemporary rates of total Pb loadings to terrestrial ecosystems are estimated at
approximately 1 to 2 mg/m2year (CD, p. AX7-36). In association with the Great Lakes Water
Quality Agreement between the United States and Canada, a deposition monitoring network was
established to estimate regional atmospheric inputs to the Great Lakes (Voldner and Eisenreich,
1989). Based on measurements from that network, total Pb deposition to three of the Great
Lakes (Lakes Superior, Michigan and Erie) in the early 1990s was estimated to be on the order of
1.5 -2 mg/m2-year (CD, pp. 2-57 and 2-60; Sweet et al., 1998).
For Lakes Superior and Michigan, dry deposition estimates were greater than those for
wet deposition by a factor of 1.5 to 2, while dry deposition to Lake Erie was estimated to be less
than 80% of wet deposition (CD, pp. 2-57 and 2-60; Sweet et al., 1998). In the mid-Atlantic
region during the 1990s, dry deposition was estimated to be equal to or lower than wet
deposition, contributing <50% of total deposition (CD, Section 2.3.2; Scudlark et al., 2005).
Reports of wet deposition for this region during the 1990s range from nearly 400 to just over 600
ug/m2-year (CD, Section 2.3.2).
2.7 OUTDOOR DUST AND SOIL
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). Both media may play a substantial role in human and
ecological exposures. With regard to human exposures, contaminated soil can be a potential
source of Pb exposure, particularly for children (CD, Section 3.2). Another source of children's
exposure, as discussed in the CD (Sections 3.2 and 4.4), is house dust, which may be derived
from Pb in outdoor dust and soil as well as from ambient air Pb.
2.7.1 Outdoor Dust
Outdoor dust refers to particles deposited on outdoor surfaces. Lead in outdoor dust has
been associated with active point sources as well as well as older urban areas. For example, a
50% reduction in dust Pb levels accompanied a 75% reduction in airborne Pb concentrations
associated with replacement of a smelting facility in Canada (CD, pp. 3-23 to 3-24).
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Additionally, Caravanos and others (2006b) have described Pb in dust (particulate matter)
deposited on surfaces in New York City. Lead levels have been found to be higher in dust on or
near roadways, or in older urban areas as compared to newer or rural areas (CD, Sections 3.2.3
and 3.2.4; Caravanos et al 2006a,b). As with surface soil, contact with outdoor dust may
contribute to incidental ingestion of environmental contaminants including Pb. Additionally, as
stated in the CD (p. 2-62), the "resuspension of soil-bound Pb particles and contaminated road
dust can be a significant source of airborne Pb". Resuspension, thus, provides a pathway for Pb
transport into residences and its contribution to Pb in house dust. As mentioned in Section 2.1.2,
particles containing Pb may be resuspended into the air by a range of processes including wind
and vehicular traffic, as well as other mechanical processes including pedestrian traffic,
agricultural operations, and construction.
2.7.2 Soil
A reservoir of 0.5 to 4 g/m2 gasoline additive-derived Pb is estimated to exist in U.S.
soils (CD, p. AX7-36), with most contained in the upper soil horizons (O + A horizons). Studies
have indicated that industrial Pb can be strongly sequestered by organic matter and by secondary
minerals such as clays and oxides of Al, Fe, and Mn, (CD, pp. AX7-24 to AX7-39).
Accordingly, migration (e.g, to groundwater) and biological uptake of Pb in ecosystems is
considered to be relatively low, with variability of Pb mobility in different systems influenced by
factors including elevation and climate, vegetation type, acidity, and soil composition (CD,
Sections 2.3.5 and AX7.1.2.3). Generally then, forest floors are considered to currently act as
net sinks for Pb, and burial or movement of Pb over time down into lower soil/sediment layers
also tends to sequester it away from more biologically active parts of the watershed, unless later
disturbed or redistributed (CD, p. AX7-36). In areas of exposed soil, however, there is potential
for interaction with airborne Pb (as discussed in Sections 2.7.1 and 2.1.2).
As discussed below (Section 2.7.2.1), findings to date indicate that those systems less
influenced by current point sources are still responding to reduced Pb deposition rates associated
with reduced atmospheric emissions of Pb, including those associated with the phase-out of
leaded gasoline (see Section 2.3.2.3). Situations near point sources and those involving
historically deposited Pb near roadways are less well characterized. Section 2.7.2.2 summarizes
estimates of soil Pb concentrations since the time of the last review.
2.7.2.1 Temporal Trends
Variability among soil system characteristics that influence Pb mobility contributes to
differences in current and projected temporal trends in soil concentrations (e.g., CD, pp. 3-18 to
3-19, Sections 3.2.1-3.2.2, and pp. AX7-33 to AX7-34).
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Studies of forest soils have concluded that the time for soils to respond to reduced Pb
deposition rates (e.g., associated with Pb gasoline phase-out) is shorter than previously believed.
For example, Miller and Friedland (1994) projected that a 37% reduction in Pb concentration in
northern hardwood and subalpine forest soils would occur within 17 years and 77 years,
respectively. Kaste and Friedland (2003) traced atmospherically deposited Pb within forest soils
in Vermont and found similar response times of 60 and 150 years for the two forest soils,
respectively. They also concluded that the penetration of atmospherically delivered Pb in soils is
currently limited to the upper 20 cm and that the heterogeneous distribution of Pb in soils would
seem to indicate that the release of Pb to groundwater will be dispersed thereby reducing the
likelihood of a large pulse to groundwater. This study and those of Wang and Benoit (1997),
Johnson et al. (1995), and Zhang (2003) conclude that forest surface soils do not act as sinks
under current deposition rates for Pb and that a gradual migration into mineral soils is occurring,
making the possibility of a large pulse to groundwater in the future from past Pb pollution
unlikely. Studies of the role of acidification in Pb mobility in sandy soils (e.g., NJ pine barrens),
however, suggest a greater risk of mobilization of Pb and organic matter into these mineral soils,
with subsequent inputs to associated stream waters (CD, p. AX7-91).
Studies in urban areas of southern California, where Pb has accumulated from past
sources, suggests an environment in which Pb may remain at the soil surface (and other
surfaces), contributing to air concentrations via resuspension for the near-term (CD, pp. 2-65 to
2-67 and 3-18 to 3-19). Figure 2-38 illustrates how the temporal trend in surface soil
concentrations at a location may be influenced by the rate of resuspension. Harris and Davidson
(2005) suggested that typical long-term values for resuspension rate fall in the range of 10"11 to
10"7 per second, based on wind speeds, with the range of 10"11 to 10"10 proposed as a range
appropriate to California's south coast air basin. Under these assumptions, the model predicted
that the occurrence of resuspension at this rate would lead to little to no reduction in soil Pb
concentration in southern California over the next few hundred years (CD, pp. 2-65 to 2-67 and
3-18 to 3-20).
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E
7i>
c
o
~
£
**
c
o
0
c
o
O
A = 1x10'%
A = 1x10"10/s
A = 1x10-%
= 1x10'8/s
0.010
0.009
0.008
0.007
0.006
0.005
0.004
0.003
0.002
0 0.001
0.000
1970 1975 1980 1985 1990 1995 2000
Year
Source: Reprinted from Harris and Davidson (2005). Units for resuspension rate (A) are per second (/s).
Figure 2-38. Modeled soil concentrations of Pb in the South Coast Air Basin of California
based on four resuspension rates (A).
Temporal trends in surface soils near established point sources are not well characterized.
Information described in the CD for areas surrounding smelters after implementation of pollution
controls, although showing declines in Pb concentrations in outdoor dustfall, street dust and
indoor dustfall, has not indicated a noticeable decline in soil Pb concentrations (CD, pp. 3-23 to
3-24). Further, Pb concentrations in "clean" soil placed in areas influenced by current sources
have been reported to exhibit increasing temporal trends (USEPA, 2006c). Concentrations of Pb
in the very top layer of material (within the upper 1 inch of soil, analyzed using portable x-ray
fluorescence) at locations less than a mile from a primary Pb smelter exhibited statistically
significant increasing concentration over a four year period, with the average monthly change in
Pb concentration ranging from 1 to 8 mg/kg (USEPA, 200 Ib, 2006c). Estimates of associated
steady-state surface soil Pb concentrations or the expected longer-term temporal pattern for this
situation have not been made.
2.7.2.2 Current Surface Soil Concentrations
Present concentrations of Pb in forest surface soils range from 40 to 100 mg/kg while
natural background levels would be expected to be <1 mg/kg (CD, Section AX7.1.2.3). Urban
and roadside soils and those in areas of long-term Pb emissions from point sources have much
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higher concentrations of Pb, ranging up to hundreds to tens of thousands of mg/kg (CD, Section
3.2.1). For example, Pb surface soil concentrations near smelters have been found to range from
thousands of mg/kg (dry weight) within approximately 100-250 meters, dropping to 200 mg/kg
and below by distances of approximately 3-5 km (CD, Table 3-4). Soil Pb concentrations of
500-1100 mg/kg have been reported near U.S. mines that are no longer active (CD, Table 3-6).
2.8 SURFACE WATER AND SEDIMENT
The primary source of Pb in aquatic systems is atmospheric deposition. Lead is also
carried into water bodies via wastewater effluent from municipalities and industry, stormwater
runoff, erosion, and accidental discharges (CD, p. AX7-142). MostPb occurring in aquatic
systems is associated with particles, with the distribution between particle-bound and dissolved
form being influenced by water chemistry as well as suspended sediment levels (CD, pp. AX7-
117 to AX7-118; CD, Section AX7.2.2). The ratio of Pb in suspended solids to Pb in filtrate has
been described to vary from 4:1 in rural streams to 27:1 in urban streams (CD, p. AX7-118).
Water columns have been described as "transient reservoirs" for pollutants (CD, p. 2-75).
Once deposited to sediments, whether Pb is available for resuspension back into the water
column with potential transport further down a watershed versus being buried into deeper
sediments depends on the aquatic system. In open ocean waters (generally characterized by
depth and distance from continental sources), resuspension to surface waters is unlikely. In more
shallow systems, and additionally those influenced by land sources (e.g., stormwater runoff as
well as point sources), resuspension may play a significant role in water column concentrations.
For example, studies in San Francisco Bay, the southern arm of which as an average depth of 2
m, have indicated that Pb particles may be remobilized from surface sediments into the water
column (CD, AX7-141).
2.8.1 Temporal Trends
As discussed in the CD, many studies have investigated trends in Pb concentration in
sediment and surface waters (CD, Section AX7.2.2), with declines documented in many systems
and usually attributed to the phasing out of leaded gasoline.
Using sediment cores, temporal changes in Pb deposition and associated sediment Pb
concentration have been documented. Sediment cores from the Okefenokee Swamp indicate that
Pb concentrations were approximately 0.5 mg/kg prior to industrial development, reached a
maximum of approximately 31 mg/kg from about 1935 to 1965, and following passage of the
Clean Air Act in 1970 concentrations declined to about 18 mg/kg in 1990 (CD, p. AX7-141).
Researchers investigating trends in metals concentrations (roughly from 1970-2001) in sediment
cores from 35 reservoirs and lakes in urban and reference settings found that number of lakes
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exhibiting decreasing trends in Pb concentration outnumbered increasing trends (83% versus
6%). Mass accumulation rates of Pb in cores, adjusted for background concentrations, decreased
from the 1970s to the 1990s, with a median change of 246%. The largest decreases were found in
lakes located in dense urban watersheds, although anthropogenic mass accumulation rates in
dense urban lakes remained elevated over those in lakes in undeveloped watersheds, indicating
that urban fluvial source signals can overwhelm those from regional atmospheric sources (CD, p.
AX7-141; Mahler et al, 2006).
Figure 2-39 presents data on Pb concentrations in lake sediments from 12 lakes in the
Great Lakes area. Consistent with other studies, this study showed a peak in Pb concentrations
consistent with peak use of leaded gasoline in the U.S. in the mid 70's and declining
concentrations in most lake sediments through the mid 1990's.
1995
Litttefield*
•Elk
Higgins
Mullett
Crystal M
Gratiot
Cadillac
Paw Paw
Cass
•Gull
• Crystal B
Whitmore
1815
0.00 0.20 0.40 0.60 0.80 1.00 1.20
Lead sediment concentrations normalized to peak
Source: Yohn et al. (2004).
Figure 2-39. Pb concentrations in sediment samples in 12 Michigan lakes. The
concentrations are normalized by the peak Pb concentration in each lake;
peak Pb concentrations ranged from approximately 50 to 300 mg/kg.
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2.8.2 Current Concentrations
An analysis of data from the United States Geological Survey (USGS) National Water-
Quality Assessment (NAWQA) program is described in the CD. The NAWQA data set
encompasses data, collected over the past 15 years, on Pb concentrations in flowing surface
waters, bed sediment, and animal tissue for more than 50 river basins and aquifers throughout the
country (CD, Section AX7.2.2.3). Based on analysis of these data, the mean dissolved Pb
concentration in ambient surface waters of the U.S. is estimated to be 0.66 ug/L (range 0.04 to 30
ug/L), as compared to a mean of 0.52 ug/L (range 0.04 to 8.4 ug/L) for the "natural" locations.
The term "ambient" was used by NAWQA to describe the combined contribution of natural and
anthropogenic sources, and a separate set of samples was identified for natural locations (e.g.,
"forest", "rangeland", and "reference" sites). The mean concentration of Pb in "ambient" bulk
sediment (<63 microns, grain size) is 120 ug/g dry weight (range 0.5 to 12,000 ug/g), as
compared to a mean of 109 ug/g dry weight (range 0.5 to 12,000 ug/g) for "natural" locations.
Geographic distribution of Pb concentrations in surface waters and sediments in this data
set are presented in Figures 2-40 and 2-41 (CD, Figures AX7-2.2.7 and AX7-2.2.9). Areas with
high surface water Pb concentrations were observed in Washington, Idaho, Utah, Colorado,
Arkansas, and Missouri, with the maximum measured Pb concentration occurring at a site in
Idaho with a land use classified as mining (CD, p. AX7-131). As was seen with surface water Pb
concentrations, the highest measured sediment Pb concentrations were found in Idaho, Utah, and
Colorado. And also similar to the surface water findings, seven of the top 10 sediment Pb
concentrations recorded were measured at sites classified as mining land use (CD, p. AX7-133).
As described in the CD, dissolved surface water concentrations reported for lakes have been
generally much lower than the NAWQA values for lotic waters (CD, AX7-138).
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Legend
Surface Water Dissolved Pb (iig/L)
o Non-detect
0 0.51 - 5.44 (<99th precentile)
0 5.45 - 29.78 (>99th percentile)
Figure 2-40. Spatial distribution of dissolved lead in surface water (N = 3445). [CD, Figure
AX7-2.2.7.]
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Legend
Bulk Sediment <63u Total Lead (ug/g)
0 0.50- 18.00 (1st Quartile)
0 18.01 -28.00 (2nd Quartile)
• 28.01 -49.00 (3rd Quartile)
• 49.01 - 12000.00 (4th Quartile)
Figure 2-41. Spatial distribution of total lead in bulk sediment <63 um (N = 1466). [CD,
Figure AX7-2.2.9]
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component, 1, Model description. J. Geophys. Res., 108(D6), 4183-4200, doi:10.1029/2001JD001409.
Byun, D.W., and Ching, J.K.S. (Eds.), 1999. Science Algorithms of the EPA Models-3 Community Multiscale Air
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3 POLICY-RELEVANT ASSESSMENT OF HEALTH EFFECTS
EVIDENCE
3.1 INTRODUCTION
This chapter assesses key policy-relevant information on the known and potential health
effects associated with exposure to ambient lead (Pb). The presentation here summarizes the
qualitative assessment of health evidence contained in the CD, as a basis for the evidence-based
assessment of primary standards for Pb presented in Chapter 5 of this document. The focus is on
health endpoints associated with the range of exposures considered to be most relevant to current
exposure levels. This presentation also gives particular attention to those endpoints for which
there is quantitative health evidence available in this review that provides a foundation for the
quantitative health risk assessment discussed in Chapter 4 and used in the risk-based assessment
of primary standards for Pb presented in Chapter 5.
The presentation in this chapter recognizes several key aspects of the health evidence for
Pb. First, because exposure to atmospheric Pb particles occurs not only via direct inhalation of
airborne particles, but also via ingestion of deposited particles (e.g., associated with soil and
dust), the exposure being assessed is multimedia and multi-pathway in nature, occurring via both
the inhalation and ingestion routes. In fact, ingestion of indoor dust has been recognized as a
significant Pb exposure pathway, particularly for young children (CD, p. 3-27 to 3-28). Second,
the exposure index or dose metric most commonly used and associated with health effects
information is an internal biomarker (i.e., blood Pb). Additionally, the exposure duration of
interest (i.e., that influencing internal dose pertinent to health effects of interest) may span
months to potentially years, as does the time scale of the environmental processes influencing Pb
deposition and fate. Lastly, the nature of the evidence for the health effects of greatest interest
for this review is epidemiological data strongly supported by toxicological data that provide
biological plausibility and insights on mechanisms of action.
At the time of the last review, Pb was recognized to produce multiple effects in a variety
of tissues and organ systems across a range of exposure levels, with blood Pb levels of 10-15
ug/dL being recognized as levels of concern for impaired neurobehavioral development in
infants and children (USEPA, 1990). The current CD recognizes the existence of a wide array of
Pb-induced deleterious effects, including several in children and/or adults that are induced by
blood Pb levels extending well below 10 ug/dL, to below 5 ug/dL and possibly lower (CD,
Section 8.4).
In recognition of the multi-pathway aspects of Pb, and use of an internal exposure metric
in health risk assessment, Section 3.2 describes our understanding of the internal disposition or
distribution of Pb, and the use of blood Pb as an internal exposure or dose metric. Section 3.3
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discusses the nature of Pb-induced health effects, giving emphasis to those with the strongest
evidence, particularly those associated with the range of current exposure levels. Potential
impacts of Pb exposures on public health, including recognition of potentially susceptible or
vulnerable subpopulations, are discussed in Section 3.4. Finally, Section 3.5 summarizes key
policy-relevant conclusions about Pb-related health effects.
3.2 INTERNAL DISPOSITION - BLOOD LEAD AS DOSE METRIC
The health effects of Pb, discussed in the CD and summarized in Section 3.3 below, are
remote from the portals of entry to the body (i.e., the respiratory system and gastrointestinal
tract). Consequently, the internal disposition and distribution of Pb is an integral aspect of the
relationship between exposure and effect. This section summarizes the current state of
knowledge of Pb disposition pertaining to both inhalation and ingestion routes of exposure as
described in the CD.
Inhaled Pb particles deposit in the different regions of the respiratory tract as a function
of particle size (CD, pp. 4-3 to 4-4). Lead associated with smaller particles, which are
predominantly deposited in the pulmonary region, may, depending on solubility, be absorbed
into the general circulation or transported via phagocytic cells to the gastrointestinal tract (CD,
pp. 4-3). Lead associated with larger particles, that are predominantly deposited in the head and
conducting airways (e.g., nasal pharyngeal and tracheobronchial regions of respiratory tract),
may be transported by mucociliary transport into the esophagus and swallowed, thus making its
way to the gastrointestinal tract (CD, pp. 4-3 to 4-4), where it may be absorbed into the blood
stream. Thus, Pb can reach the gastrointestinal tract either directional through the ingestion route
or indirectly following inhalation.
The absorption efficiency of Pb from the gastrointestinal (GI) tract varies with particle
size, as well as with the chemical form or matrix in which it is contained (CD, pp. 4-8 to 4-9).
One line of evidence for this comes from research using animal models to estimate relative
bioavailability (KB A) by comparing the absorbed fraction of ingested Pb for different test
materials relative to that for a highly water-soluble form of Pb. Relative bioavailability of Pb
from contaminated soils from different industrial sites (e.g., near Pb smelters, mines, etc), as
assessed in such models, have been found to differ markedly, with RBA values ranging from 6 to
100% (CD, pp. 4-8 to 4-10; Casteel et al., 2006). As stated in the CD, "variations in size and
mineral content of the Pb-bearing grains are the suspected cause of variations in the rate and
extent of GI absorption of Pb" occurring in soil from different contaminated locations (CD, p. 4-
9).
In addition to characteristics associated with the ingested Pb, GI absorption of Pb also
varies with an individual's physiology (e.g., maturity of the GI tract), and nutritional status (e.g.,
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iron and calcium deficiency increases absorption), as well as the presence of food in the GI tract
(CD, Section 4.2.1, pp. 4-5 to 4-8). With regard to GI tract maturity, estimates of Pb GI
absorption reported in the past for young children (-40-50%) are higher than those reported for
adults (CD, pp. 4-5 to 4-6). Several studies have reported that the presence of food in the GI
tract reduces the absorption of water-soluble Pb (CD, p. 4-6). A contributing factor to this
phenomenon is the presence of calcium, iron, and phosphate in the food, which depresses Pb
absorption (CD, pp. 4-6 to 4-7). Animal studies have also indicated that Vitamin D, which
regulates calcium absorption, enhances Pb absorption from the GI tract (CD, p. 4-7).
Once in the blood stream, where approximately 99% of the Pb associates with red blood
cells, the Pb is quickly distributed throughout the body (e.g., within days) with the bone serving
as a large, long-term storage compartment, and soft tissues (e.g., kidney, liver, brain, etc) serving
as smaller compartments, in which Pb may be more mobile (CD, Sections 4.3.1.4 and 8.3.1.).
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).
In several recent studies investigating the relationship between Pb exposure and blood Pb
in children (e.g., Lanphear and Roghmann 1997; Lanphear et al., 1998), blood Pb levels have
been shown to reflect Pb exposures, with particular influence associated with exposures to Pb in
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surface dust. Further, as stated in the CD "these and other studies of populations near active
sources of air emissions (e.g., smelters, etc.), substantiate the effect of airborne Pb and
resuspended soil Pb on interior dust and blood Pb" (CD, p. 8-22).
As mentioned earlier, blood Pb, although subject to influence by Pb levels in all organs
and tissues, is generally described as reflecting recent exposures (CD, Section 4.3.1.4). Inhaled
or ingested Pb quickly enters the blood, and Pb in the blood is available for exchange with the
soft and skeletal tissues, conceptually viewed as the fast (half-life of-28 days) and slow (half-
life may be decades in adults) turnover pools, respectively (CD, Section 4.3.1.4). Simulations
using biokinetic models indicate that blood Pb levels in adults achieve a new quasi-steady state
within 75-100 days (approximately 3-4 times the blood elimination half-life) subsequent to
abrupt increases in Pb uptake (CD, pp. 4-25 to 4-26). Similar models indicate a quicker response
of blood Pb levels in children (CD, p. 4-27 and Figure 4-5). Additionally, response of the blood
to reduction of a relatively brief Pb exposure appears to be faster than for an exposure of several
years, with estimated half-lives of approximately 9 months as compared to 30 months for the
longer exposure response (CD, pp. 4-25 to 4-26).
Blood Pb levels are extensively used as an index or biomarker of exposure by national
and international health agencies, as well as in epidemiological (CD, Sections 4.3.1.3 and 8.3.2)
and toxicological studies of Pb health effects and dose-response relationships (CD, Chapter 5).
The prevalence of the use of blood Pb as an exposure index or biomarker is related to both the
ease of blood sample collection (CD, p. 4-19; Section 4.3.1) and by findings of association with a
variety of health effects (CD, Section 8.3.2). As noted above, blood Pb levels respond to
elevations in exposure. Accordingly, the U.S. Centers for Disease Control and Prevention
(CDC), and its predecessor agencies, have for many years used blood Pb level as a metric for
identifying children at risk of adverse health effects and for specifying particular public health
recommendations (CDC, 1991; CDC, 2005). In 1978, when the current Pb NAAQS was
established, the CDC recognized a blood Pb level of 30 ug/dL as a level warranting individual
intervention (CDC, 1991). In 1985, the CDC recognized a level of 25 ug/dL for individual child
intervention, and in 1991, they recognized a level of 15 ug/dL for individual intervention and a
level of 10 ug/dL for implementing community-wide prevention activities (CDC, 1991; CDC,
2005). In 2005, with consideration of a review of the evidence by their advisory committee,
CDC revised their statement on Preventing Lead Poisoning in Young Children., specifically
recognizing the evidence of adverse health effects in children with blood Pb levels below 10
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ug/dL and the data demonstrating that no "safe" threshold for blood Pb had been identified, and
emphasizing the importance of preventive measures (CDC, 2005).l
Since 1976, the CDC has been monitoring blood Pb levels nationally through the
National Health and Nutrition Examination Survey (NHANES). This survey has documented
the dramatic decline in mean blood Pb levels in the U.S. population that has occurred since the
1970s and that coincides with regulations regarding leaded fuels, leaded paint, and Pb-containing
plumbing materials that have reduced Pb exposure among the general population (CD, Sections
4.3.1.3 and 8.3.3; Schwemberger et al., 2005). Although levels in the U.S. general population,
including geometric mean levels in children aged 1-5, have declined, mean levels have been
found to differ among children of different socioeconomic status (SES) and other demographic
characteristics (CD, p. 4-21). The health effects associated with blood Pb levels are extensively
discussed in the CD, while those of particular policy relevance for this review are summarized in
subsequent subsections of this chapter.
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).
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
With the 2005 statement, CDC identified a variety of reasons, reflecting both scientific and practical
considerations, for not lowering the 1991 level of concern, including a lack of effective clinical or public health
interventions to reliably and consistently reduce blood Pb levels that are already below 10 ug/dL, the lack of a
demonstrated threshold for adverse effects, and concerns for deflecting resources from children with higher blood
Pb levels (CDC, 2005).
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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 as compared to contributions from other sources.
3.3 NATURE OF EFFECTS
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).2
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 levels of Pb exposure. These are neurological, hematological and
immune effects for children, and neurological, hematological, cardiovascular and renal effects
for adults (See Tables 3-1 and 3-2), 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
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
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.htmX
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endpoints being induced by distinctly lower than previously demonstrated Pb exposures indexed
by blood Pb levels extending well below 10 ug/dL in children and/or adults" (CD, p. 8-25).
Tables 3-1 and 3-2 (CD, Tables 8-5 and 8-6) indicate some health effects associated with blood
Pb levels that extend below 5 ug/dL, and use the notation "(???)" to indicate that some studies
have observed these effects at the lowest blood levels considered. That is, threshold levels for
these effects cannot be discerned from the currently available studies.
The endpoints identified above and included in Tables 3-1 and 3-2 are important
considerations for this review and are described briefly in sections below, while detailed
discussion of the evidence is presented in the CD.
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Table 3-1. Summary of Lowest Observed Effect Levels for Key Lead-Induced Health Effects in Children (reproduced from
CD, Table 8-5).
Lowest Observed Effect
Blood Lead Level
Neurological Effects
Hematological Effects
Immune Effects
30 (ig/dL
Increased urinary 5-
aminolevulinic acid
15 (ig/dL
Behavioral disturbances
(e.g., inattention, delinquency)
Altered electrophysiological
responses
Erythrocyte protoporphyrin
(EP) elevation
10 (ig/dL
5 (ig/dL
0 (ig/dL
Effects on neuromotor function
CNS cognitive effects
(e.g., IQ deficits)
Inhibition of 5-aminolevulinic
acid dehydratase (ALAD)
Pyrimidine -5' -nuclotidase
(Py5N) activity inhibition
Effects on humoral (| serum IgE)
and cell-mediated (J, T-cell
abundance) immunity
Note: Arrows depict cases where weight of overall evidence strongly substantiates likely occurrence of type of effect in association with blood-Pb
concentrations in range of 5-10 ng/dL, or possibly lower, as implied by (???). Although no evident threshold has yet been clearly established for those
effects, the existence of such effects at still lower blood-Pb levels cannot be ruled out based on available data.
Source: Adapted/updated from Table 1-17 of U.S. Environmental Protection Agency (1986a).
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Table 3-2. Summary of Lowest Observed Effect Levels for Key Lead-Induced Health Effects in Adults (reproduced from CD,
Table 8-6).
Lowest Observed Effect
Blood Lead Level
Neurological Effects
Hematological Effects Cardiovascular Effects
Renal Effects
30 ug/dL
Peripheral sensory nerve
impairment
Erythrocyte
protoporphyrin (EP)
elevation in males
Impaired Renal Tubular
Function
20 ug/dL
15 ug/dL
Cognitive impairment
Postural sway
Erythrocyte
protoporphyrin (EP)
elevation in females
Increased urinary
5-aminolevulinic acid
10 ug/dL
5 ug/dL
0 ug/dL
Inhibition of
5-aminolevulinic acid
dehydratase (ALAD)
Elevated blood pressure
Elevated serum creatine
(J, creatine clearance)
Note: Arrows depict cases where weight of overall evidence strongly substantiates likely occurrence of type of effect in association with blood-Pb
concentrations in range of 5-10 ug/dL, or possibly lower, as implied by (???). Although no evident threshold has yet been clearly established for those
effects, the existence of such effects at still lower blood-Pb levels cannot be ruled out based on available data.
Source: Adapted/updated from Table 1-16 of U.S. Environmental Protection Agency (1986a).
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3.3.1 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 ug/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).
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 ug/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
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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 |ig/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 ug/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):
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 Pb-
induced per sever ation, 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 (<1Q ng/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 ug/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 ug/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.
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3.3.2 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 ug/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
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).
3.3.3 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 ug/dL). A recent meta-analysis by Nawrot and others
(2005), that included a range of blood Pb levels from 2.3 to 63.8 ug/dL, reported an association
of increased systolic blood pressure and decreased diastolic pressure with increased blood Pb
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level, including levels below 10 ug/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 ug/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
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).
3.3.4 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 ug/dL, to as low as ~2 to 4 ug/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"
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(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).
3.3.5 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
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 ug/dL, in children and adults (Tables 3-1 and 3-2; CD, Table 6-7). Blood
Pb concentrations at and above approximately 15 ug/dL, in children, and 15-30 ug/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).
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3.3.6 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 cell-
mediated 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 are supported by evidence in neonatal and juvenile animals and have
been reported in epidemiologic studies of children at blood Pb levels extending below 10 ug/dL
(CD, p. 6-197 and Sections 5.9.10 and 8.4.6).
3.4 LEAD-RELATED IMPACTS ON PUBLIC HEALTH
In addition to the advances in our knowledge and understanding of Pb health effects at
lower exposures (e.g., using blood Pb as the index), there has been some change with regard to
the U.S. population Pb burden since the time of the last Pb NAAQS review. For example, the
geometric mean blood Pb level for U.S. children aged 1-5, as estimated by the U.S. Centers for
Disease Control, declined from 2.7 ug/dL (95% CI: 2.5-3.0) in the 1991-1994 survey period to
1.7 ug/dL (95% CI: 1.55-1.87) in the 2001-2002 survey period (CD, Section 4.3.1.3).3 Blood Pb
levels have also declined in the U.S. adult population over this time period (CD, Section 4.3.1.3).
These observation however, should not be interpreted to mean that blood Pb levels declined in all
communities, or uniformly by this amount. As noted in the CD, "blood-Pb levels have been
declining at differential rates for various general subpopulations, as a function of income, race,
and certain other demographic indicators such as age of housing" (CD, p. 8-21).
The following discussion draws from the CD to characterize subpopulations potentially at
risk for Pb-related effects and potential public health impacts associated with exposure to
ambient Pb.
3 These levels are in contrast to the geometric mean blood Pb level of 14.9 ug/dL reported for U.S. children
(aged 6 months to 5 years) in 1976-1980 (CD, Section 4.3.1.3).
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3.4.1 At-risk Subpopulations
In considering at-risk subpopulations, 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). We also considered
evidence pertaining to 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,
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 5-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).
While early childhood is recognized as a time of increased susceptibility, a difficulty in
identifying a discrete period of susceptibility from epidemiological studies has been that the
period of peak exposure, reflected in peak blood Pb levels, is around 18-27 months when hand-
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to-mouth activity is at its maximum (CD, p. 6-60). The earlier Pb literature described the first 3
years of life as a critical window of vulnerability to the neurodevelopmental impacts of Pb (CD,
p. 6-60). Recent epidemiologic studies, however, have indicated a potential for susceptibility of
children to concurrent Pb exposure extending to school age (CD, pp. 6-60 to 6-64). It may be
that the influence of concurrent blood Pb (and exposures contributing to it) remains important
until school age with regard to the potential to affect cognitive development (CD, pp. 6-63 to 6-
64; Chen et al., 2005). The evidence indicates both the sensitivity of the first 3 years of life, and
a sustained sensitivity throughout the lifespan as the human central nervous system continues to
mature and be vulnerable to neurotoxicants (CD, Section 8.4.2.7). The animal evidence supports
our understanding of specific periods of development with increased vulnerability to specific
types of effect (CD, Section 5.3), and indicates a potential importance of exposures of duration
on the order of months. Evidence of a differing sensitivity of the immune system to Pb across
and within different periods of life stages indicates that Pb exposures of duration as short as
weeks to months may contribute to some effects. For example, the animal evidence suggests that
the gestation period is the most sensitive life stage followed by early neonatal stage, and within
these life stages, critical windows of vulnerability are likely to exist (CD, Section 5.9 and p. 5-
245).
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 consideration of public health impacts. Age or lifestage was used
to distinguish potential groups on which to focus the quantitative risk assessment (Chapter 4) in
recognition of its influence on exposure and susceptibility, and young children were selected as
the priority population for the risk assessment in consideration of the health effects evidence
regarding endpoints of greatest public health concern and in recognition of effects on the
developing nervous system as a sentinel endpoint for public health impacts of Pb (see Section
3.3). We recognize, however, other population subgroups as described above may also be at risk
of Pb-related health effects of public health concern.
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3.4.2 Potential Public Health Impact
There are several potential public health impacts associated with the current range of
population blood Pb levels, including potential impacts on population IQ, heart disease, and
chronic kidney disease (CD, Section 8.6). The quantitative implications of potential Pb-related
population impacts related to these health impacts are discussed in the CD (Sections 8.6.2, 8.6.3
and 8.6.4). With regard to IQ, it is noted that, given a somewhat uniform manifestation of Pb-
related decrements across the range of IQ scores in a population, "a downward shift in the mean
IQ value is not associated only with a substantial increase in the percentage of individuals
achieving very low scores, but also with substantial decreases in percentages achieving very high
scores" (CD, p. 8-81). For example, for a population mean IQ of 100 (and standard deviation of
15), 2.3% of the population would score above 130, but a shift of the population to a mean of 95
results in only 0.99% of the population scoring above 130 (CD, pp. 8-81 to 8-82).
In emphasizing the need to recognize distinctions between population and individual risk,
the CD notes that a "point estimate indicating a modest mean change on a health index at the
individual level can have substantial implications at the population level" (CD, p. 8-77). For
example, "the import of a decline for an individual's well-being is likely to vary depending on
the portion of the IQ distribution" such that "for an individual functioning in the low range due
to the influence of developmental risk factors other than Pb", a Pb-associated IQ decline of
several points might be sufficient to drop that individual into the range associated with increased
risk of educational, vocational, and social handicap (CD, p. 8-77). Similarly, "although an
increase of a few mmHg in blood pressure might not be of concern for an individual's well-
being, the same increase in the population mean might be associated with substantial increases in
the percentages of individuals with values that are sufficiently extreme that they exceed the
criteria used to diagnose hypertension" (CD, p. 8-77).
The magnitude of a public health impact is dependent upon the size of population
affected and type or severity of the effect. As summarized in Section 3.4.1, there are several
population groups that may be susceptible or vulnerable to effects associated with exposure to
Pb. They include young children, particularly those in families of low SES, as well as
individuals with hypertension, diabetes, and chronic renal insufficiency. Although
comprehensive estimates of the size of these groups residing in proximity to policy-relevant
sources of ambient Pb have not been developed, total estimates of these population
subpopulations within the U.S. are substantial (Table 3-3).
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Table 3-3. Population subgroups with characteristics that may contribute to increased
susceptibility or vulnerability to Pb health effects.
Estimated number
in U.S. population01
Year for estimate
Reference
Children3
Living in poverty
4.8 million
(20%)e
2005
DeNavas-Waltetal.,
2006
Adults" w.
hypertension0
-50 million
(25.6%)e
1999-2002
NCHS, 2005
Adults" w.
Diabetes
18 million
(8.7%)e
2002
CDC, 2003
Adults" w. chronic
kidney disease
19.2 million
(11%)e
1988-1994
Coresh etal., 2005
aChildren less than 6 years of age.
blndividuals greater than 20 year of age.
cHypertension, defined as blood pressure of 140/90 millimeters of mercury (mm Hg) or higher, using blood
pressure lowering medications, or having been told at least twice by a physician or other health professional
that they had high blood pressure (medical history).
dNote that there may be overlap among some groups (i.e., individuals may be counted in more than one
subgroup).
ePercent of age group.
The limited information available on air and surface soil concentrations of Pb indicates
elevated concentrations near stationary sources as compared with areas remote from such sources
(CD, Sections 3.2.2 and 3.8). The air quality analyses presented in Chapter 2 indicate
dramatically higher Pb concentrations at monitors near sources as compared with those more
remote (Section 2.3.2.4). We are handicapped, however, in our ability to characterize the size of
at-risk populations in areas influenced by policy-relevant sources of ambient Pb by the
significant limitations of our monitoring and emissions information. For example, size and
spatial coverage limitations of the current Pb monitoring network limits our ability to
characterize the levels of airborne Pb in the U.S. today (see Section 2.3.2.1). Further, the
available information on emissions and locations of sources indicates that the network is
inconsistent in its coverage of the largest sources identified in the 2002 NEI, with monitors
within a mile of only 2 of 26 facilities in the 2002 NEI with emissions greater than 5 tpy (Sect
2.3.2.1). Additionally, there are various uncertainties and limitations associated with source
information in the NEI (Section 2.2.5).
In recognition of the significant limitations associated with the currently available
information on Pb emissions and airborne concentrations in the U.S. and associated exposure of
potentially at-risk populations, we have summarized the information in several different ways.
None of these summaries is precisely what might be desired for this analysis, however, all are
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informative with regard to considering the prevalence of airborne Pb emissions (and associated
airborne Pb concentrations) and exposure of human populations.
Air quality analyses of the limited monitoring network indicates the numbers of
monitoring sites exceeding alternate NAAQS levels, with consideration of different statistical
forms (Section 2.3.2.5), and these analyses are summarized with regard to population size in
counties home to those monitoring sites (Appendix 5.A). Information for the monitors and from
the NEI indicates a range of source sizes in proximity to monitors at which various levels of Pb
are reported. Together this information suggests that there is variety in the size of sources (in
terms of Pb emissions, tpy) that may influence air Pb concentrations that may be of interest in
this review. Identifying specific emissions levels of sources expected to result in air Pb
concentrations of interest, however, would be informed by a comprehensive analysis using
detailed source characterization information that has not been feasible within the time and data
constraints of this review. Consequently, we have instead developed a summary of the emissions
and demographic information for Pb sources that includes estimates of the numbers of people
residing in counties in which the aggregate Pb emissions from NEI sources is greater than or
equal to 0.1 tpy (Table 3-4) or in counties in which the aggregate Pb emissions is greater than or
equal to 0.1 tpy per 1000 square miles (Table 3-5).
Table 3-4. Population size in counties with Pb emissions, by total emissions (tpy).
Total Pb
Emissions in
County (tpy)*
£10.0
5.0-10.0
1.0-5.0
0.5-1.0
0.1 -0.5
>0.1
Number of
Counties
20
37
346
320
1,165
1,888
Population
(1,000's)
25,756
20,180
116,496
42,995
56,287
261,715
Underage 5
population
(1,000's)
1,949
1,430
7,979
2,871
3,687
17,915
3 2002 NEI.
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Table 3-5. Population size in counties with Pb emissions, by emissions density.
Total Pb Emissions
Density in County
(tpy/1000 square miles)
> 10.0
5.0-10.0
1.0-5.0
0.5-1.0
0.1 -0.5
total > 0.1
Number of
Counties
77
80
463
301
1,105
2,026
Population
(1,000's)
32,911
39,278
108,700
32,589
52,352
265,829
Under age 5 population
(1,000's)
2,185
2,888
7,377
2,200
3,521
18,172
a2002NEI.
Additionally, the potential for historically deposited Pb near roadways to contribute to
increased risks of Pb exposure and associated risk to populations residing nearby is suggested in
the CD and also investigated in Chapter 4 of this document. Although estimates of the number
of individuals, including children, living within close proximity to roadways specifically
recognized for this potential have not been developed, these numbers may be substantial.4
3.5 SUMMARY AND CONCLUSIONS
Based on the available health effects evidence and the evaluation and interpretation of
that evidence in the CD, summarized briefly above, the following conclusions have been drawn:
• Lead exposures occur both by inhalation and by ingestion. Ingestion of Pb-
contaminated dust has a strong influence on blood Pb levels in children.
• Children, in general and especially low SES children, are at increased risk for Pb
exposure and Pb-induced adverse health effects. This is due to several factors,
including enhanced exposure to Pb via ingestion of soil Pb and/or dust Pb due to
childhood hand-to-mouth activity and poor nutritional status.
• Once inhaled or ingested, Pb is distributed by the blood, with long-term storage
accumulation in the bone. Bone Pb levels provide a strong measure of cumulative
exposure which has been associated with many of the effects summarized below,
For example, the 2005 American Housing Survey, conducted by the U.S. Census Bureau indicates that
some 14 million (or approximately 13% of) housing units are "within 300 feet of a 4-or-more-lane roadway, railroad
or airport" (U.S. Census Bureau, 2006). Additionally, estimates developed for Colorado, Georgia and New York
indicate that approximately 15-30% of the populations in those states reside within 75 meters of a major roadway
(i.e., a "Limited Access Highway", "Highway", "Major Road" or "Ramp", as defined by the U.S. Census Feature
Class Codes) (ICF, 2005).
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although difficulty of sample collection has precluded widespread use in
epidemiological studies to date.
• Blood levels of Pb are well accepted as an index of exposure (or exposure metric) for
which associations with the key effects (see below) have been observed. In general,
associations with blood Pb are most robust for those effects for which past exposure
history poses less of a complicating factor, i.e., for effects during childhood.
• Epidemiological studies have observed significant associations between Pb exposures
and a broad range of health effects. Many of these associations have been found at
levels of blood Pb that are currently relevant for the U.S. population, with children
having blood Pb levels of 5-10 ug/dL or, perhaps somewhat lower, being at notable
risk.
• Pb exposure is associated with a variety of neurological effects in children, notably
intellectual attainment, attention, and school performance. Both qualitative and
quantitative evidence, with further support from animal research, indicates a robust
effect of Pb exposure on neurocognitive ability at blood Pb levels levels in the range of
5 to 10 ug/dL, and some analyses appear to show Pb effects on intellectual attainment
in young children with blood Pb levels ranging from 2 to 8 ug/dL
• Deficits in cognitive skills may have long-term consequences over a lifetime. Poor
academic skills and achievement can have enduring and important effects on objective
parameters of success in real life as well as increased risk of antisocial and delinquent
behavior.
• For the quantitative risk assessment for neurocognitive ability in young children
(described in Chapter 4), the staff concludes that that it is appropriate to use nonlinear
concentration-response models that reflect the epidemiological evidence of a higher
slope of the blood Pb concentration-response relationship at lower blood Pb levels.
• For children, the evidence is also robust for Pb-induced disruption of heme synthesis at
blood Pb levels of approximately 15 ug/dL and higher. At blood Pb levels on the order
of 10 ug/dL, and slightly lower, associations have been found with effects to the
immune system, resulting in altered macrophage function, increased IgE levels and
associated increased risk for autoimmunity and asthma.
• In adults, epidemiological studies have consistently demonstrated associations between
Pb exposure and increased risk of adverse cardiovascular outcomes, including
increased blood pressure and incidence of hypertension, as well as cardiovascular
mortality. These associations have been observed with bone Pb and, for some studies
with blood Pb levels below 10 ug/dL. Animal evidence provides confirmation of Pb
effects on cardiovascular functions. For these Pb effects, particularly susceptible
subpopulations include those with a higher baseline blood pressure. For example,
African Americans, as a group, have greater incidence of elevated blood pressure than
other ethnic groups.
• Renal effects in adults, evidenced by reduced renal function, have also been associated
with Pb exposures indexed by bone Pb levels and also with blood Pb below 10 ug/dL,
with the potential adverse impact of such effects being enhanced for susceptible
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subpopulations including those with diabetes, hypertension, and chronic renal
insufficiency.
• Other Pb associated effects in adults occurring at or just above 10 ug/dL include
hematological (e.g., impact on heme synthesis pathway) and neurological effects, with
animal evidence providing support of Pb effects on these systems and evidence
regarding mechanism of action.
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4 CHARACTERIZATION OF HEALTH RISKS
4.1 INTRODUCTION
This chapter summarizes the human exposure and health risk assessments conducted in
support of the current review (throughout the remainder of this chapter, 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 assessments is
required). There are two phases to the risk assessment for the current review: pilot and full-
scale. The pilot phase was presented in the first draft Staff Paper and accompanying technical
report (USEPA, 2006a, 2006b; ICF, 2006), and was the subject of a review by the Clean Air
Scientific Advisory Committee (CASAC) on February 6 and 7, 2007 (Henderson, 2007a). The
initial full-scale analyses were presented in the July 2007 draft report (USEPA, 2007a) and were
the subject of a CASAC review at a public meeting on August 28 and 29, 2007 (Henderson,
2007b). In response to CASAC recommendations, additional analyses using a core modeling
approach were conducted to complete the full-scale assessment. The complete full-scale
assessment and background for these analyses, including CASAC advice on the pilot and draft
full-scale assessment and the risk assessment performed for the previous review, are presented in
the final Risk Assessment Report (USEPA, 2007b).
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. Not only are we constrained by the
timeframe allowed for this review (Section 1.2.3) in the context of the breadth of information to
address, we are also constrained by significant limitations with regard to data and tools needed
for the assessment. Further, the multimedia and persistent nature of Pb and the role of multiple
exposure pathways contributes significant additional complexity to the assessment as compared
to other assessments that focus only on the inhalation pathway.
Due to the limited data, models, and time available, we are not able to fully and
completely characterize in this risk assessment all of the various complexities associated with Pb.
Consequently, in our efforts to focus on and characterize risk associated with the ambient air-
related sources and exposures, we have made a number of simplifying assumptions in a number
of areas. 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). As illustrated in Figure 2-1 people are also exposed to Pb that originates from
nonair sources, including leaded paint or drinking water distribution systems. For purposes of
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this assessment, however, the Pb from these nonair sources is collectively referred to as "policy-
relevant background" l. Although Pb in diet and drinking water sources may derive from Pb
emitted into the ambient air, the contribution from air pathways to these exposure pathways
could not be explicitly modeled, such that these exposures are treated as policy-relevant
background. 2
In the Risk Assessment Report (USEPA, 2007b), we have made every effort to
completely describe the assessment design, data and methodology, to identify and describe
limitations and simplifying assumptions, and to characterize our understanding of the associated
uncertainty in the exposure and risk estimates. Due to the time constraints for preparation of this
Staff Paper, the risk assessment is only briefly summarized in this Chapter. Key limitations and
uncertainties associated with the design and associated results are briefly summarized in Section
4.2.7. We direct the reader to the Risk Assessment Report for a more complete presentation.
The remainder of this chapter is organized as follows. Section 4.1.1 provides an
overview of the human health risk assessment completed in the last review of the Pb NAAQS in
1990 (USEPA, 1990a). Section 4.1.2 describes advice received from CASAC during this
review. Section 4.2 provides a summary of the exposure and risk assessment, following which
are separate sections dedicated to summaries of the key findings of the exposure assessment
(Section 4.3) and risk assessment (Section 4.4).
4.1.1 Overview of Risk Assessment from Last Review
The risk assessment conducted in support of the last review used a case study approach to
compare air quality scenarios 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, 1990; USEPA, 1989). The case studies in that analysis, however, focused
exclusively on Pb smelters including two secondary and one primary smelter and did not
consider exposures in a more general urban context. Additionally, the analysis focused on
children (birth through 7 years of age) and middle-aged men. The staff evaluated impacts of
alternate NAAQS on numbers of children and men with blood Pb levels above levels of concern
based on health effects evidence at that time. The primary difference between the risk
assessment approach used in the current analysis and the assessment completed in 1990 involves
the risk metric employed. 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
1 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.
2 Further, although paint is a policy-relevant background_source, for this analysis, it may be reflected
somewhat in estimates developed for policy-relevant sources, due to modeling constraints (see USEPA, 2007b).
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ug/dL), the current analysis estimates changes in health risk, specifically 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.
4.1.2 CASAC Advice on Pilot and Initial Risk Analyses in this Review
The staff consulted with the CASAC on the draft analysis plan for the risk assessment
(USEPA, 2006c) in June, 2006 (Henderson, 2006). Some key comments provided by CASAC
members on the Analysis Plan included: (a) placing a higher priority on modeling the child IQ
metric than the adult endpoints (e.g., cardiovascular effects), (b) recognizing the importance of
indoor dust loading by Pb contained in outdoor air as a factor in Pb-related exposure and risk for
sources considered in this analysis, and (c) concurring with use of the IEUBK biokinetic blood
Pb model. Staff subsequently developed the pilot phase assessment, intended to test the risk
assessment methodology being developed for the full-scale assessment. The pilot is described in
the first draft Staff Paper and accompanying technical report (USEPA, 2006b; ICF 2006), which
was discussed by the CASAC Pb panel on February 6-7 (Henderson, 2007a).
Results from the pilot assessment, together with comments received from CASAC and
the public, informed decisions on the design of the full-scale analysis. The full-scale analysis
included a substitution of a more generalized urban case study for the location-specific near
roadway case study evaluated in the pilot. In addition, a number of changes were made in the
exposure and risk assessment approaches, including the development of a new indoor dust Pb
model focused specifically on urban residential locations and specification of additional IQ loss
concentration-response (C-R) functions to provide greater coverage for potential impacts at
lower exposure levels.
The draft full-scale assessment was presented in the July 2007 draft risk assessment
report (USEPA, 2007a) that was released for public comment and provided to CASAC for
review. In their review of the July draft risk assessment report, the CASAC Pb Panel made
several recommendations for additional exposure and health risk analyses (Henderson, 2007b).
Results from the initial full-scale analyses, along with comments from CASAC and the public
resulted in additional modifications and enhancements to the full-scale assessment, resulting in a
final version of the full-scale assessments which is described in this chapter and presented in
greater detail in the accompanying Risk Assessment Report and associated appendices (USEPA,
2007b). These include specification of additional IQ loss concentration-response functions and
the addition of a set of location-specific urban case studies.
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4.2 DESIGN OF EXPOSURE AND RISK ASSESSMENTS
This section provides an overview of key elements in the design of the risk assessment.
In the discussion below, we highlight key aspects of the assessment design, inputs, and methods,
and we also highlight key uncertainties and limitations for each. In so doing, we have identified
throughout this section, subsections of the Risk Assessment Report that provide the
corresponding detail.
In this section, we first focus on how this assessment builds on the health effects evidence
discussed in Chapter 3 with discussion of the endpoint, metric and model used here for risk
quantitation (Section 4.2.1). We also discuss key aspects of the assessment design (Sections
4.2.2 and 4.2.3) and the modeling approaches (Sections 4.2.4 through 4.2.6). In this discussion,
we only briefly summarize the modeling elements and refer the reader to the Risk Assessment
Report for full descriptions of methodologies employed. Lastly, in Section 4.2.7, we highlight
key limitations of the design and associated uncertainties in the resultant estimates. A more
complete presentation of limitations, sensitivity of particular inputs or models, and uncertainties
occurs in the Risk Assessment Report (USEPA, 2007b, Sections 3.5, 4.3 and 5.3.3).
4.2.1 Health Endpoint, Risk Metric and Concentration-response Functions
Of the health endpoints described in Section 3.3, the health endpoint on which we
focused in the quantitative health risk assessment for this review is developmental neurotoxicity
in children, 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 ug/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 ug/dL, and some analyses indicate
Pb effects on intellectual attainment of young children ranging from 2 to 8 ug/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
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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
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 ug/dL increase in blood Pb may have a greater effect
on IQ at blood Pb levels below 10 ug/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
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8-89).3 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
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 concentration-
response relationship described by these functions is greater at the lower blood Pb levels (e.g.,
less than 10 ug/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 ug/dL, 10 to 20 ug/dL, and 20 to 30 ug/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 ug/dL basis, estimates of
IQ decrement associated with blood Pb levels (concurrent, 24-month, peak, lifetime average or
lifetime cumulative) below 10 ug/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 ug/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
numbers 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)
3 Similarly, a nonlinear concentration-response relationship was observed for the relationship between
blood Pb levels and blood pressure in adults (CD, pp. 8-83 to 8-89).
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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 ug/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 ug/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 ug/dL was significantly greater than that observed for those with
a maximal blood lead level > 7.5 ug/dL (p-0.015)" and that "environmental lead exposure in
children who have maximal blood lead levels < 7.5 ug/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 7 cohorts (Lanphear et al.,
2005). The Rochester data included IQ test and concurrent blood Pb measurements taken at age
6 (Lanphear et al., 2005). The linear slope observed for this subset of the pooled dataset,
however, was notably greater than that previously reported for the low blood Pb subset of the
Rochester cohort at age 5 described above, and greater than those slopes from other studies for
blood Pb < 10 ug/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 ug/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
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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 4.1.2) 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 ug/dL and
• Population stratified dual linear function for concurrent blood Pb, derived from the
pooled dataset stratified at 7.5 ug/dL peak blood Pb.
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. 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 ug/dL and 10 ug/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
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were fitted to the data above and below the outpoint. 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 ug/dL and 244 children had maximal blood lead levels less than 10 ug/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
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 ug/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 (log-
linear function with low-exposure linearization, log-linear function with cutpoint, dual linear
function, stratified at 7.5 |ig/dL peak blood Pb, and dual linear function, stratified at 10 |ig/dL
peak blood Pb), we have assigned greatest confidence to risk estimates derived using the log-
linear 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.
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4.2.2 Case Studies
For this risk assessment, we have employed a case study approach to inform our
understanding of risks associated with exposure of children in the U.S. to Pb associated with
outdoor ambient air under current conditions, under conditions that would just meet the current
NAAQS, and under conditions associated with just meeting a range of alternative NAAQS. The
case study approach is described in Sections 2.2 (and subsections) and 5.1.3 of the Risk
Assessment Report (USEPA, 2007b); the assessment scenarios are described in Sections 2.3 (and
subsections) and 5.1.1 of that report.
The four types of case studies included in the assessment are the following:
• Location-specific urban case studies: Three urban case studies focused on specific
urban areas (Cleveland, Chicago and Los Angeles) have been modeled to provide
perspectives on the magnitude of ambient air Pb-related risk in specific urban
locations. Ambient air Pb concentrations are characterized using source-oriented and
other Pb-TSP monitors in these cities.
• General urban case study: Represents a nonlocation-specific analysis which uses
several simplifying assumptions regarding ambient air Pb levels and demographics to
produce a simplified representation of urban areas intended to inform our assessment
of the impact of changes in ambient Pb concentrations on risk.
• Primary Pb smelter case study: This case study is modeled to estimate risk for children
living in an area currently not in attainment with the current NAAQS, that is impacted
by Pb emissions from a primary Pb smelter. As such, this case study characterizes risk
for a specific highly exposed population and also provides insights on risk to child
populations living in areas near large sources of Pb emissions.
• Secondary Pb smelter case study: This case study was included in the initial analyses
for the full-scale assessment as an example of areas influenced by smaller point sources
of Pb emissions. As discussed in Section 4.2.7 below, however, we have recognized a
variety of significant limitations in the approaches employed for this case and
associated large uncertainties in these results which preclude the use of this case study
as illustrative of the larger set of areas influenced by similarly sized Pb sources. We
note that risk estimates for this case study (presented in detail in the Risk Assessment
Report) are lower than those for the other case studies.
4.2.3 Air Quality Scenarios
Air quality scenarios modeled for this analysis include (a) a current conditions scenario
for the location-specific urban case studies, the general urban case study, and the secondary Pb
smelter case study; (b) a current NAAQS scenario for the location-specific urban case studies,
the general urban case study, and the primary Pb smelter case study; and (c) a range of
alternative NAAQS scenarios for all case studies. The alternative NAAQS scenarios include 0.5,
0.2, 0.05, and 0.02 |ig/m3 (as maximum monthly averages) and 0.2 |ig/m3 (as a maximum
quarterly average). The current NAAQS scenario for the urban case studies involved a "rolling
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up" of ambient air Pb levels from current conditions. While EPA staff recognizes that it is
extremely unlikely that Pb concentrations in urban areas (see Section 2.3.2.4) would rise to meet
the current NAAQS and there are limitations and uncertainties associated with the approach used
(as described in Section 4.2.7 below), this scenario was included to provide some perspective on
risks associated with the current NAAQS relative to current conditions.
Details of the assessment scenarios, including a description of the derivation of Pb
concentrations for air and other media are presented in Sections 2.3 (and subsections) and
Section 5.1.1 of the Risk Assessment Report (USEPA, 2007b).
4.2.4 Categorization of Policy-relevant Exposure Pathways
To inform policy aspects of the Pb NAAQS review, we have attempted to parse the
assessment estimates for blood Pb and IQ loss into the fraction associated with background
sources (e.g., diet and drinking water) versus that associated with policy-relevant pathways,
which include inhalation, outdoor soil/dust ingestion and indoor dust ingestion. We have further
categorized the policy-relevant pathways into one of two categories, "recent air" or "past air".
Conceptually, the recent air category includes those pathways involving Pb that is or has recently
been in the outdoor ambient air, including inhalation and ingestion of indoor dust Pb derived
from recent ambient air (i.e., air Pb that has penetrated into the residence recently and loaded
indoor dust). Past air includes exposure contributions from ingestion of outdoor soil/dust that is
contacted on surfaces outdoors, and ingestion of indoor dust Pb that is derived from past air
sources (i.e., impacts from Pb that was in the ambient air in the past and has not been recently
resuspended into ambient air). In this assessment, as discussed further below, that portion of
indoor dust Pb not associated with recent air is classified as "other" and, due to technical
limitations, includes not only past air impacts, but also contributions from Pb paint. The reader
is referred to Sections 2.4.3 and 3.2.2 of the Risk Assessment Report for additional detail on
partitioning of exposure and risk between policy-relevant and background exposure pathways.
In simulating reductions in exposure associated with reducing ambient air Pb levels
through alternative NAAQS (and increases in exposure if the current NAAQS was reached in
certain case studies), our modeling has only affected the exposure pathways we categorize as
recent air (inhalation and ingestion of that portion of indoor dust associated with outdoor ambient
air). We have not simulated decreases in past air-related exposure pathways (e.g., reductions in
outdoor soil Pb levels following reduction in ambient air Pb levels and a subsequent decrease in
exposure through incidental soil ingestion and the contribution of outdoor soil to indoor dust).
This approach is likely to underestimate reductions in ambient air related exposure and risk.
Consequently, incremental reductions in exposure and risk estimated for alternative NAAQS
considered in the full-scale analysis, which reflect simulated reductions in the recent air
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category, are likely to be underpredictions of the impact of changes to the NAAQS on total Pb
exposure and health risk.
Additionally, there is uncertainty related to parsing out exposure and risk between
background sources and policy-relevant exposure pathways (and subsequent parsing of recent air
and past air) resulting from a number of technical limitations. Key among these is that, while
conceptually, Pb paint contributions to indoor dust Pb would be considered background and
included in modeling background exposures, due to technical limitations related to indoor dust
Pb modeling, ultimately, Pb paint was included as part of "other" indoor dust Pb (i.e., as part of
past air exposure). This means that total exposure and risk associated with past air would be
overestimated due to inclusion of Pb paint as part of "other" indoor dust Pb ingestion.
Uncertainty related to parsing of exposure and risk between background sources and policy-
relevant exposure pathways are discussed in Sections 2.4.3, 3.2.2 and 3.4 of the Risk Assessment
Report.
In summary, because of limitations in the assessment design, data and modeling tools, the
risk attributable to policy-relevant exposure pathways is bounded on the low end by the risk
estimated for the "recent air" category and on the upper end by the risk estimated for the "recent
air" plus "past air" categories.
4.2.5 Overview of Analytical Steps
The risk assessment includes four analytical steps, briefly discussed below. The reader is
referred to Sections 2.4.4, 3.1, 3.2, 4.1, and 5.1 of the Risk Assessment Report for additional
detail.
• Characterization ofPb in ambient air: The characterization of outdoor ambient air Pb
levels uses one of three basic approaches in each case study: (a) establishment of
exposure zones using source-oriented and non-source oriented monitors (location-
specific urban case studies), (b) establishment of a single exposure zone with uniform
levels of Pb in exposure media (general urban case study), or (c) air dispersion
modeling of Pb released from operations associated with a particular facility (point
source case studies).
• Characterization of outdoor soil/dust and indoor dust Pb concentrations: Outdoor soil
Pb levels were estimated using empirical data (including site-specific and/or national
datasets) and/or fate and transport modeling. Indoor dust Pb levels were predicted
using a combination of (a) regression-based models that relate indoor dust to ambient
air Pb and/or outdoor soil Pb, and (b) mechanistic models that predict indoor dust Pb
based on key mechanisms (e.g., exchange of outdoor air with indoor air, deposition
rates for Pb to indoor surfaces, house cleaning rates). For the point source case studies,
regression-based models obtained from the literature or developed based on site-
specific data were used, and a hybrid empirical-mechanistic model was developed and
used for the urban case studies. The model for urban case studies was developed as the
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available regression-based models had been developed largely based on residential
exposures near large point sources and were not considered representative of more
general urban exposures.
• Characterization of blood Pb levels: Blood Pb levels for each exposure zone are
derived from central-tendency blood Pb concentrations estimated using the Integrated
Exposure and Uptake Biokinetic (IEUBK) model. Concurrent or lifetime average
blood Pb is estimated from the IEUBK outputs as described in Section 3.2.1.1 of the
Risk Assessment Report. For the point source and location-specific urban case studies,
a probabilistic exposure model is used to generate population distributions of blood Pb
concentrations based on:
(a) the central tendency blood Pb levels for each exposure zone,
(b) demographic data for the distribution of children (less than 7 years of age)
across exposure zones in a given study area, and
(c) a GSD intended to characterize interindividual variability in blood Pb (e.g.,
reflecting differences in behavior and biokinetics related to Pb).
For the general urban case study, as demographic data for a specific location are not
considered, the GSD is applied directly to the central tendency blood Pb level to estimate
a population distribution of blood Pb levels.
• Risk characterization (estimatingIQ loss): Concurrent or lifetime average blood Pb
levels generated for each simulated child in each case study population are converted
into total Pb-related IQ loss estimates using the concentration-response functions
described in Section 4.2.1 (See Section 4.2.6 and Section 5.3.1 of the Risk Assessment
Report). The estimates of IQ loss are presented in two ways: (a) population-weighted
distributions of IQ loss from which specific population percentiles are identified, and
(b) for the location-specific urban case studies, population risk incidence distributions
providing estimates of the number of children with specific amounts of IQ loss in a
particular case study. There are a range of uncertainties associated with the
development and application of these concentration-response functions that are
summarized in Sections 4.2.1 and 4.2.7.
The urban case studies differ from the point source case studies in terms of how ambient
air Pb levels were characterized and in the specific mix of modeling and empirical data used to
characterize Pb levels in exposure media (e.g., outdoor soil and indoor dust). Key elements of
the approaches used in each of the case study categories are summarized below and described in
more detail in the Risk Assessment Report.
• Location-specific urban case studies: Study areas were defined based on the monitoring
data for that city in the 2003-2005 dataset analyzed in Chapter 2, with the monitor
locations used to define the outer extent of each study area. This resulted in study
areas with varying dimensions for the three cities including: Cleveland (5 miles by 5
miles), Chicago (20 miles by 5 miles) and Los Angeles (40 miles by 20 miles). For the
Cleveland and Chicago case studies, two types of exposure zones were modeled: (a)
zones associated with source oriented monitors and (b) zones associated with non
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source oriented monitors. Source oriented exposure zones extend one mile out from
each source-oriented monitor, and ambient air Pb for all children in those zones is
assumed to be at the level of that monitor. Children in the study area not associated
with these source-oriented zones are assigned ambient air Pb levels associated with the
nearest non source oriented monitor. This approach essentially assumes that source
oriented monitors have an impact close by, and that other monitors represent air Pb
levels more common across the study area. Because the Los Angeles case study does
not have any source oriented monitors, children in the study area were simply assigned
to the nearest monitor. All three case studies were modeled using the same soil Pb
levels as that used in the general urban case study (i.e., these were not location-
specific). The remainder of the modeling used the same core modeling approach as
employed for the general urban case study (see below). Demographic data (i.e., child
counts) were based on US Census 2000 block group data. See Sections 5.1 and 5.2 of
the Risk Assessment Report for details on these case studies.
• General urban case study: This case study was intended to characterize exposure and
risk for a child urban population under several simplifying assumptions including (a)
ambient air Pb levels for a given air quality scenario are assumed to be uniform across
the study area and (b) demographics are assumed uniform across the study area. This
essentially translates into a single study area with uniform ambient air Pb levels and
population total. Two current conditions scenarios were assessed for this case study:
(a) one based on the mean Pb-TSP level in U.S. urban areas of more than a million
population, and (b) a second high-end scenario based on the 95th percentile Pb-TSP
level for U.S. urban areas of more than a million people. No demographic data were
used in this case study, since it is not location specific.
• Primary and secondary Pb smelter case studies: Both of these case studies were
modeled using air dispersion model-derived ambient air Pb levels. Each was also
modeled using both a 10 km radius study area surrounding the facility and a smaller
1.5 km subarea, that omitted a large number of lower air Pb exposed children that
influenced the full study area results toward "lower risk" exposure and risk
distributions. These case studies used either U.S. Census 2000 block-level data
(secondary Pb smelter case study) or a combination of block and block group-level
data (primary Pb smelter case study) as the basis for generating population risk
projections. There are differences in the specific mix of empirical data and modeling
used for the two case studies to characterize Pb levels in indoor dust and outdoor
soil/dust.
4.2.6 Generating Multiple Sets of Risk Results
In the initial analyses for the full-scale assessment, staff implemented multiple modeling
approaches for each case study scenario in an effort to characterize the potential impact on
exposure and risk estimates of uncertainty associated with the limitations in the tools, data and
methods available for this risk assessment and with key analytical steps in the modeling
approach (e.g., prediction of indoor dust Pb levels given changes in outdoor ambient air Pb,
prediction of IQ loss given specific Pb blood levels). These multiple modeling approaches are
described in Section 2.4.6.2 of the Risk Assessment Report. In consideration of comments
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provided by CASAC (Henderson, 2007b) on these analyses regarding which modeling approach
they felt had greater scientific support, we identified a smaller set of modeling combinations as
the core approach for the subsequent new analyses (presented in Chapter 5 of the Risk
Assessment Report). The core modeling approach is described in detail in Sections 5.1.2,5.2 and
5.3 of the Risk Assessment Report (USEPA, 2007b). This approach includes the following key
elements:
• the hybrid indoor dust model specifically developed for urban residential applications,
• the IEUBK blood Pb model,
• the concurrent blood Pb metric,
• a geometric standard deviation (GSD) for concurrent blood Pb of 2.1 to characterize
interindividual variability in blood Pb levels, and
• four different functions relating concurrent blood Pb to IQ loss, including two log-
linear models (one with low-exposure linearization and one with a cutpoint) and two
dual-linear models with stratification, one stratified at 7.5ug/dL peak blood Pb and the
other at 10 ug/dL peak blood Pb (see Section 4.2.1).
The core modeling approach utilizes one overall modeling approach for estimating
exposure for each case study and then combines this with the four concentration-response
functions referenced above to derive four sets of risk results for each case study. Although we
have included risk results based on applying all four concentration-response models to provide
coverage for uncertainty related to this key modeling step, for reasons described in Section 4.2.1
above, we have greater confidence in the log-linear with low-exposure linearization
concentration-response function (LLL) and the results generated using that function are
emphasized below in summarizing risk estimates.
Exposure and risk estimates generated for the initial full set of modeling approaches
applied to the general urban case study and the two point source case studies are presented in
detail in Chapters 3 and 4 of the Risk Assessment Report (USEPA, 2007b). While the estimates
for the core modeling approach described above are emphasized in presenting the exposure and
risk results in this chapter, the fuller set of results provide additional perspective on uncertainty,
especially in relation to exposure modeling.
In addition to analyzing multiple modeling approaches to address potential uncertainty in
the overall analysis and illustrate the potential impact of that uncertainty on estimated exposure
and risk levels, we have also evaluated performance of models applied in the assessment (see
Sections 3.5.1 and 3.5.2 of the Risk Assessment Report) and performed sensitivity analyses to
characterize the potential impact of uncertainty in key analysis steps on exposure and risk
estimates (see Sections 4.3.2, 5.3.3.4 of the Risk Assessment Report).
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4.2.7 Key Limitations and Uncertainties
As recognized in Section 4.1 above, we have made a number of simplifying assumptions
in a number of areas of this assessment due to the limited data, models, and time available. We
have attempted to completely describe these assumptions, and related limitations and
uncertainties of the assessment design and results in the Risk Assessment Report (USEPA,
2007b). Key assumptions, limitations and uncertainties are only briefly identified below, and the
reader is referred to the Risk Assessment Report for detailed discussion. The aspects of the
assessment discussed below are considered by staff to be particularly important to the
interpretation of the exposure and risk estimates. .
Limitations in Assessment and Case Study Designs and Associated Uncertainty
• Temporal aspects: As described in Section 2.4.1 of the Risk Assessment Report,
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 of the Risk Assessment Report). These aspects are a
simplification of population exposures that contributes uncertainty to our exposure and
risk estimates.
• General urban case study: This case study differs from the others in several ways
(described in more detail in Risk Assessment Report, 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 (Risk
Assessment Report, Sections 2.4.2, 3.1.1 and 4.3.1). 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 substantial uncertainty and presumably an upward bias in
risk, particularly for large areas, across which air concentrations may vary
substantially.
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• 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 performance4. 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
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
(presented in Chapters 3 and 4 of the Risk Assessment Report) 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.
4 The information supporting the air dispersion modeling for the primary Pb smelter case study (see Section
3.5.1.1 of the Risk Assessment Report) provides substantially greater confidence in estimates for that case study.
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Limitations in Estimation of Ambient Air Pb concentrations and Associated Uncertainty
• Location-specific urban case studies: As recognized in Section 2.3.2.1, 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 group (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 of the Risk Assessment
Report. 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 block groups 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 significant 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 of the Risk Assessment
Report 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
substantial 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 outcome such as increases in emissions from
just one specific industrial operation that could lead 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, attainment of 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 of the Risk Assessment Report 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 attain
alternative NAAQS (see Sections 2.3.1 and 5.2.2.1 of the Risk Assessment Report).
We recognize that there is significant 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 that just meet lower alternative standards. For
example, control measures might be targeted only at the specific area exceeding
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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 significant uncertainty associated with estimates
for the alternate NAAQS scenarios.
Limitations in Estimation of Indoor Dust and Outdoor Soil/Dust Pb Concentrations and
Associated Uncertainty
• 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 of the Risk Assessment
Report, however, there is potentially significant 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.
• Estimates of indoor dust Pb concentrations for the urban case studies (application of
the hybrid model): The hybrid mechanistic-empirical model for estimating indoor dust
Pb for the urban case studies (see Section 3.1.4.1 of the Risk Assessment Report) has
several sources of uncertainty that could significantly impact its estimates. These
include: (a) failure to consider house-to-house variability in factors related to
infiltration of outdoor ambient air Pb indoors and subsequent buildup on indoor
surfaces, (b) limitations in data available on the rates and efficiency of indoor dust
cleaning and removal, (c) limitations in the method for converting model estimates of
dust Pb loading to dust Pb concentration needed for blood Pb modeling, and (d) the
approach employed to partition estimates of dust Pb concentration into "recent air" and
"other" components (see Section 5.3.3.4 of the Risk Assessment Report). These last
two sources of uncertainty reduce our confidence in estimates of apportionment of dust
Pb between "recent air" and "other". In recognition of this limitation, we have, in
evaluating exposure and risk reduction trends related to reducing ambient air Pb levels,
focused on changes in total blood Pb rather than on estimates of "recent air" blood Pb.
See Section 4.3.1 of the Risk Assessment report for additional discussion of
uncertainty associated with indoor dust modeling for the urban case studies, and
Section 5.3.3.4 of the Risk Assessment Report for discussion of a sensitivity analysis
of the approach used in estimating the "other" category of indoor dust Pb.
• 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 of the Risk Assessment Report), 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
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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 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, 3.5.1.3 and 4.3.1 of the Risk Assessment Report for additional
discussion.
Limitations in Estimation of Blood Pb Concentrations and Associated Uncertainty
• Characterizing interindividual variability using a GSD: There is uncertainty
associated with the GSD specified for each case study (see Sections 3.2.3 and 5.2.2.3
of the Risk Assessment Report for additional detail on GSDs). Two factors are
described here as contributors to that uncertainty. Interindividual variability in blood
Pb levels for any study population (as described by the GSD) will reflect, to a certain
extent, spatial variation in media concentrations, including outdoor ambient air Pb
levels and indoor dust Pb levels. For each case study, there is significant uncertainty in
the specification of spatial variability in ambient air Pb levels and associated indoor
dust Pb levels, as noted above. In addition, there are a limited number of datasets for
different types of residential child populations from which a GSD can be derived (e.g.,
NHANES datasets for more heterogeneous populations and individual study datasets
for likely more homogeneous populations near specific industrial Pb sources). This
uncertainty associated with the GSDs introduces significant uncertainty in exposure
and risk estimates for the 95th population percentile.
• Exposure pathway apportionment for higher percentile blood Pb level and IQ loss
estimates: 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 significant 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. Because of this uncertainty related to pathway apportionment, as
mentioned earlier, we have placed greater emphasis on estimates of total Pb exposure
and risk in evaluating the impact of the current NAAQS and alternative NAAQS
relative to current conditions.
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Limitations in Estimation oflQ Loss and Associated Uncertainty
• Relating bloodPb levels to IQ loss: Specification of the quantitative relationship
between blood Pb level and IQ loss is subject to significant uncertainty at lower blood
Pb levels (e.g., below 5 |ig/dL concurrent blood Pb). As discussed in Section 4.2.1,
there are limitations in the datasets and concentration-response analyses available for
characterizing the concentration-response relationship at these lower blood Pb levels.
For example, the pooled international dataset analyzed by Lanphear and others (2005)
includes relatively few children with blood Pb levels below 5 |ig/dL and no children
with levels below 1 |ig/dL (see Section 4.2.1). 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 |ig/dL (see Section
4.3). In recognition of the uncertainty in specifying a quantitative concentration-
response relationship at such levels, our core modeling approach involves the
application of four different functions to generate a range of risk estimates (see Section
4.2.6 and Section 5.3.1 of the Risk Assessment Report). The range of absolute IQ loss
seen for a given case study/air quality scenario combination when modeled using the
four concentration-response functions is typically close to a factor of 3. However, we
note that the relative difference (in terms of percent change) in IQ loss between
individual_air quality scenarios (i.e., the pattern of risk reduction across air quality
scenarios) is fairly consistent across all four models. However, the function producing
higher overall risk estimates (the dual linear function, stratified at 7.5 |ig/dL, peak
blood Pb) will also produce larger absolute reductions in IQ loss compared with the
other three functions.
4.3 EXPOSURE ASSESSMENT
Exposure results generated for the full-scale analysis are summarized in Tables 4-1 and
4-2 at the end of this section. These tables include estimates of blood Pb levels for the median
and 95th population percentile, respectively. Each table presents estimated blood Pb levels
resulting from total Pb exposure across all pathways (policy-relevant and background), as well as
estimates of percent contribution from "recent air" and "recent plus past air" exposure categories.
As noted in Section 4.2.4 (and Section 3.4 of the Risk Assessment Report), given the various
limitations of our modeling tools, the contribution to blood Pb levels from air-related exposure
pathways and current levels of Pb emitted to the air (including via resuspension) are likely to fall
between contributions attributed to "recent air" and those attributed to "recent plus past air".
Key uncertainties regarding partitioning dust Pb into "recent air" and "other" categories are
summarized in Section 4.2.7.
Time limitations in preparing this Staff Paper have resulted in our providing here only a
brief summary of the exposure assessment results. However, these results need to be understood
in the context of the broader and more comprehensive and detailed presentation provided in the
Risk Assessment Report (USEPA, 2007b). Listed below are key observations related to the
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exposure assessment based on estimates presented in Tables 4-1 and 4-2. This section is
organized by ambient air quality scenario category beginning with observations regarding
estimated exposures under current conditions, followed by observations related to the current
NAAQS, and concluding with observations regarding the alternative NAAQS scenarios.
In presenting these observations, we reference both median and 95th population percentile
estimates of concurrent blood Pb levels. It is important to note that 5 percent of the child study
population at each case study would have exposures above the high-end exposures presented
here, although due to technical limitations we believe that it is not possible at this point to
reasonably predict the distribution of exposures for that top 5 percent.
Current Conditions
This section presents observations regarding the blood Pb estimates for the current
conditions scenarios.
• Current Pb concentrations for the three location-specific urban case studies in terms of
maximum quarterly average are 0.09, 0.14 and 0.36 ug/m3 for the study areas in Los
Angeles, Chicago and Cleveland, respectively. In terms of maximum monthly average
the values are 0.17 ug/m3' 0.31 ug/m3 and 0.56 ug/m3 for the study areas in Los
Angeles, Chicago and Cleveland, respectively.
• Median estimates of total blood Pb level range from 1.7 to 1.8 ug /dL for the three
location-specific urban case studies, with the percent associated with ambient air Pb
estimated to fall between the estimate for recent air (17%) and that for recent plus past
air (71%) (see Table 4-1). Estimates for the 95th percentile range from 5.9 to 6.1 ug/dL
with the percent associated with ambient air Pb estimated to fall between 15% and 68%
(see Table 4-2).
• Two current conditions scenarios were considered for the general urban case study:
0.14 ug/m3 as a maximum quarterly average, the mean for large urban areas, and 0.87
ug/m3 as a maximum quarterly average, the high-end estimate for large urban areas
(see Table 4-1). Median estimates of total blood Pb for these two scenarios were very
similar at 1.9 and 2.1 ug/dL (see Table 4-1). In both cases, the percent associated with
ambient air Pb is estimated to fall between 32% and 76% of total Pb exposure.
Estimated total blood Pb levels for the 95th percentile of the distribution are 6.5 ug/dL
for the mean scenario and 7.2 ug/dL for the high-end scenario (see Table 4-2).
Current NAAQS
This section presents observations regarding blood Pb estimates for the current NAAQS
scenario in which ambient air Pb levels are simulated to just meet the current NAAQS level of
1.5 ug/m3, as a maximum quarterly average.
• Estimates of median total blood Pb for the current NAAQS scenario in the three
location-specific urban case studies range from 2.1 to 3.0 ug/dL with the percent
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associated with ambient air estimated to fall between the estimates for recent air (50-
63%) and those for recent plus past air (81-86%) (see Table 4-1). Estimates of total
blood Pb exposures for the 95th percentile range from 7.4 to 10.2 ug/dL with the
percent associated with ambient air estimated to fall between the estimates for recent
air (31-65%) and those for recent plus past air (73-87%) (see Table 4-2). The
similarity of the proportion of the contribution from ambient air Pb to total blood Pb
for the median and 95th percentile child scenarios suggests the key role played by the
interindividual GSD in determining 95th percentile exposures for the location-specific
urban case studies (i.e., spatial gradients, which are less significant in these urban case
studies, are less important in producing elevated levels of exposure). Both the median
and 95th percentile blood Pb estimates for current NAAQS suggest a significant
increase in blood Pb levels compared with blood Pb levels estimated for the current
conditions (see last section). Specifically, median blood Pb levels are approximately 1
|ig/dL higher under the current NAAQS scenario compared with the current conditions
scenario, while 95th percentile levels range from 1.5 to 4 |ig/dL higher under the
current NAAQS scenario.
• Blood Pb estimates for the general urban case study are slightly higher than estimates
for the location-specific urban case studies, with a median estimate for total blood Pb
of 3.1 |ig/dL and a 95th percentile estimate of 10.6 |ig/dL (see Tables 4-1 and 4-2,
respectively). Contributions of ambient air Pb to total blood Pb are somewhat higher in
this case study than they are for the location-specific urban case studies. This finding
is expected since the location-specific urban case study assumes that the entire study
area is at the current NAAQS level, while the location-specific urban case studies will
have portions of the study areas at that level and the remainder at levels notably lower
than the current NAAQS. Estimates for the median child for both the mean and high-
end current conditions scenarios indicate a decrease of about 1 |ig/dL in total blood Pb
for the current conditions scenario compared with the current NAAQS scenario. The
difference is even more pronounced for the 95th percentile child, with the mean current
conditions scenario differing by 4 |ig/dL from the current NAAQS scenario and the
high-end current conditions scenario differing by 3.4 |ig/dL.
• For the primary Pb smelter case study (full study area) the estimate of median total
blood Pb is 1.5 |ig/dL with 53% of this resulting from recent plus past air (see Table 4-
1). The 95th percentile estimate for total blood Pb is 4.6 |ig/dL, with 61% of this
coming from recent plus past air (see Table 4-2). Both the median and 95th percentile
total blood Pb estimates are significantly lower than those generated for the urban
scenarios, reflecting in part the fact that characterization of ambient air Pb levels for
the primary Pb smelter did not consider contributions from other sources besides the
smelter (e.g., resuspension of road dust, other industrial sources) that might contribute
to exposures further from the facility. This underprediction bias is more important for
areas further from the facility which include a large segment of the modeled study
population. By contrast, the general urban case study and location-specific urban case
studies are based on monitoring data, which will reflect the contribution from all Pb
sources in the vicinity of the monitors.
• Blood Pb estimates for the 1.5 km subarea of the primary Pb smelter case study were
markedly elevated for the current NAAQS scenario compared with the corresponding
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estimates for the full study area. This reflects the fact that the analysis of the subarea
focused on a subpopulation experiencing significantly greater ambient air Pb levels
(due to their proximity to the facility). The median estimate of total blood Pb level is
4.6 |ig/dL with 87% of that exposure resulting from recent plus past air Pb (see Table
4-1). The 95th percentile estimate of total blood Pb level is 12.3 |ig/dL, with 83% of
that exposure resulting from recent plus past air Pb (see Table 4-2). The larger fraction
of total Pb exposure associated with ambient air Pb for the subarea compared with the
full study area (83 to 87% compared with 53 to 61%) indicates that elevated blood Pb
levels for the subarea result from significantly greater ambient air Pb contributions.
Alternative NAAQS
This section presents observations regarding the blood Pb estimates resulting from
modeling of exposure assuming that each of the case studies just meets each of the alternative
NAAQS levels.
• The current air Pb concentrations in the study areas of the three location-specific urban
case studies fall near and within the upper end of the range of alternate NAAQS
considered (i.e., 0.2 |ig/m3 maximum monthly average to 0.5 |ig/m3 maximum
quarterly average). In modeling alternate NAAQS for these case studies, we only
considered those alternative NAAQS that were either equal to or lower than current
conditions at each location. This meant that the three lowest alternative NAAQS were
considered for Chicago, all of the alternative NAAQS were considered for Cleveland
and only the two lowest were considered for Los Angeles. The remaining case studies
were evaluated for the full set of alternative NAAQS.
• For the two location-specific urban case studies at which the higher alternative
NAAQS (0.5 and 0.2 |ig/m3, maximum monthly average and 0.2 |ig/m3 maximum
quarterly average) were simulated, median estimates of total blood Pb levels range
from 1.7 to 1.8 |ig/dL (see Table 4-1). This range is similar to the estimates for current
conditions for the three case studies, which is expected given the similarity of current
conditions to these alternative NAAQS. Estimates of median total blood Pb levels for
the lowest alternative NAAQS (0.02 and 0.05 |ig/m3 maximum monthly average) show
a slight reduction compared with current conditions (i.e., equal to or less than a 0.2
|ig/dL reduction - see Table 4-1). Estimates of 95th percentile total blood Pb levels for
the higher alternative NAAQS (0.5 and 0.2 |ig/m3 maximum monthly average and 0.2
|ig/m3 maximum quarterly average) range from 5.7 to 6.0 |ig/dL (see Table 4-2). These
estimates differ from those for current conditions by about 0.4 |ig/dL or less. Estimates
of 95th percentile total blood Pb levels under the lowest alternative NAAQS (0.02 and
0.05 |ig/m3 maximum monthly average) shows a significantly larger reduction in blood
Pb levels compared to current conditions, with this drop ranging from roughly 0.5 to
0.8 |ig/dL (see Table 4-2).
• The primary Pb smelter (full study area) shows no discernable reduction in median
total blood Pb levels across any of the alternative NAAQS (see Table 4-1), again
reflecting the fact that characterization of ambient air Pb levels for this study area did
not consider sources other than the facility and consequently, portions of the study area
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further from the facility have relatively low ambient air-related exposures. However, a
moderate reduction in 95th percentile total blood Pb levels is seen across the alternative
NAAQS, with the highest alternative NAAQS (0.5 |ig/m3 maximum monthly average)
estimated at 4.2 |ig/dL and the lowest alternate NAAQS (0.02 |ig/m3 maximum
monthly average) estimated at 3.8 |ig/dL (see Table 4-2).
• The primary Pb smelter (1.5km subarea) presents a much more substantial trend in
reduction of estimated median total blood Pb levels across alternative NAAQS.
Median total blood Pb levels are estimated at 3.2 |ig/dL for the highest alternative
NAAQS (0.5 |ig/m3 maximum monthly average), decreasing to 2.3 |ig/dL for the
intermediate alternative NAAQS (0.2 |ig/m3 maximum monthly average) and finally
dropping to 1.6 |ig/dL for the lowest alternative NAAQS (0.02 |ig/m3 maximum
monthly average) (see Table 4-1). This trend is also seen for 95th percentile estimates
of total blood Pb levels, but it is even stronger, with the highest alternative NAAQS
estimated at 8.5 |ig/dL, dropping to 6.1 |ig/dL (for the 0.2 |ig/m3 maximum monthly
average NAAQS) and reaching 4.2 |ig/dL with the lowest alternative NAAQS (see
Table 4-2).
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Table 4-1. Summary of blood Pb estimates for median total blood Pb.
Air Quality Scenario
(and case study)
Location-specific (Chicago)
Current NAAQS (1.5 ug/m , max quarterly)
Current conditions
(0.14 ug/m3 max quarterly; 0.31 ug/m3 max monthly)
Alternative NAAQS (0.2 ug/nT3, max monthly)
Alternative NAAQS (0.05 ug/mj, max monthly)
Alternative NAAQS (0.02 ug/nT3, max monthly)
Policy-relevant source
contribution (percent) of
total blood Pb
Recent
Air"
63%
22%
17%
6%
1%
Recent Air
plus Past Air
83%
67%
67%
69%
63%
Concurrent
blood Pb
concentration
(total Pb exposure) a
3.0
1.8
1.8
1.6
1.6
Location-specific (Cleveland)
Current NAAQS (1.5 ug/rrr3, max quarterly)
Current conditions
(0.36 ug/m3 max quarterly; 0.56 ug/m3 max monthly)
Alternative NAAQS (0.5 ug/nT3, max monthly)
Alternative NAAQS (0.2 ug/nr3, max quarterly)
Alternative NAAQS (0.2 ug/nT3, max monthly)
Alternative NAAQS (0.05 ug/mj, max monthly)
Alternative NAAQS (0.02 ug/mj, max monthly)
57%
17%
39%
12%
6%
1%
1%
86%
67%
72%
65%
65%
63%
63%
2.1
1.8
1.8
1.7
1.7
1.6
1.6
Location-specific (Los Angeles)
Current NAAQS (1.5 ug/nr3, max quarterly)
Current conditions
(0.09 ug/m3 max quarterly; 0.17 ug/m3 max monthly)
Alternative NAAQS (0.05 ug/mj, max monthly)
Alternative NAAQS (0.02 ug/mj, max monthly)
50%
18%
13%
6%
81%
71%
69%
63%
2.6
1.7
1.6
1.6
General urban
Current NAAQS (1.5 ug/nr3, max quarterly)
Alternative NAAQS (0.5 ug/nT3, max monthly)
Current conditions -high-end (0.87 ug/m'3, max quarterly)
Alternative NAAQS (0.2 ug/m , max quarterly)
Current conditions - mean (0.14 ug/irr3, max quarterly)
Alternative NAAQS (0.2 ug/nr3, max monthly)
Alternative NAAQS (0.05 ug/nT3, max monthly)
Alternative NAAQS (0.02 ug/mj, max monthly)
61%
41%
38%
35%
32%
26%
12%
6%
84%
73%
76%
75%
74%
74%
65%
69%
3.1
2.2
2.1
2.0
1.9
1.9
1.7
1.6
Primary Pb smelter - full study area
Current NAAQS (1.5 ug/nr3, max quarterly)
Alternative NAAQS (0.5 ug/nT3, max monthly)
Alternative NAAQS (0.2 ug/nT3, max quarterly)
Alternative NAAQS (0.2 ug/mj, max monthly)
Alternative NAAQS (0.05 ug/mj, max monthly)
Alternative NAAQS (0.02 ug/nT3, max monthly)
MA
53%
79%
50%
36%
50%
64%
1.5
1.4
1.4
1.4
1.4
1.4
Primary Pb smelter - 1.5km study area
Current NAAQS (1.5 ug/nT3, max quarterly)
Alternative NAAQS (0.5 ug/nT3, max monthly)
Alternative NAAQS (0.2 ug/nT3, max quarterly)
Alternative NAAQS (0.2 ug/nT3, max monthly)
Alternative NAAQS (0.05 ug/nr3, max monthly)
Alternative NAAQS (0.02 ug/nT3, max monthly)
MA
87%
81%
72%
78%
65%
69%
4.6
3.2
2.5
2.3
1.7
1.6
a - All values 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; "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 of the Risk Assessment Report).
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th
Table 4-2. Summary of blood Pb level estimates for 95 percentile total blood Pb.
Air Quality Scenario
(and case study)
Policy-relevant
contribution (percent)
of total blood Pb
Recent
A" b
Air
Recent
plus Past
« • b
Air
Concurrent blood
Pb concentration
(total Pb
exposure)3
Location-specific (Chicago)
Current NAAQS (1.5 ug/nT3, max quarterly)
Current conditions
(0.14 ug/m3 max quarterly; 0.31 ug/m3 max monthly)
Alternative NAAQS (0.2 ug/nT3, max monthly)
Alternative NAAQS (0.05 ug/nr3, max monthly)
Alternative NAAQS (0.02 ug/nT3, max monthly)
65%
18%
25%
5%
4%
87%
68%
70%
65%
67%
10.2
6.0
6.0
5.5
5.4
Location-specific (Cleveland)
Current NAAQS (1.5 ug/nT3, max quarterly)
Current conditions
(0.36 ug/m3 max quarterly; 0.56 ug/m3 max monthly)
Alternative NAAQS (0.5 ug/nT3, max monthly)
Alternative NAAQS (0.2 ug/mj, max quarterly)
Alternative NAAQS (0.2 ug/nT3, max monthly)
Alternative NAAQS (0.05 ug/mj, max monthly)
Alternative NAAQS (0.02 ug/nT3, max monthly)
31%
15%
25%
12%
7%
2%
2%
73%
67%
70%
67%
67%
65%
66%
7.4
6.1
6.0
5.8
5.7
5.4
5.3
Location-specific (Los Angeles)
Current NAAQS (1.5 ug/rrr3, max quarterly)
Current conditions
(0.09 ug/m3 max quarterly; 0.17 ug/m3 max monthly)
Alternative NAAQS (0.05 ug/mj, max monthly)
Alternative NAAQS (0.02 ug/nT3, max monthly)
52%
19%
9%
4%
80%
68%
67%
65%
8.9
5.9
5.5
5.4
General urban
Current NAAQS (1.5 ug/nT3, max quarterly)
Alternative NAAQS (0.5 ug/nT3, max monthly)
Current conditions -high-end (0.87 ug/nT3, max quarterly)
Alternative NAAQS (0.2 ug/m , max quarterly)
Current conditions - mean (0.14 ug/rrr3, max quarterly)
Alternative NAAQS (0.2 ug/nT3, max monthly)
Alternative NAAQS (0.05 ug/mj, max monthly)
Alternative NAAQS (0.02 ug/mj, max monthly)
60%
39%
38%
34%
29%
27%
12%
7%
83%
76%
75%
74%
72%
72%
68%
67%
10.6
7.4
7.2
6.8
6.5
6.4
5.7
5.5
Primary Pb smelter - full study area
Current NAAQS (1.5 ug/rrr3, max quarterly)
Alternative NAAQS (0.5 ug/nT3, max monthly)
Alternative NAAQS (0.2 ug/mj, max quarterly)
Alternative NAAQS (0.2 ug/nT3, max monthly)
Alternative NAAQS (0.05 ug/nT3, max monthly)
Alternative NAAQS (0.02 ug/nT3, max monthly)
NA
61%
74%
60%
63%
50%
84%
4.6
4.2
4.0
4.0
3.8
3.8
Primary Pb smelter - 1.5km study area
Current NAAQS (1.5 ug/rrr3, max quarterly)
Alternative NAAQS (0.5 ug/nT3, max monthly)
Alternative NAAQS (0.2 ug/nT3, max quarterly)
Alternative NAAQS (0.2 ug/nT3, max monthly)
Alternative NAAQS (0.05 ug/nT3, max monthly)
Alternative NAAQS (0.02 ug/mj, max monthly)
NA
83%
89%
89%
80%
78%
71%
12.3
8.5
6.6
6.1
4.5
4.2
a - All values 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 of the Risk Assessment Report).
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4.4 RISK ASSESSMENT
Risk results generated for the full-scale analysis are summarized in Tables 4-3 through 4-
at the end of this section. These tables present three types of risk metrics:
• Estimates of IQ loss for all air quality scenarios: Tables 4-3 and 4-4 present IQ loss
estimates for total Pb exposure for each of the air quality scenarios simulated for each
case study. Table 4-3 presents estimates for the population median and Table 4-4
presents results for the 95th percentile. To reflect the variation in estimates derived
from the four different concentration-response functions, the lowest and highest
estimates are presented for each scenario along with estimates for the LLL model.
These are labeled in these tables as (a) low C-R function (this is either the dual linear
with stratification at 10 |ig/dL, peak blood Pb, or the log-linear with cutpoint,
depending on the blood Pb level); (b) the LLL C-R function (log-linear with low-
exposure linearization); and (c) the high C-R function (the dual linear with
stratification at 7.5 |ig/dL, peak blood Pb).
• Estimates of IQ loss under the current NAAQS air quality scenario: Tables 4-5 and
4-8 present estimated IQ loss for total Pb exposure based on simulation of just meeting
the current NAAQS for the case studies to which the core modeling approach was
applied. Specifically, Table 4-5 presents estimates of total Pb-related IQ loss for the
population median, and Table 4-6 presents estimates for the 95th percentile. Both of
these tables present estimated total IQ loss (reflecting both policy-relevant pathways
and background sources) as well as the estimated range of IQ loss associated with
policy-relevant exposures alone (bounded by estimates for "recent air" and for "recent
plus past air").
• IQ loss incidence estimates for the three location-specific urban case studies:
Estimates of the number of children projected to have total Pb-related IQ loss greater
than one point are summarized in Table 4-7, and similar estimates for IQ loss greater
than 7 points are summarized in 4-8. Also presented are the changes in incidence of
the current NAAQS and alternative NAAQS scenarios compared to current conditions.
Estimates are presented for each of the four concentration-response functions used in
the core analysis. The complete set of incidence results is presented in Risk
Assessment Report Appendix O, Section O.3.4.
Time limitations in preparing this Staff Paper have resulted in our providing here only a
brief summary of the risk assessment results. These results, however, need to be understood in
the context of the broader and more comprehensive and detailed presentation provided in the
Risk Assessment Report (USEPA, 2007b). Listed below are key observations related to the risk
assessment, and as such, they draw on estimates presented in Tables 4-3 through 4-8. These
observations are organized by type of ambient air quality scenario beginning with observations
regarding the current conditions scenarios, followed by observations regarding the current
NAAQS scenarios, and concluding with observations regarding the set of alternative NAAQS
evaluated.
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As described in Section 4.2.5 above, we included four blood Pb concentration-response
functions relating blood Pb and IQ loss in the core modeling approach to provide coverage for
uncertainty in this key modeling step. However, for the reasons described in Section 4.2.1, we
place greater confidence in one of these functions (the log-linear with low-exposure
linearization, or LLL function). We note that, risk estimates generated using the LLL model fall
intermediate between estimates generated using the other three functions. Estimates derived
using the log-linear function with cutpoint and the dual-linear function with stratification at 10
|ig/dL (peak blood Pb) yield the lowest risk estimates and the dual-linear function with
stratification at 7.5 |ig/dL (peak blood Pb) yields the highest risk estimates. Because of the
greater confidence placed in the LLL function and because it generates risk estimates generally
bounded by the other three functions, we emphasize risk estimates generated using the LLL
function in this discussion. All risk estimates discussed in the observations below, unless
otherwise noted, were generated using the LLL function (i.e., results presented in Tables 4-3
through 4-8 under the LLL heading).
It is important to point out that the range of absolute IQ loss estimates generated using the
four models for a given case study and air quality scenario is large, typically around a factor of 3.
This can be seen by considering the difference in total IQ loss between the "low C-R function"
and "high C-R function" estimates presented for any case study and air quality scenario
combination in Tables 4-3 and 4-4. However, the relative (proportional) change in IQ loss
across air quality scenarios (i.e., the pattern of risk reduction across air quality scenarios for the
same case study) is fairly consistent across all four models. This suggests that there may be
significant uncertainty in estimates of absolute IQ loss for a median or 95th percentile child with
exposures related to a given ambient air Pb level. Accordingly, we have greater confidence in
predicting incremental changes in IQ loss across air quality scenarios.
In presenting these risk observations, as with presentation of the exposure observations,
we reference both median and 95th percentile estimates of total IQ loss. It is important to note
that, similar to the exposure assessment, 5 percent of the child study population at each case
study would have risk levels above the 95th percentile IQ loss estimates presented here, although
due to technical limitations of our modeling tools, we believe that it is not possible at this point
to reasonably predict the distribution of risk levels for that top 5 percent.
Current Conditions
The following observations are with regard to estimation of Pb-related IQ loss for current
conditions at each of the case studies. Unless otherwise stated, all risk estimates discussed below
were generated using the LLL model (designated as "LLL" in the risk results tables).
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• As mentioned earlier in discussing the exposure results, current conditions for the three
location-specific urban case studies in terms of maximum quarterly average air Pb
concentrations are 0.09, 0.14 and 0.36 |ig/m3 for the study areas in Los Angeles,
Chicago and Cleveland, respectively. In terms of maximum monthly average the
ambient air Pb levels range from 0.17 |ig/m3 (maximum monthly average for Los
Angeles case study) to 0.56 |ig/m3 maximum monthly average for Cleveland case
study. The estimate for the Chicago case study is between these at 0.31 |ig/m3 as a
maximum monthly average.
• For the three location-specific urban case studies, median total IQ loss is estimated at
4.2 points using the LLL function and from 1.4 to as much as 5.2 points across
functions (see Table 4-3). Estimates for 95th percentile total IQ loss range from 7.5 to
7.6 points for the LLL and as much as 4.1 to 11.4 points across all four functions (see
Table 4-4).
• Two current conditions scenarios were considered for the general urban case study:
one based on the mean value for ambient air Pb levels in large urban areas (0.14 |ig/m3
as a maximum quarterly average) and a high-end ambient air Pb level in large urban
areas (0.87 |ig/m3 as a maximum quarterly average). Estimates of median total IQ loss
for these two scenarios were very similar at 4.5 and 4.7 points for the mean and high-
end current conditions, respectively. Estimated 95th percentile total IQ losses are also
fairly similar for the two current conditions scenarios, with the mean scenario at 7.7
points and the high-end scenario at 8 points. The range of estimates across all
concentration-response models is similar to that for the location-specific urban areas
(see above).
Current NAAQS
This section presents observations regarding estimates of Pb-related IQ loss for the
current NAAQS scenario for each of the case studies. Note, that discussion of the contribution
of recent air and recent plus past air to total IQ loss has been reserved for the current NAAQS air
quality scenario, since this scenario was modeled for all of the case studies. As noted above, all
risk estimates discussed below were generated using the LLL model (i.e., designated as "LLL" in
the risk results tables), unless otherwise noted.
• Estimates of median total IQ loss for the current NAAQS scenarios in the three
location-specific urban case studies range from 4.7 to 5.6 points, with between 2.7 and
3.4 of that coming from recent air and 3.9 to 4.7 coming from recent plus past air (see
Table 4-5). Estimates of 95th percentile total IQ loss range from 8.1 to 9 points, with
between 2.6 and 5.8 points coming from recent air and 5.9 and 7.6 points coming from
recent plus past air (see Table 4-6). Both the median and 95th percentile risk estimates
for current NAAQS are significantly higher than those for current conditions.
Specifically, median IQ loss is approximately 0.5 to 1 point higher under the current
NAAQS, while 95th percentile levels are 0.5 to 1.5 points higher under the current
NAAQS (see Tables 4-3 and 4-4, respectively). While the size of the increase in blood
Pb levels is significantly higher for the 95th percentile child than for the median, the
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size of risk increase is similar because the blood Pb increase for the 95th percentile is
taking place in a flatter portion of the concentration-response function. Conversely,
blood Pb increases for the median population percentile are occurring in a steeper
portion of the curve, thereby resulting in a similar increase in IQ loss for the median
and 95th percentile child. Risk estimates generated using the other three IQ loss
concentration-response functions provides a considerably wider range of predicted IQ
loss. For example, the estimated median total IQ loss ranges from 1.7 to 8.8 points
when all four concentration-response models are considered (compared with 4.7 to 5.6
points presented above for the LLL model alone - see Table 4-3).
• Population risk modeling completed for the three location-specific urban case studies
provides estimates of changes in the number of children with Pb-related IQ loss greater
than one point for the current NAAQS scenario compared to the current conditions
scenario. It is estimated that an additional 1% of the modeled child populations at the
three urban locations would have total IQ loss greater than 1.0 point under the current
NAAQS scenario. Specifically, the Chicago study area would have nearly 6,000 more
children with this magnitude of Pb-related IQ loss (from a total study population of
some 400,000), Cleveland would have approximately 100 (from a total study
population of 14,000) and Los Angeles would have approximately 4,000 more children
(from a total study population of approximately 370,000) with total Pb-related IQ loss
greater than 1.0 point under the current NAAQS scenario (see Table 4-7).
• By contrast, it is estimated that an additional 5% to 17% of the modeled child
populations at the three location-specific case study areas would move from having
total IQ loss below 7 IQ points to above 7 IQ points if current conditions increased to
levels near the current NAAQS. Specifically, the Chicago study area would have some
70,000 more children with Pb-related IQ loss greater than 7 points, the Cleveland study
area would have some 600 more and Los Angeles would have some 35,000 more
children with this magnitude of Pb-related IQ loss (see Table 4-8). The increases in
total IQ loss estimates are distributed across the distribution for each population with
some of the children moving between higher IQ loss categories (above the 7 point
demarcation). The prediction that shifting ambient air levels up to the current NAAQS
would have a greater impact on the number of children with Pb-related IQ loss of
greater than 7 points compared with the number with greater than one point of IQ loss
reflects the overall shape of the distribution of total Pb-related IQ loss estimates for
these study areas under current conditions. The majority of children in the three urban
study areas are projected to have IQ loss due to total Pb exposure that is significantly
greater than 1 point (the median is around 4 points - see Table 4-3). Therefore, a
hypothetical increase in ambient air Pb levels to just meet the current NAAQS is
predicted to yield a larger change in the number of children with relatively higher IQ
loss (in the range of 7 points) than on the number of children with IQ loss in the range
of 1, since current conditions estimates for Pb-related IQ loss are already well above 1
point.
• Current NAAQS scenario risk estimates for the general urban case study are slightly
higher than those for the location-specific urban case studies, with estimates for median
total IQ loss of 5.8 points and 95th percentile estimates of 9.1 points of loss (see Tables
4-3 and 4-4, respectively). The fraction of IQ loss associated with ambient air Pb
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exposure for the general urban case study is similar to that estimated for the location-
specific urban case studies. Total Pb-related IQ loss estimates (at both the median and
95th percentile) are about 1 point higher than those for the two current conditions
scenarios (see Tables 4-7 and 4-8). The similarity of the change in risk for the median
and 95th population percentiles (despite greater differences in blood Pb level
reductions), reflects the different slopes of the concentration-response functions at the
different blood Pb levels.
The primary Pb smelter case study (full study area) had an estimated median total IQ
loss for the current NAAQS scenario of 3.8 points, with approximately 1.9 points
resulting from recent plus past air (see Table 4-5). The estimate of 95th percentile total
IQ loss for the current NAAQS scenario is 6.8 points, with 4.2 points of this coming
from recent plus past air (see Table 4-6).
The 1.5 km subarea of the primary Pb smelter case study had markedly elevated risk
levels for the current NAAQS scenario, compared with the full study area. This
reflects the fact that the analysis of the subarea focused on a subpopulation
experiencing significantly greater ambient air Pb levels due to proximity to the facility.
Median total IQ loss is estimated at 6.8 points with 6.0 of those points resulting from
recent plus past air Pb (see Table 4-5). The estimate for 95th percentile total IQ loss is
9.5 points, with 8.0 of those points resulting from recent plus past air Pb (see Table 4-
6). The larger fraction of total Pb risk associated with ambient air Pb for the subarea
compared with the full study area indicates the greater impact of ambient air Pb in this
area.
Alternative NAAQS
This section presents observations regarding modeling of Pb-related IQ loss for each of
the case studies for the alternative NAAQS scenarios. As noted above, all risk estimates
discussed below were generated using the LLL model (i.e., designated as "LLL" in the risk
results tables), unless otherwise stated.
• In modeling the alternate NAAQS for the three location-specific urban case studies, we
assessed those alternative NAAQS that were either equal to or lower than current
conditions at each location. Thus, the three lowest alternative NAAQS were
considered for Chicago, all of the alternative NAAQS were considered for Cleveland
and only the two lowest were considered for Los Angeles. The remaining case studies
were evaluated for the full range of alternative NAAQS.
• For the three location-specific urban case studies, median total IQ loss for the higher
alternative NAAQS (0.5 and 0.2 |ig/m3 as monthly maximum average and 0.2 |ig/m3as
a maximum quarterly average) is estimated to range from 4.1 to 4.2 points (see Table
4-3). This range is very close to the current conditions estimates for these three case
studies, as would be expected since these alternative NAAQS fall near the range of
current conditions at these locations. Estimates of median total IQ loss for the lower
alternative NAAQS levels (0.02 and 0.05 |ig/m3 as a maximum monthly average) show
a slight reduction in total IQ loss for the median child compared with current
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conditions (i.e., equal to or less than a 0.2 IQ point reduction). Estimates of 95
th
3
percentile IQ loss for the higher alternative NAAQS (0.5 and 0.2 |ig/m as monthly
maximum average and 0.2 |ig/m3 as a maximum quarterly average) range from 7.4 to
7.5 points (see Table 4-4). Under the lower alternative NAAQS levels (0.02 and 0.05
|ig/m3 maximum monthly average) 95th percentile IQ loss estimates also show a slight
reduction in the degree of IQ loss, compared to current conditions, with this drop
ranging from roughly 0.1 to 0.3 IQ points. The relatively lower reduction in IQ loss
compared with blood Pb levels for the 95th percentile child (between the intermediate
and the lower alternative NAAQS levels) reflects the IQ loss concentration-response
functions which have a flatter curve at the higher blood Pb levels associated with the
95th percentile child, which translates into reduced magnitudes of change in IQ loss.
• Population risk modeling in the three location-specific urban case studies for a
simulated reduction in ambient air Pb levels from current conditions to just meeting the
lower alternative NAAQS levels (0.02 and 0.05 |ig/m3 as a monthly maximum
average) projects that approximately 0.5 to 1% of the modeled child populations
involved would have total IQ loss shift from above one IQ point to below one IQ point.
The size of the reduction in terms of number of children is approximately 3,000
children for the Chicago study area, 100 for the Cleveland study area and 2,000 for the
Los Angeles area (see Table 4-7).
• By contrast, a comparison of the scenarios for the lower NAAQS to current conditions
for the location-specific urban areas indicates that approximately 1 to 2 % of the total
modeled child populations involved would have total IQ loss shift from above 7 point
to below 7 points as a result of the decrease in ambient air Pb levels. The size of the
reduction in terms of number of children is approximately 8,000 children for the
Chicago study area, 300-400 for the Cleveland study area and 5,000 for the Los
Angeles study area (see Table 4-8).
• The primary Pb smelter (full study area) is estimated to have a 0.1 point reduction in
total IQ loss for the median population percentile across any of the alternative NAAQS
(see Table 4-3). A somewhat larger reduction in total IQ loss (0.3 points) is estimated
for the 95th population percentile with the highest alternative NAAQS (0.5 |ig/m3
maximum monthly average) estimated at 6.6 points and the lowest alternative NAAQS
level (0.02 |ig/m3 maximum monthly average) estimated at 6.3 |ig/dL (Table 4-4).
• The 1.5 km subarea of the primary Pb smelter risk estimates indicate a much more
substantial trend in total IQ loss reduction across alternative NAAQS scenarios for the
median population percentile. Estimates of total Pb-related IQ loss are 5.8 points for
the highest alternative NAAQS (0.5 |ig/m3 maximum monthly average), decreasing to
5.0 points with the intermediate alternative NAAQS (0.2 |ig/m3 maximum monthly
average) and dropping to 4.0 points with the lowest alternative NAAQS level (0.02
|ig/m3 maximum monthly average - see Table 4-3). This trend is also seen with the
95th population percentile, with the magnitude of IQ loss reduction matching that seen
for the median population percentile. Specifically, for the highest alternative NAAQS
the IQ loss is estimated at 8.5 points, dropping to 7.6 points for the 0.2 |ig/m3
maximum monthly average, and reaching 6.5 points for the lowest alternative NAAQS
(see Table 4-4).
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Table 4-3. Summary of risk estimates for medians of total-exposure risk distributions.
Case Study and Air Quality Scenario
Points IQ loss
(total Pb exposure)3
Low C-R
function
estimate
LLL"
High C-R
function
estimate
Location-specific (Chicago)
Current NAAQS (1 .5 |jg/m3, max quarterly)
Current conditions (0.14 |jg/m3 max quarterly; 0.31 |jg/m3 max monthly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
2.4
1.4
1.4
1.3
1.3
5.6
4.2
4.2
4.0
4.0
8.8
5.2
5.2
4.8
4.7
Location-specific (Cleveland)
Current NAAQS (1 .5 |jg/m3, max quarterly)
Current conditions (0.36 |jg/m3 max quarterly; 0.56 |jg/m3 max monthly)
Alternative NAAQS (0.5 |jg/m3, max monthly)
Alternative NAAQS (0.2 |jg/m3, max quarterly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
1.7
1.4
1.4
1.4
1.3
1.3
1.2
4.7
4.2
4.2
4.1
4.1
4.0
3.9
6.3
5.2
5.2
5.0
4.9
4.7
4.6
Location-specific (Los Angeles)
Current NAAQS (1 .5 |jg/m3, max quarterly)
Current conditions (0.09 |jg/m3 max quarterly; 0.17 |jg/m3 max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
2.1
1.4
1.3
1.3
5.3
4.2
4.0
4.0
7.7
5.1
4.8
4.7
General Urban
Current NAAQS (1 .5 |jg/m3, max quarterly)
Alternative NAAQS (0.5 |jg/m3, max monthly)
Current conditions - high-end (0.87 |jg/m3 max quarterly)
Alternative NAAQS (0.2 |jg/m3, max quarterly)
Current conditions - mean (0.14 |jg/m3 max quarterly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
2.5
1.7
1.7
1.6
1.5
1.5
1.3
1.3
5.8
4.8
4.7
4.6
4.5
4.4
4.1
4.0
9.2
6.4
6.3
5.9
5.6
5.6
5.0
4.8
Primary Pb smelter - full study area
Current NAAQS (1 .5 |jg/m3, max quarterly)
Alternative NAAQS (0.5 |jg/m3, max monthly)
Alternative NAAQS (0.2 |jg/m3, max quarterly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
1.2
1.0
0.9
0.9
0.9
0.9
3.8
3.7
3.6
3.6
3.6
3.6
4.4
4.2
4.2
4.1
4.0
4.1
Primary Pb smelter - 1 .5km subarea
Current NAAQS (1 .5 |jg/m3, max quarterly)
Alternative NAAQS (0.5 |jg/m3, max monthly)
Alternative NAAQS (0.2 |jg/m3, max quarterly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
3.7
2.6
2.0
1.9
1.4
1.3
6.8
5.8
5.2
5.0
4.2
4.0
11.2
9.4
7.4
6.9
5.1
4.8
a - Estimates below 1 .0 are rounded to one decimal place, all values below 0.05 are presented as <0.1 and values
between 0.05 and 0.1 as 0.1 . All values above 1 .0 are rounded to the nearest whole number.
b-Log-linear with low-exposure linearization concentration-response function.
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Table 4-4. Summary of risk estimates for 95th percentile of total-exposure risk
distributions.
Case Study and Air Quality Scenario
Points IQ loss
(total Pb exposure)3
Low C-R
function
estimate
LLL"
High C-R
function
estimate
Location-specific (Chicago)
Current NAAQS (1 .5 |jg/m3, max quarterly)
Current conditions (0.14 |jg/m3 max quarterly; 0.31 |jg/m3 max monthly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
4.7
4.1
4.1
4.1
4.1
9.0
7.5
7.5
7.3
7.3
12.1
11.4
11.4
11.3
11.3
Location-specific (Cleveland)
Current NAAQS (1 .5 |jg/m3, max quarterly)
Current conditions (0.36 |jg/m3 max quarterly; 0.56 |jg/m3 max monthly)
Alternative NAAQS (0.5 |jg/m3, max monthly)
Alternative NAAQS (0.2 |jg/m3, max quarterly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
4.3
4.1
4.1
4.1
4.1
4.0
4.0
8.1
7.6
7.5
7.4
7.4
7.2
7.2
11.6
11.4
11.4
11.3
11.3
11.3
11.3
Location-specific (Los Angeles)
Current NAAQS (1 .5 |jg/m3, max quarterly)
Current conditions (0.09 |jg/m3 max quarterly; 0.17 |jg/m3 max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
4.5
4.1
4.1
4.0
8.6
7.5
7.3
7.2
11.8
11.4
11.3
11.3
General Urban
Current NAAQS (1 .5 |jg/m3, max quarterly)
Alternative NAAQS (0.5 |jg/m3, max monthly)
Current conditions - high-end (0.87 |jg/m3 max quarterly)
Alternative NAAQS (0.2 |jg/m3, max quarterly)
Current conditions - mean (0.14 |jg/m3 max quarterly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
4.7
4.3
4.3
4.2
4.2
4.2
4.1
4.1
9.1
8.1
8.0
7.9
7.7
7.7
7.4
7.3
12.1
11.6
11.6
11.5
11.5
11.4
11.3
11.3
Primary Pb smelter - full study area
Current NAAQS (1 .5 |jg/m3, max quarterly)
Alternative NAAQS (0.5 |jg/m3, max monthly)
Alternative NAAQS (0.2 |jg/m3, max quarterly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
3.7
3.4
3.2
3.2
3.1
3.1
6.8
6.6
6.5
6.4
6.3
6.3
11.2
11.1
11.1
11.1
11.0
11.0
Primary Pb smelter - 1 .5km subarea
Current NAAQS (1 .5 |jg/m3, max quarterly)
Alternative NAAQS (0.5 |jg/m3, max monthly)
Alternative NAAQS (0.2 |jg/m3, max quarterly)
Alternative NAAQS (0.2 |jg/m3, max monthly)
Alternative NAAQS (0.05 |jg/m3, max monthly)
Alternative NAAQS (0.02 |jg/m3, max monthly)
5.0
4.5
4.2
4.1
3.6
3.3
9.5
8.5
7.8
7.6
6.8
6.5
12.4
11.8
11.5
11.4
11.1
11.1
a - Estimates below 1 .0 are rounded to one decimal place, all values below 0.05 are presented as <0.1 and values
between 0.05 and 0.1 as 0.1 . All values above 1 .0 are rounded to the nearest whole number.
b-Log-linear with low-exposure linearization concentration-response function.
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Table 4-5. Median IQ loss estimates for the current NAAQS scenario.
Case study
Location-specific (Chicago)
Location-specific (Cleveland)
Location-specific (Los Angeles)
General urban
Primary Pb smelter-full area
Primary Pb smelter - subarea
IQ Loss from policy relevant exposures
(recent air plus past air) a
Low C-R function
estimates
Recent
air
1.4
0.6
1.1
1.5
Recent air
+ past air
2.0
1.4
1.7
2.1
0.6 c
3.2
LLL C-R function
estimates
Recent
air
3.4
2.8
2.7
3.5
Recent
air
+ past
air
4.7
3.9
4.2
4.8
1.9
6.0
High C-R function
estimates
Recent
air
5.6
2.1
4.0
5.6
Recent air
+ past air
7.4
4.6
6.2
7.7
2.3
9.4
Total IQ loss
(total Pb exposure) b
Low C-R
function
estimates
2.4
1.7
2.1
2.5
1.2
3.7
LLL C-R
function
estimates
5.6
4.7
5.3
5.8
3.8
6.8
High C-R
function
estimates
8.8
6.3
7.7
9.2
4.4
11.2
a - These columns present the IQ loss estimated to result from policy-relevant Pb exposure, including recent air and recent plus past air.
Estimates for the low C-R function, the LLL C-R function and the high C-R function are presented. 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).
b- These columns present the estimates of total IQ loss resulting from total Pb exposure (policy-relevant plus background). Results are
presented for the low C-R function, the LLL C-R function and the high C-R function.
c- Risk estimates are not presented for recent air for the primary Pb smelter case study (See Section 3.2.2 of the Risk Assessment Report).
-th
Table 4-6. 95 percentile IQ loss estimates for the current NAAQS scenario
Case study
Location-specific (Chicago)
Location-specific (Cleveland)
Location-specific (Los Angeles)
General urban
Primary Pb smelter - full area
Primary Pb smelter - subarea
IQ Loss from policy relevant exposures
(recent air plus past air) a
Low C-R function
estimates
Recent
air
3.0
1.4
2.3
2.9
Recent
air +
past air
4.0
3.1
3.6
3.9
2.3
4.2
LLL C-R function
estimates
Recent
air
5.8
2.6
4.4
5.5
Recent
air +
past air
7.6
5.9
6.9
7.6
4.2
8.0
High C-R function
estimates
Recent
air
7.7
3.7
6.1
7.3
Recent
air +
past air
10.3
8.5
9.5
10.1
6.8
10.4
Total IQ loss
(total Pb exposure) b
Low C-R
function
estimates
4.7
4.3
4.5
4.7
3.7
5.0
LLL C-R
function
estimates
9.0
8.1
8.6
9.1
6.8
9.5
High C-R
function
estimates
12.1
11.6
11.8
12.1
11.2
12.4
a - These columns present the IQ loss estimated to result from policy-relevant Pb exposure, including recent air and recent plus past air.
Estimates for the low C-R function, the LLL C-R function and the high C-R function are presented. 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 pred cted to be associated with outdoor ambient air Pb
levels, with outdoor ambient air also potentially including resuspended, previously deposited Pb (see Section 2.4.3).
b- These columns present the estimates of total IQ loss resulting from total Pb exposure (policy-relevant plus background). Results are
presented for the low C-R function, the LLL C-R function and the high C-R function.
c- Risk estimates are not presented for recent air for the primary Pb smelter case study (see Section 3.2.2 of the Risk Assessment Report).
4-36
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1 Table 4-7. Incidence of children with >1 point Pb-related IQ loss.
Air Quality Scenario
(for location-specific urban case studies)
Chicago (total modeled child population: 396,511)
Chicago Current Conditions (Mean)
Current NAAQS (1.5 ug/m3 Maximum Quarterly)
Alternative NAAQS (0.2 ug/m3 Maximum Monthly)
Alternative NAAQS (0.05 ug/m3 Maximum Monthly)
Alternative NAAQS (0.02 ug/m3 Maximum Monthly)
Cleveland (total modeled child population: 13,990)
Cleveland Current Conditions (Mean)
Current NAAQS (1 .5 ug/m3 Maximum Quarterly)
Alternative NAAQS (0.2 ug/m3 Maximum Quarterly)
Alternative NAAQS (0.5 ug/m3 Maximum Monthly)
Alternative NAAQS (0.2 ug/m3 Maximum Monthly)
Alternative NAAQS (0.05 ug/m3 Maximum Monthly)
Alternative NAAQS (0.02 ug/m3 Maximum Monthly)
Los Angeles (total modeled child population: 372,252)
Los Angeles Current Conditions (Mean)
Current NAAQS (1.5 ug/m3 Maximum, Quarterly)
Alternative NAAQS (0.05 ug/m3 Maximum Monthly)
Alternative NAAQS (0.02 ug/m3 Maximum Monthly)
dual linear - stratified at
7.5 |Jg/dL peak blood Pb
Incidence of
>1 point IQ
loss
391 ,602
395,797
391,158
389,572
389,176
13,809
13,893
13,770
13,789
13,759
13,729
13,720
282,216
285,272
281,112
280,740
Delta
(change in
incidence
compared to
current
conditions)
4,195
-444
-2,030
-2,427
84
-38
-20
-50
-80
-88
3,056
-1,104
-1 ,476
log -linear with
linearization
Incidence
of
>1 point,
IQ loss
389,754
395,528
389,461
387,407
386,630
1 3,745
13,857
13,703
13,720
13,694
13,642
13,628
280,711
284,945
279,658
279,057
Delta
(change in
incidence
compared to
current
conditions)
5,773
-293
-2,347
-3,125
112
-42
-25
-51
-103
-117
4,234
-1 ,053
-1 ,654
dual linear - stratified at
10 |J/dL peak blood Pb
Incidence
of>1
point, IQ
loss
271 ,031
347,415
271 ,444
253,775
249,865
10,664
9,221
9,497
9,083
8,785
8,736
191,675
240,988
183,395
180,745
Delta
(change in
incidence
compared to
current
conditions)
76,384
412
-17,2Si,257
-21,166
1,137
-305
-3?-1-
-443
-741
-790
49,313
-8,280
-10,$29,47^
log-linear with cutpoint
Incidence
of
>1 point,
IQ loss
314,053
235,559
224,394
219,294
9,769
8,160
8,464
8,010
7,720
7,668
226,608
161,914
158,234
Delta
(change in
incidence
compared to
current
conditions)
77,795
-698
-1 1 ,864
-16,963
1,254
-354
-51
-505
-795
-846
56,134
-8,560
-12,240
4-37
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1 Table 4-8. Incidence of children with >7 points Pb-related IQ loss.
Air Quality Scenario
(location-specific urban case studies)
Chicago (total modeled child population: 396,51 1)
Chicago Current Conditions (Mean)
Current NAAQS (1 .5 ug/m3 Maximum Quarterly)
Alternative NAAQS (0.2 ug/m3 Maximum Monthly)
Alternative NAAQS (0.05 ug/m3 Maximum Monthly)
Alternative NAAQS (0.02 ug/m3 Maximum Monthly)
Cleveland (total modeled child population: 13,990)
Cleveland Current Conditions (Mean)
Current NAAQS (1 .5 ug/m3 Maximum Quarterly)
Alternative NAAQS (0.2 ug/m3 Maximum Quarterly)
Alternative NAAQS (0.5 ug/m3 Maximum Monthly)
Alternative NAAQS (0.2 ug/m3 Maximum Monthly)
Alternative NAAQS (0.05 ug/m3 Maximum Monthly)
Alternative NAAQS (0.02 ug/m3 Maximum Monthly)
Los Angeles (total modeled child population: 372,252)
Los Angeles Current Conditions (Mean)
Current NAAQS (1.5 ug/m3 Maximum, Quarterly)
Alternative NAAQS (0.05 ug/m3 Maximum, Monthly)
Alternative NAAQS (0.02 ug/m3 Maximum, Monthly)
dual linear - stratified at
7.5 ug/dL peak blood Pb
Incidence of
> 7 points
IO Inss
136,709
244,401
136,067
120,706
117,819
4,834
6,139
4,525
4,806
4,424
4,106
4,051
94,684
158,171
87,303
83,909
Delta
(change in
incidence
compared to
current
conditions)
107,692
-642
-16,003
-18,890
1,305
-309
-28
-410
-728
-783
63,487
-7,382
-10,775
log -linear with
linearization
Incidence
of
> 7 points
IO Inss
33,664
100,159
32,546
27,367
26,027
1,212
1,858
1,073
1,180
1,026
886
866
22,665
57,834
19,781
17,939
Delta
(change in
incidence
compared to
current
conditions)
66,495
-1,118
-6,297
-7,637
647
-139
-31
-186
-326
-345
35,168
-2884
-4726
dual linear - stratified at
10 ug/dL peak blood Pb
Incidence
of
> 7 points
IO Inss
63
555
48
16
8
3
4
1
1
1
0
0
23
183
11
17
Delta
(change in
incidence
compared to
current
conditions)
492
-16
-48
-56
2
-2
-2
-2
-3
-3
160
-11
-6
log-linear with cutpoint
Incidence
of
> 7 points
IO Inss
1,015
5,226
1,007
864
690
46
105
40
43
43
24
27
732
3,771
624
498
Delta
(change in
incidence
compared to
current
conditions)
4,211
-8
-151
-325
59
-6
-3
-3
-22
-18
3,038
-109
-235
4-38
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REFERENCES
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. Available at:
http://www.epa.gov/ttn/naaqs/standards/pb/s_pb_cr_td.html
Henderson, R. (2006) 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. Memorandum to Stephan Johnson, EPA Administrator, from Dr.
Rogene Henderson. July. Available at http://www.epa.gov/sab/pdf/casac-con-06-006.pdf.
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. 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.
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.; Roberts, R. (2005) Low-level environmental lead exposure and children's intellectual
function: an international pooled analysis. Environ. Health Perspect. 113: 894-899.
Rothenberg, S.J.; Rothenberg, J.C. (2005) Testing the dose-response specification in epidemiology: public health
aand policy consequences for lead. Environ. Health Perspect. 113: 1190-1195.
Slob, W.; Moerbeek, M.; Rauniomaa, E.; Piersma, A. H. (2005) A statistical evaluation of toxicity study designs
for the estimation of the benchmark dose in continuous endpoints. Toxicol. Sci. 84: 167-185.
U.S. Environmental Protection Agency. (1989) Review of National Ambient Air Quality Standard for Pb: Exposure
Analysis Methodology and Validation. Research Triangle Park, NC: Office of Air Quality Planning and
Standards. EPA-450/2-89-011. June.
U.S. Environmental Protection Agency. (1990) Review of the national ambient air quality standards for Pb:
assessment of scientific and technical information. OAQPS staff report. Research Triangle Park, NC:
Office of Air Quality Planning and Standards; report no. EPA-450/2-89-022. Available from: NTIS,
Springfield, VA; PB89-207914.
U.S. Environmental Protection Agency. (2006a) Draft 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 Standards, Research Triangle Park, NC.
U.S. Environmental Protection Agency. (2006b) 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.
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U.S. Environmental Protection Agency. (2007a) 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. EPA-
452/D-07-001a. and EPA-452/D-07-001b.
U.S. Environmental Protection Agency. (2007b) Lead: Human Exposure and Health Risk Assessments for Selected
Case Studies, 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. EPA-452/R-07-
014a and EPA-452/R-07-014b.
4-40
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5 THE PRIMARY LEAD NAAQS
5.1 INTRODUCTION
This chapter presents staff conclusions and recommendations for the Administrator to
consider in deciding whether the existing primary Pb standard should be revised and, if so, what
revision is appropriate. Our conclusions and recommendations are based on the assessment and
integrative synthesis of information presented in the CD, staff analyses and evaluations presented
in Chapters 2 through 4 herein, and the comments and advice of CAS AC and interested parties
who commented on an early draft of this document and the related Risk Assessment Report.
In recommending policy options for the Administrator's consideration, we note that the
final decision on retaining or revising the current primary Pb standard is largely a public health
policy judgment to be made by the Administrator. The Administrator's final decision should
draw upon scientific information and analyses about health effects, population exposure and
risks, as well as judgments about the appropriate response to the range of uncertainties that are
inherent in the scientific evidence and analyses. Our approach to informing these judgments,
discussed more fully below, is based on a recognition that the available health effects evidence
generally reflects a continuum consisting of ambient levels at which scientists generally agree
that health effects are likely to occur, through lower levels at which the likelihood and magnitude
of the response become increasingly uncertain.
This approach is consistent with the requirements of the NAAQS provisions of the Act
and with how EPA and the courts have historically interpreted the Act. These provisions require
the Administrator to establish primary standards that, in the Administrator's judgment, are
requisite to protect public health with an adequate margin of safety. In so doing, the
Administrator seeks to establish standards that are neither more nor less stringent than necessary
for this purpose. The Act does not require that primary standards be set at a zero-risk level but
rather at a level that avoids unacceptable risks to public health, including the health of sensitive
groups.
The following discussion starts with background information on the current standard in
Section 5.2, including both the basis for derivation of the current standard and considerations and
conclusions from the Staff Paper prepared in the last review. The general approach used in this
current review to evaluate the adequacy of the current standard and identify policy alternatives is
summarized in Section 5.3. Staff conclusions and recommendations with regard to the adequacy
of the current standard are discussed in Section 5.4, and conclusions and recommendations with
regard to elements of alternative standards for consideration are discussed in Section 5.5. Key
uncertainties and research recommendations related to setting a primary lead standard are
identified in Section 5.6.
5-1
-------
5.2 BACKGROUND ON THE CURRENT STANDARD
5.2.1 Basis for Setting the Current Standard
The current primary standard is set at a level of 1.5 ug/m3, measured as lead in TSP, not
to be exceeded by the maximum arithmetic mean concentration averaged over a calendar quarter.
The standard was set in 1978 to provide protection to the public, especially children as the
particularly sensitive population subgroup, against Pb-induced adverse health effects (43 FR
46246). The basis for selecting each of the elements of the standard is described below.
5.2.1.1 Level
EPA's objective in selecting the level of the current standard was "to estimate the
concentration of lead in the air to which all groups within the general population can be exposed
for protracted periods without an unacceptable risk to health" (43 FR 46252). Consistent with
section 109 of the Clean Air Act, the Agency selected a level for the current standard that was
below the concentration that was at that time identified as a threshold for adverse health effects
(i.e., 40 ug/dl blood Pb), so as to provide an adequate margin of safety. As stated in the notice of
final rulemaking,
"This estimate was based on EPA's judgment in four key areas:
(1) Determining the 'sensitive population' as that group within the general population
which has the lowest threshold for adverse effects or greatest potential for exposure.
EPA concludes that young children, aged 1 to 5, are the sensitive population.
(2) Determining the safe level of total lead exposure for the sensitive population,
indicated by the concentration of lead in the blood. EPA concludes that the
maximum safe level of blood lead for an individual child is 30 ug Pb/dl and that
population blood lead, measured as the geometric mean, must be 15 ug Pb/dl in order
to place 99.5 percent of children in the United States below 30 ug Pb/dl.
(3) Attributing the contribution to blood lead from nonair pollution sources. EPA
concludes that 12 ug Pb/dl of population blood lead for children should be attributed
to nonair exposure.
(4) Determining the air lead level which is consistent with maintaining the mean
population blood lead level at 15 ug Pb/dl [the maximum safe level]. Taking into
account exposure from other sources (12 ug Pb/dl), EPA has designed the standard to
limit air contribution after achieving the standard to 3 ug Pb/dl. On the basis of an
estimated relationship of air lead to blood lead of 1 to 2, EPA concludes that the
ambient air standard should be 1.5 ug Pb/m3." (43 FR 46252)
EPA's judgments in these key areas, as well as margin of safety considerations, are discussed in
the following subsections.
5-2
-------
5.2.1.1.1 Sensitive Population
The assessment of the science that was presented in the 1977 CD (USEPA, 1977),
indicated young children, aged 1 to 5, as the population group at particular risk from Pb
exposure. Children were recognized to have a greater physiological sensitivity than adults to the
effects of Pb and a greater exposure. In identifying young children as the sensitive population,
EPA also recognized the occurrence of subgroups with enhanced risk due to genetic factors,
dietary deficiencies or residence in urban areas. Yet information was not available to estimate a
threshold for adverse effects for these subgroups separate from that of all young children.
Additionally, EPA recognized both a concern regarding potential risk to pregnant women and
fetuses, and a lack of information to establish that these subgroups are more at risk than young
children. Accordingly, young children, aged 1 to 5, were identified as the group which has the
lowest threshold for adverse effects of greatest potential for exposure (i.e., the sensitive
population) (43 FR 46252).
5.2.1.1.2 Maximum Safe Blood Level
In identifying the maximum safe exposure, EPA relied upon the measurement of Pb in
blood (43 FR 46252-46253). The physiological effect of Pb that had been identified as occurring
at the lowest blood Pb level was inhibition of an enzyme integral to the pathway by which heme
(the oxygen carrying protein of human blood) is synthesized, i.e., delta-aminolevulinic acid
dehydratase (5-ALAD). The 1977 CD reported a threshold for inhibition of this enzyme in
children at 10 ug Pb/dL. The 1977 CD also reported a threshold of 15-20 ug/dL for elevation of
protoporphyrin (EP), which is an indication of some disruption of the heme synthesis pathway.
EPA concluded that this effect on the heme synthesis pathway (indicated by EP) was potentially
adverse. EPA further described a range of blood levels associated with a progression in
detrimental impact on the heme synthesis pathway. At the low end of the range (15-20 ug/dL),
the initial detection of EP associated with blood Pb was not concluded to be associated with a
significant risk to health. The upper end of the range (40 ug/dL), the threshold associated with
clear evidence of heme synthesis impairment and other effects contributing to clinical symptoms
of anemia, was regarded as clearly adverse to health. EPA also recognized the existence of
thresholds for additional adverse effects (e.g., nervous system deficits) occurring for some
children at just slightly higher blood Pb levels (e.g., 50 ug/dL). Additionally, EPA stated that the
maximum safe blood level should not be higher than the blood Pb level recognized by the CDC
as "elevated" (and indicative of the need for intervention). In 1978, that level was 30 ug/dL1.
1 The CDC subsequently revised their advisory level for children's blood Pb to 25 ug/dL in 1985, and to 10
ug/dL 1991. More details on this level are provided in Section 3.2.
5-3
-------
Having identified the maximum safe blood level in individual children, EPA next made a
public health policy judgment regarding the target mean blood level for the U.S. population of
young children (43 FR 46252-46253). With this judgment, EPA identified a target of 99.5
percent of this population to be brought below the maximum safe blood Pb level. This judgment
was based on consideration of the size of the sensitive subpopulation, and the recognition that
there are special high-risk groups of children within the general population. The population
statistics available at the time (the 1970 U.S. Census) indicated a total of 20 million children
younger than 5 years of age, with 15 million residing in urban areas and 5 million in center cities
where Pb exposure was thought likely to be "high". Concern about these high-risk groups
influenced EPA's determination of 99.5 percent, deterring EPA from selecting a population
percentage lower than 99.5 (43 FR 46253). EPA then used standard statistical techniques to
calculate the population mean blood Pb level that would place 99.5 percent of the population
below the maximum safe level. Based on the then available data, EPA concluded that blood Pb
levels in the population of U.S. children were normally distributed with a geometric standard
deviation of 1.3. Based on standard statistical techniques, EPA determined that a thus described
population in which 99.5 percent of the population has blood Pb levels below 30 ug/dL would
have a geometric mean blood level of 15 ug/dL. EPA described 15 ug/dL as "the maximum safe
blood lead level (geometric mean) for a population of young children" (43 FR 46247).
5.2.1.1.3 Nonair Contribution
When setting the current NAAQS, EPA recognized that the air standard needed to take
into account the contribution to blood Pb levels from Pb sources unrelated to air pollution.
Consequently, the calculation of the current NAAQS included the subtraction of Pb contributed
to blood Pb from nonair sources from the estimate of a safe mean population blood Pb level.
Without this subtraction, EPA recognized that the combined exposure to Pb from air and nonair
sources would result in a blood Pb concentration exceeding the safe level (43 FR 46253).
In developing an estimate of this nonair contribution, EPA recognized the lack of detailed
or widespread information about the relative contribution of various sources to children's blood
Pb levels, such that an estimate could only be made by inference from other empirical or
theoretical studies, often involving adults. Additionally, EPA recognized the expectation that the
contribution to blood Pb levels from nonair sources would vary widely, was probably not in
constant proportion to air Pb contribution, and in some cases may alone exceed the target mean
population blood Pb level (43 FR 46253-46254).
The amount of blood Pb attributed to nonair sources was selected based primarily on
findings in studies of blood Pb levels in areas where air Pb levels were low relative to other
locations in U.S. The air Pb levels in these areas ranged from 0.1 to 0.7 ug/m3. The average of
5-4
-------
the reported blood Pb levels for children of various ages in these areas was on the order of 12
ug/dL. Thus, 12 ug/dL was identified as the nonair contribution, and subtracted from the
population mean target level of 15 ug/dL to yield a value of 3 ug/dL as the limit on the air
contribution to blood Pb.
5.2.1.1.4 Air Pb Level
In determining the air Pb level consistent with an air contribution of 3 ug Pb/dL, EPA
reviewed studies assessed in the 1977 CD that reported changes in blood Pb with different air Pb
levels. These studies included a study of children exposed to Pb from a primary Pb smelter,
controlled exposures of adult men to Pb in fine particulate matter, and a personal exposure study
involving several male cohorts exposed to Pb in a large urban area in the early 1970s (43 FR
46254). Using all three studies, EPA calculated an average slope or ratio over the entire range of
data. That value was 1.95 (rounded to 2 ug/dL blood Pb concentration to 1 ug/m3 air Pb
concentration), and is recognized to fall within the range of values reported in the 1977 CD. On
the basis of this 2-to-l relationship, EPA concluded that the ambient air standard should be 1.5
ug Pb/m3 (43 FR 46254).
5.2.1.1.5 Margin of Safety
In consideration of the appropriate margin of safety during the development of the
current NAAQS, EPA identified the following factors: (1) the 1977 CD reported multiple
biological effects of Pb in practically all cell types, tissues and organ systems, of which the
significance for health had not yet been fully studied; (2) no beneficial effects of Pb at then
current environmental levels were recognized; (3) data were incomplete as to the extent to which
children are indirectly exposed to air Pb that has moved to other environmental media, such as
water, soil and dirt, and food; (4) Pb is chemically persistent and with continued uncontrolled
emissions would continue to accumulate in human tissue and the environment; and (5) the
possibility that exposure associated with blood Pb levels previously considered safe might
influence neurological development and learning abilities of the young child (43 FR 46255).
Recognizing that estimating an appropriate margin of safety for the air Pb standard was
complicated by the multiple sources and media involved in Pb exposure, EPA chose to use
margin of safety considerations principally in establishing a maximum safe blood Pb level for
individual children (30 ug Pb/dL) and in determining the percentage of children to be placed
below this maximum level (about 99.5 percent). Additionally, in establishing other factors used
in calculating the standard, EPA used margin of safety considerations in the sense of making
careful judgment based on available data, but these judgments were not considered to be at the
precautionary extreme of the range of data available at the time (43 FR 46251).
5-5
-------
EPA further recognized that, because of the variability between individuals in a
population experiencing a given level of Pb exposure, it was considered impossible to provide
the same margin of safety for all members in the sensitive population or to define the margin of
safety in the standard as a simple percentage. EPA believed that the factors it used in designing
the standards provided an adequate margin of safety for a large proportion of the sensitive
population. The Agency did not believe that the margin was excessively large or on the other
hand that the air standard could protect everyone from elevated blood Pb levels (43 FR 46251).
5.2.1.2 Averaging Time, Form, and Indicator
The averaging time for the current standard is a calendar quarter. In the decision for this
aspect of the standard, the Agency also considered a monthly averaging period, but concluded
that "a requirement for the averaging of air quality data over calendar quarter will improve the
validity of air quality data gathered without a significant reduction in the protectiveness of the
standards." As described in the notice for this decision (43 FR 46250), this conclusion was
based on several points, including the following:
• An analysis of ambient measurements available at the time indicated that the
distribution of air Pb levels was such that there was little possibility that there could be
sustained periods greatly above the average value in situations where the quarterly
standard was achieved.
• A recognition that the monitoring network may not actually represent the exposure
situation for young children, such that it seemed likely that elevated air Pb levels when
occurring would be close to Pb air pollution sources where young children would
typically not encounter them for the full 24-hour period reported by the monitor.
• Medical evidence available at the time indicated that blood Pb levels re-equilibrate
slowly to changes in air exposure, a finding that would serve to dampen the impact of
short-term period of exposure to elevated air Pb.
• Direct exposure to air is only one of several routes of total exposure, thus lessening the
impact of a change in air Pb on blood Pb levels.
The statistical form of the current standard is as a not-to-be-exceeded or maximum value.
EPA set the standard as a ceiling value with the conclusion that this air level would be safe for
indefinite exposure for young children (43 FR 46250).
The indicator is total airborne Pb collected by a high volume sampler (43 FR 46258).
EPA's selection of Pb-TSP as the indicator for the standard was based on explicit recognition
both of the significance of ingestion as an exposure pathway for Pb that had deposited from the
air and of the potential for Pb deposited from the air to become re-suspended in respirable size
particles in the air and available for human inhalation exposure. As stated in the final rule, "a
significant component of exposure can be ingestion of materials contaminated by deposition of
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lead from the air," and that, "in addition to the indirect route of ingestion and absorption from the
gastrointestinal tract, non-respirable Pb in the environment may, at some point become respirable
through weathering or mechanical action" (43 FR 46251).
5.2.2 Policy Options Considered in the Last Review
During the 1980s, EPA initiated a review of the air quality criteria and NAAQS for Pb.
CAS AC and the public were fully involved in this review, which led to the publication of a
criteria document with associated addendum and a supplement (USEPA, 1986a, 1986b, 1990a),
an exposure analysis methods document (USEPA, 1989) and a staff paper (USEPA, 1990b).
Total emissions to air were estimated to have dropped by 94 percent between 1978 and
1987, with the vast majority of it attributed to the reduction of Pb in gasoline. Accordingly, the
focus of the last review was on areas near stationary sources of Pb emissions. Although such
sources were not considered to have made a significant contribution (as compared to Pb in
gasoline) to the overall Pb pollution across large, urban or regional areas, Pb emissions from
such sources were considered to have the potential for a significant impact on a local scale. Air
Pb concentrations, and especially soil and dust Pb concentrations had been associated with
elevated levels of Pb absorption in children and adults in numerous Pb point source community
studies. Exceedances of the current NAAQS were found at that time only in the vicinity of
nonferrous smelters or other point sources of Pb.
In summarizing and interpreting the health evidence presented in the 1986 CD and
associated documents, the 1990 Staff Paper described the collective impact on children of the
effects at blood Pb levels above 15 ug/dL as representing a clear pattern of adverse effects
worthy of avoiding. This is in contrast to EPA's identification of 30 ug/dL as a safe blood Pb
level for individual children when the NAAQS was set in 1978. The Staff Paper further stated
that at blood Pb levels of 10-15 ug/dL there was a convergence of evidence of Pb-induced
interference with a diverse set of physiological functions and processes, particularly evident in
several independent studies showing impaired neurobehavioral function and development.
Further, the available data did not indicate a clear threshold in this blood Pb range. Rather, it
suggested a continuum of health risks down to the lowest levels measured.2
For the purposes of comparing the relative protectiveness of alternative Pb NAAQS, the
staff conducted analyses to estimate the percentages of children with blood Pb levels above 10
ug/dL and above 15 ug/dL for several air quality scenarios developed for a small set of
stationary source exposure case studies. These analyses omitted the subset of young children,
whom it was considered could not be substantially affected by any changes in atmospheric Pb
2 In 1991, the CDC reduced their advisory level for children's blood Pb from 25 ug/dL to 10 ug/dL.
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emissions under different standards, such as those with excessive pica3 and/or those living in
overtly deteriorated Pb-paint homes. The results of the analyses of child populations living near
two Pb smelters indicated that substantial reductions in Pb exposure could be achieved through
just meeting the current Pb NAAQS. According to the best estimate analyses, over 99.5% of
children living in areas significantly affected by the smelters would have blood Pb levels below
15 ug/dL if the current standard was achieved. Progressive changes in this number were
estimated for the alternative monthly Pb NAAQS levels evaluated in those analyses, which
ranged from 1.5 ug/m3 to 0.5 ug/m3.
In light of the health effects evidence available at the time, the Staff Paper presented air
quality, exposure, and risk analyses, and other policy considerations, as well as the following
staff conclusions with regard to the primary Pb NAAQS (USEPA, 1990b, pp. xii to xiv):
1) "The range of standards ... should be from 0.5 to 1.5 ug/m3."
2) "A monthly averaging period would better capture short-term increases in lead
exposure and would more fully protect children's health than the current quarterly
average."
3) "The most appropriate form of the standard appears to be the second highest monthly
averages {sic} in a 3-year span. This form would be nearly as stringent as a form that
does not permit any exceedances and allows for discounting of one "bad" month in 3
years which may be caused, for example, by unusual meteorology."
4) "With a revision to a monthly averaging time more frequent sampling is needed,
except in areas, like roadways remote from lead point sources, where the standard is
not expected to be violated. In those situations, the current l-in-6 day sampling
schedule would sufficiently reflect air quality and trends."
5) "Because exposure to atmospheric lead particles occurs not only via direct inhalation,
but via ingestion of deposited particles as well, especially among young children, the
hi-volume sampler provides a reasonable indicator for determining compliance with a
monthly standard and should be retained as the instrument to monitor compliance
with the lead NAAQS until more refined instruments can be developed."
Based on its review of a draft Staff Paper, which contained the above recommendations,
the CASAC strongly recommended to the Administrator that EPA should actively pursue a
public health goal of minimizing the lead content of blood to the extent possible, and that the Pb
NAAQS is an important component of a multimedia strategy for achieving that goal (CASAC,
1990, p. 4). In noting the range of levels recommended by staff, CASAC recommended
consideration of a revised standard that incorporates a "wide margin of safety, because of the risk
3 Pica is an eating disorder typically defined by persistent cravings to eat non-food items.
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posed by Pb exposures, particularly to the very young whose developing nervous system may be
compromised by even low level exposures" (id., p. 3). More specifically, CASAC judged that a
standard within the range of 1.0 to 1.5 ug/m3 would have "relatively little, if any, margin of
safety;" that greater consideration should be given to a standard set below 1.0 ug/m3; and, to
provide perspective in setting the standard, it would be appropriate to consider the distribution of
blood Pb levels associated with meeting a monthly standard of 0.25 ug/m3, a level below the
range considered by staff (id.).
After consideration of the documents developed during the review, EPA chose not to
propose revision of the NAAQS for Pb. During the same time period, the Agency published and
embarked on the implementation of a broad, multi-program, multi-media, integrated national
strategy to reduce Pb exposures (USEPA, 1991). As part of implementing this integrated Pb
strategy, the Agency focused efforts primarily on regulatory and remedial clean-up actions aimed
at reducing Pb exposures from a variety of non-air sources judged to pose more extensive public
health risks to U.S. populations, as well as on actions to reduce Pb emissions to air, particularly
near stationary sources. This focus reflected in part the dramatic reduction of Pb in gasoline that
occurred since the standard was set in 1978, which resulted in orders-of-magnitude reductions in
airborne emissions of Pb, and a significant shift in the types of sources with the greatest Pb
emissions. EPA established standards for Pb-based paint hazards and Pb dust cleanup levels in
most pre-1978 housing and child-occupied facilities. Additionally, EPA has developed standards
for the management of Pb in solid and hazardous waste, oversees the cleanup of Pb
contamination at Superfund sites, and has issued regulations to reduce Pb in drinking water
(http://www.epa.gov/lead/regulation.htm). Beyond these specific regulatory actions, the
Agency's Lead Awareness Program has continued to work to protect human health and the
environment against the dangers of Pb by conducting research and designing educational
outreach activities and materials (http://www.epa.gov/lead/). Actions to reduce Pb emissions to
air during the 1990s included enforcement of the NAAQS, as well as the promulgation of
regulations under Section 112 of the Clean Air Act, including national emissions standards for
hazardous air pollutants (including Pb compounds) at primary and secondary Pb smelters, as well
as other Pb sources.
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5.3 APPROACH FOR CURRENT REVIEW
To evaluate whether it is appropriate to consider retaining the current primary Pb
standard, or whether consideration of revisions is appropriate, we adopted an approach in this
review that builds upon the general approach used in the initial setting of the standard, as well as
that used in the last review, and reflects the broader body of evidence and information now
available. As summarized above, the 1978 rulemaking decisions were based on an integration of
information on health effects associated with exposure to ambient Pb; expert judgment on the
adversity of such effects on individuals; and policy judgments as to when the standard is
requisite to protect public health with an adequate margin of safety, which were informed by air
quality and related analyses, quantitative exposure and risk assessments when possible, and
qualitative assessment of impacts that could not be quantified. The decision in the last review
not to propose any revision to the Pb NAAQS was made in conjunction with the Agency's
implementation of a broad integrated Pb strategy to reduce Pb exposures through various
regulatory and awareness programs that focused on important non-air sources judged to pose
more extensive public health risks, reflecting in part the dramatic reduction of Pb in gasoline
since the standard was set.
In conducting this assessment, staff is again aware of the dramatic alteration in the basic
patterns of air lead emissions in the U.S. since the standard was set that was evident during the
last review as well. In addition to the dramatic reduction of Pb in gasoline, an additional
circumstance that has changed since the standard was set is the enactment of Clean Air Act
Amendments of 1990, which amended the Clean Air Act Section 112 to list Pb compounds as
hazardous air pollutants (HAP) and to require technology-based and risk-based standards, as
appropriate, for major stationary sources of HAP. Staff is also aware that these significantly
changed circumstances have raised the question in this review of whether it is still appropriate to
maintain a NAAQS for Pb or to retain Pb on the list of criteria pollutants. As a result, this
assessment considers the status of Pb as a criteria pollutant and assesses whether revocation of
the standard is an appropriate option for the Administrator to consider.
In developing conclusions and identifying policy options for the Pb standard in this
review, staff has taken into account both evidence-based and quantitative exposure- and risk-
based considerations. A series of general questions frame our approach to reaching conclusions
and identifying options for consideration by the Administrator in deciding whether to retain or
revise the current primary Pb standard. Our review of the adequacy of the current standard
(section 5.4) addresses questions such as the following:
• To what extent does newly available information reinforce or call into question
evidence of associations with effects identified in the last review?
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• To what extent has evidence of new effects and/or sensitive populations become
available since the last review?
• To what extent have important uncertainties identified in the last review been reduced
and have new uncertainties emerged?
• To what extent does newly available information reinforce or call into question any of
the basic elements of the current standard?
• To what extent does available information and current circumstances regarding basic
patterns of air lead emissions in the U.S. reinforce or call into question the need to
maintain a standard for Pb or to retain Pb on the list of criteria pollutants?
To the extent that the available information suggests that revision of the current standard may be
appropriate to consider, we also address whether the currently available information supports
consideration of a standard that is either more or less protective by addressing questions such as
the following:
• Is there evidence that associations, especially likely causal associations, extend to air
quality levels that are as low as or lower than had previously been observed, and what
are the important uncertainties associated with that evidence?
• Are exposures of concern and health risks estimated to occur in areas that meet the
current standard; are they important from a public health perspective; and what are the
important uncertainties associated with the estimated risks?
To the extent that there is support for consideration of a revised standard, we then consider the
specific elements of the standard (section 5.5) to identify ranges of standards (in terms of an
indicator, averaging time, level, and form) that we conclude would be appropriate for the
Administrator to consider in making public health policy judgments, based on the currently
available information, as to the degree of protection that is requisite to protect public health with
an adequate margin of safety. In so doing, we address the following questions:
• Does the evidence provide support for considering a different Pb indicator?
• Does the evidence provide support for considering different averaging times?
• What ranges of levels and forms of alternative standards are supported by the evidence,
and what are the uncertainties and limitations in that evidence?
• To what extent do specific levels and forms of alternative standards reduce the
estimated exposures of concern and risks attributable to Pb, and what are the
uncertainties associated with the estimated exposure and risk reductions?
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5.4 ADEQUACY OF THE CURRENT STANDARD
In considering the adequacy of the current standard, staff first considered whether it is
appropriate to maintain a NAAQS for Pb or to retain Pb on the list of criteria pollutants. As
noted above, this question has arisen in this review as a result of the dramatic alteration in the
basic patterns of air Pb emissions in the U.S. since the standard was set, that primarily reflects
the dramatic reduction of Pb in gasoline, which resulted in orders-of-magnitude reductions in
airborne emissions of Pb and a significant shift in the types of sources with the greatest Pb
emissions. In addition, Section 112 of the Clean Air Act was amended in 1990 to include Pb
compounds on the list of hazardous air pollutants and to require EPA to establish technology-
based emission standards for those listed major source categories emitting Pb compounds, and to
establish risk-based standards, as appropriate, for those categories of sources.
In considering this issue, staff notes that CASAC specifically examined several scientific
issues and related public health (and public welfare) issues that the CASAC Lead Review Panel4
judged to be essential in determining whether delisting Pb or revoking the Pb NAAQS would be
appropriate options for the Administrator to consider. In its letter to the Administrator of March
27, 2007, based on its review of the first draft Staff Paper (Henderson, 2007a; Attachment A),
CASAC's examination of these issues was framed by the following series of questions:
(1) Does new scientific information accumulated since EPA 's promulgation of the
current primary Lead NAAQS of 1.5 fj,g/m3 in 1978 suggest that science previously
overstated the toxicity of lead?
(2) Have past regulatory and other controls on lead decreased PbB [blood lead]
concentrations in human populations so far below levels of concern as to suggest
there is now an adequate margin of safety inherent in those PbB levels?
(3) Have the activities that produced emissions and atmospheric redistribution of lead in
the past changed to such an extent that society can have confidence that emissions
will remain low even in the absence of NAAQS controls?
(4) Are airborne concentrations and amounts of lead sufficiently low throughout the
United States that future regulation of lead exposures can be effectively accomplished
by regulation of lead-based products and allowable amounts of lead in soil and/or
water?
(5) If lead were de-listed as a criteria air pollutant, would it be appropriately regulated
under the Agency's Hazardous Air Pollutants (HAP) program?
For the reasons presented in its March 2007 letter (Attachment A), the CASAC Lead
Review Panel judged that the answer to each of these questions was "wo," leading the Panel to
4 This Lead Panel includes the statutorily defined seven-member CASAC and additional subject-matter
experts needed to provide an appropriate breadth of expertise for this review of the Pb NAAQS.
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conclude that "the existing state of science is consistent with continuing to list ambient lead as a
criteria pollutant for which fully-protective NAAQS are required" (id, p. 5). Further, in a
subsequent letter to the Administrator of September 27, 2007, based on its review of the second
draft Risk Assessment Report (Henderson, 2007b; Attachment B), CASAC strongly reiterated its
opposition to any considered delisting of Pb, and expressed its unanimous support for
maintaining fully-protective NAAQS (id., p. 2).
Staff also notes the receipt of comments from the public on this issue. In a comment
submitted on the first draft Staff Paper, an industry group urged the Agency to delist Pb as a
criteria pollutant. Many other public comments on this issue, received as comments on the first
draft Staff Paper, were against revoking the Pb NAAQS or delisting Pb as a criteria pollutant.
Staff concurs with the reasoning that the CASAC Lead Review Panel used in reaching its
judgments on these questions and agrees with the conclusion that currently available information
does not support either delisting Pb as a criteria pollutant or revoking, and not replacing, the
current Pb NAAQS. In particular, we note that notwithstanding the dramatic changes in the
basic patterns of air Pb emissions in the U.S. since the standard was set, Pb continues to be
emitted into the ambient air from numerous and diverse mobile and stationary sources. Further,
currently available studies provide evidence of adverse health effects associated with blood lead
levels and environmental exposures well below those previously identified, and we note that
there is now no discernable threshold for such effects in contrast to the thresholds that had
previously been inferred. While there is substantial evidence that segments of the population
continue to have blood lead levels that are clearly of concern, there is only limited evidence on
which to base an assessment of the extent to which airborne Pb contributes to these blood Pb
levels. Nonetheless, we believe that the available information is sufficient to infer that ambient
air Pb contributes to air pollution that may reasonably be anticipated to negatively impact public
health and that further reductions in ambient air Pb would likely benefit public health. In the
absence of evidence to the contrary, we believe it is appropriate to retain the NAAQS authority
as part of a broad strategy to control Pb exposures in sensitive populations. Further, we note that
there is the potential for lead emissions to increase above present levels in the absence of a Pb
NAAQS, such as through increased capacity at Pb processing facilities or through the possible
conversion of secondary Pb smelting facilities to primary smelting operations. In addition, while
we recognize that airborne Pb emissions can be reduced for some source categories through
regulatory actions under the hazardous air pollutant program, that program is focused on
stationary sources and is not directed toward other types of sources, including mobile sources
and related resuspension, that contribute to Pb in the ambient air. For the reasons identified here,
we recommend that consideration not be given to delisting Pb as a criteria pollutant or to
revoking, and not replacing, the Pb NAAQS.
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Having reached the general conclusion that it is appropriate to maintain a NAAQS for Pb,
we discuss below the available evidence (section 5.4.1) and quantitative exposure- and risk-
based considerations (section 5.4.2) to more fully inform consideration of the adequacy of the
current standard. We also take into account the views expressed by CASAC and public
commenters (section 5.4.3) in reaching staff conclusions on the adequacy of the current standard
(section 5.4.4).
5.4.1 Evidence-based Considerations
In considering the broad array of health effects evidence assessed in the CD with respect
to the adequacy of the current standard, staff has focused on those health endpoints associated
with the Pb exposure and blood levels most pertinent to ambient exposures (Chapter 3).
Additionally, we give particular weight to evidence available today that differs from that
available at the time the standard was set with regard to its support of the current standard.
First, with regard to the sensitive population, the susceptibility of young children to the
effects of Pb is well recognized, in addition to more recent recognition of effects of chronic
exposure to low level Pb with advancing age (CD, Sections 5.3.7 and pp. 8-73 to 8-75). As
summarized in Chapter 3 and discussed in detail in the CD, the prenatal period and early
childhood are periods of increased susceptibility to Pb exposures, with robust evidence of
adverse effects on the developing nervous system that generally appear to persist into later
childhood and adolescence (CD, Section 6.2). Thus, while we also recognize the sensitivity of
the elderly and other particular subgroups (e.g., see Section 3.4.1), as at the time the standard
was set, young children continue to be recognized as the key sensitive population for Pb
exposures.
With regard to the exposure levels at which adverse health effects occur, the current
evidence demonstrates the occurrence of adverse health effects at appreciably lower blood Pb
levels than those demonstrated by the evidence at the time the standard was set. At the time the
standard was set the physiological effects identified as occurring at the lowest blood Pb levels
were those associated with production of anemia. EPA recognized as clearly adverse the
impairment of heme synthesis and other Pb-related effects which were identified to result in
clinical symptoms of anemia in children above a blood Pb level of 40 |ig/dl, thus identifying this
blood Pb level as an approximate threshold for adverse health effects of Pb (USEPA, 1977; 43
FR46252).5
5 At the time the standard was set, inhibition of delta-aminolevulinic acid dehydratase (5-ALAD), an
enzyme integral to hemoglobin synthesis, was demonstrated to occur at a blood Pb level as low as 10 ug/dL. Effects
of lead on cellular synthesis of heme, as indicated by elevation of EP was considered potentially adverse to the
health of young children and EP elevation could be correlated with blood Pb levels as low as 15 to 20 ug/dL.
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This change in the evidence since the time the standard was set is reflected in changes
made by the CDC in their advisory level for Pb in children's blood, and changes they have made
in their characterization of that level (described more fully in Section 3.2). In 1978, when the
current Pb NAAQS was established, the CDC recognized a level of 30 ug/dL blood Pb as
warranting individual intervention (CDC, 1991). In 2005, with consideration of a review of the
evidence by their advisory committee, CDC revised their statement on Preventing Lead
Poisoning in Young Children, specifically recognizing the evidence of adverse health effects in
children with blood Pb levels below 10 ug/dL and the data demonstrating that no "safe"
threshold for blood Pb had been identified (CDC, 2005).
As discussed extensively in the CD and summarized in Chapter 3, the current evidence
demonstrates the occurrence of a variety of adverse health effects, including those on the
developing nervous system, associated with blood Pb levels extending well below 10 |ig/dL to 5
|ig/dL and possibly lower. For example, we note in particular the international pooled analysis
by Lanphear and others (2005), studies of individual cohorts such as the Rochester, Boston, and
Mexico City cohorts (Canfield et al., 2003a; Canfield et al., 2003b; Bellinger and Needleman,
2003; Tellez-Rojo et al., 2006), the study of African-American inner-city children from Detroit
(Chiodo et al., 2004), and the cross-sectional analysis of a nationally representative sample from
the NHANES, in which the mean blood Pb level was 1.9 |ig/dL (Lanphear et al., 2000).
Further, current evidence does not indicate a threshold for the more sensitive health
endpoints such as adverse effects on the developing nervous system (CD, pp. 5-71 to 5-74 and
Section 6.2.13). This differs from the Agency's inference of a threshold of 40 |ig/dL blood Pb
for the most sensitive health endpoint identified in the 1978 rulemaking, i.e., impairment of heme
synthesis and other effects which result in childhood anemia.
As when the standard was set in 1978, we recognize that there remain today contributions
to blood Pb levels from nonair sources. Estimating contributions from nonair sources is
complicated by the persistent nature of Pb. For example, Pb that is a soil or dust contaminant
today may have been airborne yesterday or many years ago. The studies currently available and
reviewed in the CD that evaluate the multiple pathways of Pb exposure do not usually
distinguish between outdoor soil/dust Pb resulting from historical emissions and outdoor
soil/dust Pb resulting from recent emissions. Further, while indoor dust Pb has been identified as
being a predominant contributor to children's blood Pb, available studies do not distinguish the
However, because of an absence of evidence of impairment on heme synthesis at levels below 40 ug/dL, EPA did
not recognize the inhibition of 5-ALAD at lower blood Pb levels as adverse to health. At that time, EPA stated that
it considered that above blood levels of 30 ug/dL, EP elevation has progressed to the extent that it should be
considered an adverse health effect, and that the effects on heme synthesis seen at 40 ug/dL and above are clearly
adverse (43 FR 46251-46253).
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different pathways (air-related and other) contributing to indoor dust Pb. The exposure
assessment performed for this review has employed available data and methods to develop
estimates intended to inform a characterization of these pathways (see Chapter 4).
Consistent with reductions in air Pb concentrations6 which contribute to blood Pb, nonair
contributions have also been reduced. For example, Pb contributions to diet have been reported
to have declined significantly since 1978, perhaps as much as 70% or more between then and
1990 (WHO, 1995) and the 2006 CD identifies a drop in dietary Pb intake by 2 to 5 year olds of
96% between the early 1980s and mid 1990s. The 1977 CD included a dietary Pb intake
estimate for the general population of 100 to 350 |ig Pb/day (USEPA 1977, p. 1-2) and the 2006
CD cites recent studies indicating a dietary intake ranging from 2 to 10 jig Pb/day (CD, Section
3.4 and p. 8-14). Reductions in elevated blood Pb levels in urban areas indicate that other nonair
contributions to blood Pb (e.g., drinking water distribution systems, and Pb-based paint) have
also been reduced since the late 1970s. In their March 2007 letter to the Administrator, the
CASAC Pb Panel recommended that 1.0-1.4 |ig/dL or lower be considered as an estimate of the
nonair component of blood Pb.
As in 1978, the evidence demonstrates that Pb in ambient air contributes to Pb in blood,
with the pertinent exposure routes including both inhalation and ingestion (CD, Sections 3.1.3.2,
4.2 and 4.4; Hilts et al., 2003). In 1978, the evidence indicated a quantitative relationship
between ambient air Pb and blood Pb - i.e., the ratio describing the increase in blood Pb per unit
of air Pb - that ranged from 1:1 to 1:2 (USEPA, 1977). In setting the standard, the Agency relied
on a ratio of 1:2, i.e., 2 |ig/dL blood Pb per 1 |ig/m3 air Pb (43 FR 46252). The evidence now
and in the past on this relationship is limited by the circumstances in which the data are
collected. We do not have specific measurements of Pb in blood that derived from Pb that had
been in the air. Rather, we have estimates of the relationship between Pb concentrations in air
and Pb levels in blood, developed from populations in differing Pb exposure circumstances,
which inform us on this point. Many of the currently available reviews of estimates for air-to-
blood ratios, which include air contributions from both inhalation and ingestion exposure
pathways, indicate that such ratios generally fall between 1:3 to 1:5, with some as high as 1:10 or
higher (USEPA 1986a, pp. 11-99 to 11-100 and 11-106; Brunekreef, 1984). Findings of a recent
study of changes in children's blood Pb levels associated with reduced Pb emissions and
associated air concentrations near a Pb smelter in Canada indicates a ratio on the order of 1:7
(CD, pp. 3-23 to 3-24; Hilts et al., 2003). In their advice to the Agency, CASAC identified
values of 1:5 as used by the World Health Organization (2000) and 1:10 as supported by an
6 As described in Section 2.3.2.2, air Pb concentrations nationally are estimated to have declined more than
90% since the early 1980s.
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empirical analysis of changes in air Pb and changes in blood Pb between 1976 and the time when
the phase-out of Pb from gasoline was completed (Henderson, 2007a). While there is
uncertainty in the absolute value of the air-to-blood relationship, the current evidence indicates a
notably greater ratio, with regard to increase in blood Pb, than the 1978 1:2 relationship e.g., on
the order of 1:3 to 1:10.
Taken together, the dramatic reduction in the blood Pb level associated with adverse
health effects, the evidence for a greater ratio between blood Pb and air Pb, and the lack of
evidence for a threshold for the sensitive health endpoint, we conclude that the current evidence
calls into question the adequacy of the current standard. In particular, there is now no recognized
safe level of Pb in children's blood and studies appear to show adverse effects at mean
concurrent blood Pb levels as low as 2 |ig/dL (CD, pp. 6-31 to 6-32; Lanphear et al., 2000), as
compared to EPA's view in 1978 that 30 |ig/dL was a maximum safe blood Pb level for an
individual child, and that 15 |ig/dL was the maximum safe blood Pb level for a population of
young children (43 FR 46246-46256). Further, while the nonair contribution to blood Pb has
declined, perhaps to a range of 1.0-1.4 |ig/dL, the air-to-blood ratio appears to be higher at
today's lower blood Pb levels than the estimates at the time the standard was set, with current
estimates on the order of 1:3 to 1:5 and perhaps up to 1:10. Therefore, considered in light of the
framework employed in setting the standard in 1978, the more recently available evidence
suggests a level for the standard that is lower by an order of magnitude or more.
5.4.2 Exposure- and Risk-based Considerations
In addition to the evidence-based considerations, staff has also considered exposures and
health risks estimated to occur upon just meeting the current Pb standard to help inform
judgments about the extent to which exposure and risk estimates may be judged to be important
from a public health perspective, taking into account key uncertainties associated with the
estimated exposures and risks.
As discussed in the previous section (Section 5.4.1), young children are the sensitive
population of primary focus in this review. Accordingly, as described in Chapter 4, the exposure
and risk assessment estimates Pb exposure for children (less than 7 years of age), and associated
risk of neurocognitive effects in terms of IQ decrements. In addition to the risks (IQ decrement)
that we quantitatively estimated, we recognize that there may be long-term adverse consequences
of such deficits over a lifetime, that there is evidence of other health effects occurring at similar
or higher exposures for young children, and that other health evidence demonstrates associations
between Pb exposure and adverse health effects in adults (see Chapter 3). As in Chapter 4
(Section 4.4), we focus predominantly on risk estimates derived using the log-linear with low-
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exposure linearization (LLL) concentration-response function, also noting the range associated
with the other three functions.
In interpreting the quantitative risk estimates for IQ decrement, we are aware of the
significant implications of potential shifts in the distribution of IQ for the exposed population
(e.g., CD, Sections 8.6.1 and 8.6.2; Bellinger, 2004; Needleman et al., 1982; Weiss, 1988; Weiss,
1990). As noted in the CD, a modest change in the mean for a health index at the individual
level can have substantial implications at the population level (CD, p. 8-77). For example for an
individual functioning in the low range of IQ due to the influence of risk factors other than Pb, a
Pb-associated IQ decline of a few points might be sufficient to drop that individual into the range
associated with increased risk of educational, vocational, and social handicap (CD, p. 8-77).
Further as noted in Section 3.4.2, given a somewhat uniform manifestation of Pb-related
decrements across the range of IQ scores in a population, "a downward shift in the mean IQ
value is not associated only with a substantial increase in the percentage of individuals achieving
very low scores, but also with substantial decreases in percentages achieving very high scores"
(CD, p. 8-81). As recognized in Section 5.4.3, the CASAC Pb Panel has advised on this point
that "a population loss of 1-2 IQ points is highly significant from a public health perspective"
(Henderson, 2007a, p. 6).
In considering the risk estimates, we will describe both those for the median and for an
upper percentile, the 95th. In so doing, we emphasize that in setting the standard in 1978, the
Agency accorded risk management significance to the 99.5th percentile by selecting a mean
blood Pb level intended to bring 99.5 percent of the population to or below the then described
maximum safe blood Pb level (see Section 5.2.1.1.2). Similarly, in their advice to the Agency in
this review, CASAC stated that "the primary lead standard should be set so as to protect 99.5%
of the population" (Henderson, 2007a, p. 6). In considering estimates from the quantitative
assessment that will inform conclusions consistent with this objective, however, we and CASAC
also recognize uncertainties in our risk estimates at the edges of the distribution and
consequently report the 95th percentile as our estimate of the high end of the risk distribution
(Henderson, 2007b, p. 3). In so doing, however, we note that there are individuals in the
population expected to have higher risk, the consideration of which is important given the risk
management objectives for the current standard with regard to the 99.5th percentile.
As summarized in Chapter 4 and discussed in more detail in the Risk Assessment Report
(e.g., Sections 2.4.3, 3.2.2 and 3.4), in addition to estimating IQ loss associated with the
combined exposure to Pb from all exposure pathways, we have estimated IQ loss for two policy-
relevant categories of exposure pathways. These are "recent air", which conceptually is intended
to include contributions to blood Pb associated with Pb that has recently been in the air, and
"past air", intended to include contributions to blood Pb associated with Pb that was in the air in
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the past but not in the air recently. In the exposure modeling conducted for the risk assessment,
the exposure pathways assigned to the "recent air" category were inhalation of ambient air Pb
and ingestion of the component of indoor dust Pb that is predicted to be associated with ambient
air concentrations. The exposure pathways assigned to the "past air" category were ingestion of
outdoor soil/dust Pb and ingestion of the component of indoor dust Pb not assigned to recent air.
As recognized in Chapter 4 and described more fully in Sections 2.4.3 and 3.2.2 of the Risk
Assessment Report, there are various limitations associated with our modeling tools that affected
our estimates for these two categories. As a result, blood Pb levels and associated risks of
greatest interest in this review - those associated with exposure pathways involving ambient air
Pb and current levels of Pb emitted to the air (including via resuspension) - are likely to fall
between estimates for recent air and those for the sum of recent plus past air. Accordingly, we
have considered that those two sets of estimates provide a range of interest, with regard to
policy-relevant Pb, in this review.
In considering the adequacy of the current standard, it is important to note that the
standard is currently met throughout the country with very few exceptions. Further, the national
composite average maximum quarterly mean based on 198 active monitoring sites during 2003-
2005 is 0.17 |ig/m3, an order of magnitude below the current standard (Section 2.3.2.4). Review
of the current monitoring network in light of current information on Pb sources and emissions,
however, indicates that we do not have monitors near many of the larger sources and leads us to
conclude that we are likely underestimating the extent of occurrences of relatively higher Pb
concentrations (Section 2.3.2.1).
We have estimated exposure and risk associated with current conditions in a general
urban case study and in three location-specific urban case studies in areas where air
concentrations fall significantly below the current standard. For the general urban case study,
which is a simplified representation of urban areas (see summary of limitations and uncertainties
in Section 4.2.7), median estimates of total Pb-related IQ loss range from 1.5 to 6.3 points
(across all four concentration-response functions), with estimates based on the LLL function of
4.5 and 4.7 points, for the mean and high-end current conditions scenarios, respectively (Table 4-
3). Associated estimates for exposure pathway contributions to total IQ loss (LLL estimate) at
the population median in these two scenarios indicate that IQ loss associated with policy-relevant
Pb falls somewhere between 1.3 and 3.6 points (Risk Assessment Report, Table 5-9 entries for
recent air and recent air plus past air). At the 95th percentile for total IQ loss (LLL estimate), IQ
loss associated with policy-relevant Pb is estimated to fall somewhere between 2.2 and 6.0 points
(Risk Assessment Report, Table 5-9).
For the three location-specific urban case studies, median estimates of total Pb-related IQ
loss for current conditions range from 1.4 to 5.2 points (across all four concentration-response
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functions), with estimates based on the LLL function all being 4.2 points (Table 4-3).7 Median
IQ loss associated with policy-relevant Pb (LLL function) is estimated to fall between 0.6 to 2.9
points IQ loss (Risk Assessment Report, Table 5-9). The 95th percentile estimates for total Pb-
related IQ loss across the three location-specific urban case studies range from 4.1 to 11.4 points
(across all four concentration-response functions), with estimates based on the LLL function
ranging from 7.5 to 7.6 points (Table 4-4). At the 95th percentile for the three location-specific
urban case studies, IQ loss associated with policy-relevant Pb (LLL function) is estimated to fall
between 1.2 to 5.2 points IQ loss (Risk Assessment Report, Table 5-10).
In order to more completely consider exposure and risk associated with the current
standard, we have developed estimates for a case study (including additional focus on a small
subarea) based on air quality projected to just meet the standard in a location of the country
where air concentrations do not meet the current NAAQS (the primary Pb smelter case study).
In so doing, we consider it extremely unlikely that air concentrations in urban areas across the
U.S. that are currently well below the current standard would increase to just meet the standard.
However, we recognize the potential for air Pb concentrations in some areas currently well
below the standard to increase to just meet the 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 scenario (increased Pb concentrations to just meet
the current standard) in a general urban case study and in three location-specific urban case
studies. In this scenario, we note substantial uncertainty in simulating how the profile of Pb
concentrations might change in the hypothetical case where concentrations increase to just meet
the current standard.
Turning first to the estimates of total blood Pb for the current NAAQS scenario simulated
for the location-specific urban case studies (e.g., Tables 4-1 and 4-2), we consider the extent to
which exposures associated with increased air Pb concentrations that simulate just meeting the
current standard are estimated to increase blood Pb levels in young children. The magnitude of
this for the median total blood Pb ranges from 0.3 |ig/dL (an increase of 20 percent) in the case
of the Cleveland study area, for which current conditions are estimated to be approximately one
fourth of the current NAAQS, up to approximately 1 |ig/dL (an increase of 50 to 70%) for the
7 As described in Chapter 4 (and Section 5.2.3.2 of the Risk Assessment Report), although the maximum
quarterly average concentration for the highest monitor in each study area differs among the three areas by a factor
of 4 (0.09 to 0.36 ug/m3), the population weighted air Pb concentrations for these three study areas are more similar
and differ by approximately a factor of 2, with the study area with highest maximum quarterly average concentration
having a lower population weighted air concentration that is more similar to the other two areas. This similarity in
population weighted concentrations explains the finding of similar total IQ loss across the three study areas.
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Chicago and Los Angeles study areas, for which current conditions are estimated to be at or
below one tenth of the current NAAQS.
Our estimates of IQ loss (for child with median total IQ loss estimate) associated with
recent air plus past air Pb at exposures allowed by just meeting the current NAAQS in the
primary Pb smelter case study (Table 4-5) differ when considering the full study area (10 km
radius) or the 1.5 km radius subarea. Estimates for median IQ loss associated with the recent air
plus past air category of exposure pathways for the full study area range from 0.6 point to 2.3
points (for the range of concentration-response functions), while these estimates for the subarea
range from 3.2 points to 9.4 points IQ loss (Tables 4-5). The estimates (recent plus past) for the
median based on the LLL concentration-response function are 1.9 points IQ loss for the full
study area and 6.0 points for the subarea. The 95th percentile estimates of total IQ loss in the
subarea range from 5.0 to 12.4 points, with an associated range for the recent air plus past air of
4.2 to 10.4 points (Table 4-6).
For the current NAAQS scenario in the three location-specific case studies, estimates of
IQ loss associated with policy-relevant Pb for the median total IQ loss range from 0.6 points loss
(recent air estimate using low-end concentration-response function) to 7.4 points loss (recent
plus past air estimate using the high-end concentration-response function) (Table 4-5). The
corresponding estimates based on the LLL concentration-response function range from 2.7 points
(lowest location-specific recent air estimate) to 4.7 points IQ loss (highest location-specific
recent plus past air estimate) (Table 4-5). The comparable estimates of IQ loss for children at the
95th percentile range from 2.6 to 7.6 points for the LLL concentration-response function (Table
4-6).
Further, in comparing current NAAQS scenario estimates to current conditions estimates
for the three location-specific urban case studies, we estimate a difference in total Pb-related IQ
loss for the median of about 0.5 to 1.4 points using the LLL C-R function and a similar
magnitude of difference for the 95th percentile (Tables 4-3 and 4-4). The corresponding estimate
for the general urban case study is 1.1 to 1.3 points higher total Pb-related IQ loss for the current
NAAQS scenario compared to the two current conditions scenarios (Tables 4-3 and 4-4).
Our estimates of median and 95th percentile IQ loss associated with policy-relevant Pb
exposure for air quality scenarios under current conditions (which meet the current NAAQS)
and, particularly those reflecting increased air Pb concentrations simulated to just meet the
current standard, indicate levels of IQ loss that may reasonably be judged to be highly significant
from a public health perspective. Further, for the three location-specific urban case studies, the
estimated differences in incidences of children with IQ loss greater than one point and with IQ
loss greater than seven points in comparing current conditions to those associated with the
current NAAQS indicate the potential for significant numbers of children to be negatively
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affected if air Pb concentrations increased to levels just meeting the current standard. Estimates
of the additional number of children with IQ loss greater than 1 point (based on the LLL
concentration-response function) in these three study areas for the current NAAQS scenario
compared to current conditions range from 100 to 6,000 across the 3 locations (Table 4-7). The
corresponding estimates for the additional number of children with IQ loss greater than 7 points
for the current NAAQS as compared to the current conditions scenario range from 600 to 35,000
(Table 4-8). These latter values for the change in incidence of children with greater than 7 points
Pb-related IQ loss represent 5 to 17 percent of the children (aged less than 7 years of age) in
these study areas. This increase corresponds to approximately a doubling in the number of
children with this magnitude of Pb-related IQ loss in the study area most affected.
Beyond our finding of estimated decrements in IQ for policy-relevant exposures
associated with the current NAAQS that are clearly of a magnitude that might be reasonably be
judged to be highly significant from a public health perspective, there are other, unquantified
adverse neurocognitive effects that may occur at similarly low exposures which might
additionally contribute to reduced academic performance, which may have adverse consequences
over a lifetime (Section 3.3.1; CD, pp. 8-29 to 8-30). Additional impacts at low levels of
childhood exposure summarized in Chapter 3 and described in detail in the CD, that were not
quantified in the risk assessment, include: other neurological effects (sensory, motor, cognitive
and behavioral), immune system effects (including some related to allergic responses and
asthma), and early effects related to anemia. Taken together, we judge that the quantified IQ
effects associated with the current NAAQS and other, non-quantified effects are important from
a public health perspective, indicating a need for consideration of revision of the standard to
provide an appreciable increase in public health protection.
5.4.3 CASAC Advice and Recommendations
In our consideration of the adequacy of the current standard, in addition to the evidence-
and risk/exposure-based information discussed above, we have also considered the advice and
recommendations of CASAC, based on their review of the CD and the earlier draft of this
document and the related technical support document, as well as comments from the public on
earlier drafts of this document and the related technical support document8. With regard to the
public comments, those that addressed adequacy of the current standard concluded that the
current standard is inadequate and should be revised, suggesting appreciable reductions in the
All written comments submitted to the Agency will be available in the docket for this rulemaking, as will
be transcripts of the public meetings held in conjunction with CAS AC's review of the earlier draft of this document
and the first draft of the related technical support document, and of draft and final versions of the CD on which this
document is based.
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level. No comments were received expressing the view that the current standard is adequate.
One comment was received arguing not that the standard was inadequate but rather that
conditions justified that it should be revoked.
In both the 1990 review and this review of the standard set in 1978, CAS AC has
recommended consideration of more health protective NAAQS. In CASAC's review of the 1990
staff paper, as discussed in Section 5.2.2, they generally recommended consideration of levels
below 1.0 |ig/m3, specifically recommended analyses of a standard set at 0.25 |ig/m3, and also
recommended a monthly averaging time (CASAC, 1990). In two letters to the Administrator
during the current review, CAS AC has consistently recommended that the primary NAAQS
should be "substantially lowered" from the current level of 1.5 |ig/m3 to a level of "0.2 |ig/m3 or
less" (Henderson, 2007a, b). CASAC drew support for this recommendation from the current
evidence, described in the CD, of health effects occurring at dramatically lower blood Pb levels
than those indicated by the evidence available when the standard was set. Citing specific studies
(referenced in their March 2007 letter, Attachment A), CASAC stated (Henderson, 2007a, p. 3):
Despite the dramatic decrease in environmental lead exposure, lead toxicity
remains a major public health problem. Environmental lead exposure in children has
been associated with increased risks for reading problems, school failure, Attention
Deficit Hyper activity Disorder (ADHD), delinquency, and criminal behavior (6-10).
Among U.S. children, eight to fifteen years old, those in the highest quintile (> 2 ^g/dl) of
lead exposure were four times more likely to have doctor-diagnosed ADHD (11).
Moreover, there is no evidence of a threshold for the adverse consequences of lead
exposure; studies show that the decrements in intellectual (cognitive) functions in
children are proportionately greater at PbB [blood Pb] concentrations < 10 /ug/dl, the
concentration considered acceptable by the Centers for Disease Control (11-14).
Lead's effects extend beyond childhood. In adults, lead exposure is a risk factor
for some of the most prevalent diseases or conditions of industrialized society, including
cardiovascular disease and renal disease (16-20). There is also compelling evidence
that the risks for mortality from stroke and myocardial infarction are increased at PbB
[blood Pb] concentrations below 10 jug/dl, which is considerably lower than those
considered acceptable for adults (19). Finally, although less definitive, there is also
evidence that lead exposure during pregnancy is a risk factor for spontaneous abortion
or miscarriage at PbB [blood Pb] concentrations < 10 ug/dl (21).
CASAC concluded that the current Pb NAAQS "are totally inadequate for assuring the necessary
decreases of lead exposures in sensitive U.S. populations below those current health hazard
markers identified by a wealth of new epidemiological, experimental and mechanistic studies",
and stated that "Consequently, it is the CASAC Lead Review Panel's considered judgment that
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the NAAQS for Lead must be decreased to fully-protect both the health of children and adult
populations" (Henderson, 2007a, p. 5).
5.4.4 Staff Conclusions and Recommendations
Staff has considered the appropriateness of maintaining a NAAQS for Pb and the
retention of Pb on the list of criteria pollutants in light of the changed circumstances since the
current standard was set in 1978. We conclude that the currently available information does not
support either delisting Pb as a criteria pollutant or revoking, and not replacing, the current Pb
NAAQS, a conclusion that has also been reached by CASAC. Accordingly, we recommend that
Pb not be delisted as a criteria pollutant and that the Pb NAAQS not be revoked.
In considering the adequacy of the current standard, staff gives great weight to the body
of available evidence that is much expanded from that available when the current standard was
set, and which demonstrates the occurrence of adverse health effects at appreciably lower blood
Pb levels than those demonstrated by the evidence at the time the standard was set. Further, the
current health effects evidence and findings in our exposure assessment, like the information
available at the time the standard was set, supports the conclusion that airborne Pb exposure
pathways (by inhalation and ingestion) contribute to blood Pb levels in young children, and that
the proportion of the contribution from these pathways to total blood Pb levels is likely larger
than that estimated when the standard was set and may be several times higher.
In areas projected to just meet the current standard, the quantitative estimates of risk (for
IQ decrement) associated with policy-relevant Pb indicate risk of a magnitude that may
reasonably be judged to be highly significant from a public health perspective, and which
CASAC has judged to be so. Further, although the current monitoring data indicate few areas
with airborne Pb near or just exceeding the current standard, the staff recognizes significant
limitations with our current monitoring network that indicate we are likely underestimating the
extent of occurrences of relatively higher Pb concentrations (Section 2.3.2.1).
In summary, staff draws conclusions with regard to the adequacy of the current standard
from both the evidence and from the exposure and risk assessments, in light of related limitations
and uncertainties. We conclude that the overall body of evidence clearly calls into question the
adequacy of the current standard and provides strong support for consideration of an alternative
Pb standard that would provide an appreciable improvement in health protection for sensitive
groups, including most notably young children, against an array of effects, most importantly
including effects on the developing nervous system. We also conclude that risks projected to
remain upon meeting the current standard, based on the exposure and risk assessment, are
indicative of risks to sensitive groups that can reasonably be judged to be important from a
public health perspective, which reinforces our conclusion that consideration should be given to
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revising the level of the standard so as to provide increased public health protection.
Accordingly, we recommend that the Administrator consider revision of the primary NAAQS for
Pb. Staff conclusions and recommendations for the indicator, averaging time, form and level for
an alternative, more protective primary standard for Pb are discussed in the following sections.
5.5 ELEMENTS OF THE STANDARD
The four elements of the standard - indicator, averaging time, form and level serve to
define the standard and must be considered collectively in evaluating the health and welfare
protection afforded by the standards. In the previous section, staff concluded that the current
standard is not adequate and should be revised. In considering a revision to the current standard
in the subsequent sections, we consider each of the four elements of the standard as to how they
might be revised to provide a primary standard for Pb that is requisite to protect public health.
5.5.1 Indicator
The indicator for the current standard is Pb-TSP. When the standard was set, EPA
considered identifying Pb-PMio as the indicator in response to comments expressing concern that
because only a fraction of airborne particulate matter is respirable, an air standard based on total
air Pb is unnecessarily stringent. The Agency responded that while it agreed that some Pb
particles are too small or too large to be deposited in the respiratory system, a significant
component of exposures can be ingestion of materials contaminated by deposition of Pb from the
air. In addition to the route of ingestion and absorption from the gastrointestinal tract,
nonrespirable Pb in the environment may, at some point, become respirable through weathering
or mechanical action. EPA concluded that total airborne Pb, both respirable and nonrespirable
fractions should be addressed by the air standard.
In the 1990 Staff Paper, staff reconsidered this issue in light of information regarding
limitations of the high-volume sampler used for the Pb-TSP measurements and concurred with
the continued use of TSP as the indicator (USEPA, 1990):
Given that exposure to lead occurs not only via direct inhalation, but via ingestion of
deposited particles as well, especially among young children, the hi-vol provides a more
complete measure of the total impact of ambient air lead ... Despite its shortcomings, the
staff believes the high-volume sampler will provide a reasonable indicator for
determination of compliance ...
In their advice to the Agency CAS AC recommended consideration of a change in the
indicator to utilize low-volume PMio sampling (Henderson, 2007a, b). In so doing, CASAC
recognized the "importance of coarse dust contributions to total Pb ingestion", that a scaling of
the NAAQS level would be needed to accommodate the loss of very large coarse-mode Pb
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particles, and that concurrent Pb-PMio and Pb-TSP sampling would be needed to inform
development of scaling factors (Henderson, 2007b). The September 2007 CASAC letter states
that the CASAC Lead Panel "strongly encourages the Agency to consider revising the Pb
reference method to allow sample collection by PMio, rather than TSP samplers, accompanied by
analysis with low-cost multi-elemental techniques like X-Ray Fluorescence (XRF) or Inductively
Coupled Plasma-Mass Spectroscopy (TCP-MS)." While recognizing the importance of coarse
dust contributions to total Pb exposure via the ingestion route and acknowledging that TSP
sampling is likely to capture additional very coarse particles which are excluded by PMio
samplers, the Panel raised some concerns. The concerns regarded the precision and variability of
TSP samplers and the inability to efficiently capture the non-homogeneity of very coarse
particles in a national monitoring network. The Panel indicated that these concerns may need to
be addressed in implementing additional monitoring sites and an increased frequency of sample
collection that might be required with the substantial reduction in the level of the standard and
the shorter averaging time that they recommend (Henderson, 2007b).
In considering the appropriate indicator, staff concurs with previous Agency conclusions
that the health evidence indicates that Pb in all particle size fractions, not just respirable Pb,
contributes to Pb in blood and to associated health effects. Additionally, the currently available
information, from a very small set of collocated Pb-TSP and Pb-PMio monitoring sites, does not
support the derivation of a scaling factor which might be used to derive a level for the standard in
terms of Pb-PMio as the indicator.
The staff recognizes, however, that an indicator that exhibits low spatial variability is
desirable such that it facilitates implementation of an effective monitoring network, i.e., one that
assures identification of areas with the potential to exceed the NAAQS. For an indicator with
low spatial variability, attainment/nonattainment outcomes would be less sensitive to exact
placement of monitors. However, staff notes that there is an inherent tension between the
perspective that Pb-TSP has high spatial variability and the expectation that a national scaling
factor between the two indicators is possible.
There are several options that staff suggests be considered that might improve the
available database and facilitate consideration of such a move in the future, while retaining Pb-
TSP as the indicator for the NAAQS at this time. For example, the Administrator might consider
describing a FEM in terms of PMio that might be acceptably applied on a site-by-site basis where
an appropriate relationship between Pb-TSP and Pb-PMio can be developed based on site-
specific data. Alternatively, use of such a FEM might be approved, in combination with more
limited Pb-TSP monitoring, in areas where the Pb-TSP data indicate ambient Pb levels are well
below the NAAQS level. These examples are intended purely for purposes of illustrating the
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types of options the Administrator might consider. Specific details of any options will need to be
supported by appropriate data analyses.
To the extent that Pb-PMi0 exhibits less spatial variability and that a scaling factor can be
developed from Pb-PMi0 data to level for the standard in terms of Pb-TSP, staff recommends the
Administrator consider moving to a Pb-PMio indicator in the future. One of the issues to
consider when moving to a Pb-PMio indicator is whether regulating concentrations of Pb-PMio
will lead to appropriate controls on Pb emissions from sources with a large percentage of Pb in
the greater than 10 micron size range (e.g., fugitive dust emissions from Pb smelters). It is
reasonable to believe that Pb-PMi0/Pb-TSP ratios are sensitive to distance from emissions
sources (due to faster deposition of larger particles). As such, the use of a Pb-PMio indicator
may have a significant influence on the degree of Pb controls needed from emission sources.
In conclusion, the staff recommends retaining Pb-TSP as the indicator for the primary
standard, coincident with activities intended to encourage collection and development of datasets
that will improve our understanding of national and site-specific relationships between PMio and
Pb-TSP to support a more informed consideration of indicator during the next review. Staff
suggests that such activities be inclusive of uses of FEMs such as those described above where
sufficient data are available to adequately demonstrate a relationship between Pb-TSP and Pb-
PMio.
5.5.2 Averaging Time and Form
In considering alternative Pb standards that would provide increased public health
protection, staff has taken into account both evidence-based and exposure- and risk-based
considerations. We have also considered analyses of monitoring data comparing metrics
reflecting both averaging time and form.
The basis for the averaging time of the current standard (Section 5.2.1.2) reflects
consideration of the evidence available when the Pb NAAQS were promulgated in 1978. At that
time, the Agency had concluded that the level of the standards, 1.5 |ig/m3, would be a "safe
ceiling for indefinite exposure of young children" (43 FR 46250), and that the slightly greater
possibility of elevated air lead levels within the quarterly averaging period as contrasted to the
monthly averaging period proposed in 1977 (43 FR 63076), was not significant for health. These
conclusions were based in part on the Agency's interpretation of the health effects evidence as
indicating that 30 |ig/dL was the maximum safe level of blood Pb for an individual child.
As discussed in Sections 3.3 and 5.4.1, the currently available health effects evidence
indicates a variety of neurological effects, as well as immune system and hematological effects,
associated with levels below 10 |ig/dL as a central tendency metric of study cohorts of young
children. Further, there is currently no blood Pb level for an individual child recognized to be
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without adverse effect. Accordingly, to the extent that air Pb contributes to variation in blood
Pb, we currently cannot identify a safe ceiling for indefinite exposure of young children.
Additionally, several aspects of the current health effects evidence for Pb pertain to the
consideration of averaging time:
• Children are exposed to ambient Pb via inhalation and ingestion, with Pb taken into the
body absorbed through the lungs and through the gastrointestinal tract. Studies on Pb
uptake, elimination and distribution show that Pb is absorbed into peripheral tissues in
adults within a few days (USEPA 1986a; USEPA 1990b, p. IV-2) Absorption of Pb
from the gastrointestinal tract appears to be greater and faster in children as compared
to adults (CD, Section 4.2.1). Once absorbed, it is quickly distributed from plasma to
red blood cells and throughout the body.
• Lead accumulates in the body and is only slowly removed, with bone Pb serving as a
blood Pb source for years after exposure and as a source of fetal Pb exposure during
pregnancy (CD, Sections 4.3.1.4 and 4.3.1.5).
• Blood Pb levels, including levels of the lexicologically active fraction, respond quickly
to increased Pb exposure, such that an abrupt increase in Pb uptake rapidly changes
blood Pb levels, with the time to reach a new quasi-steady state with the total body
burden after such an occurrence projected to be approximately 75 to 100 days (CD, p.
4-27).
• The elimination half-life, which describes the time for blood Pb levels to stabilize after
a reduction in exposure, for the dominant phase for blood Pb responses to changes in
exposure is on the order of 20 to 30 days for adults (CD, p. 4-25). Blood elimination
half-lives are influenced by contributions from bone. Given the tighter coupling in
children of bone stores with blood levels, children's blood Pb is expected to respond
more quickly than adults (CD, pp. 4-20 and 4-27).
• Data from NHANES II and an analysis of the temporal relationship between gasoline
consumption data and blood lead data generally support the inference of a prompt
response of children's blood Pb levels to changes in exposure in that children's blood
lead levels and the number of children with elevated blood Pb levels appear to respond
to monthly variations in Pb emissions from Pb in gasoline (EPA, 1986a, p. 11-39;
Rabinowitz and Needleman, 1983; Schwartz and Pitcher, 1989).
• The evidence with regard to sensitive neurological effects is limited in what it indicates
regarding the specific duration of exposure associated with effect, although it indicates
both the sensitivity of the first 3 years of life and a sustained sensitivity throughout the
lifespan as the human central nervous system continues to mature and be vulnerable to
neurotoxicants (CD, Section 8.4.2.7). The animal evidence supports our understanding
of periods of development with increased vulnerability to specific types of effect (CD,
Section 5.3), and indicates a potential importance of exposures on the order of months.
• Evidence of a differing sensitivity of the immune system to Pb across and within
different periods of life stages indicates a potential importance of exposures as short as
weeks to months duration. For example, the animal evidence suggests that the
gestation period is the most sensitive life stage followed by early neonatal stage, and
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within these life stages, critical windows of vulnerability are likely to exist (CD,
Section 5.9 and p. 5-245).
Further, evidence described in the CD and the risk analyses described in Chapter 4
indicate that ingestion of dust is the predominant exposure pathway for young children to policy-
relevant Pb, and that there is a strong association between indoor dust Pb levels and children's
blood Pb levels. Further, a recent study of dustfall near an open window in New York City
indicates that airborne Pb can contribute Pb in dust on interior surfaces at a median loading of
4.8 ug/ft2 (52 ug/m2) per week (CD, p. 3-28; Caravanos et al., 2006). The response time in
rooms or houses with closed windows would be slower. But this study indicates that during
times when windows are open, there is a relatively rapid response of indoor dust Pb loading to
airborne Pb.
While some of these aspects of the health effects evidence would be consistent with a
quarterly averaging time, taken as a whole, and in combination with information on potential
response time for indoor dust Pb levels, there is support for consideration of an averaging time
shorter than a calendar quarter.
Another consideration cited in selection of the averaging time when the standards were
first promulgated in 1978 was that an analysis of ambient measurements available at the time
indicated that the distribution of air Pb levels was such that there was little possibility that there
could be sustained periods greatly above the average value in situations where the quarterly
standard was achieved. This may have been related to the pattern of lead emissions at the time
the standard was set, which differed from the pattern today in that, due to emissions from cars
and trucks at that time, emissions were more spatially distributed. The air quality analysis in
Chapter 2 for 2003-2005 indicates the presence of areas in the U.S. currently where temporal
variability does create differences between average quarterly levels and levels sustained for
shorter than quarterly periods. For example, four percent of the monitoring sites in the 3-year
analysis dataset that meet the current standard as an average over a calendar quarter exceed the
level of the current standard when considering an average for any individual month (see Section
2.3.2.5). The same analysis indicates that this number is as high as ten percent for some alternate
lower levels.
In further considering the appropriate form of the standard that might accompany a
shorter averaging time, the staff has considered analyses using the air quality data for 2003-2005
(Section 2.3.2). Maximum quarterly average and various monthly statistics were derived for
each year across the three year Pb-TSP dataset and also for the entire three year period. The
latter time period is consistent with the three calendar year attainment period that has been
adopted for the ozone and particulate matter NAAQS subsequent to the promulgation of the Pb
NAAQS, and was a recommendation of the 1990 Staff paper. For the three year period, the
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monthly statistics derived are maximum monthly mean, second maximum monthly mean,
average of three overall highest monthly means and average of three annual maximum monthly
means; these statistical forms were also considered in the 1990 Staff paper. Additionally, the
maximum and 2nd maximum monthly means for each year of the three year data set were
derived, as well as the averages of these individual year statistics.
With regard to comparison of monthly forms with the maximum quarterly mean, the
average Pb-TSP maximum monthly mean among all 189 sites in the analysis is notably higher
(nearly a factor of two) than the average of the average maximum quarterly mean among these
sites. Further, this difference is slightly greater for source-oriented sites than non source-
oriented sites or urban sites (e.g., a factor of approximately 1.8 as compared to one of
approximately 1.6), indicating perhaps an influence of variability in emissions. The alternate
forms of a monthly averaging time that were analyzed yield an across-site average that is similar
although slightly higher than the quarterly average (e.g., Figure 2-8).
In the 1990 Staff Paper, analyses of computer simulated values and of Pb measurements
collected at point source oriented sites included consideration of six different forms for a
monthly average (USEPA, 1990b). These analyses focused on forms computed over a three
calendar year attainment period to be consistent with multi-year formats adopted for the ozone
and particulate matter NAAQS (subsequent to the promulgation of the Pb NAAQS). From these
analyses, staff demonstrated that the maximum monthly average yielded the highest design
value9, and, among an intermediate design value set of three alternatives, the simplest form was
the second maximum month.
The analyses described in Chapter 2 consider both a period of three calendar years and
one of an individual calendar year (with the form of the current standard being the maximum
quarterly mean in any one year). These analyses indicate that with regard to either single-year or
3-year statistics for the 2003-2005 dataset, a 2nd maximum monthly mean yields very similar,
although just slightly greater, numbers of sites exceeding various alternate levels as a maximum
quarterly mean, with both yielding fewer exceedances than a maximum monthly mean. Other
forms included in a subset of the analyses involved the average of metrics across a three-year
period, such as the average of the 3 maximum monthly means in a three-year period and the
average of three annual maximum monthly means.
In considering whether it is appropriate to change the form to apply to a three-year
period, as is common practice for NAAQS for other pollutants, from the current single-year
period, staff took into account the following. In a three-year approach, a monitor would be
9 The design value is the estimated air concentration at a specific location in terms of the standard (i.e., with
regard to averaging time, form and indicator).
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considered to be in violation of the NAAQS as of a certain date if in any of the three previous
calendar years with sufficiently complete data, the value of the selected form of the indicator
(e.g., maximum monthly average, second maximum monthly average) exceeded the level of the
NAAQS. A monitor, once having violated the NAAQS, would not be considered to have
attained the NAAQS until three years have passed without such an exceedance. This three-year
approach would provide more stability in the air quality management process, and would help
ensure that areas initially found to be violating the NAAQS have effectively controlled the
contributing lead emissions before being redesignated to attainment/maintenance. Analysis of
Pb concentration data has shown instances in which a monitor has exceeded a Pb concentration
in the range recommended for consideration in one year, not exceeded it the second year, and
exceeded it again in the third year, apparently not due to substantial changes in nearby emissions
but rather to meteorological variability and the monitoring schedule. A three-year approach for
Pb would be consistent with the period used for the current NAAQS for PMio, PM2.5, and ozone.
EPA established three-year periods for those NAAQS to provide more stability to the air quality
management process, consistent with CASAC recommendation made during the respective
NAAQS review processes.
In their review of the 1990 Staff Paper during the last review, the CASAC Pb panel
concurred with the staff recommendation to express the lead NAAQS as a monthly standard not
to be exceeded more than once in three years. Similarly, the current CASAC, in their advice to
the Agency during this review, has recommended that the Agency consider changing from a
calendar quarter to a monthly averaging time (Henderson, 2007a). In making that
recommendation, CASAC emphasized support from studies that suggest that blood Pb
concentrations respond at shorter time scales than would be captured completely by quarterly
values, as indicated by their description of their recommendation for adoption of a monthly
averaging time as "more protective of human health in light of the response of blood lead
concentrations that occur at sub-quarterly time scales" (Henderson, 2007a).
With regard to form of the standard, the current CASAC Pb Panel stated that one could
"consider having the lead standards based on the second highest monthly average, a form that
appears to correlate well with using the maximum quarterly value", while also indicating that
"the most protective form would be the highest monthly average in a year" (Henderson, 2007a).
The following observations support consideration of an averaging time on the order of a
calendar month or quarter: 1) the health evidence indicates that very short exposures can lead to
increases in blood Pb lead levels, 2) the time period of response of indoor dust Pb to airborne Pb
can be on the order of weeks and, 3) the health evidence indicates that adverse effects may occur
with exposures during relatively short windows of susceptibility, such prenatally and as in
developing infants. The staff also recognizes the limited evidence specific to the consideration
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of the particular duration of sustained airborne Pb levels having the potential to contribute to the
adverse health effects identified as most relevant to this review.
After considering the current evidence and analyses of air quality, we conclude that this
information provides support for an averaging time at least as short as a calendar quarter and for
considering a change of the averaging time to a calendar month. In considering a form for a
monthly averaging time, staff concludes that a form of maximum or second maximum would be
appropriate. Further, staff concludes that it is appropriate to also consider changing the duration
of the time period evaluated in considering attainment, to a three-year period as is common
practice for NAAQS for other pollutants (from the current single-year period).
5.5.3 Level
Staffs consideration of alternative levels for the primary Pb NAAQS that would provide
greater protection against the array of Pb-related adverse health effects than that afforded by the
current standard builds on our conclusion that the overall body of evidence indicates that the
current standard is inadequate to protect public health and should be appreciably lower (Section
5.4).
5.5.3.1 Evidence-based Considerations
As an initial matter, staff recognizes obstacles that preclude using the epidemiological
evidence directly as the basis for selecting appropriate levels for the Administrator to consider.
As summarized in Chapter 3 and discussed in greater depth in the CD (Sections 4.3 and 6.1.3),
the epidemiological evidence that associates Pb exposures with health effects uses blood Pb as
the dose metric. Further, for the health effects receiving greatest emphasis in this review
(neurological effects on the developing nervous system), no threshold levels can be discerned
from the evidence. As was recognized at the time of the last review, estimating a threshold for
toxic effects of Pb on the central nervous system entails a number of difficulties (CD, pp. 6-10 to
6-11). The task is made still more complex by support in the evidence for a nonlinear rather than
linear relationship of blood Pb with neurocognitive decrement, with greater risk of decrement-
associated changes in blood Pb at the lower levels of blood Pb in the exposed population
(Section 3.3.7; CD, Section 6.2.13).
As the evidence cannot be used directly as the basis for selecting levels, we turn to
somewhat more indirect uses of the evidence, such as the framework applied in the establishment
of the standard. As discussed above (Sections 3.3 and 5.4.1), the body of evidence is much
expanded from that available when the current standard was set. In the following discussion we
have applied the 1978 framework to the currently available evidence.
With regard to the sensitive population, staff identifies young children, the same
population identified in 1978, as the key sensitive population for Pb exposures. Our recognition
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of young children as the sensitive population is based on the evidence summarized in Sections
3.3 and 5.4.1 and described in more detail in the CD.
As recognized in Section 5.4.1 above, the current evidence demonstrates the occurrence
of adverse health effects, including those on the developing nervous system, associated with
blood Pb levels extending well below 10 |ig/dL to 5 |ig/dL and possibly lower. Further, the
current evidence does not indicate a threshold for the more sensitive health endpoints such as
adverse effects on the developing nervous system (CD, pp. 5-71 to 5-74 and Section 6.2.13).
This differs from the Agency's inference in the 1978 rulemaking of a threshold of 40 |ig/dL
blood Pb for effects of Pb considered clearly adverse to health, i.e., impairment of heme
synthesis and other effects which result in anemia. Thus, the level of Pb in children's blood
associated with adverse health effect has dropped by more than a factor of 8, from 40 ug/dL to
less than 5 ug/dL, with some studies indicating Pb effects on intellectual attainment of young
children at blood Pb levels ranging from 2 to 8 ug/dL (CD, Sections 6.2, 8.4.2 and 8.4.2.6),
including a finding of similar Pb-related effects in a study of a nationally representative sample
of children in which the mean blood Pb level was 1.9 |ig/dL (CD, pp. 6-31 to 6-32; Lanphear et
al., 2000).
As when the standard was set in 1978, we recognize that there remain today important
contributions to blood Pb levels from nonair sources.10 As discussed in Section 5.4.1, these
contributions have been reduced since 1978, with estimates of reduction in the dietary
component of 70 to 95 percent (CD, Section 3.4). The evidence is limited with regard to the
aggregate reduction since 1978 of all nonair sources to blood Pb. However, the available
evidence and some preliminary analysis led CASAC to recommend consideration of 1.0 to 1.4
ug/dL or lower as an estimate of the nonair component of blood Pb (Henderson, 2007a,
Appendix D). The value of 1.4 ug/dL was the geometric mean blood Pb level derived from a
simulation of current nonair exposures using the IEUBK model (Henderson, 2007a, pp. F-60 to
F-61).
Regarding the relationship between air and blood, while the evidence demonstrates that
airborne Pb influences blood Pb concentrations through a combination of inhalation and
ingestion exposure pathways, estimates of the quantitative relationship (i.e., air-to-blood ratio)
available in the evidence vary (USEPA, 1986a; Brunekreef, 1984) and there is uncertainty as to
the values that pertain to current exposures. Studies summarized in the 1986 CD typically yield
estimates in the range of 1:3 to 1:5, with some as high as 1:10 or higher (with regard to the air-
10 It should be noted that deposition of airborne Pb is a major source of Pb in food (as is house dust, which
may also be attributable to deposition of ambient air lead) (CD p. 3-54). Thus, although the risk assessment
characterizes dietary Pb as "background," reductions in ambient air Pb have the potential to reduce exposures
through dietary Pb as well.
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influenced increase in blood Pb) (USEPA, 1986a; Brunekreef, 1984). Findings in a more recent
study identified in the 2006 CD of blood Pb response to reduced air concentrations indicate a
ratio on the order of 1:7 (CD, pp. 3-23 to 3-24; Hilts et al., 2003). A value of 1:5 has been used
by the World Health Organization (2000).
Simply applying the framework relied upon in setting the standard in 1978 to the
currently available information illustrates the need for a level for the standard that is appreciably
lower than the current level. For example, replacement of the 1978 blood Pb target of 15 |ig/dL
for the child population geometric mean with a level of 2 |ig/dL reflecting, not a recognized
"safe" exposure level as was the case with 15 |ig/dL in 1978, but some of the lowest population
levels associated with adverse effect in the current evidence (e.g., CD, p. E-9)11, and subsequent
subtraction of 1 to 1.4 |ig/dL, representing nonair sources, yields 0.6 to 1 |ig/dL as a target for
the air contribution to blood Pb. Division of the air target by 5, consistent with currently
available information on the ratio of air Pb to blood Pb, yields a level of 0.1 to 0.2 |ig/m3. We
note, however, that we cannot today identify a blood Pb level considered safe from all adverse
health effects. Thus, putting the current evidence into the framework by which the current
NAAQS was derived, and recognizing that today's evidence provides no evidence of a threshold
for the most sensitive effects, indicates a level for the NAAQS that is lower than the current level
by approximately an order of magnitude or more. The evidence, while indicating the potential
for adverse effects at or below the level used here in application of the 1978 framework, does not
provide specificity with regard to the public health implications associated with lower levels that
might directly inform our consideration of the lower part of a range for the standard.
5.5.3.2 Exposure- and Risk-based Considerations
To inform staff judgments about a range of levels for the standard that could provide an
appropriate degree of public health protection, in addition to considering the health effects
evidence (see preceding section), staff has also considered the quantitative estimates of exposure
and health risks attributable to policy-relevant Pb upon meeting specific alternative levels of
alternative Pb standards and the uncertainties in the estimated exposures and risks. As discussed
above (Section 5.4.2), staff has based this evaluation on the exposure and risk assessment results
presented in Chapter 4, in which exposures have been estimated for children of less than 7 years
of age in six case studies. We also estimated the risk of adverse neurocognitive effects in terms
of IQ decrements associated with total and policy-relevant Pb exposures, including incidence of
11 It is important to note that the 1978 target of 15 was described as the geometric mean level associated
with a 99.5 percentile of 30 ug/dL which the Agency described as a "safe level" for an individual child, while
current epidemiological evidence using a large national database has identified an association with IQ decrement of
blood Pb levels for which the geometric mean blood Pb concentration was 2 ug/dL (Lanphear et al., 2000).
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different levels of IQ loss in three of the six case studies (Chapter 4). In so doing, we are
mindful of the important uncertainties and limitations that are associated with the exposure and
risk assessments, as discussed in Chapter 4. For example, with regard to the risk assessment,
important uncertainties include those related to estimation of blood Pb concentration-response
functions, particularly for blood Pb concentrations at and below the lower end of those
represented in the epidemiological studies characterized in the CD; these uncertainties are
described in Section 4.2.1.
As discussed in Section 4.2.7, we recognize important limitations in the design of, and
data and methods employed in, the exposure and risk analyses, and the associated uncertainties
with regard to the results. For example, the available monitoring data for Pb, relied upon for
estimating current conditions for the urban case studies is quite limited, in that we do not have
monitors near some of the larger known Pb sources (Section 2.3.2.1), which provides the
potential for underestimation of current conditions. Additionally, we are uncertain about the
proximity of existing monitors to other Pb sources not represented in the NEI but with the
potential to influence exposures, such as old urban roadways and areas where housing with Pb
paint has been demolished. All of these limitations raise uncertainty as to whether these data
adequately capture the magnitude of ambient Pb concentrations to which the target population is
currently exposed. Additionally, we did not have sufficient information and tools to evaluate all
relevant sensitive groups (e.g., adults with chronic kidney disease) or all Pb-related adverse
health effects (e.g., neurological effects other than IQ decrement, immune system effects, adult
cardiovascular or renal effects), and the scope of our analyses was generally limited to estimating
exposures and risks in six case studies intended to illustrate a variety of Pb exposure situations
across the U.S., with three of them focused on specific areas in three cities. Thus, it is clear that
national-scale public health impacts of ambient Pb exposures associated with meeting the current
or alternative standards are larger than the quantitative estimates of Pb-related incidence of IQ
decrement summarized in Chapter 4.
As mentioned in Section 5.4.3 and summarized in Chapter 4, we recognize limitations in
our ability to characterize the contribution of policy-relevant Pb to total Pb exposure and Pb-
related health risk. For example, given various limitations of our modeling tools, 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", and we recognize limitations associated with our indoor dust Pb models
that affect our ability to discern differences in the recent air category among different alternate
air quality scenarios.
With these limitations in mind, we first consider the estimates of IQ loss associated with
policy-relevant Pb at air Pb concentrations near those currently occurring in urban areas as
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illustrated by conditions in the three cities chosen for the location-specific urban case studies,
e.g., 0.09 to 0.36 |ig/m3 as a maximum quarterly average or 0.17 to 0.56 |ig/m3 as a maximum
monthly average. Recognizing, as described above, that estimates of IQ loss 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", we
consider ranges reflecting those two categories. Further, as in Section 5.4.2 and for reasons
discussed in Sections 4.2.1, 4.2.7 and 4.4, we focus on risk estimates derived using the LLL
concentration-response function.
As described in Section 5.4.2, the ambient air Pb related IQ loss (based on LLL function)
associated with the median IQ loss for current conditions in the three location-specific case
studies (see Table 5-9 of the Risk Assessment Report) is estimated to fall between the estimates
for recent air (0.6-0.7 points) and those for recent plus past air (2.9 points). This range appears
to be of a magnitude that may reasonably be judged to be highly significant from a public health
perspective in that it overlaps with the range of 1-2 points in IQ loss (see Sections 5.4.2 and
5.4.3). Comparable estimates for the current conditions scenarios in the general urban case study
are still more significant with median estimates for IQ loss for the general urban case study
ranging from 1.3 to 1.8 points for recent air and from 3.2 to 3.6 points for recent plus past air.
The estimates of IQ loss for the 95th percentile (for the current conditions scenario in all of the
urban case studies) extends higher than those for the median, ranging from 1.2-3.1 (recent air) to
5.2-6.0 points (recent plus past).
As mentioned previously, a current conditions scenario was not assessed for the primary
Pb smelter case study, in which current air Pb concentrations across the study area are generally
near or greater than the current NAAQS. However, this case study illustrates the potential
impact of alternative NAAQS levels in comparison to the current NAAQS. Accordingly, we
compare total IQ loss estimates across air quality scenarios, while noting that the simulations of
alternative NAAQS scenarios in the risk assessment involve changes only to the recent air
category of policy-relevant pathways and, consequently, likely provide an underestimate of IQ
loss associated with all policy-relevant Pb. In the subarea of this case study, where risks are
driven by the adjacent point source, reductions in median IQ loss (based on the LLL function) of
1.0 point, 1.8 points, 2.6 points and 2.8 points are estimated for the alternative NAAQS scenarios
for the 0.5, 0.2, 0.05 and 0.02 |ig/m3 levels (in terms of a maximum monthly average),
respectively (Table 4-3). From this it can be seen that in a situation where risks are driven by air
concentrations that just meet the standard across the area assessed (as compared to a situation
where most of the area is well below the standard), an appreciable difference in risk is seen
between the levels of 0.5 and 0.2 |ig/m3, and additionally between those levels and the lower
alternative standard levels assessed. The difference between the alternative standard levels of
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0.5 and 0.2 |ig/m3 is seen to a smaller degree in the general urban case study. The difference
becomes quite small in the Cleveland case study because as mentioned above, few in the
population of that case study reside in the area with the highest air concentrations. Rather, the
vast majority of the population in that case study resides in areas of notably lower levels.
As recognized above, differences in total IQ loss for different air quality scenarios reflect
changes in the risk assessment simulations only to the recent air category of policy-relevant
pathways. We also looked directly at risk estimates for the recent air category, which as
recognized in Chapter 4 are likely underestimates of risk contributed by ambient air-related Pb;
we observe that estimates of "recent air" IQ loss (based on the LLL function) at the 95th
percentile of population total IQ loss are greater than one point for current conditions scenarios
in all three location-specific urban case studies (Risk Assessment Report, Table 5-10). As noted
above, ambient air Pb levels in these case studies extend down to 0.09 ng/m3 maximum quarterly
average (0.17 |J,g/m3 maximum monthly average). The estimates for recent air (for the LLL
concentration-response function) associated with the 95th percentile total IQ loss in the two
current conditions scenarios of the general urban case study are 2.2 and 3.1 points.
Next we consider the extent to which alternative standard levels below current conditions
are estimated to reduce blood Pb levels and associated health risk in young children (Tables 4-1
through 4-4), turning first to the estimates of total blood Pb. In the general urban case study,
blood Pb levels for the median of the population associated with the lowest alternative NAAQS
(0.02 |ig/m3) are estimated to be reduced from levels in the two current conditions scenarios by
14% (0.3 ng/dL) and 24% (0.5 ng/dL), respectively. For the 95th percentile of the population,
the estimated reductions are similar in terms of percentage, but are higher in absolute values (1.7
and 1.0 |ig/dL). For the three location-specific urban case studies, median blood Pb estimates
associated with the lowest alternative standard are reduced from those associated with current
conditions by approximately 10% in the Chicago and Cleveland study areas and 6% in the Los
Angeles study area; similar percent reductions are estimated at the 95th percentile total blood Pb.
For the localized subarea of the primary Pb smelter case study, a 65% reduction in both median
and 95th percentile blood Pb (3 and 8.1 |ig/dL, respectively) is estimated for the lowest
alternative NAAQS as compared to the current NAAQS.12
We next consider the extent to which specific levels of alternative Pb standards reduce
the estimated risks in terms of IQ loss attributable to policy-relevant exposures to Pb (Tables 4-3
and 4-4). For the general urban case study, estimated reductions in median Pb-related IQ loss
12 This can be compared to reductions in blood Pb, for the primary Pb smelter case study subarea estimated
to be associated with a change in the level from the current standard to the 0.2 ug/m3 level (either averaging time)
which are approximately 45-50% for both the median and 95th percentile values.
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associated with reduced exposures at the lowest alternative NAAQS level (0.02 |ig/m3) were 0.5
and 0.7 points (LLL function) for the two current conditions scenarios. Reductions at the 95th
percentile were of a similar magnitude. Among the three location-specific case study areas,
estimated reductions in median Pb-related IQ loss associated with reduced exposures at the
lowest alternate NAAQS as compared to current conditions range from 0.4 to 0.6 points for the
high-end concentration-response function to 0.1 to 0.2 points for the low-end concentration-
response functions, with estimates for the LLL function ranging from 0.2 to 0.3 points. The
reduction at the 95th percentile, based on the LLL function, is 0.3-0.4 points. Reduced exposures
associated with the lowest alternative NAAQS in the primary Pb smelter case study subarea as
compared with the current NAAQS (which is not currently met by this area) were more
substantial, ranging from 2.8 points at the median and 3 points at the 95th percentile (based on
LLL function).
In considering estimated reductions in Pb-associated IQ loss discussed above, we observe
that estimates for the 95th percentile of the population are quite similar to (for the LLL
concentration-response function) or smaller (for the high- and low-end concentration-response
functions) than those at the median for all case studies. This is because of the nonlinear
relationship between IQ decrement and blood Pb level such that relatively smaller IQ decrement
is associated with changes in blood Pb at higher blood Pb levels.
As summarized in Section 4.4, reduction in air Pb concentrations from current conditions
to meet the lower alternative NAAQS (0.02 and 0.05 |ig/m3, maximum monthly mean) is
estimated to reduce the number of children having Pb-related IQ loss greater than one point by
one half to one percent in each of the three location-specific urban case studies. More
specifically, within the three study areas this corresponds to a range of approximately 100 to
3,000 fewer children having total IQ loss greater than 1.0 for an alternative standard of 0.02
|ig/m3, maximum monthly mean. Further, just meeting the lowest alternative standard in these
three study areas is estimated to reduce the number of children having an IQ loss greater than
seven points by one to two percent. This corresponds to a range of approximately 350 (for the
Cleveland study area) up to 8,000 (for the Chicago study area) fewer children with total Pb-
related IQ loss greater than 7.0.
In summary, in staffs view (as noted above), a population IQ loss of 1-2 points may
reasonably be judged to be highly significant from a public health perspective, and is judged to
be so by CASAC (Section 5.4.3). Estimates of IQ loss associated with policy-relevant Pb are of
a magnitude that appears to fall near or within this range for air quality scenarios involving levels
at or above 0.09 |ig/m3 (maximum quarterly mean, or 0.17 maximum monthly mean). Estimated
reductions in risk associated with reducing air Pb concentrations from current conditions (in the
urban case studies) to the two lower alternative levels evaluated (0.02 and 0.05 |ig/m3) appear to
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range from a few tenths to just below one IQ point (for the LLL concentration-response function)
(and up to 1.5 IQ points for the highest concentration-response function). In considering changes
in risk across the population associated with the two lower alternative levels (as compared to
current conditions), we estimate reductions in the number of children with total Pb-related IQ
loss greater than 1 or greater than 7 on the order of hundreds to thousands of children in the three
location-specific urban case studies.
In considering the exposure and risk information with regard to a level for the standard,
staff notes that at the time the standard was set, the Agency recognized a particular blood Pb
level as "safe". Today, current evidence does not support the recognition of a "safe" level. This
is generally reflected in the concentration-response functions used in the risk assessment and in
CAS AC recommendations on these functions with regard to a lack of a threshold. We therefore
have considered a different approach in this review.
In considering these risk estimates, we note our conclusion and CASAC's
recommendation regarding the high public health significance of a population loss of 1 to 2 IQ
points, our recognition in Sections 5.4.1 and 5.4.2 of other unquantified health effects, and the
significant implications of potential shifts in the distribution of IQ for the exposed population
summarized in Section 5.4.2. Based on these factors and the range of estimates summarized
above for IQ loss associated with policy-relevant Pb for the current conditions scenarios of the
location-specific case studies, staff concludes that reducing the NAAQS to a level of 0.1 to 0.2
|ig/m3 or less would provide appreciable improvement in the protection of public health from air-
related ambient Pb relative to that afforded by the current standard.
In considering standard levels below 0.1 |ig/m3, staff has considered risk as well as blood
Pb reduction that might be achieved. Notable reductions in blood Pb are estimated for the lower
alternative standards as compared to the current conditions scenarios. As has been recognized
previously (e.g., Sections 2.1 and 3.1), ambient air Pb is one of several sources of Pb exposure to
children in the U.S. Accordingly, the NAAQS is one of several regulatory tools the Agency
brings to the national task of eliminating blood Pb poisoning in the U.S.13
In considering the public health significance of IQ loss, we have started with our
conclusion that a population loss of 1-2 IQ points may reasonably be judged to be highly
significant from a public health perspective. We also note that some may judge that any IQ loss
at the population level is of potential public health significance. That is, there is no amount of IQ
loss at the population level that is clearly recognized as being of no importance from a public
health perspective. Thus, the magnitude of IQ loss that could be allowed by a standard that
13 The President's Task Force on Environmental Health Risks to Children (Executive Order 13045, as
amended) has identified childhood blood Pb poisoning as a priority public health issue in the United States.
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protects public health with an adequate margin of safety is clearly a public health policy
judgment to be made by the Administrator.
In considering the magnitudes of IQ loss estimated in our assessment for the range of
alternative levels that we considered, we focused on total IQ loss and also on the contribution to
total IQ loss from policy-relevant pathways. In so doing, we recognize that an IQ loss of 1-2
points may reasonably be judged to be highly significant from a public health perspective and we
also recognize that nonair contributions to total Pb-related IQ loss are estimated to reach and
exceed that amount, with air Pb contributions generally of a much smaller magnitude. Thus, we
recognize that it may be appropriate to consider smaller estimates of IQ loss (e.g., less than 1
point IQ loss) in identifying the appropriate target for the policy-relevant component.
Placing weight on incremental changes in policy-relevant Pb-related IQ loss of less than
one point IQ would lead to consideration of the lower standard levels evaluated in the risk
assessment as part of a judgment as to what standard would protect public health with an
adequate margin of safety. Staff also recognizes, however, the significant uncertainties in the
quantitative risk estimates and that uncertainty in the estimates increases with increasing
difference of the air quality scenarios from current conditions (See Section 4.2.7). Thus, to the
extent that incremental exposure reductions achieved through lowering the NAAQS are
concluded to contribute to notable incremental reductions in children's blood Pb and to
associated reductions in health effects, staff suggests that consideration of NAAQS levels below
0.1 |ig/m3 (e.g., the lower levels included in the risk assessment of 0.02 and 0.05 |ig/m3) may be
appropriate to consider.
Thus, if the policy goal for the Pb NAAQS was to be defined so as to provide protection
that limited estimates of IQ loss from policy-relevant sources to no more than 1-2 points IQ loss
at the population-level, we note that standard levels in the range of 0.1 to 0.2 |ig/m3 may achieve
that goal. We also note that even with lower levels of the standard evaluated, while the range of
policy-relevant IQ loss estimates is lower, the upper end of the range still extends up to and in
some cases above 1 point IQ loss. We note, however, appreciably greater uncertainty associated
with these estimates that increases with increasing difference of the alternative standards from
current conditions (See Section 4.2.7).
Alternatively, if the policy goal was to be defined so as to provide somewhat greater
public health protection by limiting the air-related component of risk to somewhat less than 1
point IQ loss at the population level, this would suggest greater consideration for standards in the
lower part of the range evaluated (0.02-0.05 |ig/m3). Such a goal might reflect recognition that
nonair sources, in and of themselves, are estimated to contribute 1-2 points or more of IQ loss,
such that the incremental risk for policy-relevant Pb is adding to a level of total Pb exposure that
is already in a range that can be reasonably judged to be highly significant from a public health
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perspective. We note, however that considering standards in this lower range places greater
weight on the more highly uncertain risk estimates and thus would be more precautionary in
nature.
5.5.3.3 CASAC Advice and Recommendations
Beyond the evidence- and risk/exposure-based information discussed above, in our
consideration of the level for the NAAQS, we have also considered the advice and
recommendations of CASAC, based on their review of the CD and the earlier draft of this
document and the related technical support document, as well as comments from the public on
earlier drafts of this document and the related technical support document. Comments from the
public that pertained to the level of the standard recommended an appreciable reduction in the
level, e.g., setting it at 0.2 |ig/m3 or less.
In their advice to the Agency during this review CASAC has recognized the importance
of both the health effects evidence and the exposure and risk information in selecting the level
for the standard (Henderson, 2007a,b). In two separate letters, CASAC has stated that it is the
unanimous judgment of the CASAC Lead Panel that the primary NAAQS should be
"substantially lowered" to "a level of about 0.2 |ig/m3 or less", reflecting their view of the health
effects evidence (Henderson, 2007a,b). The CASAC Lead Panel also performed some
preliminary calculations to provide input to the staff in terms of the range of alternate standards
appropriate to consider in carrying out the risk assessment (Henderson, 2007a, Appendix D).
The CASAC calculations included an approach relating air Pb levels to blood Pb levels using the
framework employed in the setting of the current NAAQS in 1978, while another related air Pb
levels to blood Pb levels, and then related blood Pb to IQ loss. The results of these calculations
and subsequent advice (Henderson, 2007b) led us to include a range of alternate NAAQS levels
from 0.2 to 0.02 |ig/m3.
The CASAC Pb Panel also provided advice regarding how the Agency should consider
IQ loss estimates derived from the risk assessment in selecting a level for the standard. The
Panel stated that they consider a population loss of 1-2 IQ points to be "highly significant from a
public health perspective". Further they recommended that "the primary Pb standard should be
set so as to protect 99.5% of the population from exceeding that IQ loss." We anticipate further
advice from CASAC with regard to level at the time of their review of the ANPR14.
14 As described in Section 1.2.3, EPA plans to sign an ANPR for the Pb NAAQS around the end of
November 2007 for publication in the Federal Register, consistent with the new NAAQS process. A public meeting
of the CASAC Pb Panel is now being planned for mid-December.
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5.5.3.4 Staff Conclusions and Recommendations
Staffs consideration of alternative levels for the primary Pb standard builds on our
conclusion that the overall body of evidence clearly calls into question the adequacy of the
current standard with regard to health protection afforded to at-risk populations (Section 5.4.4).
We believe that the available information provides strong support for consideration of a range of
standard levels that are appreciably below the level of the current standard in order to provide
increased public health protection for these populations. The support for this conclusion is
drawn from consideration of the evidence and also from consideration of the quantitative risk
and exposure information.
Consideration of the health effects evidence (Section 5.5.3.1) leads the staff to conclude
that it is reasonable to consider a range for the level of the standard, for which the upper part is
represented by 0.1 to 0.2 ug/m3. We note that such levels (in terms of monthly or quarterly
averaging time) are currently seen in many urban areas across the U.S. The evidence, while
indicating the potential for adverse effects at or below these levels, does not provide specificity
with regard to the public health implications associated with lower levels that might directly
inform our consideration of the lower part of a range for the standard. This evidence-based
conclusion takes into account the wealth of evidence characterizing adverse effects, particularly
those to young children, at much lower blood Pb levels than were indicated by the evidence
when the standard was set, the lack of evidence today for a threshold associated with the effects
of greatest public health concern, and the evidence for quantitative relationships between air Pb
and blood Pb that support a higher contribution to blood Pb by air Pb. This conclusion is
consistent with advice and recommendations from CASAC.
Having reached this evidence-based conclusion on the upper part of the range of levels
appropriate for consideration, staff then considered the exposure and risk assessment (Section
5.5.3.2), first as to what extent it provided support for the evidence-based conclusions. In this
assessment, population IQ loss of a magnitude that may reasonably be judged to be highly
significant from a public health perspective was estimated to be associated with policy-relevant
Pb in air quality scenarios involving levels at or above 0.17 ug/m3 Pb with a maximum monthly
averaging time and form (or levels at and above 0.09 ug/m3 with a maximum quarterly averaging
time and form). Thus, the exposure and risk information supports and extends the evidence-
based conclusion by indicating that important reductions in blood Pb and in Pb-associated IQ
loss may be gained or maintained by reducing the level of the standard (in conjunction with a
monthly averaging time) by at least an order of magnitude to approximately 0.15 ug/m3 or
lower.
Staff then considered the exposure and risk assessment with regard to the lower part of
the range of alternative levels for the standard. In looking at the lower standard levels evaluated
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in the quantitative risk assessment, we recognize increasing uncertainty in both the total risk
attributable to Pb exposure as well as that portion attributable to policy-relevant Pb. Further, we
note that the extent to which the estimates of policy-relevant risks may reasonably be judged to
be important from a public health perspective becomes less clear at these lower levels.
Nonetheless, consideration of the risk estimates in a similar framework to that applied when the
current standard was set recognizes the importance of incremental contributions from air-related
sources to total blood Pb and the associated health risks. Thus, to the extent one places weight
on risk estimates for the lower standard levels, we believe these risk results may suggest
consideration of a range of levels that extend down to the lowest levels assessed in the risk
assessment, 0.02 to 0.05 |ig/m3.
In conclusion, staff judges that a level for the standard set in the upper part of our
recommended range (0.1-0.2 |ig/m3, particularly with a monthly averaging time) is well
supported by the evidence and also supported by estimates of risk associated with policy-relevant
Pb that overlap with the range of IQ loss that may reasonably be judged to be highly significant
from a public health perspective, and is judged to be so by CASAC. A standard set in the lower
part of the range would be more precautionary in nature in that it would place weight on the
more highly uncertain range of estimates from the risk assessment.
To provide some perspective on the implications of alternative primary standards (within
the range of levels recommended above and within the alternate averaging times and forms
focused on in Section 5.5.2), staff analyzed the 2003-2005 Pb-TSP dataset described in Section
2.3 to estimate the percentage of counties, and the populations in those counties, that likely
would not attain various Pb standards. We note that given the limitations of the current
monitoring network recognized in Section 2.3.2.1, the estimates of percentage of counties are
likely to be underestimates. This analysis, shown in Appendix 5.A for various forms and levels
of the standards, was not considered as a basis for the above staff conclusions and
recommendations.
5.5.4 Summary of Staff Conclusions and Recommendations on the Primary Pb
NAAQS
Staff recommendations for the Administrator's consideration in making decisions on the
primary standard for Pb, together with supporting conclusions from section 5.4 and 5.5, are
briefly summarized below. In making these recommendations, staff is mindful that the Act
requires standards to be set that, in the Administrator's judgment, are requisite to protect public
health with an adequate margin of safety, such that the standards are to be neither more nor less
stringent than necessary. Thus, the Act does not require that NAAQS be set at zero-risk levels,
but rather at levels that avoid unacceptable risks to public health.
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(1) Staff concludes that the currently available information does not support either
delisting Pb as a criteria pollutant or revoking, and not replacing, the current Pb
NAAQS, a conclusion that has also been reached by CASAC. Accordingly, we
recommend that Pb not be delisted as a criteria pollutant and that the Pb NAAQS not
be revoked.
(2) Staff concludes that the overall body of evidence clearly calls into question the
adequacy of the current standard and provides strong support for consideration of a
Pb standard that would provide an appreciable increase in health protection for
sensitive groups, including most notably young children, against an array of effects,
most importantly including effects on the developing nervous system. We also
conclude that risks estimated to remain upon meeting the current standard, based on
the exposure and risk assessment, are indicative of risks to sensitive groups that can
reasonably be judged to be highly significant from a public health perspective, which
reinforces our conclusion that consideration should be given to revising the level of
the standard so as to provide increased public health protection. Accordingly, we
recommend that the Administrator consider revision of the primary NAAQS for Pb.
(3) Staff concludes that it is appropriate to continue to use Pb-TSP as the indicator to
address effects associated with exposure to Pb. Based on the available information,
and with consideration of the views of CASAC and public commenters, we conclude
that currently available information does not provide a basis for considering an
alternative indicator at this time. Staff notes interest in obtaining data concerning
relationships between Pb-TSP and PMio that might facilitate consideration of this
issue in the next review.
(4) Staff concludes that it is appropriate to consider changing the standard to a monthly
averaging time or to retaining the quarterly averaging time. In considering a form for
a monthly averaging time, staff concludes that a form of maximum or second
maximum would be appropriate to consider. Further, staff concludes that it is
appropriate to also consider changing the duration of the time period evaluated in
considering attainment to a three-year period as is common practice for NAAQS for
other pollutants (from the current single-year period).
(5) Staff concludes that it is appropriate for the Administrator to consider an appreciable
reduction in the level of the standard, reflecting our judgment that a standard
appreciably lower than the current standard could provide an appropriate degree of
public health protection and would likely result in important improvements in
protecting the health of sensitive groups. We recommend that consideration be given
to a range of standard levels from approximately 0.1-0.2 |ig/m3 (particularly in
conjunction with a monthly averaging time) down to the lower levels included in the
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exposure and risk assessment, 0.02 to 0.05 |ig/m3. In so doing, staff recognizes the
substantial complexity in the assessment of exposures and risks and the increasing
uncertainty in the risk estimates at these lower levels.
5.6 SUMMARY OF KEY UNCERTAINTIES AND RESEARCH
RECOMMENDATIONS RELATED TO SETTING PRIMARY STANDARD
Staff believes it is important to highlight key uncertainties associated with establishing
standards for Pb. Such key uncertainties and recommendations for health-related research,
model development, and data gathering are outlined below. In some cases, research in these
areas can go beyond aiding standard setting to aiding in the development of more efficient and
effective control strategies. We note, however, that a full set of research recommendations to
meet standards implementation and strategy development needs is beyond the scope of this
discussion. Staff has identified the following key uncertainties and research questions that have
been highlighted in this review of the health-based primary standards:
• A critical aspect to the risk assessment conducted for this review is the concentration-
response function for the relationship between blood Pb levels in children and neurological
effects, specifically IQ decrement. The functions applied in the assessment are derived from
a recent analysis of pooled datasets from a number of studies (Lanphear et al., 2005). A
particular area of uncertainty in our application of this analysis to our assessment is with
regard to the specification of the concentration-response relationship at the lower blood Pb
levels, particularly below 5 ug/dL, where the pooled analysis was quite limited with regard to
number of observations. Additional epidemiological research involving substantial
populations with blood Pb levels in this lower range would help to reduce uncertainty in
predicting IQ loss at these lower exposure levels.
• The prediction of blood Pb levels in children and other at-risk subgroups would benefit from
research in a number of areas including:
• Temporal scale associated with changes in blood Pb levels associated with changes
in ambient air Pb;
• Interindividual variability in blood Pb levels and methods for characterizing
interindividual variability, including consideration of both empirical and
mechanistic methods;
• Apportionment of blood Pb levels with regard to exposure pathway contributions,
particular distinctions pertinent to policy-relevant exposures and background
sources;
• Prediction of blood Pb levels for subgroups other than young children, including
adults with consideration for the full period of exposure from childhood into
adulthood; and
• Model performance evaluation, with emphasis on applications pertaining to blood
Pb response to ambient air-related pathways and responses to changes in exposures
for those pathways.
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• An important element in the Pb NAAQS risk assessment is the characterization of the
relationship between indoor dust Pb levels and levels of Pb in the ambient air, particularly
with regard to the influence of changes in ambient air Pb on indoor dust Pb. Research in this
topic area generally, as well as in specific environments, and also with regard to aspects
associated with mechanistic modeling (e.g., air exchange rates, home cleaning frequency and
efficiency) would contribute to improved models and methods for use in subsequent reviews.
• The spatial and temporal characterization of ambient air Pb levels in urban residential areas is
a key element of the exposure and risk assessment completed as part of the Pb NAAQS
review. Current limitations in this area contribute uncertainty to our characterization of
ambient air Pb levels and associated exposures. Research in the area of characterizing spatial
variation in air Pb concentrations in different environments and related to different air
sources would help to reduce this uncertainty. An examples of a particular aspects of interest
include the potential for systematic trends in the relationship between ambient air Pb (in
terms of both spatial and temporal patterns) and the distribution of urban residential
populations (e.g., are there elevated ambient air Pb levels in the vicinity of older roads due to
resuspension in the vicinity of higher-density residential populations?).
• An important aspect to this review is the relationship between ambient air Pb levels and soil
Pb levels, including the temporal dynamics of that relationship and variation in that for
different environments. Research to improve our understanding of these areas would
contribute to reducing associated uncertainty with regard to characterization of the
relationship between air and soil Pb and the impact of changes in air Pb on outdoor soil Pb
levels over time.
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Bellinger, D. C. and Needleman, H. L. (2003) Intellectual impairment and blood lead levels [letter]. N. Engl. J. Med.
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Brunekreef, B. (1984) The relationship between air lead and blood lead in children: a critical review. Science of the
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Canfield, R. L.; Henderson, C. R., Jr.; Cory-Slechta, D. A.; Cox, C.; Jusko, T. A.; Lanphear, B. P. (2003a)
Intellectual impairment in children with blood lead concentrations below 10 ug per deciliter. N. Engl. J.
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Canfield, R. L., Kreher, D. A., Cornwell, C., and Henderson, C. R., Jr. (2003b) Low-level lead exposure, executive
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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
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the 2nd Draft Lead Human Exposure and Health Risk Assessments. September 27, 2007.
Hilts, S. R. (2003) Effect of smelter emission reductions on children's blood lead levels. Sci. Total Environ. 303: 51-
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Lanphear, B. P.; Dietrich, K. N.;Auinger, P.; Cox, C. (2000) Cognitive deficits associated with blood lead
concentrations <10 ug/dL in US children and adolescents. Public Health Reports. 115: 521-529.
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N.; Bornschein, R.; Greene, T.; Rothenberg, S. J.; Needleman, H. L.; Schnaas, L.; Wasserman, G.;
Graziano, J.; Roberts, R. (2005) Low-level environmental lead exposure and children's intellectual
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Needleman, H. L., Leviton, A., Bellinger, D. (1982) Lead-associated intellectual deficit [letter]. N. Engl. J. Med.
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Rabinowitz, M. and Needleman, H.L. (1983) Petrol Lead sales and umbilical cord blood lead levels in Boston,
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Schwartz, J., and Pitcher, H. (1989) The relationship between gasoline lead and blood lead in the United States. J
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Tellez-Rojo, M. M, Bellinger, D. C., Arroyo-Quiroz, C., Lamadrid-Figueroa, H., Mercado-Garcia, A., Schnaas-
Arrieta, L., Wright, R. O., Hernandez-Avila, M., Hu, H. (2006) Longitudinal associations between blood
lead concentrations < 10 ug/dL and neurobehavioral development in environmentally-exposed children in
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Weiss, B. (1988) Neurobehavioral toxicity as a basis for risk assessment. Trends Pharmacol. Sci. 9: 59-62.
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6 ASSESSMENT OF THE SECONDARY STANDARD
6.1 INTRODUCTION
This chapter presents information in support of the review of the secondary NAAQS for
lead (Pb). The presentation of welfare effects information summarizes policy-relevant aspects of
the assessment of welfare effects evidence contained in the CD. Staff conclusions and
recommendations on the secondary standard are based on the assessment and integrative
synthesis of the welfare effects evidence presented in the CD, staff analyses and evaluations
presented in Chapter 2 and in this chapter, and the comments and advice of CASAC and
interested parties who commented on an early draft of this document and on the pilot phase Risk
Assessment Report (USEPA, 2006; ICF, 2006).
Welfare effects addressed by the secondary NAAQS include, but are not limited to,
effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, weather,
visibility and climate, damage to and deterioration of property, and hazards to transportation, as
well as effects on economic values and on personal comfort and well-being. The presentation in
this chapter recognizes several key aspects of the welfare evidence for Pb. Lead is persistent in
the environment and accumulates in soils, aquatic systems (including sediments), and some
biological tissues of plants, animals and other organisms, thereby providing long-term,
multipathway exposures to organisms and ecosystems. Additionally, we recognize there have
been a number of widespread uses of Pb especially as an ingredient in automobile fuel by also in
other products such as decorative paints, lead-acid batteries, and some pesticides which have
significantly contributed to widespread increases in Pb concentrations in the environment (e.g.,
CD, Chapters 2 and 3).
In this chapter, we first present key policy-relevant information on the welfare effects
associated with exposure to ambient Pb in section 6.2. Next in Section 6.3, we summarize the
screening level ecological risk assessment conducted in support of the review the details of
which are presented in the pilot phase Risk Assessment Report.1 In Section 6.4, we assess the
secondary standard, draw conclusions and present recommendations for the Administrator to
consider in deciding whether the existing secondary Pb standard should be revised and if so,
what revision is appropriate.
1 As recognized in the December 2006 draft Staff Paper, a full-scale ecological risk assessment has not
been performed for this review.
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6.2 WELFARE EFFECTS
In this section we present a summary of the policy-relevant welfare effects evidence that
is presented in detail in the CD. Key effects and concentration responses for other criteria
pollutants are much more fully understood than for Pb. In the case of Pb, it is difficult to
generalize effects due to the nature of the data and the general lack of community or population
level information on the effects of Pb. Therefore, in this section we describe the effects of Pb on
ecosystems by grouping known effects into categories of organisms and summarizing the limited
available information for broader ecosystem effects of Pb.
6.2.1 Effects in Terrestrial Ecosystems
Ecosystems near smelters, mines and other industrial sources of Pb have demonstrated a
wide variety of adverse effects including decreases in species diversity, loss of vegetation,
changes to community composition, decreased growth of vegetation, and increased number of
invasive species. Apportioning these effects between Pb and other stressors is complicated
because these point sources also emit a wide variety of other heavy metals as well as SC>2 which
may cause toxic effects. There are no field studies which have investigated effects of Pb
additions alone but some studies near large point sources of Pb have found significantly reduced
species composition and altered community structures. While these effects are significant, they
are spatially limited: the majority of contamination occurs within 20 to 50 km of the emission
source (CD, AX7.1.4.2).
By far, the majority of Pb found in terrestrial ecosystems was deposited in the past during
the use of Pb additives in gasoline. This gasoline-derived Pb was emitted predominantly in small
size particles which were widely dispersed and transported across large distances. The evidence
indicates that many sites receiving Pb predominantly through such long-range transport have
accumulated large amounts of Pb in soils (CD, pi AX7-98). There is little evidence that sites
exposed as a result of this long range transport of Pb have experienced significant effects on
ecosystem structure or function (CD, AX7.1.4.2, p. AX7-98). Strong complexation of Pb by soil
organic matter may explain why few ecological effects have been observed (CD, p. AX7-98).
Studies have shown decreasing levels of Pb in vegetation which seems to correlate with
decreases in atmospheric deposition of Pb resulting from the removal of Pb additives to gasoline
(CD, AX 7.1.4.2). Little work, however, has been done investigating the effect of residual long
term, low-level metal concentration on species diversity.
As stated in the CD (Section 7.1), terrestrial ecosystems remain primarily sinks for Pb but
amounts retained in various soil layers vary based on forest type, climate, and litter cycling.
Once in the soil, the migration and distribution of Pb is controlled by a multitude of factors
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including pH, precipitation, litter composition, and other factors which govern the rate at which
Pb is bound to organic materials in the soil (CD, Section 2.3.5).
Like most metals the solubility of Pb is increased at lower pH. However, the reduction of
pH may in turn decrease the solubility of dissolved organic material (DOM). Given the close
association between Pb mobility and complexation with DOM, a reduced pH does not
necessarily lead to increased movement of Pb through terrestrial systems and into surface waters.
Studies have shown that in areas with moderately acidic soil (i.e., pH of 4.5 to 5.5) and abundant
DOM, there is no appreciable increase in the movement of Pb into surface waters compared to
those areas with neutral soils (i.e., pH of approximately 7.0). This appears to support the theory
that the movement of Pb in soils is limited by the solubilization and transport of DOM. In sandy
soils without abundant DOM, moderate acidification appears likely to increase outputs of Pb to
surface waters (CD, AX 7.1.4.1).
Forest harvesting and management practices have significant and lasting effects on
organic matter cycling in forest ecosystems. Clear cutting, as well as other methods of tree
removal, leads to decreased organic matter for several years after harvesting and organic matter
remaining in soils is exposed to higher temperatures and moisture which tend to increase rates of
decomposition. Despite these effects, studies have shown very little to no mobilization of Pb
from soils to surface waters following clear cutting. On possible explanation for this is that
mineral soils (those below the biologically active, organic layer of soil) are efficient in capturing
and retaining mobilized Pb. Loss of Pb in particulate form due to runoff and erosion in clear cut
areas remains a potential source of Pb to surface waters (CD, Section 7.1.5 and associated
Annex).
As described in Chapter 2 (Sections 2.6 to 2.8) and in the CD (Chapter 7 and the Chapter
7 Annex), Pb emitted anthropogenically into the atmosphere accumulates in surface soils and
vegetation throughout the United States as a result of wet and dry deposition. The following
discussion relies heavily on information presented in Chapters 2, 7, 8 of the CD and the Chapter
7 Annex of the CD.
6.2.1.1 Pathways of Exposure
The main pathways of exposure to Pb for animals are inhalation and ingestion.
Inhalation exposures, which would be limited to areas immediately surrounding point sources,
are not thought to be common and little information is available about inhalation in wildlife.
Ingestion constitutes the main pathway of exposure for most organisms whether by incidental
ingestion or prey contamination. For higher organisms which may ingest either contaminated
plants or soils/sediments, the form and species of Pb ingested influences uptake and toxicity as
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does the presence of other heavy metals. The relative toxicity of metal mixtures and their effects
on Pb toxicity is complex and varies greatly by species and metal.
For plants, direct deposition onto surfaces and uptake of dissolved Pb by roots is the main
exposure route (CD, Section 7.1.3). While the migration and biological uptake of Pb in
ecosystems is relatively low compared to other metals, there are many factors that may affect the
mobility of Pb, including elevation and climate, vegetation type, acidity, and soil composition.
The bioavailability and accessibility of Pb to plants is determined largely by the soil pH,
chemical form of Pb, presence of other metals, and source of the Pb in the ecosystem. Low pH
soils enhance bioavailability to plants and Pb chlorides and acetates are more bioavailable than
Pb oxides. These factors directly relate to the ability of Pb complexes to enter pore water in soils
and sediments and thereby enter root tissues.
6.2.1.2 Effects of Lead on Energy Flow and Biogeocycling
Lead in soils and leaf litter can have a significant adverse effect on energy flow in
terrestrial ecosystems through reducing the rate of litter decomposition and by decreasing
photosynthetic rates in plants, both of which alter the ecosystem carbon cycling and may reduce
the ability of trees and other plants to obtain nutrients from the soil (CD, AX7.1.4.3). Recent
studies have associated high Pb concentrations in soils, such as those found near point sources,
with reduced fungal and bacterial activity. This can lead to interruptions in various metabolic
pathways by either reducing symbiotic relationships between the roots of some types of plants
and fungi and/or bacteria or by tying up nutrients needed for plant growth (CD, AX7.1.4.3).
In less contaminated areas removed from point sources, there is little evidence that Pb
represents a threat to energy flow or carbon cycling or that large pulses of Pb are likely to enter
surface waters. Recent studies have shown that atmospheric deposition of Pb has decreased
dramatically (>95%) over the last three decades and residence times in soils (the time for Pb to
move out of the biologically active layers of soil) can varying greatly, for example from about 60
years in deciduous forests to 150 years in coniferous stands (CD, AX7.1.2.2).
6.2.1.3 Tools for Identifying Ecotoxicity in Terrestrial Organisms
In recognition of a need by EPA's Superfund Program to identify the potential for
adverse effect from various pollutants in soils to ecosystems, a multi-stakeholder group,
consisting of federal, state, private sector, and academic participants developed Ecological Soil
Screening Levels (Eco-SSLs) for various pollutants including Pb. Eco-SSLs describe the
concentrations of contaminants in soils that would result in little or no measurable effect on
ecological receptors (USEPA, 2005a). They are intentionally conservative in order to provide
confidence that contaminants that could present an unacceptable risk are not screened out early
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in the evaluation process (intended to be a specific site under consideration of the Superfund
Program). At or below these levels, adverse effects are considered unlikely. These values are
defined in the Ecological Soil Screening Levels for Lead (USEPA, 2005a) as "concentrations of
contaminants in soil that are protective of ecological receptors that commonly come into contact
with soil or ingest biota that live in or on soil." They were derived separately for four general
categories of ecological receptors: plants, soil invertebrates, birds, and mammals.
In the case of plants and soil invertebrates, Eco-SSLs are expressed as concentration of
Pb in soil (mg Pb /kg soil) and were developed with consideration of characteristics affecting
bioavailability (e.g., pH, organic content, etc). The development of Eco-SSLs for avian and
mammalian wildlife involved a two step process: 1) derivation of a toxicity reference value
(TRV) in mg contaminant per kg body weight per day from available literature, and 2)
application of the TRV with information on soil intake, foraging habits, diet, contaminant uptake
by prey for a single species to derive an Eco-SSL in mg Pb per kg soil. In general for avian and
mammalian wildlife categories, a single TRV was developed (e.g., the reference dose for the
most sensitive of the adverse ecological effects on birds) for all species in each category.
However, default assumptions regarding incidental soil ingestion, foraging techniques,
contaminant intake by prey, and overall diet composition generally resulted in different Eco-SSL
values, expressed as soil concentrations, for the different species in each receptor category. The
receptor category Eco-SSL was then set equal to the lowest species-specific Eco-SSL (USEPA,
2005a; ICF, 2006). The Eco-SSLs for Pb, as developed by EPA Superfund Program, for
terrestrial plants, birds, mammals, and soil invertebrates are 120 mg/kg, 11 mg/kg, 56 mg/kg and
1700 mg/kg, respectively. Section 2.7 discusses current concentrations of Pb in soils. Values
range from 40 to 100 mg Pb/kg soil in remote forests where historic deposition of Pb from
gasoline would be presumed to be the major source to hundreds to tens of thousands of mg/kg
near point sources.
By comparing known or modeled soil concentrations of Pb to the Eco-SSL value derived
for each receptor group, Eco-SSL values can be used to identify locations for which further
analyses are warranted to determine adverse effects from Pb. Soil screening values, including
Eco-SSLs, were used in this way in the ecological screening analyses conducted for this
assessment.
6.2.1.4 Effects on Plants
As discussed in Section 7.3.1 of the CD, atmospheric deposition of Pb onto vegetation is
the primary route of exposure to plants from atmospheric Pb. Lead enters plant tissues primarily
through direct transport, whether by surface deposition or through the soil. There is some uptake
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through root cell walls via pore water but little Pb is translocated to other parts of the plant by
this mechanism. Most Pb that does enter plant tissues is deposited in the roots.
Toxicity to plants occurs over a broad range of soil Pb concentrations (tens to thousands
of mg/kg) due in part, to the interaction between various soil processes and the bioavailability of
Pb to plants (CD, Section 7.1.4). Laboratory studies have shown great variation in toxicity to
plants based on the route of exposure and the form of Pb to which the plants are exposed. Two
main factors make it very difficult to determine concentration responses for plants in the field: 1)
the large number of confounding factors that need to be controlled for, and 2) the lack of good
field sites without multiple metal exposures. The 1986 CD (USEPA, 1986) indicated that most
plants experience reduced growth when Pb concentrations in pore water exceed 2 to 10 mg/kg
and when soil concentrations exceed 10,000 mg/kg under conditions of low bioavailability (e.g.,
high pH, oxide rather than acetate forms, etc.) Under increased bioavailability, Pb would cause
reduced growth at much lower levels (e.g. <100 mg/kg). More recent studies have indeed
indicated effects at much lower levels than 10,000 mg/kg in the laboratory. For example, at
2,800 mg Pb/kg dry weight of soil, adverse effects on growth were found for radish shoots when
exposed to Pb chloride in mildly acidic sandy loams and at 12,000 mg/kg for shoots under
similar exposures to Pb oxide (CD, Section AX7.1.4). Root cell elongation, another indicator of
growth, was inhibited in ryegrass at <2.5 mg/kg Pb chloride and absence of root growth was
observed at 5 mg/kg. Elevated toxicity was also found for red spruce and ryegrass when exposed
to Pb under low pH conditions (CD, Section AX7.1.3.1). There is a wide breadth of studies
discussed in the CD for various plants in the laboratory which indicate that Pb in concentrations
found in soils near point sources could reduce plant growth. Despite this information, there are
very few reports of phytotoxicity from Pb exposure under field conditions. Indeed two studies
cited in Section AX7.1.3.2 of the CD found no indication of toxicity in plants exposed to high
soil concentrations of Pb and other heavy metals near mining sites despite relatively high
concentrations of Pb in the vegetation (4000 jig/g in Leita et al., 1989). Overall, the
phytotoxicity of Pb is considered relatively low because little Pb enters plants from soil and what
Pb does enter into plant tissue is deposited in roots where it is either detoxified or sequestered.
6.2.1.5 Effects on Birds and Mammals
The primary source of Pb exposure to birds and mammals is through dietary intake of
both contaminated food items and incidental ingestion of soils/sediments. Direct inhalation of
Pb rarely accounts for more than 10 to 15% of daily exposures and drinking water exposure is
not a significant source of Pb for most organisms (CD, AX7.1.3.1).
Physiological effects from Pb exposure in birds and mammals include increased lipid
peroxidation (fat breakdown) and effects on blood component production (CD, Section
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AX7.1.2.5). Lipid peroxidation and fatty acid changes have been linked to changes in immune
system response and bone formation. Other adverse effects may include changes in juvenile
growth rates; delay of reproductive maturity; behavioral effects, such as decreased predator
avoidance or lack of balance and coordination; and mortality. This cascade of effects has the
potential to influence populations by reducing the number of organisms and the rate at which
they are replaced, as well as altering food web composition.
Toxic effects to birds from Pb exposure have been observed over a wide range of doses in
laboratory studies, usually measuring reproductive success, but little to no data are available on
field populations. Studies have found few significant effects in birds below doses of 100 mg/kg
in the diet and there is evidence that wide ranges of effects levels may be expected. Even in
studies focused on reproductive effects in the same species, effects from doses ranging from <1
to >100 mg Pb/kg bw/day have been observed (CD, AX7.1.3.5). This variation is also true for
other effects (e.g. behavioral and physiological effects) which have been observed at lower
doses. As described in Section AX7.1.3.3 of the CD, no data are available on inhalation
exposures of birds and very little research has been done since the 1986 CD on toxicity from Pb
to birds not exposed to sediment (waterfowl).
Soil Pb concentrations and potential toxicity to birds has been considered in the
development of Eco-SSLs by EPA's Office of Solid Waste and Emergency Response (USEPA,
2005b). A soil Pb concentration of 11 mg/kg dry weight of soil was derived as the Eco-SSL for
birds (woodcock) (CD, Section AX7.1.4). This concentration is commonly exceeded in many
areas including those not influenced by point sources (CD, Sections 3.2 and AX7.1.2.3).
Toxic effects to mammals from Pb exposure have also been observed over a wide range
of doses in laboratory studies with little information available for field populations or exposures.
Recent studies indicate that effects on wildlife survival would likely occur at higher doses than
the 2 to 8 mg/kg-day reported in the 1986 CD. Several studies have recently reported no
observed adverse effect levels (NOAELs) for survival ranging from 3.5 to as high as 3200
mg/kg-day (CD, AX7.1.3.3). No inhalation studies were found to evaluate endpoints in
mammals and in those studies used to develop toxicity endpoints, organisms were dosed using
either ingestion or gavage (tube feeding) which may not necessarily simulate exposure levels in
the field.
A Pb Eco-SSL has been derived for mammals (shrews) at 56 mg/kg dry weight of soil
based in part on toxicity reference values established for reproductive and growth effects
(USEPA, 2005b). Soil concentrations exceeding 56 mg Pb/kg are not uncommon in
urban/industrial locations or near major roadways and may indeed also occur in areas influenced
by deposition of gasoline derived Pb without current Pb emission sources (CD, Section 3.2 and
AX7.1.2.3).
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Several behavioral and physiological processes seem to alter the toxicity of Pb in birds
and mammals. Nutritionally deficient diets, especially those low in calcium, lead to increased
uptake of Pb from the diet. Studies have also shown that younger animals and females are
generally more sensitive to Pb, insectivorous animals may be more highly exposed than
herbivores, and higher trophic level organism are less exposed than lower trophic level
organisms.
6.2.1.6 Effects on Decomposers and Soil Invertebrates
Elevated concentrations of Pb in soils can lead to decreased decomposition rates either by
direct toxicity to specific groups of decomposers, by deactivating enzymes excreted by
decomposers to break down organic material or by binding with organic matter and making it
resistant to the action of decomposers. Direct adverse effects to invertebrates, such as
earthworms and nematodes, include decreased survival, growth and reproduction. Toxicity has
been observed in soil invertebrates and microorganisms at concentrations of hundreds to
thousands of mg Pb/kg soil with significant variation due to soil parameters such as pH and
amount of organic matter (CD, Section AX 7.1.2).
As discussed in CD Section 7.1.4, an Eco-SSL of 1700 mg/kg dry weight of soil has been
derived for soil invertebrates (USEPA, 2005). This concentration does not appear to be
commonly exceeded in areas not directly influenced by point sources (CD, Sections 3.2 and
AX7.1.2.3).
Several physiological mechanisms for reducing Pb toxicity have been found among
invertebrates and microorganisms. These include enzyme mediated detoxification in two species
of spider, Pb storage in waste nodules in earthworms and storage as an inert compound,
pyromorphite, in nematodes. Avoidance of contaminated substrates and reduced feeding has
also been observed in invertebrates.
6.2.1.7 Summary
Lead exists in the environment in various forms which vary widely in their ability to
cause adverse effects on ecosystems and organisms. Current levels of Pb in soil also vary widely
depending on the source of Pb but in all ecosystems Pb concentrations exceed what is thought to
be natural background levels. The deposition of gasoline-derived Pb into forest soils has
produced a legacy of slow moving Pb that remains bound to organic materials despite the
removal of Pb from most fuels and the resulting dramatic reductions in overall deposition rates.
For areas influenced by point sources of air Pb, concentrations of Pb in soil may exceed by many
orders of magnitude the concentrations which are considered harmful to laboratory organisms.
Adverse effects associated with Pb include neurological, physiological and behavioral effects
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which may influence ecosystem structure and functioning. Eco-SSLs have been developed for
Superfund site characterizations to indicate concentrations of Pb in soils below which no adverse
effects are expected to plants, soil invertebrates, birds and mammals. Values like these may be
used to identify areas in which there is the potential for adverse effects to any or all of these
receptors based on current concentrations of Pb in soils.
6.2.2 Effects in Aquatic Ecosystems
Atmospheric Pb enters aquatic ecosystems primarily through the erosion and runoff of
soils containing Pb and deposition (wet and dry). While overall deposition rates of atmospheric
Pb have decreased dramatically since the removal of Pb additives from gasoline, Pb continues to
accumulate and may be re-exposed in sediments and water bodies throughout the U.S (CD,
Section 2.3.6).
Several physical and chemical factors govern the fate and bioavailability of Pb in aquatic
systems. A significant portion of Pb remains bound to suspended particulate matter in the water
column and eventually settles into the substrate. Species, pH, salinity, temperature, turbulence
and other factors govern the bioavailability of Pb in surface waters (CD, Section 7.2.2).
6.2.2.1 Tools for Identifying Ecotoxicity in Aquatic Organisms
Ambient Water Quality Criteria (AWQC) have been developed by U.S. EPA to provide
guidance to states and tribes to use in adopting water quality standards. AWQC values are
available for freshwater and marine environments and for chronic and acute exposures. These
values vary with water hardness and are based on the amount of dissolved Pb in the water
column. They are derived from toxicity testing on aquatic organisms, including fish,
invertebrates and algae and are considered to be values below which no adverse effect is
anticipated (USEPA, 1993). Therefore these values are useful in identifying locations for which
there is the potential for adverse effect from Pb. Section 4.4 describes how these criteria were
used in the risk characterization for the ecological analyses that accompany this review.
A number of sediment ecotoxicity screening values have been developed to identify the
concentration of Pb in sediment at which the potential for adverse effects occur. EPA has
recently published an equilibrium partitioning method for sediment which incorporates the
bioavailability of Pb and allows for mixtures of metals but may not account for ingestion of
sediment by sediment dwelling organisms. There are other alternative approaches for deriving
sediment criteria which are based more directly upon comparisons between concentrations of Pb
in sediment and associated effects from toxicity tests. These methods do not account for
bioavailability or metal mixtures but are compatible with data available from current water
quality databases.
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6.2.2.2 Effects in Marine/Estuarine Ecosystems
This section gives a brief overview of the information available for Pb in marine and
estuarine systems. Most Pb in marine systems is in the inorganic form, complexed with chloride
and carbonate ions. Increasing salinity increases the amount of Cl" and COj2' complexation and
reduces concentration of free Pb2+ thereby producing compounds with lower bioavailability.
There is less data available for the effects of Pb on saltwater organisms and ecosystems but
studies indicate lower concentrations of Pb in oceans and large lakes. Toxicity data as expressed
in both the AQWC guidelines (USEPA, 1993) and CD, AX7.2.2, indicate a much higher
threshold for effects in saltwater environments.
6.2.2.2.1 Pathways of Exposure
Sources of Pb to marine and estuarine ecosystems include runoff from contaminated
watersheds, direct atmospheric deposition and turnover of contaminated sediment in areas of
high turbulence. Lead is primarily found in the open ocean in the dissolved form and is available
in sediment in a variety of complexed forms. Lead concentrations in oceans were found to be
much lower than those measured in freshwater lotic environments and studies with estuarine
organisms have also shown reduced toxicity with increasing salinity, most likely due to increased
complexation with Cl" ions thereby reducing bioavailability. Studies in the Pacific Ocean near
Hawaii have found concentrations of total Pb between 5-11 ng/kg (CD, Section 7.2.2).
6.2.2.2.2 Effects on Organisms and Communities
Hematological and neurological responses, including red blood cell destruction, enzyme
inhibition and spinal curvature, were the most commonly reported effects in aquatic vertebrates.
Demonstrated effects in invertebrates include alteration of reproduction rates and reduced
growth.
Studies with marine protozoa indicate that at water column concentrations of 0.02 to 1.0
mg Pb/L, abundance, biomass and diversity are reduced. In an estuarine community, Pb was
found to affect species abundance when sediment concentrations reached 1343 mg/kg dry
weight. Inhibition of embryo development in commercial shellfish has been documented at
water concentrations of 50|ig/L (CD, AX 7.2.4.3).
The toxicity of Pb in the marine or estuarine environment is highly dependent on salinity.
A study of mysid shrimp reported a lethal concentration for 50% of the test organisms (LCso) of
1140 |ig/L at a salinity of 5% and an LCso of 4274 |ig/L at 25 % salinity. There is also some
evidence of gender sensitivity in that male copepods were more sensitive to Pb in sediment than
females. Smaller fish have been shown to be more sensitive than larger fish of the same species.
Studies on invertebrates have also shown that deposit feeders were most affected by elevated
substrate concentrations.
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6.2.2.3 Effects in Freshwater Ecosystems
This section gives a brief overview of information available for Pb in freshwater systems.
Most Pb in freshwater systems is in the inorganic form. Speciation is important in bioavailability
and is dependent upon factors such as pH, temperature and water hardness. In freshwater, Pb
typically forms strong inorganic complexes with OH" and COs2" and weak complexes with Cl".
Organic Pb compounds in freshwater, which may increase bioavailability, arise from both natural
and anthropogenic sources. Concentrations of various forms of organic Pb complexes are largely
dependent on pH and water hardness.
6.2.2.3.1 Pathways of Exposure
The bioavailability and accessibility of Pb to aquatic organisms is determined largely by
the species of Pb that forms in the ecosystem. In an acidic environment (pH<4) the ionic form,
which is the more toxic form, of most metals generally predominates. As pH increases,
carbonate, oxide, hydroxide, and sulfide complexes usually predominate and tend to be less
toxic. Water hardness also influences toxicity by providing competition in the form of calcium
and magnesium to Pb binding sites on biological membranes. Therefore, Pb is least toxic in
neutral to basic pH levels and at increased water hardness. A further discussion of speciation
and toxicity can be found in Section AX7.2.2.1 of the CD.
The U.S. Geological Survey (USGS) has developed the National Water Quality
Assessment (NAWQA) program which is a nationwide water quality monitoring program. In the
NAWQA program, data have been collected on Pb concentrations in surface water, bulk
sediment, and fish tissue in many watersheds throughout the U.S. While the data are not
representative of the entire U.S., the NAWQA database is the most comprehensive national
database available. The mean concentration of Pb in U.S. surface waters sampled in the
NAWQA program between 1991 and 2003 was 0.66 |ig/L (ranging from 0.04 to 30) and in bulk
sediment was 120.11 |ig/g (ranging from 0.5 to 12,000 |ig/g) (CD, AX7.2.2.2 and Section 2.2).
6.2.2.3.2 Effects at an Ecosystem Level
Aquatic ecosystems near point sources such as smelters, mines and other industrial
sources of Pb have demonstrated a wide variety of effects including reduced species diversity,
abundance and richness; decreased primary productivity, and alteration of nutrient cycling.
Apportioning these effects between Pb and other stressors is problematic since these point
sources also emit a wide variety of other heavy metals which may cause toxic effects in aquatic
systems.
Lead exposure may adversely affect organisms at different levels of organization, i.e.,
individual organisms, populations, communities, or ecosystems. Generally, however, there is
insufficient information available for single contaminants in controlled studies to permit
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evaluation of specific impacts on higher levels of organization (beyond the individual organism).
Potential effects at the population level or higher are, of necessity, extrapolated from individual
level studies. Available population, community, or ecosystem level studies are typically
conducted at sites that have been contaminated or adversely affected by multiple stressors
(several chemicals alone or combined with physical or biological stressors). Therefore, the best
documented links between Pb and effects on the environment are with effects on individual
organisms.
However, several recent studies have attributed the presence of Pb to reduced primary
productivity, increased respiration, and alterations of community structure. Specifically,
dissolved Pb at concentrations from 6 to 80 mg/L (concentrations higher than those found in the
NAWQA database) was found to reduce primary productivity and increase respiration in an algal
community. Laboratory microcosm studies have indicated reduced species abundance and
diversity in protozoan communities exposed to 0.02 to 1 mg Pb/L (CD, Section AX 7.2.5). Field
studies have associated the presence or bioaccumulation of Pb with reductions in species
abundance, richness, or diversity, particularly in sediment-dwelling communities (CD, Section
AX7.2.5). Most of the available data for Pb effects in aquatic ecosystems comes from either
laboratory studies which focused on only a few aspects of the natural system thereby neglecting
some of the factors known to influence bioavailability of Pb, or from complex natural systems
with many stressors and various sources of anthropogenic Pb, particularly direct mining waste
inputs (CD, AX7.2.5.2). Thus, the effects of atmospheric Pb on aquatic ecological condition
remain to be defined.
There is a paucity of data in the general literature that explores the effects of Pb in
conjunction with all or several of the various components of ecological condition as defined by
the EPA (Young and Sanzone, 2002). Recent studies have attributed the presence of Pb to
adverse effects on biotic conditions such as abundance, diversity, reduced primary productivity,
and alteration of community structure (CD, Section 7.2.5). It is difficult to apportion effects
between Pb ands other stressors, however, and these studies did not generally account for
modifying factors that may mediate or exacerbate Pb effects.
Lead concentrations in sediment vary with depth and are attributable to increased
anthropogenic inputs over the last few decades. Several studies have been undertaken to identify
regional sources of Pb in eastern North America and the Great Lakes and have found positive
correlations between Pb isotope ratios in the Great Lakes and known aerosol emissions from
current and historic industrial sources in Canada and the U.S. These studies seem to indicate that
current emissions are contributing somewhat to Pb in sediments (CD, AX7.2.2.3). Resuspension
of historically deposited Pb in sediments may also constitute a source of Pb in some systems for
the foreseeable future (CD, AX 7.2.2.3).
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6.2.2.3.3 Effects on Algae and Aquatic Plants
As primary producers in aquatic systems, algae and aquatic plants are vital to ecosystem
function and provide the foundation upon which the food web depends. Therefore impacts to
these organisms can create a chain of effects that impacts the entire ecosystem. Algae and
aquatic plants are exposed to Pb by either uptake from the water column or sediment. Pb is most
bioavailable in the divalent form (Pb2+) and as such is adsorbed onto cell walls and accumulates
in the cell wall or surface of the plasma membrane of aquatic plants and algae (CD, AX7.2.3.1).
Bioconcentration of Pb, the accumulation of Pb inside an organism, may be quite high for both
algae and aquatic plants and have made them effective in the remediation of contaminated areas.
In aquatic plants as in terrestrial plants, Pb tends to be sequestered (bound and stored) in roots
much more than in shoots although some wetland plants have been found to accumulate high
levels of Pb in shoots as well. Within the plants the sequestered Pb tends to be metabolically
unavailable until a certain concentration is reached which appears to be species specific.
Growth inhibition is exhibited by algae and aquatic plants over a broad range of Pb
concentrations in water (1000 to > 100,000 |ig/L) due in part, to the interaction between various
biochemical factors and the bioavailability of Pb to these organisms (CD, AX7.2.3.1). Clinical
signs of Pb toxicity in algae include deformation and disintegration of cells, shortened
exponential growth phase, and inhibition of pigment synthesis which may ultimately lead to cell
death. As reported in the CD (Section AX7.2.3.1), studies have shown growth inhibition of
Closterium acerosum, a freshwater algae, at concentrations of 1,000 |ig/L Pb nitrate exposure
and an effects concentration for 50% of the test population (EC50) for growth inhibition of
Scenedesmus quadricauda has been reported at 13,180|ig/L. Other species of algae such as
Synechococcus aeruginosus were much more tolerant and required concentrations in excess of
82,000 |ig/L to elicit significant growth inhibition. In aquatic plants, toxicity studies have
focused on the effects of Pb on plant growth, chlorophyll concentration and protein content. An
ECso of 1,100 |ig/L was reported for growth inhibition for Azollapinnata, an aquatic fern, when
exposed to Pb nitrate for 4 days. Studies with duckweed, Lemna gibba, have reported an ECso of
3,750jig/L under the same conditions. These studies indicate the possibility of adverse impacts
to algae and aquatic plants at concentrations which may be found in the vicinity of direct
discharges from point sources but which would not be expected from ambient deposition.
There are two main mechanisms by which algae and plants may moderate Pb toxicity:
sequestration in roots or cell walls, and production of enzymes which complex Pb to make it
metabolically inactive. Studies have shown phytochelatins, polypeptides which chelate heavy
metal ions and make them biologically unavailable to the organism, may be synthesized in
response to exposure to heavy metals (CD, AX7.1.2.4).
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6.2.2.3.4 Effects on Invertebrates
Aquatic invertebrates serve an important role in aquatic ecosystems as both consumers of
detrital material and as a prey source for many other organisms. Therefore, adverse impacts to
invertebrates can dramatically alter or reduce ecosystem function. Invertebrates may accumulate
Pb in tissue through ingestion of food and water and adsorption from water. Dietary Pb may
contribute significantly to the chronic toxicity of Pb through ingestion of food which has
accumulated Pb or by incidental ingestion of sediments. Studies which relate the effects of
dietary exposure and toxic effects in aquatic systems are rare; however, it may be assumed that
both dietary and waterborne exposures are important to overall Pb toxicity (CD, AX7.2.4.3).
Exposure to Pb can result in reduction of growth rates and reproductive rates as well as
cause increased mortality. As discussed in Section 4.3.2.1 of this document, both acute and
chronic toxicity of Pb can be significantly influenced by water hardness and pH. A study by
Borgmann et.al (2005) with Hyalella azteca, a freshwater amphipod, showed a 23-fold increase
in acute toxicity in soft water (18 mg CaCOs/L) compared to hard water (124 mg CaCOs/L).
The influence of pH on Pb toxicity varies between invertebrate species. Studies have reported
increasing mortality with decreasing pH in some bivalves, cladocerans, amphipods, gastropods
and mayflies while some crustaceans and gastropods have shown no relationship between pH
and mortality under identical conditions. For the amphipod H. azteca, the lowest observed effect
concentration (LOEC) for survival in hard water at pH 8.27 was 192 |ig/L as dissolved Pb and
466 |ig/L as total Pb leading to the conclusion that both waterborne and dietary Pb contributed to
this reduced survival (CD, AX7.2.4.3). Overall, adverse effects for the most sensitive
invertebrates studied, amphipods and waterfleas, occurred at concentrations ranging from 0.45 to
8,000 ng/L. Exposures to Pb in sediment can also produce toxic effects in sediment dwelling
invertebrates. Acute effects in the water flea, Daphnia magna, included reduced mobility after
exposure to 7,000 mg Pb/kg dw for 48 hours while chronic exposure of midges to sediments
containing 31,900 mg Pb/kg dw resulted in 100% mortality over 14 days (CD, AX7.2.4.3).
Based on recorded Pb concentrations in the NAWQA database, there are some surface
waters and sediments in the U.S where effects on sensitive invertebrates would be expected but
apportioning these concentrations between air and nonair sources has not been done.
There are several mechanisms by which invertebrates detoxify Pb. Lead may be
concentrated in some invertebrates by formation of granules which may be eventually excreted,
sequestered within the exoskeleton and glandular cells, or bound to membranes in gills and other
tissues. Avoidance behaviors have been documented for the aquatic snail, Physella columbiana,
but few studies were found that reported avoidance behaviors in invertebrates.
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6.2.2.3.5 Effects on Fish and Waterfowl
Both the ingestion of contaminated sediment and prey items as well as direct absorption
from water contributes to fish exposures to Pb. Dietary effects of Pb are not well studied in fish
but evidence supports that higher tissue concentrations have been found in fish with direct
contact with sediment. Gale et al. (2002) found a good correlation between sediment
concentration and tissue concentrations in suckers and small sunfish, which feed directly from
the sediment, but not in smallmouth bass, which feed at a higher trophic level. Bioconcentration
does occur in freshwater fish and bioconcentration factors (BCFs) for brook trout and bluegill of
42 and 45, respectively, have been reported (CD, AX7.2.3.1). Studies have also shown that fish
accumulate Pb more rapidly in low pH environments and when diets are calcium deficient.
Lead has been observed to have adverse effects on the production of some enzymes
which affect locomotor function as well as adverse blood chemistry effects in some fish.
Symptoms of Pb toxicity in fish include the production of excess mucous, spinal deformity,
anemia, darkening of the dorsal region, degeneration of the caudal fin, destruction of spinal
neurons, enzyme inhibition, growth inhibition, renal pathology, reproductive effects, and
mortality (CD, AX7.2.4.3). As in other organisms, Pb speciation, water pH and water hardness
play an important role in the toxicity of Pb. Spinal deformities were found to occur at much
lower Pb concentrations in soft water than in hard water. Maximum acceptable threshold
concentrations (MATC), the maximum concentrations at which no adverse effects were seen,
have been reported (CD, AX7.2.4.3) for rainbow and brook trout in soft water as 4.1 to 7.6 |ig/L
Pb and 58 to 118|ig/L Pb respectively. A LC50 of 810 |ig/L was found using fathead minnows at
a pH of 6-6.5 while at the same water hardness the LCso was >5,400 |ig/L at a pH range of 7 -
8.5. Other studies have shown alterations in blood chemistry in fish from chronic and acute
exposures ranging from 100 to 10,000 |ig/L Pb (CD, Section AX8.2.3.3). Therefore, given the
concentrations of Pb found in surface waters in the NAWQA database, there are likely adverse
effects to fish populations in some locations of the U.S. It is not clear what the ambient air
contributions of Pb are at these locations.
There are several physiological and behavioral mechanisms by which fish reduce
exposure and absorption of Pb. While the avoidance response to Pb in fish has not been well
studied, it is known for other metals and is thought likely for Pb (CD, AX7.2.3.2). As in other
organisms, gender and age are important variables in determining the adverse effects of Pb with
females and young fish being more sensitive to Pb.
Incidental ingestion of contaminated sediment is the primary route of exposure for
waterfowl. Studies by Beyer et al. (2000) in the Coeur d'Alene watershed near mining and
smelting activity have shown a range of effects for waterfowl based on sediment concentrations
and corresponding blood Pb levels. This study suggested that a NOAEL blood concentration of
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0.20 mg/kg wet weight Pb corresponded to a sediment concentration of 24 mg/kg Pb.
Subclinical poisoning (LOEL) occurred in swans when sediment concentration was 530 mg/kg
Pb which corresponded to a 0.68 mg/kg blood Pb level. Some mortality may occur with
sediment concentrations as low as 1800 mg/kg Pb and an LCso was found in swans at 3,600
mg/kg Pb in sediment. While these values are somewhat site specific and are dependent on the
bioavailability of the Pb as well as the overall health and diet of the animals, the correlation
between blood Pb levels and effects should be applicable irrespective of location-specific
variables. Given current concentrations of Pb in sediment, it is likely that some adverse effects
are occurring in waterfowl exposed to point sources of Pb, whether through deposition or direct
discharge.
6.2.2.4 Summary
Lead exists in the aquatic environment in various forms and under various chemical and
physical parameters which determine the ability of Pb to cause adverse effects either from
dissolved Pb in the water column or Pb in sediment. Current levels of Pb in water and sediment
also vary widely depending on the source of Pb. Conditions exist in which adverse effects to
organisms and thereby ecosystems may be anticipated given experimental results. It is unlikely
that dissolved Pb in surface water constitutes a threat to ecosystems that are not directly
influenced by point sources. For Pb in sediment, the evidence is less clear. It is likely that some
areas with long term historical deposition of Pb to sediment from a variety of sources as well as
areas influenced by point sources have the potential for adverse effects to aquatic communities.
The long residence time of Pb in sediment and its ability to be resuspended by turbulence make
Pb likely to be a factor for the foreseeable future. Criteria have been developed to indicate
concentrations of Pb in water and sediment below which no adverse effects are expected to
aquatic organisms. These values may be used to identify areas in which there is the potential for
adverse effects to receptors based on current concentrations of Pb in water and sediment.
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6.3 SCREENING LEVEL RISK ASSESSMENT
A screening level ecological risk assessment was performed in the pilot phase of the risk
assessment for this review. The screening level assessment is described in detail in Lead Human
Exposure and Health Risk Assessments and Ecological Risk Assessment for Selected Areas, Pilot
Phase (ICF, 2006). Funding constraints have precluded performance of a full-scale ecological
risk assessment. The discussion here is focused on the screening level assessment performed in
the pilot phase (ICF, 2006) and takes into consideration CASAC recommendations with regard
to interpretation of this assessment (Henderson, 2007a, b).
6.3.1 Overview of Analyses
The screening level risk assessment involved multiple case studies and a national-scale
surface water and sediment screen. The case studies included areas surrounding a primary Pb
smelter and a secondary Pb smelter, as well as a near roadway nonurban location. An additional
case study for an ecologically vulnerable location was identified and described, but schedule
constraints precluded risk analysis for this location (ICF, 2006).
The case study analyses were designed to estimate the potential for ecological risks
associated with exposures to Pb emitted into ambient air. Soil, surface water, and/or sediment
concentrations were estimated from available monitoring data or modeling analysis, and then
compared to ecological screening benchmarks to assess the potential for ecological impacts from
Pb that was emitted into the air (Figure 6-1). Results of these comparisons are not definitive
estimates of risk, but rather serve to identify those locations at which there is the greatest
likelihood for adverse effect. Similarly, the national-scale screening assessment evaluated the
potential for ecological impacts associated with the atmospheric deposition of Pb at surface
water and sediment monitoring locations across the United States.
Figure 6-1 illustrates the use of information and models in each phase of the analysis.
Table 6-1 specifies the information types and models used for each case study, and for the
national-scale screen. The reader is referred to the pilot phase Risk Assessment Report (ICF,
2006) for details on the use of this information and models in the screening assessment. As
indicated in these exhibits, the specific approach for each case study differed based on the nature
of the case study (e.g., type of source, land use) and the site-specific measurements available. In
cases where the available measurements were not sufficient to characterize the study area (e.g.,
due to insufficient spatial coverage), these data were used for performance evaluation of
modeling tools.
6-17
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Figure 6-1. Overview of Ecological Screening Assessment.
Exposure Assessment: Estimating Media Concentrations
National-Scale
Secondary Pb Surface Water &
Smelter Case Study Sediment Screening
Assessment
Primary Pb
Smelter
Case Study
Near Roadway
N on-urban
Case Study
Site-specific soil,
surface water, and
sediment
monitoring data
Site-specific soil
monitoring data
Stack and fugitive
emissions
estimates
Air dispersion
model
Lead deposition
rates across study
area
Soil
mixing
model
Outdoor soil
concentrations
Water column
concentrations
Sediment
concentrations
Risk
Chara cterizatio n
Ecological
Effects
Assessment
Soil
screening values
Freshwater
screening values
Sediment
screening values
Media-specific Hazard Quotients
6-18
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Table 6-1. Models and Measurements Used for Ecological Risk Screening Assessment.
Primary Pb Smelter
Location Missouri
Spatia, extent and resolution App^mjejr 6Jm dieter,
Secondary Pb
Smelter
Alabama
U.S. Census
blocks
Near Roadway Non-Urban National-Scale Aquatic
Corpus Christi, Texas Surface water bodies in
Atlee, Virginia the U.S.
trS^r^di^o 47-^^r°m
r°Af °f
-------
6.3.2 Measures of Exposure and Effect
The measures of exposure for these analyses are total Pb concentrations in soil, dissolved
Pb concentrations in fresh surface waters (water column), and total Pb concentrations in
freshwater sediments. These exposure concentrations were estimated for the three case studies
and the national-scale screening analyses as described below:
• For the primary Pb smelter case study, measured concentrations of total Pb in soil,
dissolved Pb in surface waters, and total Pb in sediment were used to develop point
estimates for sampling clusters thought to be associated with atmospheric Pb
deposition, rather than Pb associated with nonair sources, such as runoff from waste
storage piles.
• For the secondary Pb smelter case study, concentrations of Pb in soil were estimated
using fate and transport modeling based on EPA's MPE methodology (USEPA, 1998)
and data available from similar locations.
• For the near roadway nonurban case study, measured soil concentration data collected
from two interstate sampling locations, one with fairly high-density development
(Corpus Christi, Texas) and another with medium-density development (Atlee,
Virginia), were used to develop estimates of Pb in soils for each location.
• For the national-scale surface water and sediment screening analyses, measurements of
dissolved Pb concentrations in surface water and total Pb in sediment for locations
across the United States were compiled from available databases. Air emissions,
surface water discharge, and land use data for the areas surrounding these locations
were assessed to identify locations where atmospheric Pb deposition may be expected
to contribute to potential ecological impacts. The exposure assessment focused on
these locations.
The Hazard Quotient (HQ) approach was used to compare estimated Pb media
concentrations with ecotoxicity screening values for soils, surface waters, and sediments around
the primary Pb smelter, for soils only around the secondary Pb smelter case study location and
the near roadway non-urban case study locations, and for surface water and sediment in the
national-scale screen. The HQ is calculated as the ratio of the media concentration to the
ecotoxicity screening value. The HQ is represented by the following equation:
HQ = (estimated Pb media concentration) / (ecotoxicity screening value)
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For each case study, HQ values were calculated for each location where either modeled
or measured media concentrations were available. Separate soil HQ values were calculated for
each ecological receptor group for which an ecotoxicity screening value has been developed (i.e.,
birds, mammals, soil invertebrates, and plants). HQ values less than 1.0 suggest that Pb
concentrations in a specific medium are unlikely to pose significant risks to ecological receptors
whereas HQ values greater than 1.0 indicate that the expected exposure exceeds the ecotoxicity
screening value.
6.3.3 National-Scale Screen and Case Studies
This section provides an overview of the study locations included in the ecological
screening risk assessment performed in support of the NAAQS review. A national-scale screen
was conducted to look at current Pb concentrations in freshwaters and sediments throughout the
U.S. and three case study locations were selected: 1) near a primary Pb smelter, 2) near
secondary Pb smelter and 3) a near roadway non-urban location. The primary and secondary Pb
smelter case studies represent large and moderate sized point source scenarios, respectively,
while the near roadway non-urban location represents a more ubiquitous exposure from historic
emissions of gasoline Pb along major roadways.
6.3.3.1 National-Scale Screen
A national-scale assessment was performed using the NAWQA database to identify
locations in the U.S. in which concentrations of Pb in surface water and/or sediment exceed
established screening values and for which ambient air Pb is likely to be a major factor. These
locations were identified using the methodology described below and in the risk assessment
report (ICF, 2006).
6.3.3.1.1 Fresh Surface Waters
A screening-level ecological risk assessment for aquatic ecosystems was conducted for
Pb concentrations in fresh surface waters of the United States to identify areas in which there are
concentrations in excess of EPA recommended national ambient water quality criteria (AWQC),
both chronic and acute, for the protection of freshwater aquatic life. In this assessment, we
identified locations at which Pb concentrations exceeded the EPA AWQC and for which air
sources are likely to be the major contributor to the Pb concentrations in the water (i.e., there are
no other obvious sources of Pb to the water).
As the geographic coverage achieved in this surface water screen is based entirely on the
geographic coverage of available measurements of dissolved Pb in the selected database, it was
important that the most appropriate dataset be used. It was concluded that of the commonly
available databases, including NWIS, STORET and NAWQA, the NAWQA data set is most
6-21
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appropriate for a nationwide aquatic risk screen for several reasons. The inclusion of dissolved
Pb as an analyte is limited in all of the databases (total Pb is measured more often). None of the
databases provide the co-located measurements of water hardness in the same records as the
measurements of dissolved Pb. STORET and NWIS include samples from more locations in the
United States than does the NAWQA data set, but the sampling and reporting protocols
represented in STORET and NWIS are less consistent from site to site. Data for dissolved Pb in
NWIS are predominantly from the 1980s, and therefore do not represent current conditions. The
NAWQA data set, on the other hand, provides representative (though not complete) coverage of
the United States. The portion of the database containing samples through 2004 is what has been
used in this assessment (USGS, 2004). The NAWQA data set also provides a consistent
approach to sampling and analysis of the elements using consistent quantitation limits across the
country. Given the sampling design for NAWQA and the consistency of the data across the
country, it is considered to be more appropriate for a national-scale aquatic risk screen than the
other two data sets and was therefore used for this screen.
6.3.3.1.2 Lead in Sediments
Possible risks to sediment dwelling organisms were also examined at locations identified
in the surface water screen by comparing total Pb concentrations in sediments to ecotoxicity
benchmarks for sediments, generally referred to as sediment quality criteria. The preferred
approach for sediment data was to obtain them from surface water sampling locations in the
NAWQA database. Sediment sampling data were not always available at the same locations as
surface water samples. Therefore, some of the sites of interest do not have sediment samples
available from the same location. Where an exact match was not found, a nearby sampling
location was identified on the basis of latitude, and longitude, and name of the site location.
6.3.3.2 Ecologically Vulnerable Location
A literature search was conducted to identify an acidified forest or non-urban acidified
watershed ecosystem to which the following criteria could be applied:
• Potential for increased bioavailability of Pb due to soil and water acidification;
• Relatively distant from point sources of Pb emissions;
• Relatively high elevation which may be subject to comparatively higher deposition of
Pb due to wind speed and precipitation as well as longer residence time; and
• Availability of data on trends (temporal, elevation, etc.) of Pb concentration in various
environmental media.
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Based on these criteria, we selected the Hubbard Brook Experimental Forest (HBEF) in
the White Mountains of New Hampshire for the ecologically vulnerable case study. The FffiEF
was established by the USDA Forest Service in 1955 as a major center for hydrologic research in
New England. The USDA proposed to use the small watershed approach at Hubbard Brook to
study linkages between hydrologic and nutrient flux and cycling in response to natural and
human disturbances, such as air pollution, forest cutting, land-use changes, increases in insect
populations and climatic factors. The first grant was awarded by the National Science
Foundation (NSF) to Bormann and Likens in 1963 to support the research at the HBEF. Since
that time there has been continuous support from the NSF and the U.S. Department of
Agriculture (USDA) Forest Service. In 1988 the HBEF was designated as a Long-Term
Ecological Research (LTER) site by the NSF. On-going cooperative efforts among diverse
educational institutions, private institutions, government agencies, foundations and corporations
have resulted in one of the most extensive and longest continuous data bases on the hydrology,
biology, geology and chemistry of natural ecosystems in the United States. This historical record
makes HBEF uniquely suited to the purpose of this review. Findings with regard to atmospheric
deposition and Pb mobility in soil at this location are described in the CD (CD, pp. AX7-98). As
discussed earlier, an assessment of Pb related ecological risks for this case study location is not
presented in this document.
6.3.3.3 Primary Pb Smelter Case Study
The primary Pb smelter case study location is one of the largest primary Pb smelters in
the world, is the only remaining operating Pb smelter in the United States, and is also the longest
operating smelter in the world, sustaining nearly continuous operation since 1892. Further
information on the surroundings and demographics in the vicinity of the primary Pb smelter can
be found in the risk report (ICF, 2006). Portions of this study area comprise an active Superfund
site and are subject to ongoing evaluation under the Superfund program administered by the EPA
Office of Solid Waste and Emergency Response. Methods used in conducting ecological risk
assessment for the 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.
The primary Pb smelter property is bordered on the east by the Mississippi River, on the
southeast by Joachim Creek, on the west and north-northwest by residential areas, and on the
south-southwest by a slag pile. A large part of the slag pile is located in the floodplain wetlands
of Joachim Creek and the Mississippi River.
Ecological features near the facility include the Mississippi River, streams, emergent and
scrub-shrub wetlands, and successional and mature bottomland hardwood forest tracts (ELM,
2005). Bottomland hardwood forests and agricultural fields are present to the west, south, and
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east of the characterization area between the smelter's slag storage area and Joachim Creek. The
most mature bottomland hardwood forest is adjacent to Joachim Creek. Immediately south of
the facility is a mixture of floodplain forest, emergent marsh, and scrub-shrub wetland habitat
that is populated by willow trees. Throughout much of the year, migratory birds such as the red-
tailed hawk, belted kingfisher, and great blue herons utilize the habitat near the facility. The
federally threatened bald eagle has been spotted on facility property, which is known to be within
the habitat for the bird. The facility is also within the habitat of the Indiana bat, which is on the
federal and state endangered species lists. In addition, the state and federally endangered pallid
sturgeon has been identified in the Mississippi River adjacent to and downstream of the facility.
The pink mucket, scaleshell, and gray bat also occur in Jefferson County and are on both the
state and federal endangered lists.
6.3.3.4 Secondary Pb Smelter Case Study
The secondary Pb smelter location falls within the Alabama Coastal Plain in Pike County,
Alabama. It is located in an area of disturbed forests, and is less than 2 km from Big Creek,
which is part of the Pea River watershed. Big Creek is located approximately 0.5 m south
southeast from the center of the facility. The surrounding area includes emergent and scrub
shrub wetlands, forests, freshwater creeks, ponds, rivers, croplands, pastureland, and developed
urban areas. The Pea River watershed drains into the Gulf of Mexico. The watershed is
underlain by coastal plain sediments, including sand, clay, and limestone; and the topography
can be characterized as gentle to moderate rolling hills (CPYRWMA 2006). Diversity of
terrestrial and aquatic animal species is relatively high. The Choctawhatee and Pea River basins,
in which the secondary Pb smelter is located, contain 43 species of marine, estuarine, and
freshwater fish species (Cook and Kopaska-Merkel, 1996). Anadromous fish species (i.e.,
saltwater fish that must spawn in freshwater) found in the Pea River basin include the following:
the threatened Gulf sturgeon, Alabama shad, striped bass, and skipjack herring. The Pea River
basin also provides habitat for 20 species of freshwater mussels (Cook and Kopaska-Merkel,
1996), as well as numerous species of snails, snakes, and other invertebrates. Terrestrial species
supported in this region include a variety of birds, mammals, invertebrates, and vascular plants.
Other terrestrial fauna found in the region include migratory birds, small mammals and
invertebrate species. A total of 34 vascular flora from Pike County are listed by the Alabama
Natural Heritage Inventory Program as state endangered, threatened, or of special concern
(Alabama Natural Heritage Inventory 2001). According to NatureServe and the U.S. Fish and
Wildlife Service (USFWS), no species in Pike County are on the federal endangered species list
(Outdoor Alabama, 2003). A few species, however, are recognized as candidates for the federal
list.
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6.3.3.5 Near Roadway Nonurban Case Study
The Houston, Texas near roadway urban case study for the human health risk assessment
used surrogate soil Pb concentration data measured at a sampling location in downtown Corpus
Christi, Texas (Turer and Maynard, 2003). For the ecological screening assessment, nonurban
case study locations that provide soil concentration data were sought with the expectation that
ecological receptors would be more likely to occur along roads in less developed areas compared
to the downtown location evaluated in the human health risk assessment. Terrestrial wildlife
may forage in Pb contaminated soils alongside highways, particularly on roads traversing
undeveloped areas.
From the literature search for studies of Pb in near roadway soils, two nonurban sites for
which soil Pb levels are available were identified for use in the ecological risk assessment. These
locations are: (1) Interstate 37 near oil refineries in Corpus Christi, Texas (Turer and Maynard,
2003) and (2) Interstate 95 in Atlee, Virginia, which connects to a moderately traveled, two-lane
road (Speiran, 1998).
Land cover data from 1992 within 1 mile of the Corpus Christi, Texas study location
showed 59 percent industrial, 10 percent low intensity residential, and 25 percent high intensity
residential (Vogelmann et al., 2001). The remaining 5 percent of the surrounding area includes
shrubland, row crops, pasture, grasses, and forested upland, including evergreen forest and
deciduous forest.
The 1992 land cover data within 1 mile of the Atlee, Virginia study locations showed 26
percent developed: 2 percent low-intensity residential and commercial and 24 percent industrial
and transportation. The remaining 74 percent included 25 percent deciduous forest, 14 percent
woody wetlands, and 12 percent pasture (Vogelmann et al., 2001). Smaller proportions of mixed
forest, evergreen forest, row crops, and transitional (barren) areas were also found.
6.3.4 Screening Values
The following is a brief summary of specific ecological screening values selected for use
in the risk assessment. The main discussion of the development and derivation of these tools can
be found in Section 6.2.1.3 of this document and in the risk report (ICF, 2006). This discussion
summarizes the ways in which the tools were used for this assessment to identify potential for
effect from Pb exposure to specific ecological receptor groups in either the case studies or the
national-scale screening assessment using the NAWQA monitoring database.
6.3.4.1 Soil Screening Values
In developing soil screening values for use in this assessment, assumptions inherent in the
derivation of the Superfund Eco-SSLs were examined, and as appropriate, augmented or
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replaced with current species-specific information. For example, the assumptions employed for
deriving the Eco-SSLs for avian and mammalian wildlife from the corresponding TRVs were
examined (ICF, 2006). Soil screening values were derived for this assessment using the Eco-
SSL methodology (described in Section 6.2.1.3) with the TRVs for Pb (USEPA, 2005b) and
consideration of the inputs on diet composition, food intake rates, incidental soil ingestion, and
contaminant uptake by prey. The soil screening values shown in Table 6-2 for plants and soil
invertebrates are the Eco-SSL values (USEPA, 2005a) while the screening values for birds and
mammals are based on the Eco-SSL methodology but with modified inputs specific to this
assessment (see Section 7.1.3.1 and Appendix L, of ICF, 2006).
Table 6-2. Soil Screening Values for Pb for Ecological Receptors
Ecological
Receptor
Plants 1
Soil Invertebrates 1
Birds 2
Mammals 2
Soil Concentration
(mg Pb/kg soil, dry weight)
120
1700
38
112
1 Values obtained from Ecological Soil Screening
Levels for Lead, Interim Final (USEPA, 2005a).
2 Values obtained by refinement described in risk
report (ICF, 2006).
6.3.4.2 Surface Water Screening Values
As described in Section 7.1.3.2 of the pilot phase Risk Assessment Report (ICF, 2006,
hardness-specific surface water screening values were calculated from the EPA recommended
AWQC (1984). Values were derived for the primary Pb smelter case study location and the
national-scale screen based on site-specific water hardness data. AWQC values for chronic
exposures are called the criterion continuous concentration (CCC) and for acute exposures are
called the criterion maximum concentration (CMC), and they are available for freshwater and
marine environments. A CCC is generally considered to be exceeded when a 4-day average
water concentration exceeds the CCC more than once every three years (USEPA, 1984).
6.3.4.3 Sediment Screening Values
This risk screen uses sediment criteria developed by MacDonald et al (2000) which focus
on total Pb concentrations in sediment. These criteria for Pb include a threshold effect
concentration (TEC) and a probable effect concentration (PEC) of 35.8 mg/kg dry weight and
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128 mg/kg dry weight, respectively. The methodology for these sediment criteria is described
more fully in section 7.1.3.3 and Appendix M of the pilot phase Risk Assessment Report (ICF,
2006).
6.3.5 Results for Case Studies and Comparison to Screening Value
To identify locations in which Pb concentrations in soil, water and/or sediment may be
potentially harmful to resident biota, each case study location was assessed using either empirical
data or model estimated Pb concentrations for each medium. These concentrations were then
compared to screening values as described in Section 6.4.2. The HQ approach was then used to
compare estimated exposures for geographic areas around the case study sites with ecotoxicity
benchmark values for each of three media: soil, surface water, and sediment. HQs less than or
equal to one suggest that ecological risks are negligible. HQs greater than one indicate a
potential for adverse effects and a more refined analysis of sensitive receptors would be needed
to more completely assess risk
6.3.5.1 National-scale Surface Water Screen
Based on EPA's reevaluation of AWQC for metals (USEPA, 1993), the CCC for
relatively soft water (50 mg/L CaCOs) is 1.2 |ig/L, and higher for waters of greater hardness.
Therefore, the initial screen of dissolved Pb concentrations in surface water looked for
measurements equal to or greater than 1.2 |ig/L. This resulted in 42 sampling locations for
which one or more measurements exceeded that screening value. The individual dissolved Pb
measurements at these stations are provided in the risk assessment report (ICF, 2006b). For a
more refined risk assessment, a given location would not usually be represented with a single
sampling measurement of dissolved Pb. However, for purposes of this risk screen, given the
limited analyses for dissolved Pb, all 42 sampling locations were retained for analysis. Next, the
location-specific CCC and CMC values were determined based on water hardness for those
locations. A review of the data on water hardness in the NAWQA data set for 1994 to 2004
indicated that the initial screening value of 1.2 |ig/L was too high to identify all locations for
which dissolved Pb concentrations exceeded the CCC for the protection of aquatic life. Many
waters in the United States are softer than anticipated (i.e., measured CaCOs concentrations
down to 1 mg/L).
A second screen was therefore conducted in which dissolved Pb measurements greater
than the quantitation limit (QL) but less than 1.2 |ig/L were reviewed. In the second screen, for
each sampling location with one or more dissolved Pb measurements above the QL but less than
1.2 |ig/L co-located measurements of CaCOs were used to calculate a site-specific CCC as
described above. To attempt to isolate those locations where air derived Pb is the major source
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of Pb to water, land-use information was obtained from the NAWQA database for each location
in which the derived HQ was greater than 1.0. The available categories of land use in the dataset
separated mining sites but did not separate other activities which are likely to produce Pb (e.g.
smelting sites were included in the industrial category). While some mining activities produce
air emissions of Pb (see Table 2-5), the data are lacking to apportion Pb between air and nonair
sources at mining sites. Therefore, results for locations with mining as the land use category
were separated from the other land use types.
Table 6-3 summarizes the HQs for the 15 non-mining sites for which the chronic HQs
exceed 1.0, indicating potential for adverse effect. These locations are in areas classified in the
NAWQA database as urban and mixed, but also include forest, rangeland, and a "reference" site
in Alaska. The HQ for the Alaska reference site is based on one measurement of dissolved Pb
and one hardness measurement. Thus, the uncertainty associated with this HQ is high (ICF,
2006).
Table 6-3. Results of Aquatic Risk Screen - Locations at which Dissolved Pb
Measurements Exceed AWQC, Excluding Mining Sites. a
Basin
ID
45 RIOG
44 UCOL
2 CONN
46 NROK
46 NROK
46 NROK
47 GRSL
2 CONN
31 OZRK
58 OAHU
1 NECB
2 CONN
46 NROK
31 OZRK
31 COOK
State
NM
CO
CT
WA
WA
ID
UT
CT
MO
HA
Rl
CT
ID
AR
AK
Station
ID
8331000
3.85E+14
1127000
12422500
12422000
12392155
4.05E+14
1119375
7018100
16212700
1112900
1124000
12419000
7050500
6.01E+14
Land
Use
Mixed
Other/Mixed
Mixed
Urban
Urban
Forest
Rangeland
Mixed
Forest
Mixed
Mixed
Mixed
Mixed
Mixed
Reference
Lead
CCC
(ug/L)
2.9
0.89
0.36
0.99
0.99
0.17
5.8
0.18
3.7
0.17
0.44
0.30
0.37
2.6
0.11
Pb Measurements
No.
>CCC
/ Total
N
1/12
1/4
3/22
4/28
2/20
4/17
1/2
5/20
1/2
1/2
3/3
11/23
2/26
1/8
1/1
No. < QL,
which
is > CCC
0
2
14
24
18
10
0
13
0
1
0
9
16
0
0
Hazard Quotient
Mean
[Pb]/
CCC
1.03
1.09
1.13
1.14
1.17
1.32
1.45
1.68
1.89
1.98
2.51
2.53
2.69
3.46
14.91
Max
[Pb]/
CCC
1.03
1.09
1.31
1.25
1.17
1.54
1.45
2.09
1.89
1.98
3.53
3.33
4.27
3.46
14.91
Max
[Pb]/
CMC
0.04
0.04
0.05
0.05
0.05
0.06
0.06
0.08
0.07
0.08
0.14
0.13
0.17
0.14
0.61
a In order of increasing Hazard Quotient for the mean CCC aquatic toxicity benchmark. Additional
information characterizing these locations is provided in the risk report (ICF, 2006).
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When the 15 sampling locations in Table 6-3 are compared to NEI data, only three appear
to be near facilities emitting relatively large quantities of Pb to the atmosphere (i.e., more than 1
ton per year): one is in Oahu, Hawaii, one in Jewett City, Connecticut, and one in Manville,
Rhode Island. An additional two sampling locations appear to be within 50 km of facilities
emitting relatively large quantities of Pb, both in Connecticut; however, whether these facilities
are close enough to influence the Pb concentrations in the water column at these sampling sites is
unknown. Of the three sampling locations within 20 km of facilities emitting more than 1 ton of
Pb per year, the location in Rhode Island might also be receiving a portion of its Pb from
upstream discharges from metal ore processing facilities; there are six such discharges out of 14
National Pollutant Discharge Elimination System (NPDES) permitted facilities upstream of this
sampling location. More information on emissions for these 15 locations can be found in the risk
report (ICF, 2006).
In summary, the national-scale screen of surface water data identified 15 locations with
water column levels of dissolved Pb that were greater than hardness adjusted chronic criteria for
the protection of aquatic life (with one location having a HQ of 15), indicating a potential for
adverse effect if concentrations were persistent over chronic periods; acute criteria were not
exceeded at any of these locations. The extent to which air emissions of Pb have contributed to
these surface water Pb concentrations is unclear.
6.3.5.2 National-scale Sediment Screen
Sediment characterization for the 15 sites identified in the AWQC screen was performed
using the hazard quotient method, where measures of total Pb concentrations in sediments were
compared with the sediment TEC and PEC values for the protection of sediment dwelling
organisms. The first step involved attempting to find matching sediment sampling locations in
the NAWQA database. It was not always possible to find co-located sediment and surface water
samples. It was expected, therefore, that some of the 15 sites of interest would not have
sediment samples available from the same location. Where an exact match was not found, a
nearby sampling location on the same water body was identified.
Table 6-4 shows the HQs for measured total Pb concentrations in sediments at 12 of the
15 surface water locations for which data were available. The HQs are calculated by dividing
sediment concentrations by the sediment screening values, which as described in Section 6.3.4.3,
are the TEC and PEC for sediment dwelling organisms from the consensus-based approach to
sediment quality criteria (MacDonald et al., 2000).
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Table 6-4. Concentrations of Total Pb in Sediments at Locations Near or Matching the 15
Sites at which Dissolved Pb Concentrations Exceeded the AWQC, Excluding
Mining Sites.
Basin ID
45
2
46
46
47
31
58
1
2
46
31
31
RIOG
CONN
NROK
NROK
GRSL
OZRK
OAHU
NECB
CONN
NROK
OZRK
COOK
State
NM
CT
WA
ID
UT
MO
HA
RI
CT
ID
AR
AK
Land
Use
Mixed
Mixed
Urban
Forest
Rangeland
Forest
Mixed
Mixed
Mixed
Mixed
Mixed
Reference
Match
Yes
Near
Near
Yes
Yes
Yes
Yes
Yes
Near
Yes
Yes
Yes
Total [Pb]
(nig/kg dry
sediment)
23
68
47.3
24.9
2900
2300
59
240
68
1620
28
239
SW
HQa:
max
[Pb]/CCC
1.03
1.13
1.14
1.32
1.45
1.89
1.98
2.51
2.53
2.69
3.46
14.91
Pb Emissions
(tons/year) (b)
Fac<
20km
0.068
6.1
0.39
0.0
0.0
0.0
4.9
4.1
0.081
0.34
0.0
0.0
Fac<
50km
0.095
7.0
0.43
0.0
0.36
0.34
4.9
11.7
11.3
0.43
0.01
0.0
No.
Upstream
NPDES
permits for
metals
0
0
0
1
1
ND
ND
6
0
4
0
0
Sediment
Hazard
Quotients
[Pb]/
TEC
0.64
1.9
1.3
0.70
81
64
1.6
6.7
1.9
45
0.78
6.7
[Pb]/P
EC
0.18
0.53
0.37
0.19
23
18
0.46
1.9
0.53
13
0.22
1.9
a Data collected for corresponding surface water locations
Abbreviations:
[Pb] = total Pb concentration in sediments (mg/kg dry sediment). CCC = Criterion Continuous Concentration
(or chronic AWQC). TEC = threshold effect concentration, and PEC = probable effect concentration, both
from the consensus-based sediment quality criteria approach published by MacDonald et al. (2000; 2003).
Table 6-4 presents the HQs for sediments at the 9 matching and 3 nearly matching
locations at which dissolved Pb concentrations in the water column exceeded the CCC (i.e.,
chronic AWQC) for the protection of aquatic organisms in surface waters. Nine of the TEC-
based HQs exceeded 1.0, and three were less than 1.0. The three sites with HQs less than 1.0 are
unlikely to pose risks to benthic aquatic communities based on the available data. Lead
concentrations at these three sites were considered to be less likely to be affected by current air
emissions of Pb from point sources (i.e., Pb emissions were less than 0.07 tons per year at all
three locations).
Five of the PEC-based HQs exceeded 1.0, indicating probable adverse effects to sediment
dwelling organisms. None of these locations were likely to be dominated, however, by current
air emissions. One location in Idaho was downstream from several NPDES-permitted discharges
of metals to surface waters (10th entry). Two other locations were found in Utah and Montana
and it is possible that these concentrations reflect historical sediment contamination from mining
operations.
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Of the locations for which air emissions of Pb appear to be more likely to be contributing
to ongoing Pb contamination of surface water and sediments (i.e. locations in Connecticut,
Hawaii, and Rhode Island, respectively), only one, the Blackstone River in Manville, Rhode
Island, is also likely to receive significant current Pb inputs from upstream NPDES-permitted
sites. In addition to Pb contamination of sediment through deposition of current air emissions to
surface waters, sediment at these three locations may have received Pb from atmospheric
deposition in the past as well as from current and past erosion of soils containing current and
historic deposits of Pb, particularly from leaded gasoline. The Quienebaug River in Connecticut
(a near match between the Jewett City and Clayville locations) and the water body at
Waikakalaua Street near Wahiawa, Oahu, Hawaii, had no other obvious inputs of Pb within 20
km than the air point sources. Both of those locations are in "mixed" urbanized areas, and may
also have historic Pb deposition from leaded gasoline and ongoing inputs of Pb to sediments
from erosion of soils contaminated by leaded gasoline. A further discussion of methodology for
the sediment screen can be found in the risk assessment report (ICF, 2006).
In summary, sediment Pb concentrations at some sites are high enough that the likelihood
that they would cause adverse effects to sediment dwelling organisms may be considered
"probable". However, the contribution of air emissions to these concentrations is unknown.
6.3.5.3 Primary Pb Smelter Case Study
A Characterization Area Investigation (CAI) was performed at the primary Pb smelter
facility by ELM Consulting in 2005. The investigation area included the smelter, slag areas, and
several haul roads within a 2.1 km radius from the facility as well as two "reference areas",
presumed to be outside the area of influence of the smelter, 6 to 7 km south of the facility. The
area was evaluated for the potential for ecological impacts to soil, sediment, and surface water
from Pb originating from the facility. Data collected as part of the CAI were used here.
To develop soil concentrations for this assessment, surface soil data were grouped into 3
geographic clusters: the west bank of Joachim Creek and two "reference areas": Crystal City and
Festus Memorial Airport. Surface water and sediment samples were taken from backwater and
low flow areas along Joachim Creek both upstream and downstream of the facility 800 m, 1.6
km and 3.2 km from the smelter. Additional samples were taken from the Mississippi River and
a nearby pond. Details on the sampling methods used by ELM can be found in the risk
assessment report (ICF, 2006).
HQs calculated for each of the sampling clusters developed for this case study are
provided here: soil results are listed in Table 6-5, surface water results are presented for Table 6-
6, and sediment results are presented in Table 6-7. HQs equal to or greater than 1.0 are bolded.
All three of the soil sampling clusters (including the "reference areas") had HQs that exceeded
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1.0 for birds. The west bank of the Joachim Creek samples had HQs greater than 1.0 for plants
and mammals also. The surface water sampling clusters all had HQs less than 1.0 as results were
all below the detection limit of 3.0|ig/L. However, three sediment sample clusters in Joachim
Creek (1, 2, and 3), the U-shaped pond, and one drainage area had HQs greater than 1.0.
Table 6-5. HQs for Soils for Primary Pb Smelter Case Study.
Location of Sample Cluster
1 - West Bank of Joachim Creek
2 - Crystal City 1
3 - Near Festus Airport 1
HQfor
Plants
3.55
0.54
0.40
HQfor Soil
Invertebrates
0.25
0.04
0.03
HQ for Birds
11.19
1.70
1.28
HQfor
Mammals
3.80
0.58
0.43
1 Control samples taken outside perceived influence of the smelter.
Table 6-6. HQs Calculated for Surface Waters for Primary Pb Smelter Case Study.
Sample Location
and Cluster ID
HQ using CCC
(Chronic)
HQ using CMC
(Acute)
Joachim Creek
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
0.39
0.40
0.39
0.41
0.41
0.02
0.02
0.02
0.02
0.02
Mississippi River
Upstream
Near Facility
Downstream
0.54
0.49
0.48
0.02
0.02
0.02
Emission Deposition
Cluster 1
CHRDDP
RRDP-02
DAM UP
0.69
0.24
0.47
0.40
0.03
0.01
0.02
0.02
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Table 6-7. HQs Calculated for Sediments in Surface Waters for Primary Pb Smelter Case
Study.
Location and
Cluster Sample ID
Hazard Quotient
Joachim Creek
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
1.0
1.6
2.2
0.84
0.96
Mississippi River
Upstream
Near Facility
Downstream
0.41
0.84
0.34
Pond and Drainage Areas
U-shaped Pond
Cluster
ED1
ED2
4.8
3.1
0.41
In summary, the concentrations of Pb in soil and sediments exceed screening values for
these media indicating potential for adverse effects to terrestrial organisms (plants, birds and
mammals) and to sediment dwelling organisms. While the contribution to these Pb
concentrations from air as compared to nonair sources is not quantified, air emissions from this
facility are substantial (see Appendix D, USEPA 2007; ICF 2006).
6.3.5.4 Secondary Pb Smelter Case Study
For the secondary Pb smelter case study, as described in Section 6.2.3, two sets of model-
predicted average Pb soil concentrations were used as exposure estimates for the pilot phase
screening ecological risk assessment. The first set of concentrations was estimated using the
MPE methodology (ICF, 2006). These estimated soil concentrations for the secondary Pb
smelter were compared to empirical data obtained from a surrogate location. Based on this
comparison, which suggested that modeled soil Pb concentrations for this case study might be
significantly underestimated, we included a second characterization of soil concentrations.
Specifically, measurements from a surrogate secondary Pb smelter location were used to "scale"
up the modeled surface generated for this case study location to more closely match the
empirical data obtained from the surrogate location (at specified distances from the facility). The
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averages for 1-, 5-, or 10-km interval distances from the secondary Pb smelter facility and the
associated soil HQs calculated for each interval are presented in Table 6-8.
Distance Range
(m)
0
1000
2000
3000
4000
5000
10000
1000
2000
3000
4000
5000
10000
20000
0
1000
2000
3000
4000
5000
10000
1000
2000
3000
4000
5000
10000
20000
Modeled Soil Concentration
Datasets (mg/kg)
Total Pb Soil Cone, with
Background
86.6
20.7
17.3
16.3
15.8
15.4
15.1
Scaled 3x Total Pb Soil
Cone, with Background
260
62.3
51 .8
48.9
47.3
46.2
45.4
HQfor
Plants
HQ for Soil
Invertebrates
HQfor
Birds
HQfor
Mammals
0.72
0.17
0.14
0.14
0.13
0.13
0.13
0.05
0.012
0.010
0.010
0.009
0.009
0.009
2.28
0.54
0.46
0.43
0.42
0.41
0.40
0.77
0.18
0.15
0.15
0.14
0.14
0.13
2,17
0.52
0.43
0.41
0.39
0.39
0.38
0.15
0.037
0.030
0.029
0.028
0.027
0.027
6.84
1.64
1.36
1.29
1.24
1.22
1.19
2.32
0.56
0.46
0.44
0.42
0.41
0.41
s HQ values greater than 1.0 are highlighted in bold type.
Table 6-8. HQs Calculated for Soils for Secondary Pb Smelter Case Study.3
The modeled soil concentrations within 1 km of the facility showed HQs of greater than
1.0 for avian wildlife. All soil concentrations for locations greater that 1 km from the facility
were associated with HQs less than 1.0 for this dataset. The three-times-higher-scaled soil
concentration dataset, developed based on soil data from similar locations, resulted in avian HQs
greater than 1.0 for all distance intervals evaluated, including the farthest interval modeled, 10 to
20 km from the facility. The scaled soil concentrations within 1 km of the facility also showed
HQs greater than 1.0 for plants, birds, and mammals.
In summary, the estimates of Pb concentration in soils associated with the secondary Pb
smelter case study were associated with HQs above 1 for plants, birds and mammals, indicating
potential for adverse effect to those receptor groups.
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6.3.5.5 Near Roadway Nonurban Case Study
Table 6-9 presents the HQ calculated for the Corpus Christi, Texas, near roadside soil
concentration data. HQs for birds were greater than 1.0 at all but one of the distances from the
road. Mammalian HQs also were greater than 1.0 at the 2 m sampling distance from the
roadway. Finally, plants also had HQs ranging from 2.83 and 5.42 at the 2 m distance. However
at the further distance from the roadway (4 m), birds and mammals still had HQs greater than 1.
In summary, HQs above one were concluded for plants, birds and mammals, indicating
potential for adverse effect to these receptor groups.
Table 6-9. HQs Calculated for Soils Near Roadway Nonurban Case Study.
Sample location -
distance from
roadway
2m
2m
2m
4 m
Sample
depth
2.5 cm
10cm
20 cm
2.5 cm
Total Pb
concentratio
n (mg/kg)
340
650
15
140
HQfor
Plants
2.83
5.42
0.13
1.17
HQfor Soil
Invertebrates
0.20
0.38
0.019
0.082
HQfor
Birds
8.95
17.1
0.395
3.68
HQfor
Mammals
3.04
5.80
0.13
1.25
6.3.6 Discussion
The screening-level ecological risk assessment briefly described above is described in
detail in the pilot phase Risk Assessment Report and appendices (ICF, 2006). The results for the
ecological screening assessment for the three case studies and the national-scale screen for
surface water and for sediment indicate a potential for adverse effect from ambient Pb to
multiple ecological receptor groups in terrestrial and aquatic locations. The screening
assessment did not provide clear categorization of contributions from air and nonair sources
although air Pb emissions are estimated to be substantial in some locations assessed. More
refined analyses, which were beyond the funding limitations for the current Pb NAAQS review,
would be necessary in order to more completely characterize risk to various receptors from
ambient Pb, and to more completely characterize locations as to contributions from air and
nonair sources of Pb.
6.3.7 Uncertainty and Variability
This section briefly summarizes uncertainties and limitations associated with the primary
Pb smelter case study, the secondary Pb smelter case study, the near roadway non-urban case
study and associated with the national-scale surface water and sediment screens that are
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presented in the pilot phase Risk Assessment Report and appendices (ICF, 2006). Note that
some limitations for the ecotoxicity screening values are described where they are introduced in
Section 6.2.2.1.
Uncertainties that apply across case studies include, but are not limited to, the following:
• The ecological risk screen is limited to specific case study locations and other locations
for which dissolved Pb data were available and evaluated in the national-scale surface
water and sediment screens. Efforts were made to ensure that the Pb exposures
assessed were attributable to airborne Pb or naturally occurring Pb and not dominated
by nonair sources. However, there is uncertainty as to whether other sources might
have actually contributed to the Pb exposure estimates.
• A limitation to using the selected ecotoxicity screening values is that they might not be
sufficient to identify risks to some threatened or endangered species or unusually
sensitive aquatic ecosystems (e.g., CD, p. AX7-110).
• The methods and database from which the surface water screening values (i.e., the
AWQC for Pb) were derived is somewhat dated and new data and approaches may now
be available to estimated the aquatic toxicity of Pb (CD, Section AX7.2.1.2). For
example, EPA is evaluating whether pH may be a better indicator of bioavailability
compared to water hardness (CD, Section AX7.2.1.3).
• No adjustments were made for sediment-specific characteristics that might affect the
bioavailability of Pb in sediments in the derivation of the sediment quality criteria used
for this ecological risk screen (CD, Sections 7.2.1 and AX7.2.1.4; Appendix M, ICF,
2006). Similarly, characteristics of soils for the case study locations were not
evaluated for measures of bioavailability.
• Although the screening value for birds used in this analysis is based on reasonable
estimates for diet composition and assimilation efficiency parameters, it was based on a
conservative estimate of the relative bioavailability of Pb in soil and natural diets
compared with water soluble Pb added to an experimental pellet diet. A recent site-
specific determination of a soil concentration protective of birds that consume soil
invertebrates suggested that the values of 38 mg/kg or even 83 mg/kg are still overly
conservative. This is possibly because the assimilation efficiency of Pb in soils and
natural foods compared with the assimilation efficiency of Pb acetate added to pelleted
diets is much less than 50 percent (Appendix L, ICF, 2006).
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6.3.7.1 Primary Pb Smelter Case Study
The ELM Sampling and Analysis Plan (ELM, 2003) was designed to investigate possible
ecological risks from all sources of Pb (and other contaminants) attributable to the primary Pb
smelter without a need to attribute the source of Pb in ecologically sensitive areas (ELM, 2003;
ELM, 2005). For purposes of the Pb NAAQS review, it is important to distinguish areas
impacted primarily from current or historic air deposition of Pb from areas impacted primarily
from nonair sources (e.g. erosion of mining waste piles, surface runoff from exposed mining
ores, direct waste discharges to water). While those areas impacted from these nonair sources
are likely to be impacted from air deposition as well, availability of data and tools limited our
ability to apportion Pb among these sources in these areas. Therefore, these analyses attempt to
focus on those areas where it may be possible to identify effects from policy relevant sources.
The soil sampling locations within a 2.1 km radius were all in areas that might have been
subject to Pb inputs from Joachim Creek during flooding events. As such, the stations might not
represent the concentrations of Pb in soils that result from air emissions from the smelter. This
limitation may overstate the risks from deposition of Pb emitted from the facility.
6.3.7.2 Secondary Pb Smelter Case Study
The ecological risk screen used modeled rather than measured media concentration data
because measured data were not available for the case study location. Data were available for
similar locations and these data were compared to the modeled results. These results appeared to
vary three-fold; therefore, scaled modeled data is also reported in this assessment. A full
discussion of the modeling steps and the fate and transport modeling limitations and
uncertainties are described in the risk assessment report (ICF, 2006).
6.3.7.3 Near Roadway Nonurban Case Study
Few measurements were available to evaluate ecological impacts of contaminated soils
near roadways in less developed areas where ecological receptors may be anticipated to occur.
The measured soil data for the Corpus Christi, Texas location 2 m from the roadway ranged from
15 mg Pb/kg at 20-cm depths to 650 mg Pb/kg at 10-cm depths. The Pb concentrations selected
at the Atlee, Virginia location ranged from 17 mg/kg 15m from the roadway to 540 mg/kg 2 m
from the roadway; both samples were collected from 7.5-to 15-cm soil depths. It is uncertain
how representative of other roadways these data are.
The assessment did not address surface water ecosystem impacts of Pb from near
roadway runoff of Pb contaminated soils. This may underestimate risks to aquatic receptors via
this exposure pathway.
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6.3.7.4 National-scale Surface Water Screen
The analysis revealed only two or three NAWQA water column sampling locations
nationwide where there appear to be risks to the aquatic community from Pb that may have
originated from atmospheric deposition. However, this is likely to be a large underestimate of
the true number of such sites for several reasons:
• The NAWQA Study Units cover less than 50 percent of the land area of the United
States.
• Dissolved Pb was an analyte at only 16 percent of all NAWQA sampling locations.
• Dissolved Pb was measured only once or twice at many locations.
• For waters with a hardness of less than 47 mg/L as CaCO3, the CCC for dissolved Pb
is less than the quantitation limit for dissolved Pb that was used until the fall of 2000
(i.e., 1 |ig/L).
• Fewer than 15 percent of samples analyzed for dissolved Pb between 1994 and 2004
were assessed with the lower quantitation limit of 0.08 |ig/L, which is a value that is
sufficiently low to match the CCC for waters with a hardness as low as 4.7 mg/L
CaCO3.
The first two points above suggest that the number of such sites nationwide might easily
be at least ten times higher than what was represented in the NAWQA database. In addition,
where the land use around a sampling location was classified as "mining," no investigation was
conducted to determine whether air emissions from a nearby smelter might also be contributing
to the Pb in the water. Emissions information summarized in Section 2.2 indicates that such
contribution is likely.
There are a variety of sources of uncertainty in the results presented for the sampling
locations for which there were some data, including the following:
• Many sampling locations are represented by only one or two measurements of
dissolved Pb.
• The water hardness for some sampling locations was not measured or is represented by
only one or two measurements.
• Where there are multiple measures of both dissolved Pb and water hardness at a given
location, no attempt was made to match sampling dates and times to develop time-
specific CCC values.
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• The water hardness measured at some locations was less than the lowest value of 20
mg/L of CaCO3 used to develop the equation to calculate a CCC. The CCC equation
is not necessarily valid at values less than 20 mg/L CaCO3.
• It is not known how quickly dissolved Pb concentrations changed at any of the
locations.
• The database supporting the current AWQC for Pb is over 20 years old; new AWQC
for Pb may be available in the near future.
6.3.7.5 National-scale Sediment Screen
This analysis was limited to those 15 locations from the NAWQA database at which
dissolved concentrations of Pb in surface waters exceeded the chronic AWQC for Pb. Those 15
locations are believed to represent a small fraction of surface waters in the U.S. for reasons given
above. Results of this analysis cannot conclusively link any of the locations with probable
adverse effects of Pb in sediments on benthic communities to ongoing air emissions of Pb.
Evidence from sediment cores of historical trends, however, illustrates the influence of airborne
Pb on sediments (Section 2.8).
An additional limitation is that where the land use around a sampling location was
classified as "mining", no investigation was conducted to determine whether air emissions from
a nearby smelter might also be contributing to the Pb in the water and sediments. It was assumed
that direct runoff and erosion from the mining sites to the surface waters would have contributed
to the bulk of the Pb in sediments.
Further limitations accrue from the sediment sampling data. There were only nine exact
matches and three near matches between the 15 surface water sampling locations of interest and
locations at which sediment samples also were analyzed. Furthermore, there was a single
sediment sample at each of the locations of interest, some of which were taken in the early
1990s.
Finally, no adjustments were made for sediment-specific characteristics that might affect
the bioavailability of Pb in sediments in the derivation of the sediment quality criteria used for
this risk screen.
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6.4 THE SECONDARY LEAD NAAQS
6.4.1 Introduction
This section and subsections present staff conclusions and recommendations for the
Administrator to consider in deciding whether the existing secondary Pb standard should be
revised and, if so, what revision is appropriate. The staff conclusions and recommendations
presented here are based on the assessment and integrative synthesis of information presented in
the CD, staff analyses and evaluations presented in Chapter 2 and the preceding sections of this
chapter, and the comments and advice of CASAC who commented on an early draft of this
document and the related Risk Assessment Report.
In recommending policy options for the Administrator's consideration, we note that the
final decision on retaining or revising the current secondary Pb standard is largely a public policy
judgment to be made by the Administrator. The Administrator's final decision should draw upon
scientific information and analyses about welfare effects, exposure and risks, as well as
judgments about the appropriate response to the range of uncertainties that are inherent in the
scientific evidence and analyses. In evaluating whether it is appropriate to consider retaining the
current secondary Pb standard, or whether consideration of revisions is appropriate, we intend to
focus on the extent to which a broader body of scientific evidence is now available that would
inform such decisions.
Section 109 of the Clean Air Act requires the Administrator to establish a secondary
standard that, in the judgment of the Administrator, are requisite to protect the public welfare
from any known or anticipated adverse effects associated with the presence of the pollutant in
the ambient air. In so doing, the Administrator seeks to establish standards that are neither more
nor less stringent than necessary for this purpose. The Act does not require that secondary
standards be set to eliminate all risk of adverse welfare effects, but rather at a level requisite to
protect public welfare from those effects that are judged by the Administrator to be adverse.
We discuss the background for the current standard in Section 6.4.2, and the general
approach used in considering the adequacy of the current standard and alternatives in Section
6.4.3. Further, staff conclusions and recommendations for the Administrator to consider in
deciding whether the existing secondary Pb standard should be revised and, if so, what revised
standard is appropriate is discussed in Section 6.4.4, and consideration of the elements of the
standard is discussed in Section 6.4.5.
6.4.2 Background on the Current Standard
The current standard was set in 1978 to be identical to the primary standard(1.5 ug Pb/m3,
as a maximum arithmetic mean averaged over a calendar quarter), the basis for which is
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summarized in section 5.2.1. At the time the standard was set, the Agency concluded that the
primary air quality standard would adequately protect against known and anticipated adverse
effects on public welfare, as the Agency stated that it did not have evidence that a more
restrictive secondary standard was justified. In the rationale for this conclusion, the Agency
stated that the available evidence cited in the 1977 CD indicated that "animals do not appear to
be more susceptible to adverse effects from lead than man, nor do adverse effects in animals
occur at lower levels of exposure than comparable effects in humans" (43 FR 46256). The
Agency recognized that Pb may be deposited on the leaves of plants and present a hazard to
grazing animals. With regard to plants, the Agency stated that Pb is absorbed but not
accumulated to any great extent by plants from soil, and that although some plants may be
susceptible to Pb, the Pb is generally present in a form that is largely nonavailable to them.
Further the Agency stated that there was no evidence indicating that ambient levels of Pb result
in significant damage to manmade materials and Pb effects on visibility and climate are minimal.
The secondary standard was subsequently considered during the 1980s in development of
the 1986 CD (USEPA, 1986) and the 1990 Staff Paper (USEPA, 1990). In summarizing staff
conclusions and recommendations at that time, staff stated that a qualitative assessment of
available field studies and animal toxicological data suggested that "domestic animals and
wildlife are as susceptible to the effects of lead as laboratory animals used to investigate human
lead toxicity risks." Further, in a departure from the conclusions when the standard was set, staff
highlighted concerns over potential ecosystem effects of Pb due to its persistence. In the 1990
Staff Paper, staff stated the following:
The available data raise concerns about the continued accumulation of lead in soil and
sediment reservoirs. Due to the persistence of lead in the environment, such
accumulations are expected to continue as long as inputs exceed outputs. Thus, even at
relatively low deposition rates, lead could affect the ecosystem over time.
In summary, while the available data are limited and do not provide clear quantitative
relationships, they generally support the need for limiting lead emissions to protect
against potential ecosystem effects. Indications are that the emission reductions achieved
since promulgation of the current standards in 1978, particularly when coupled with
reduction achieved by the phasedown of lead in gasoline ... may have mitigated or
delayed the potential risk for lead-induced ecosystem effects occurring in many areas of
the country. In urban centers, along roadsides, and in the immediate vicinity of major
stationary sources that have experienced a long-term historical accumulation of lead,
and where the natural soil sinks for lead may be approaching or have exceeded their
capacity to bind lead, the more sensitive components of the ecosystem (e.g., soil
microbes) may remain at some risk that is difficult to quantify at present.
Accordingly, the staff concluded that pending development of a stronger database that
more accurately quantifies ecological effects of different lead concentrations, consideration
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should be given to retaining a secondary standard at or below the level of the then-current
secondary standards of 1.5 |ig/m3.
6.4.3 Approach for the Current Review
In developing conclusions and identifying options for the Pb standard in this review, staff
has taken into account both evidence-based and risk-based approaches. A series of general
questions frame our approach to reaching conclusions about the adequacy of the current standard
and identifying options for consideration by the Administrator as to whether consideration
should be given to retaining or revising the current secondary Pb standard. Examples of
questions that we address in our review include the following:
• To what extent has evidence of new effects and/or sensitive organisms/ecosystems
become available since the last review and to what extent are we able to characterize
these effects?
• To what extent does newly available information support or call into question any of
the basic elements of the current standard?
• Is there evidence of associations, especially likely causal associations, in areas that
meet the current standard? What are the important uncertainties associated with that
evidence?
Our approach to considering these questions recognizes that the available welfare effects
evidence generally reflects laboratory-based evidence of toxicological effects on specific
organisms exposed to concentrations of Pb at which scientists generally agree that adverse
effects are likely to occur. It is widely recognized, however, that environmental exposures are
likely to be at lower concentrations and/or accompanied by significant confounding factors (e.g.,
other metals, acidification), which increases our uncertainty about the likelihood and magnitude
of the organism and ecosystem response.
6.4.4 Adequacy of the Current Standard
In considering the adequacy of the current standard, staff has considered the
environmental effects evidence presented in detail in the CD and summarized above in Section
6.2, the screening-level risk assessment summarized above in Section 6.3, and CASAC advice
and recommendations contained in their letters (Appendices A and B) and voiced during their
public meetings on the first draft staff paper (February 6-7, 2007) and on the second draft lead
human exposure and health risk assessment (August 28-29, 2007).
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6.4.4.1 Evidence-based Considerations
In considering the welfare effects evidence with respect to the adequacy of the current
standard, we consider not only the array of evidence newly assessed in the CD but also that
assessed in the 1986 CD and summarized in the 1990 Staff Paper. As discussed extensively in
the latter two documents, there was a significantly improved characterization of environmental
effects of Pb in the ten years after the Pb NAAQS was set. And, in the subsequent nearly 20
years, many additional studies on Pb effects in the environment are now available. These studies
are described in detail in the CD, and summarized in Section 6.2. Some of the more significant
aspects of the evidence available since the standard was set that are relevant to our consideration
of the adequacy of the current standard include the following.
• A more quantitative determination of the mobility, distribution, uptake, speciation, and
fluxes of atmospherically delivered Pb in terrestrial ecosystems. These studies show
that the binding of Pb to organic materials in the soil slows its mobility through soil
and may prevent uptake by plants (CD, Sections 7.1.2, 7.1.5, AX7.1.4.1, AX7.1.4.2,
AX7.1.4.3 and AX7.1.2). Therefore, while atmospheric deposition of Pb has
decreased, Pb may be more persistent in some ecosystems than others and may remain
in the active zone of the soil, where exposure may occur, for decades (CD, Sections
7.1.2, AX7.1.2 and AX7.1.4.3).
• Plant toxicity may occur at lower levels than previously identified as determined by
data considered in development of Eco-SSLs (CD, pp. 7-11 to 7-12, AX7-16 and
Section AX7.1.3.2), although the range of reported soil Pb effect levels is large (tens to
thousands of mg/kg soil).
• Avian and mammalian toxicity may occur at lower levels than those previously
identified (e.g., in the 1986 Lead CD) based on data considered in the development of
Eco-SSLs, although the range of Pb effect levels is large (<1 to >1,000 mg Pb/kg bw-
day) (CD, p. 7-12, Section AX7.1.3.3).
• An expanded understanding of the fate and effects of Pb in aquatic ecosystems and of
the distribution and concentrations of Pb in surface waters throughout the United States
(CD, Section AX7.2.2).
• The availability of new methods for assessing the toxicity of metals to water column
and sediment-dwelling organisms and data collection efforts, such as the USGS
NAWQA program, that monitor Pb in many U.S. aquatic ecosystems (CD, Sections
7.2.1, 7.2.2, AX7.2.2, and AX7.2.2.2). Findings indicate that in some estuarine
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systems Pb deposited during historic usage of leaded gasoline may remain in surface
sediments for decades (CD, p. 7-23).
• A larger dataset of aquatic species assessed with regard to Pb toxicity, and findings of
lower effect levels for previously untested species (CD, p. AX7-176 and Section
AX7.2.4.3).
• Currently available studies have also shown effects on community structure, function
and primary productivity, although some confounders have not been well addressed
(CD, Section AX7.1.4.2).
• A consideration of ecosystem critical loads for Pb. Such analysis had not been
previously considered in review of the Pb NAAQS. Current information on Pb critical
loads is lacking for many processes and interactions involving Pb in the environment
and work is ongoing (CD, Section 7.3).
Given the full body of current evidence, it may be concluded that despite wide variations
in Pb concentrations in soils throughout the country, Pb concentrations are in excess of
concentrations expected from geologic or other non-anthropogenic forces. In particular, the
deposition of gasoline-derived Pb into forest soils has produced a legacy of slow moving Pb that
remains bound to organic materials despite the removal of Pb from most fuels and the resulting
dramatic reductions in overall deposition rates (CD, Section AX7.1.4.3). We note that in areas
influenced by point sources of air Pb that meet the current standard, concentrations of Pb in soil
may exceed by orders of magnitude the concentrations which are considered harmful to
laboratory organisms (CD, Sections 3.2 and AX7.1.2.3).
We recognize, however, that major difficulties arise in attempting to quantify the role of
current ambient Pb in the environment. For example, some Pb deposited before the standard was
enacted is still present in soils and sediments and historic Pb from gasoline continues to move
slowly through systems as does current Pb derived from both air and nonair sources.
Additionally, the evidence of adversity in natural systems is very sparse due in no small part to
the difficulty in determining the effects of confounding factors such as multiple metals or factors
influencing bioavailability in field studies. Nonetheless, the evidence summarized above and
described in detail in the CD leads us to the following conclusions based on Pb in the
environment today and evidence of environmental Pb exposures of potential concern.
Conditions exist in which adverse effects to aquatic organisms and thereby ecosystems
may be anticipated given experimental results. While the evidence does not indicate that
dissolved Pb in surface water constitutes a threat to those ecosystems that are not directly
influenced by point sources, the evidence regarding Pb in sediment is less clear (CD, Sections
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AX7.2.2.2.2 and AX7.2.4). It is likely that some areas with long term historical deposition of Pb
to sediment from a variety of sources as well as areas influenced by point sources have the
potential for adverse effects to aquatic communities. The long residence time of Pb in sediment
and its ability to be resuspended by turbulence make Pb likely to be a factor for the foreseeable
future. Accordingly, staff concludes that the evidence suggests the occurrence of environmental
levels of Pb allowed by the current standard, set nearly thirty years ago, that may pose risk of
adverse environmental effect.
6.4.4.2 Risk-based Considerations
In addition to the evidence-based considerations discussed above, staff has also
considered the findings of the screening level ecological risk assessment taking into account key
limitations and uncertainties associated with the analyses.
The screening level risk assessment involved a comparison of estimates of environmental
media concentrations of Pb to ecological screening levels to assess the potential for ecological
impacts from Pb that was emitted into the air. Results of these comparisons are not considered to
be definite predictors of risk, but rather serve to identify those locations at which there is greatest
likelihood for adverse effect. Similarly, the national-scale screening assessment evaluated the
potential for ecological impacts associated with the atmospheric deposition of Pb released into
ambient air at surface water and sediment monitoring locations across the United States.
However, as noted above, funding constraints have precluded conducting a full-scale ecological
risk assessment for this review.
The ecological screening levels employed in the screening level risk assessment for
different media are drawn from different sources, as summarized above and described in more
detail in Section 7.1.3 of the pilot phase Risk Assessment Report. Consequently there are
somewhat different limitations and uncertainties associated with each. In general, their use here
recognizes their strength in identifying media concentrations with the potential for adverse effect
and their relative nonspecificity regarding the magnitude of risk of adverse effect.
As discussed in the previous section, as a result of its persistence, Pb emitted in the past
remains today in aquatic and terrestrial ecosystems of the Unites States. Consideration of the
environmental risks associated with the current standard is complicated by the environmental
burden associated with air Pb concentrations, predominantly in the past, that exceeded the
current standard. For example, the primary Pb smelter case study involves a facility that has
been emitting Pb for many decades, and as for much of that time there was no Pb NAAQS, air
concentrations associated with the facility may have been well above the current NAAQS.
Concentrations of Pb in soil and sediments associated with the primary Pb smelter case
study exceeded screening values for those media indicating potential for adverse effect to
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terrestrial organisms (plants, birds and mammals) and to sediment dwelling organisms. While
the contribution to these Pb concentrations from air as compared to nonair sources has not been
quantified, air emissions from this facility are substantial (see Appendix D, USEPA 2007; ICF
2006). Additionally, estimates of Pb concentration in soils associated with the secondary Pb
smelter case study were also associated with HQs above 1 for plants, birds and mammals,
indicating potential for adverse effect to those receptor groups. The industrial facility in this case
study is much younger than the primary Pb smelter and apparently became active less than ten
years prior to the establishment of the current standard.
The national-scale screens, which are not focused on particular point source locations,
indicate the ubiquitous nature of Pb in aquatic systems of the U.S. today. Further the magnitude
of Pb concentrations in several aquatic systems, nationally, exceeded screening values In the
case of the national-scale screen of surface water data, 15 locations were identified with water
column levels of dissolved Pb that were greater than hardness adjusted chronic criteria for the
protection of aquatic life (with one location having a HQ as high as 15), indicating a potential for
adverse effect if concentrations were persistent over chronic periods Further, sediment Pb
concentrations at some sites in the national-scale screen were high enough that the likelihood that
they would cause adverse effects to sediment dwelling organisms may be considered "probable".
A complicating factor in interpreting the findings for the national-scale screening
assessments is the lack of clear apportionment of Pb contributions from air as compared to
nonair sources, such as industrial and municipal discharges. While the contribution of air
emissions to the elevated concentrations has not been quantified, documentation of historical
trends in the sediments of many water bodies has illustrated the sizeable contribution that
airborne Pb can have on aquatic systems. This documentation also indicates the greatly reduced
contribution in many systems as compared to decades ago (presumably reflecting the banning of
Pb-additives from gasoline used by cars and trucks). We note, however, that the timeframe for
removal of Pb from surface sediments into deeper sediment varies across systems, such that Pb
remains available to biological organisms in some systems for much longer than in others
(Section 2.8; CD, pp.AX7-141 to AZX7-145).
The case study locations included in the screening assessment, with the exception of the
primary Pb smelter site, are currently meeting the current Pb standard, yet Pb occurs in some
locations, particularly in soil and aquatic sediment, at concentrations above the screening levels,
indicative of a potential for harm to some terrestrial and sediment dwelling organisms. While the
role of airborne Pb in determining these Pb concentrations is unclear, the historical evidence
indicates that airborne Pb can create such concentrations in sediments. Further, whether such
concentrations may be related to emissions prior to establishment of the current standard is also
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unclear. The staff suggests that such concentrations appear to indicate a potential for harm to
ecological receptors under the current standard.
6.4.4.3 CASAC Advice and Recommendations
In the CASAC letter transmitting advice and recommendations pertaining to the review
of the First Draft Pb Staff Paper and Draft Pb Exposure and Risk Assessments, the CASAC Pb
panel provided recommendations regarding the need for a Pb NAAQS, and the adequacy of the
current Pb NAAQS, as well as comments on the draft documents. With regard to the need for a
Pb NAAQS and adequacy of the current NAAQS, the CASAC letter said:
The unanimous judgment of the Lead Panel is that lead should not be delistedas a
criteria pollutant, as defined by the Clean Air Act, for which primary (public health
based) and secondary (public welfare based) NAAQS are established, and that both the
primary and secondary NAAQS should be substantially lowered.
Specifically with regard to the secondary NAAQS, the CASAC Pb Panel stated that the
December 2006 draft documents presented "compelling scientific evidence that current
atmospheric lead concentrations and deposition - combined with a large reservoir of historically
deposited lead in soils, sediments and surface waters - continue to cause adverse environmental
effects in aquatic and/or terrestrial ecosystems, especially in the vicinity of large emissions
sources." The Panel went on to state that "These effects persist in some cases at locations where
current airborne lead concentrations are below the level of the current primary and secondary
lead standards" and "Thus, from an environmental perspective, there are convincing reasons to
both retain lead as a regulated criteria air pollutant and to lower the level of the current
secondary standard."
In making this recommendation, the CASAC Pb Panel cites the persistence of Pb in the
environment, the possibility of some of the large amount of historically deposited lead becoming
resuspended by natural events, and the expectation that humans are not uniquely sensitive among
the many animal and plant species in the environment. In summary, with regard to the
recommended level of a revised secondary standard, the CASAC panel stated that:
Therefore, at a minimum, the level of the secondary Lead NAAQS should be at least as
low as the lowest-recommended primary lead standard.
The March 2007 CASAC letter also encouraged the Agency to "identify the necessary
funds to support needed continuing research on the ecological effects of airborne lead pollution
and to consider developing alternative secondary standards such as critical loads for lead, which
may be different from primary standards in indicator, averaging time, level, or form."
CASAC provided further advice and recommendations on the Agency's consideration of
the secondary standard in this review in their letter of September 2007 (Henderson, 2007b). In
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that letter, they restated their recommendation from the March letter that "at a minimum, the
secondary Lead NAAQS should be at least as low as the lowest-recommended primary lead
standard". They also recognized the role of the secondary standard in influencing the long-term
environmental burden of Pb and a need for environmental monitoring to assess the success of the
standard in this role:
The large environmental burden of historically-deposited lead is currently decreasing.
Accordingly, the goal should be to set the secondary Lead NAAQS such that there is no
reversal of the current downward trend in lead concentrations in the environment. The
limited funds available for monitoring environmental lead should be focused on this
critical task.
6.4.4.4 Staff Conclusions and Recommendations
In considering the adequacy of the current standard, staff first considered, for reasons
discussed in Section 5.4, whether it is appropriate to maintain a NAAQS for Pb or to retain Pb on
the list of criteria pollutants. Given the persistence of Pb, continued emissions of Pb by many
and varied sources (Section 2.2), and the harmful environmental effects associated with Pb
(Section 6.2; CD, Chapter 7 and Appendix AX7), staff concurs with CASAC that Pb should not
be delisted as a criteria pollutant and the secondary standard should not be revoked.
In addressing whether, in view of the information now available, the secondary standard
should be revised to provide requisite protection from Pb-related adverse effects on public
welfare, staff has considered evidence described in the CD and summarized above in Section 6.2
and Chapter 2 as well as information gained from the screening-level risk analyses summarized
above in Section 6.3.
In summary, environmental Pb levels that exist today reflect atmospheric Pb
concentrations and associated deposition, in combination with a large reservoir of historically
deposited Pb in environmental media. As discussed above in Section 6.4.4.1, the information
presented in detail in the CD and summarized in Section 6.2 indicates that there is evidence,
largely qualitative, that suggests the potential for adverse environmental impacts under the
current standard. Given the limited data on Pb effects in ecosystems, it is necessary to look at
evidence of Pb effects on organisms and extrapolate to ecosystem effects. Therefore, by looking
to laboratory studies and current media concentrations in a wide range of areas, it seems likely
that adverse effects are occurring, particularly near point sources, under the current standard.
While the role of current airborne emissions is difficult to apportion, it is conclusive that
deposition of Pb from air sources is occurring and that this ambient Pb is likely as persistent in
the environment as historically deposited Pb has been, although location-specific dynamics of Pb
in soil lead to differences among locations as to the timeframe for Pb to be retained in surface
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soils or sediments where it may be available to ecological receptors (see Section 2.7 and 2.8 and
USEPA, 2007, section 2.3.3). In addition, as noted by CASAC in alluding to the low levels of
human exposure currently associated with adverse health effects, it is reasonable to expect that
many animals and plant species in the environment may have sensitivities to Pb that are similar
to those in humans. The screening-level risk information (Section 6.4.4.2), while limited and
accompanied by various uncertainties, also suggests occurrences of environmental Pb
concentrations existing under the current standard that suggest the potential for adverse
environmental effects.
For the reasons discussed above, staff concurs with CASAC with regard to consideration
of Pb in the environment today and the adequacy of the current standard. Accordingly, we
suggest that there is a need for the Administrator to consider a revision of the current standard to
provide increased protection against reasonably anticipated adverse environmental effects.
6.4.5 Elements of the Standard
The four elements of the standard- indicator, averaging time, form and level serve to
define the standard and must be considered collectively in evaluating the welfare protection
afforded by the standards. In the previous section, staff suggested that the Administrator
consider a revision to the current standard. In considering a revision to the current standard, we
consider the four elements of the standard, the information available, and advice and
recommendations from CASAC, regarding how the elements might be revised to provide a
secondary standard for Pb that protects against adverse environmental effect.
With regard to the pollutant indicator for use in a secondary NAAQS that provides
protection for public welfare from exposure to Pb, the staff makes note of the evidence that Pb is
a persistent pollutant to which ecological receptors are exposed via multipathways. While the
evidence indicates that the environmental mobility and ecological toxicity of Pb are affected by
various characteristics of its chemical form, and the media in which it occurs, information is
insufficient to identify an indicator other than Pb-TSP that would provide protection against
adverse environmental effect in all ecosystems nationally. Accordingly, staff concludes that Pb-
TSP should be retained as the pollutant indicator for use in a secondary NAAQS.
With regard to averaging time, the staff recognizes that the evidence demonstrates that Pb
is a cumulative pollutant with environmental effects that can last many decades. In considering
the appropriate averaging time for such a pollutant the concept of critical loads may be useful
(CD, Section 7.3). However, information regarding critical loads is currently insufficient for
such use in this review. We conclude that there is not a basis at this time for recommending any
revision to the current averaging time.
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With regard to level, staff notes that there is a general lack of data that would indicate a
level of Pb in environmental media below which Pb-related adverse ecosystem effects would not
occur. In addition, the cumulative and persistent nature of Pb requires protection over long
periods of time even from accumulations resulting from small amounts of deposition. Staff also
notes the influence of airborne Pb on Pb in aquatic systems, as demonstrated by historical
patterns in sediment cores from lakes and Pb measurements (Section 2.8.1; CD, Section
AX7.2.2; Yohn et al., 2004; Boyle et al., 2005). In its advice to the Administrator, the CASAC
Pb panel indicated that a significant change to current air concentrations (e.g., via a significant
change to the standard) is likely to have significant beneficial effects on the magnitude of lead
exposures in the environment and lead toxicity impacts on natural and managed terrestrial and
aquatic ecosystems in various regions of the U.S., the Great Lakes and also U.S. territorial waters
of the Atlantic Ocean (Henderson, 2007a, Appendix E).
Staff concurs with CASAC's conclusion that the Agency lacks the relevant data to
provide a clear, quantitative basis for setting a secondary Pb NAAQS that differs from the
primary standard in indicator, averaging time, form, or level. CASAC further advised that:
To collect such data for the next Lead NAAQS review cycle, the EPA needs to initiate
new measurement activities in rural areas —including those that are remote, close to
urban and other sources, and located at high elevations — which quantify and track
changes in lead concentrations in the ambient air, soils, deposition, surface waters,
sediments and biota, along with other information as may be needed to calculate and
apply a critical loads approach for assessing environmental lead exposures and risks in
the next review cycle.
The CASAC Pb Panel also recommended that the Agency "identify the necessary funds to
support needed continuing research on the ecological effects of airborne lead pollution and to
consider developing alternative secondary standards such as critical loads for lead, which may be
different from primary standards in indicator, averaging time, level or form."
In conclusion, staff concludes that in the absence of information on which to base
independent recommendations for the secondary standard, a reduction in the level of the
secondary standard consistent with a reduction in the level of the primary standard (Section
5.5.4) would provide increased protection against adverse environmental effect. Accordingly,
staff recommends that the Administrator consider a revised secondary standard set identical to
the indicator, averaging time, form, and level of a revised primary standard.
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ATTACHMENT A
Clean Air Scientific Advisory Committee Letter
(March 27, 2007)
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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON D.C. 20460
OFFICE OF THE ADMINISTRATOR
SCIENCE ADVISORY BOARD
March 27, 2007
EPA-CASAC-07-003
Honorable Stephen L. Johnson
Administrator
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, NW
Washington, DC 20460
Subject: Clean Air Scientific Advisory Committee's (CASAC) Review of the 1st Draft
Lead Staff Paper and Draft Lead Exposure and Risk Assessments
Dear Administrator Johnson:
The Clean Air Scientific Advisory Committee (CASAC or Committee), augmented by
subject-matter-expert Panelists — collectively referred to as the CASAC Lead Review Panel
(Lead Panel) — completed its review of the Agency's 1st Draft Lead Air Quality Criteria
Document (AQCD) in September 2006 (EPA-CASAC-06-010). On December 7, 2006, Mr.
Marcus Peacock, the EPA Deputy Administrator, issued a memorandum providing his final
decisions on revisions to the process by which the National Ambient Air Quality Standards
(NAAQS) are reviewed. In this memo, Deputy Administrator Peacock directed that this revised
NAAQS review process should begin with the current, ongoing review of the NAAQS for lead.
(See URLs: http://www.epa.gov/ttnnaaqs/rn em o_process_for_revi ewing_naaqs.pdf and
http://www.epa. gov/ttnnaaqs/naaqs_process report march2006 attachments.pdf).
On February 6-7, 2007, the CASAC' s Lead Panel conducted a peer review of EPA' s
Draft Review of the National Ambient Air Quality Standards for Lead: Policy Assessment of
Scientific and Technical Information (1st Draft Lead Staff Paper, December 2006) and a related
draft technical support document, Lead Human Exposure and Health Risk Assessments and
Ecological Risk Assessment for Selected Areas: Pilot Phase, Draft Technical Report (Draft Lead
Exposure and Risk Assessments, December 2006). In addition, on March 9, 2007, the Lead
Panel held a public teleconference to review the CAS AC' s draft letter to the Administrator
resulting from its February meeting. The CASAC roster is found in Appendix A of this report,
and the Lead Panel roster is attached as Appendix B. The charge questions provided to the Lead
Panel by EPA staff are contained in Appendix C to this report, and examples of population-based
approaches to lead risk assessments for the primary Lead NAAQS are found in Appendix D. A
discussion of issues related to setting of the secondary Lead NAAQS is attached as Appendix E,
and Panelists' individual review comments are provided in Appendix F.
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At the February 6-7 public meeting, the Lead Panel expressed serious concerns both
about the EPA documents to be reviewed and the Agency's proposed rulemaking schedule for
the Lead NAAQS, as follows:
• 1st Draft Lead Staff Paper had no staff-derived options for keeping or altering the current
Lead NAAQS.
• The Draft Lead Exposure and Risk Assessments document did not have a full discussion
of the risk associated with different options for keeping or altering the Lead NAAQS.
The Lead Panel judges that, while the latter document represented a good first effort, it
was nowhere near completion.
• Under the Agency's new NAAQS review process, EPA's Staff Paper for lead will no
longer be prepared but will be replaced by a Policy Assessment (PA) for lead, to be
issued in the form of an Advance Notice of Proposed rulemaking (ANPR). However, the
Agency's proposed schedule for the Lead NAAQS review calls for completion of the
Lead Exposure and Risk Assessments document after the PA for lead is issued via the
ANPR. Thus, it was not planned for the CASAC to be given an opportunity to review a
more fully-developed, second-draft version of the Risk/Exposure Assessment (RA) prior
to the ANPR, so that the PA would not be informed by the science assessments of the
Lead Panel.
Subsequent to the February 6-7 meeting of the CASAC Lead Review Panel, Agency
officials, managers and staff held administrative discussions with the chartered members of the
CASAC to learn directly from these seven members their specific concerns with the schedule for
review of the lead standards and the revised NAAQS review process in general. The Lead Panel
is pleased to have been briefed by Agency staff during the Panel's March 9 teleconference that
EPA has modified its timeline both for the generic NAAQS review process and the current Lead
NAAQS review in particular, such that the Lead Panel will now review the 2nd draft of the
Agency's Lead Risk/Exposure Assessment this summer, prior to the issuance of the associated
PA document in the ANPR.
The CASAC Lead Review Panel used the scientific information found in the Agency's
Final Lead AQCD, which was also reviewed by the Lead Panel, in its review of EPA's 1st Draft
Lead Staff Paper and the Draft Lead Exposure and Risk Assessments document. The Lead
Panel's recommendations and the associated scientific basis for these recommendations are
presented below. The unanimous judgment of the Lead Panel is that lead should not be de-listed
as a criteria air pollutant, as defined by the Clean Air Act, for which primary (public-health
based) and secondary (public-welfare based) NAAQS are established, and that both the primary
and secondary NAAQS should be substantially lowered. It is also recommended that future
monitoring of lead exposure be conducted with low-volume PMi0 samplers rather than with total
suspended particulate (TSP) samplers, and that the averaging time be decreased from quarterly to
monthly.
The reasons for these recommendations are given below.
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Introduction
Over the past three decades, blood lead (PbB) concentrations in the U.S. population have
plummeted (1). This decline was largely due to the elimination of leaded gasoline (2). In 1976,
the Consumer Products Safety Commission restricted the allowable amount of lead in residential
paints to 0.06 percent (600 ppm) (3). Lead solder used in canned foods was also decreased —
from over 90% in 1978 to less than 5% in 1988 (4). Finally, there was a decrease in the
abundance of residential housing in which lead-based paints had been used (5). Although it is
difficult to quantify the extent of decrease in blood lead concentrations attributable to specific
sources, the 1978 NAAQS for lead were undoubtedly among the major reasons for the rapid and
widespread decrease in PbB levels in the U.S. population (6).
Despite the dramatic decrease in environmental lead exposure, lead toxicity remains a
major public health problem. Environmental lead exposure in children has been associated with
increased risks for reading problems, school failure, Attention Deficit Hyperactivity Disorder
(ADHD), delinquency, and criminal behavior (6-10). Among U.S. children, eight to fifteen
years old, those in the highest quintile (> 2 |ig/dl) of lead exposure were four times more likely
to have doctor-diagnosed ADHD (11). Moreover, there is no evidence of a threshold for the
adverse consequences of lead exposure; studies show that the decrements in intellectual
(cognitive) functions in children are proportionately greater at PbB concentrations < 10 |ig/dl,
the concentration considered acceptable by the Centers for Disease Control (11-14).
Lead's effects extend beyond childhood. In adults, lead exposure is a risk factor for some
of the most prevalent diseases or conditions of industrialized society, including cardiovascular
disease and renal disease (16-20). There is also compelling evidence that the risks for mortality
from stroke and myocardial infarction are increased at PbB concentrations below 10 |ig/dl,
which is considerably lower than those considered acceptable for adults (19). Finally, although
less definitive, there is also evidence that lead exposure during pregnancy is a risk factor for
spontaneous abortion or miscarriage at PbB concentrations < 10 |ig/dl (21). (It should be noted
that references 11 and 19 above were not cited in EPA's Final Ozone AQCD.)
Scientific Basis for Continuing or De-listing the Lead NAAQS
The CAS AC Lead Review Panel considered the implications of present scientific
understanding regarding the need for protection of public health and public welfare from
exposure to lead in the environment. One of these implications relates to the question of whether
the current science continues to support the need for lead to be listed as a criteria air pollutant for
which a NAAQS is established, or might warrant the de-listing of lead, as presented as a policy
option in the 1st Draft Lead Staff Paper. In addressing this question, the Lead Panel examined
several scientific issues and related public health and public welfare issues that are essential in
determining whether or not a pollutant such as lead should be de-listed or maintained as a criteria
air pollutant.
1. Does new scientific information accumulated since EPA 's promulgation of the current
primary Lead NAAQS of 1.5 jug/m3 in 1978 suggest that science previously overstated the
toxicity of lead! Here, the Lead Panel's answer clearly is No. The data accumulated over
the past three decades make it apparent that adverse health effects on both humans and
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other species appear at blood lead concentrations and environmental exposures well
below those previously thought to pose important risks. Indeed, if anything, this
improved scientific understanding indicates that scientific studies previously
underestimated the toxicity of lead.
2. Have past regulatory and other controls on lead decreased PbB concentrations in human
populations so far below levels of concern as to suggest there is now an adequate margin
of safety inherent in those PbB levels! Again, the Panel's answer here is No. The Nation
can take great pride in the extent to which exposures to lead have been decreased, leading
to laudable decreases in PbB concentrations to an average approaching 2 |ig/dl.
However, there remains a significant segment of the population with blood-lead
concentrations above 5 jig/dl — and some even above 10 |ig/dl — and scientific evidence
supports the contention that these PbB concentrations do not provide an adequate margin
of safety. In fact, this evidence suggests these blood lead concentrations below 5 |ig/dl
are associated with unacceptable adverse effects.
3. Have the activities that produced emissions and atmospheric redistribution of lead in the
past changed to such an extent that society can have confidence that emissions will
remain low even in the absence ofNAAQS controls! Here, the Lead Panel concludes that
the answer, once again, is No. While there have been major decreases in emissions of
lead from use of leaded gasoline, industrial and other activities, even the current air
emissions from some lead mining and reprocessing facilities produce considerable
environmental exposures once the concentrations of lead in environmental media
equilibrate. The Lead Panel concludes that past success in decreasing PbB
concentrations in human populations are due in part to NAAQS controls, and that in the
absence of such controls, there will be a significant possibility that blood-lead
concentrations would begin to rise again.
4. Are airborne concentrations and amounts of lead sufficiently low throughout the United
States that future regulation of lead exposures can be effectively accomplished by
regulation of lead-based products and allowable amounts of lead in soil and/or water?
Lead Panel concludes that the answer to this question is No. While airborne lead
concentrations have been decreased throughout much of the United States, airborne lead
remains a primary vehicle for movement of lead between different environmental
compartments. While control of airborne lead is not sufficient by itself to control
exposure to lead, it is an essential component of a successful control strategy.
Maintaining appropriate Lead NAAQS is considered by the Lead Panel to be an essential
component of a national program to decrease the ongoing adverse effects of lead in
children, adults, and in both terrestrial and aquatic ecosystems.
5. If lead were de-listed as a criteria air pollutant, would it be appropriately regulated
under the Agency's Hazardous Air Pollutants (HAP) program? The Panel's answer is
again No. The HAP program, which regulates according to use of maximum achievable
control technology (MACT), followed by an analysis of residual risk, is appropriate for
point sources. However, the most widespread source of airborne lead throughout the
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nation is the historically-deposited lead along roadways. Thus, this source of airborne
lead could not be regulated under the HAP program.
As a result of the CASACLead'Review Panel's own answers to these scientific and public
health issues, the Panel concludes that the existing state of science is consistent with continuing
to list ambient lead as a criteria air pollutant for which fully-protective NAAQS are required.
Additional Analyses to Inform Decisions About a Primary (Health-Based) NAAQS for
Lead
Despite the dramatic decreases in amounts of airborne lead exposures and human-
population blood-lead concentrations following the phase-out of leaded gasoline, lead toxicity
remains a major public health problem. As discussed above, there is increasing evidence of lead-
induced toxicity at the lowest contemporary exposures to lead — resulting in significant IQ
deficits in children (11-14), and increased frequency of ADHD (11) and cardiovascular disease
(16-19). Although less definitive, there is evidence that lead exposure is a risk factor for
spontaneous abortion and renal disease (20-21).
Although relatively few counties in the United States are out of compliance, the greatest
benefit to public health will be realized by broad decreases in airborne lead concentrations across
the U.S. population because:
1. The adverse consequences are proportionately greater at the lowest increments of lead
exposure;
2. Lead exposure is cumulative; and
3. Airborne lead exposure, in contrast with exposure to lead-based paint, is more widely
dispersed. Thus, reducing exposure from air lead will broadly reduce population blood
lead levels.
In 1978, EPA established a primary Lead NAAQS of 1.5 |ig/m3 to ensure that 99.5% of
the public did not exceed a blood-lead concentration of 30 |ig/dl, with the 99.5% figure being the
Agency's risk management (i.e., policy) choice at that time. In addition to the separate Federal
regulations that had been adopted in 1973 that requiring the phase-out of leaded gasoline, the
1978 Lead NAAQS was instrumental in helping to produce the dramatic decreases in air lead
and blood-lead concentrations over the last 30 years. However, these primary and secondary
Lead NAAQS are totally inadequate for assuring the necessary decreases of lead exposures in
sensitive U.S. populations below those current health hazard markers identified by a wealth of
new epidemiological, experimental and mechanistic studies.
Consequently, it is the CASAC Lead Review Panel's considered judgment that the
NAAQS for Lead must be decreased to fully-protect both the health of children and adult
populations.
The EPA pilot-phase human health risk assessment focused on three case study locations
(i.e., primary lead smelter, secondary lead smelter, and near-roadway urban). While the case
study approach undertaken in the risk assessment is enlightening and provides a potentially
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useful framework for understanding lead exposure for some discrete populations within the U.S.,
there are some additional considerations and analyses that the Lead Panel strongly feels could
help inform a scientifically-defensible NAAQS for lead. In particular, the Panel believes that the
risk assessment would be better informed with a "population-based" risk assessment to
supplement the current case study approach. A population-based risk assessment would
typically include two key components:
1. A quantitative description of the relationship between concentrations of lead in national
ambient air and distributions of resulting blood lead concentrations; and
2. A quantitative description of the relationship between blood lead concentrations and
impacts on IQ.
There are multiple ways in which EPA could conduct a population-based analysis, and
the Panel illustrates some possibilities in Appendix D attached to this letter. Please note that this
work does not represent a complete analysis on the part of the Lead Panel; rather, it is meant to
illustrate the Panel's thinking in this area. It will be important for EPA to consider these
approaches and to fully evaluate their pros, cons, and associated uncertainties. An adequately
comprehensive analysis should characterize the uncertainty, preferably in a quantitative manner,
in two key areas: (1) the relationship between a change in the NAAQS for lead and the
distribution of population blood lead concentrations; and (2) the relationship between blood lead
concentrations and the risk of adverse health effects. This type of review by the Agency will be
necessary to highlight the strengths and weaknesses of the available approaches and help to
elucidate a primary Lead NAAQS that is scientifically-defensible and adequately-protective of
public health.
As described in Appendix D, the Lead Panel considered three separate, but related,
population-based analytical approaches as examples to be considered in deriving an acceptable
range of alternative levels, on the basis of the scientific evidence, for setting a new level for the
primary Lead NAAQS, as follows: Approach A relates air lead levels to blood lead levels using
the approach used in previous lead NAAQS calculations; Approach B uses an epidemiologic
approach to derive an adjusted slope factor relating air lead levels to blood levels taking into
account all exposure pathways between air lead and blood lead; and Approach C relates air lead
levels to blood lead levels and then to IQ loss in children. In addition, the CASAC Lead Review
Panel considers that a population loss of 1-2 IQ points is highly significant from a public health
perspective (22). Therefore, the primary lead standard should be set so as to protect 99.5% of
the population from exceeding that IQ loss.
The three approaches provide comparable results. Given the Panel's assumptions and
preliminary analysis conducted for the three approaches, the resulting analyses indicate to the
CASAC that there is a need for a substantial reduction in the primary Lead NAAQS, to a level of
about 0.2 jug/m or less. CASAC recognizes that these preliminary calculations are dependent
upon the results of EPA's forthcoming uncertainty analyses and the current risk management
choice for the percentage of the population left at risk, as well as acceptable blood levels, IQ loss
and slope factor — the appropriateness of which all depend on certain scientific assumptions and
the risk management criteria that are chosen. Imposing more stringent criteria would result in a
lower (that is, more stringent) range of primary Lead NAAQS levels, whereas less stringent
-------
criteria would result in the calculation of a higher (i.e., less stringent) range of primary lead
standards.
Possible Revision to Lead Indicator from TSP to Low-Volume PMio
As revisions to the level, form, and averaging time of the Lead NAAQS are considered,
CASAC also recommends that EPA revise the indicator. Currently, Lead NAAQS monitoring is
predominantly based on atomic absorption analysis of fiberglass filters run on hi-volume total
suspended particulate (TSP) samplers. Most other TSP sampling was discontinued after PMio
standards were promulgated in 1987. TSP samplers capture particles with an imprecise and
variable upper particle cut size in the range of approximately 30 to 50 microns on fiberglass
filters which are not well-suited for analysis by inexpensive, multi-elemental surface beam
techniques like particle-induced X-ray emission (PIXE) or X-ray fluorescence (XRF).
Consequently TSP sampling by imprecise samplers is primarily conducted only for lead analysis
and these filters are rarely analyzed for other species.
If Lead NAAQS monitoring was based on (low-volume) PMio sampling on Teflon filters,
the resulting data would be correlated with TSP lead, as suggested by limited data in the 1st Draft
Lead Staff Paper, but would have substantially improved sampling precision. The Lead Panel
recognizes that either monitoring system would be subject to variability based on location,
particularly near sources. Other advantages of low-volume PMio sampling include:
1. Focus on those biologically-relevant particles that, when inhaled, are deposited in the
thoracic region;
2. Larger spatial-scale representativeness for population exposures to monitored particles
which remain airborne longer;
3. Could utilize more widespread PMio and "air toxics" metals sampling networks, leading
to collection of more data at lower costs;
4. Potential for inexpensive multi-elemental analysis by XRF or PIXE would provide useful
supplemental metals information for health effects studies and source apportionment;
5. Potential for automated sequential PMio samplers (not available for TSP) would be
especially useful if sampling frequency is increased from once every six days; and
6. Weighing filters would provide useful information on PMio mass; and, if collocated with
PM2.5 Federal Reference Methods (FRM), could provide needed information on PMi0-2.5
mass and speciation.
Reasons for retaining the current TSP lead indicator include: preservation of a long-term
historical record at some sites; and inclusion of very coarse (> 10 micron particle) lead which
may deposit in upper regions of the respiratory tract and ultimately be ingested, or which may
deposit on surfaces and be ingested via hand-to-mouth activity of children. Some such coarse
particles might be missed by PMIO samplers. Presumably a downward scaling of the level of the
Lead NAAQS could accommodate the loss of very large coarse-mode lead particles, and some
short period of concurrent PMio and TSP lead sampling could help develop site-specific scaling
factors at sites with highest concentrations where long-term historical records are important.
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Given the advantages of using PMw, the CASAC Lead Review Panel recommends that
the Agency revise the lead indicator to utilize low-volume PMw sampling, and also develop
equivalent analytical methods to allow use of XRF and Inductively-Coupled Plasma Mass
Spectrometry (ICP-MS) analysis.
Possible Revision to Averaging Time Used for the Lead NAAQS
A second change that should be considered with a change in the Lead NAAQS is possible
use of a different averaging time. Currently, quarterly averaging is used. However, studies
suggest that blood lead concentrations respond at shorter time scales than would be captured
completely by quarterly values. Here, the Lead Panel recommends that the Agency conduct
monthly averaging instead of quarterly.
One consideration involved in using a shorter averaging period is sampling frequency.
Currently, many of the samplers operate with sampling frequencies less than once per day, and
as infrequently as every sixth day. In the most extreme case, as few as four samples may be
involved in determining a monthly average (assuming no samples are considered invalid). This
may make the average susceptible to anomalously-high events. On the other hand, this may
motivate more frequent sampling in those areas whose air concentrations are near the level of the
Lead NAAQS, which would increase the protection of public health and significantly decrease
the impact of a single high lead exposure event. One could also consider having the lead
standards based on the second highest monthly average, a form that appears to correlate well
with using the maximum quarterly value.
The CASAC Lead Review Panel recommends adopting monthly averaging as being more
protective of human health in light of the response of blood lead concentrations that occur at
sub-quarterly time scales, and further recommends that the most protective form would be the
highest monthly average in a year. An area could choose to increase sampling frequency to
make the monthly average less susceptible to more extreme events. Such a change is consistent
with either using TSP or PMio sampling.
Secondary (Welfare-Based) NAAQS for Lead
An extended discussion of issues related to setting the secondary Lead NAAQS can be
found in Appendix E. Chapter 6 of the 1st Draft Lead Staff Paper and Chapter 7 of the "Pilot
Phase" Draft Lead Exposure and Risk Assessments technical support document present
compelling scientific evidence that current atmospheric lead concentrations and deposition —
combined with a large reservoir of historically-deposited lead in soils, sediments and surface
waters — continue to cause adverse environmental effects in aquatic and/or terrestrial
ecosystems, especially in the vicinity of large emission sources. These effects persist in some
cases at locations where current airborne lead concentrations are below the levels of the current
primary and secondary lead standards.
Thus, from an environmental perspective, there are convincing reasons to both retain
lead as a regulated criteria air pollutant and to lower the level of the current secondary
standard.
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Since concentrations of historically deposited lead in soils throughout the U.S. (averaging
0.5 to 4 g/m2 of land area) are changing only slowly — with a half-life exceeding a century —
these concentrated deposits of lead are expected to remain accessible for exchange with the
atmosphere and the rest of the biosphere into the foreseeable future. Fires, changes in land use,
or climatic events such as regional dust storms could mobilize significant quantities of lead that
would be harmful both to human health and ecosystems downwind. This potential for harm is
not adequately recognized in the 1st Draft Lead Staff Paper and the Draft Lead Exposure and
Risk Assessments technical support document, but is a concern that warrants careful continued
monitoring in the future.
In addition, while neither the 1st Draft Lead Staff Paper nor the Draft Lead Risk/Exposure
Assessments document provide a clear quantitative basis for identifying a specific lower level at
which a more protective secondary (welfare- or environmental-based) Lead NAAQS should be
set, there are no reasons to expect that humans are uniquely sensitive to lead pollution among the
millions of animal and plant species.
Therefore, at a minimum, the level of the secondary Lead NAAQS should be at least as
low as the lowest-recommended primary lead standard. The EPA is also encouraged to identify
the necessary funds to support needed continuing research on the ecological effects of airborne
lead pollution and to consider developing alternative secondary standards such as critical loads
for lead, which may be different from primary standards in indicator, averaging time, level or
form.
The CASAC continues to be pleased to provide advice to you concerning the scientific
basis for the setting of the primary and secondary Lead NAAQS. In addition, the CASAC looks
forward to continued dialog with Agency officials and staff aimed at improving EPA's NAAQS
review process in a manner that enhances the efficiency of the process while maintaining its
integrity and adherence to the stipulations of the Clean Air Act. Finally, the Committee also
looks forward to reviewing the 2nd draft of the Agency's Lead Risk/Exposure Assessment this
summer. As always, we wish Agency staff well in this important task.
Sincerely,
/Signed/
Dr. Rogene Henderson, Chair
Clean Air Scientific Advisory Committee
Appendix A - Roster of the Clean Air Scientific Advisory Committee
Appendix B - Roster of the CASAC Lead Review Panel
Appendix C - Agency Charge to the CASAC Lead Review Panel
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Appendix D - Population-Based Approaches to Risk Assessment Analyses for the Primary Lead
NAAQS
Appendix E - Issues Related to the Setting of the Secondary Lead NAAQS
Appendix F - Review Comments from Individual CASAC Lead Review Panel Members
References
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8. Denno D. Biology and Violence. New York: Cambridge University Press, 1990.
9. Needleman HL, Reiss JA, Tobin MJ, Biesecker GE, Greenhouse JB. Bone lead levels and
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10. Dietrich K, Ris M, Succop P, Berger O, Bornshein R. Early exposure to lead and juvenile
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11. Braun J, Kahn RS, Froehlich T, Auinger P, Lanphear BP. Exposures to environmental
toxicants and attention deficit hyperactivity disorder in U.S. children. Environ Health
Perspect 2006; 114:1904-1909.
12. Canfield RL, Henderson CR, Cory-Slechta DA, Cox C, Jusko TA, Lanphear BP. Intellectual
impairment in children with blood lead concentrations below 10 micrograms per deciliter. N
EnglJMed 2003;348:1517-1526.
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14. Kordas K, Canfield RL, Lopez P, et al. Deficits in cognitive function and achievement in
Mexican first-graders with low blood lead concentrations. Environ Res. 2006;100:371-386.
10
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15. Tellez-Rojo MM, Bellinger DC, Arroyo-Quiroz C, et al. Longitudinal associations between
blood lead concentrations lower than 10 |ig/dl and neurobehavioral development in
environmentally exposed children in Mexico City. Pediatrics. 2006;118:e323-330.
16. Schwartz J. Lead, blood pressure, and cardiovascular disease in men. Arch Environ Health
1995;50:31-37.
17. Nash D, Magder L, Lustberg M, Sherwin RW, Rubin RJ, Kaufmann RB, Silbergeld EK.
2003. Blood lead, blood pressure, and hypertension in perimenopausal and postmenopausal
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levels measured prospectively and risk of spontaneous abortion. Am JEpidemiol
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11
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Appendix A - Roster of the Clean Air Scientific Advisory Committee
U.S. Environmental Protection Agency
Science Advisory Board (SAB) Staff Office
Clean Air Scientific Advisory Committee (CASAC)
CHAIR
Dr. Rogene Henderson, Scientist Emeritus, Lovelace Respiratory Research Institute,
Albuquerque, NM
MEMBERS
Dr. Ellis Cowling, University Distinguished Professor At-Large, North Carolina State
University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina
State University, Raleigh, NC
Dr. James D. Crapo, Professor, Department of Medicine, National Jewish Medical and
Research Center, Denver, CO
Dr. Douglas Crawford-Brown, Director, Carolina Environmental Program; Professor,
Environmental Sciences and Engineering; and Professor, Public Policy, Department of
Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel
Hill, NC
Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of
Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT
Dr. Armistead (Ted) Russell, Georgia Power Distinguished Professor of Environmental
Engineering, Environmental Engineering Group, School of Civil and Environmental
Engineering, Georgia Institute of Technology, Atlanta, GA
Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard
Medical School, Boston, MA
SCIENCE ADVISORY BOARD STAFF
Mr. Fred Butterfield, CASAC Designated Federal Officer, 1200 Pennsylvania Avenue, N.W.,
Washington, DC, 20460, Phone: 202-343-9994, Fax: 202-233-0643 (butterfield.fred@epa.gov)
(Physical/Courier/FedEx Address: Fred A. Butterfield, III, EPA Science Advisory Board Staff
Office (Mail Code 1400F), Woodies Building, 1025 F Street, N.W., Room 3604, Washington,
DC 20004, Telephone: 202-343-9994)
A-l
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Appendix B - Roster of the CASAC Lead Review Panel
U.S. Environmental Protection Agency
Science Advisory Board (SAB) Staff Office
Clean Air Scientific Advisory Committee (CASAC)
CASAC Lead Review Panel
CHAIR
Dr. Rogene Henderson*, Scientist Emeritus, Lovelace Respiratory Research Institute,
Albuquerque, NM
MEMBERS
Dr. Joshua Cohen, Research Associate Professor of Medicine, Tufts University School of
Medicine, Institute for Clinical Research and Health Policy Studies, Center for the Evaluation of
Value and Risk, Tufts New England Medical Center, Boston, MA
Dr. Deborah Cory-Slechta, Director, Environmental and Occupational Health Sciences
Institute, a joint Institute of the Robert Wood Johnson Medical School, University of Medicine
and Dentistry of New Jersey, and Rutgers University, Piscataway, NJ
Dr. Ellis Cowling*, University Distinguished Professor At-Large, North Carolina State
University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina
State University, Raleigh, NC
Dr. James D. Crapo [M.D.]*, Professor, Department of Medicine, National Jewish Medical and
Research Center, Denver, CO
Dr. Douglas Crawford-Brown*, Director, Carolina Environmental Program; Professor,
Environmental Sciences and Engineering; and Professor, Public Policy, Department of
Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel
Hill, NC
Dr. Bruce Fowler, Assistant Director for Science, Division of Toxicology and Environmental
Medicine, Office of the Director, Agency for Toxic Substances and Disease Registry, U.S.
Centers for Disease Control and Prevention (ATSDR/CDC), Chamblee, GA
Dr. Andrew Friedland, Professor and Chair, Environmental Studies Program, Dartmouth
College, Hanover, NH
Dr. Robert Goyer [M.D.], Emeritus Professor of Pathology, Faculty of Medicine, University of
Western Ontario (Canada), Chapel Hill, NC
Mr. Sean Hays, President, Summit Toxicology, Allenspark, CO
B-l
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Dr. Bruce Lanphear [M.D.], Sloan Professor of Children's Environmental Health, and the
Director of the Cincinnati Children's Environmental Health Center at Cincinnati Children's
Hospital Medical Center and the University of Cincinnati, Cincinnati, OH
Dr. Samuel Luoma, Senior Research Hydrologist, U.S. Geological Survey (USGS), Menlo
Park, CA
Dr. Frederick J. Miller, Consultant, Gary, NC
Dr. Paul Mushak, Principal, PB Associates, and Visiting Professor, Albert Einstein College of
Medicine (New York, NY), Durham, NC
Dr. Michael Newman, Professor of Marine Science, School of Marine Sciences, Virginia
Institute of Marine Science, College of William & Mary, Gloucester Point, VA
Mr. Richard L. Poirot*, Environmental Analyst, Air Pollution Control Division, Department of
Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT
Dr. Michael Rabinowitz, Geochemist, Marine Biological Laboratory, Woods Hole, MA
Dr. Armistead (Ted) Russell*, Georgia Power Distinguished Professor of Environmental
Engineering, Environmental Engineering Group, School of Civil and Environmental
Engineering, Georgia Institute of Technology, Atlanta, GA
Dr. Joel Schwartz, Professor, Environmental Health, Harvard University School of Public
Health, Boston, MA
Dr. Frank Speizer [M.D.]*, Edward Kass Professor of Medicine, Channing Laboratory,
Harvard Medical School, Boston, MA
Dr. Ian von Lindern, Senior Scientist, TerraGraphics Environmental Engineering, Inc.,
Moscow, ID
Dr. Barbara Zielinska, Research Professor, Division of Atmospheric Science, Desert Research
Institute, Reno, NV
SCIENCE ADVISORY BOARD STAFF
Mr. Fred Butterfield, CASAC Designated Federal Officer, 1200 Pennsylvania Avenue, N.W.,
Washington, DC, 20460, Phone: 202-343-9994, Fax: 202-233-0643 (butterfield.fred@epa.gov)
* Members of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the EPA
Administrator
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Appendix C - Agency Charge to the CASAC Lead Review Panel
Charge to the CASAC Pb Panel
Within each of the main sections of the first draft Staff Paper, questions that we ask the
Panel to focus on in their review include the following:
Ambient Pb information and analyses (Chapter 2):
1. To what extent are the emissions and air quality characterizations and analyses clearly
communicated, appropriately characterized, and relevant to the review of the primary and
secondary Pb NAAQS?
2. Does the information in Chapter 2 provide a sufficient ambient Pb-related basis for the
exposure, human health and environmental effects, health risk assessment, and
environmental assessment presented in later chapters?
Pb-related health effects (Chapter 3):
1. To what extent is the presentation of evidence from the health studies assessed in the Pb
AQCD and the integration of information from across the various health-related research
areas drawn from the Pb AQCD technically sound, appropriately balanced, and clearly
communicated?
2. What are the views of the Panel on the appropriateness of staffs discussion and conclusions
in Chapter 3 on key issues related to quantitative interpretation of epidemiologic study
results, including, particularly, the form of a blood Pb-response function for neurocognitive
effects, and the form of the associated blood Pb metric?
3. What are the Panel's views on the adequacy and clarity of the discussion of potential
thresholds in concentration-response relationships presented in Chapter 3?
Human Exposure and Health Risk Analysis, Pilot-Phase (Chapter 4):
1. To what extent are the assessment, interpretation, and presentation of the results of the pilot
exposure analysis, including characterization of Pb concentrations in media, the modeling of
multi-pathway Pb exposure and application of biokinetic blood Pb models, as presented in
Chapter 4 technically sound, appropriately balanced, and clearly communicated?
2. Are the methods used to conduct the pilot exposure analysis, including the modeling of
population-level distributions of total blood Pb levels and the pathway-apportionment of
those blood Pb levels (e.g., air-inhalation, versus soil-ingestion versus dust-ingestion, versus
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background) technically sound? Does the Panel have any suggestions for improvements in
the methods used?
3. What are the Panel' s views on the staff interpretation of the performance evaluation
completed for the pilot analysis (and described in Chapter 4) with regard to the
representativeness of individual modeling steps completed for the analysis (e.g.,
characterization of ambient air and outdoor soil Pb levels and the estimation of blood Pb
levels for specific case studies)?
4. In general, are the concentration-response functions and blood Pb metrics (i.e., lifetime
average, concurrent blood lead) used in the pilot analysis appropriate for this review?
5. Are the methods used to conduct the pilot health risk assessment, including the application of
the cutpoints in relation to the concentration-response functions employed, technically
sound? Does the Panel have any suggestions for improvements in the methods used?
6. To what extent does the sensitivity analysis completed for the pilot analysis (and described in
Chapter 4) identify key sources of uncertainty and provide an assessment of their impact on
risk results?
7. As part of the NAAQS review, there is interest in attempting to differentiate Pb exposure and
health risk impacts for modeled populations between: (a) historically-deposited Pb (e.g.,
near-roadway dust/soil lead from leaded gasoline); and (b) newly-emitted Pb. Does the Panel
have specific recommendations regarding approaches that might be employed in the full-
scale assessment for this purpose?
8. What are the Panel's views on the most important issues to be addressed in the subsequent
full-scale human exposure and health assessment that will be presented in the revised
documents?
The Primary Pb NAAQS (Chapter 5)
1. What are the Panel's views on the adequacy and clarity of the presentation of the basis for
the existing standard and conclusions reached in the last review?
2. Based on the information contained in the first draft Staff Paper, as well as the AQCD, does
the Panel have recommendations with regard to specific aspects of the standard to be
considered in developing policy alternatives? For example, considering the prominence of
the soil and dust pathways for ambient Pb exposures, and the evidence regarding
environmental response times, is there reason to give more emphasis to consideration of an
alternative (shorter or longer) averaging time; and, how might this be considered in the full-
scale risk assessment given current capabilities?
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Pb-related welfare effects and screening level ecological risk assessment (Chapter 6):
1. To what extent is the presentation of evidence from the ecological studies assessed in the Pb
AQCD and the integration of information from across the various ecologically-related
research areas drawn from the Pb AQCD technically sound, appropriately balanced, and
clearly communicated?
2. Given the lack of quantitative information on Pb-related ecosystem effects, what are the
Panel's views on the presentation of this topic in chapter 6?
3. What are the Panel's views of the data sources and models used to estimate current levels of
Pb in soil, freshwater, and sediment for the case study locations?
4. To what extent are the methods used to conduct the exposure assessment and the
interpretation and presentation of the results technically sound, appropriately balanced, and
clearly communicated?
5. What are the Panel's views of the approach for addressing uncertainty in apportionment of
Pb contributions in the national-scale screen by factoring out those locations with known
non-air sources (e.g., mining, point discharges)?
6. To what extent are the assessment, interpretation, and presentation of the results of the
screening-level risk analysis, including characterization of lead concentrations in media and
the comparisons to ecological screening values, as presented in Chapter 6 and the risk
assessment report technically sound, appropriately balanced, and clearly communicated?
7. Does the Panel feel that adequate screening criteria (ecotoxicity screening values) were
selected for each of the media?
8. What are the Panel's views on the derivation of the soil screening values for birds and
mammals (i.e., using the Eco-SSL methodology)? Do the resultant values adequately reflect
current information on exposure characteristics of these organisms?
9. To what extent are the uncertainties associated with the exposure analysis clearly and
appropriately characterized in Chapter 6 and the risk assessment report?
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Appendix D - Population-Based Approaches to Risk Assessment on Analyses
for the Primary Lead NAAQS
The CASAC Lead Review Panel considered three separate, but related, population-based
analytical approaches as examples to be considered in deriving an acceptable range of alternative
levels, on the basis of the scientific evidence, for setting a new level for the primary Lead
NAAQS, as follows:
• Approach A relates air lead levels to blood lead levels using the approach established in
previous lead NAAQS calculations;
• Approach B uses an epidemiologic approach to derive an adjusted slope factor relating
air lead levels to blood levels taking into account all pathways between air lead and blood
lead; and
• Approach C relates air lead levels to blood lead levels and then to IQ loss in children.
These approaches consider existing information and the following assumptions:
• the population to be protected (99.5% of the population of children);
• the maximal acceptable blood concentration (up to 5.0 |ig/dl);
• an appropriate geometric standard deviation (GSD) for the blood lead levels in
children exposed to a given level of air lead (range 1.3-2.0);
• the non-air background (1.0-1.4 |ig/dl or lower range should be considered);
• the slope factor for the relation between air lead and blood lead for levels of blood
lead below 10 |ig/dl, with the candidate values considered being 2.0 |ig/dl per |ig/m3
(nrVdl) used in 1978, 5.0 m3/dl used by the World Health Organization (WHO) in
2000, 10.0 m3/dl noted in recent studies (see the discussion in Approach B below),
and assuming 20.0 m3/dl as a maximum; and
• the most sensitive toxicity endpoint (i.e.., IQ loss in children).
In addition, the CASAC Lead Review Panel considers that a population loss of 1-2 IQ
points is highly significant from a public health perspective (22). Therefore, the primary lead
standard should be set so as to protect 99.5% of the population from exceeding that IQ loss.
The three approaches provide comparable results. Given the Panel's assumptions and
preliminary analysis conducted for the three approaches, the resulting analyses indicate to the
CASAC that there is a need for a substantial reduction in the primary Lead NAAQS, to a level of
about 0.2 fj,g/m3 or less. CASAC recognizes that these preliminary calculations are dependent
upon the results of the Agency's forthcoming uncertainty analyses and: the values chosen for the
percent of the population left at risk; acceptable blood levels and IQ loss; and slope factor — the
appropriateness of which all depend on certain scientific assumptions and the risk management
criteria that are chosen. Imposing more stringent criteria would result in a lower (that is, more
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stringent) range of primary Lead NAAQS levels, whereas less stringent criteria would result in
the calculation of a higher (i.e., less stringent) range of primary lead standards.
Approach A
The first approach (A) relates air lead levels to blood lead (PbB) levels using a simplified
and modified empirical-deterministic approach that is essentially the same approach used in
previous EPA NAAQS (1978) and World Health Organization (WHO, 2000) guidance
documents. This approach begins with selection of a "not-to-be-exceeded" PbB value or values
based on scientific evidence. These "not-to-be-exceeded" PbB values for beginning the 1978
NAAQS and the 2000 WHO uses of the approach were 30 and 10 jig/dl respectively. The
current scientific evidence reviewed by the Panel, per the Agency's Final Lead AQCD indicates
that the concentration of lead in blood shown to be harmful has declined substantially below
those levels, to around 5 |ig/dl or less.
For example, based on current evidence, one might consider two "not-to-be-exceeded"
PbB values, such as 5.0 and 2.5 |ig/dl. These are not to be exceeded at the 99.5 percentile and,
for an illustrative GSD of 1.3, produce geometric mean values of 2.5 and 1.3 |ig/dl, respectively.
The non-air portion of these two means must be subtracted to give the air Pb-based contributions
to PbB. Panel member Dr. Paul Mushak (Appendix F) calculated the non-air portion using the
Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK).
The use of a "not-to-be-exceeded" PbB value of 5 |ig/dl in Approach A and slope factors
of 5, 10, or 20 produce corresponding suggested NAAQS values of 0.22, 0.11, or 0.06 |ig/m3.
Use of a "not-to-be-exceeded" value of 2.5 |ig/dl and the same slope factors produce air lead
values half as high, i.e., 0.11, 06 and 0.03 |ig/m3, respectively. (Note that, for the 1.25meanPbB
scenario, the non-air PbB contribution is the dominant source and modeling does not provide an
exact value; see Dr. Paul Mushak's detailed derivation comments in Appendix F, pp. F-55
through F-57.) Based on these values alone, the current primary Lead NAAQS set in 1978
should be lowered by at least a factor of seven and by as much as 50, depending on the slope
factor used (see, in particular, the individual comments of Lead Panel members Dr. Mushak and
Dr. Ian von Lindern found in Appendix F).
Approach B
Approach B is a "top-down" approach. That is, instead of estimating the effect of
inhalation alone, the effect of air lead on deposition into dust, food, etc. and the uptakes from
those pathways, an epidemiologic approach should be used to derive an adjusted slope factor
taking into account all pathways between air lead and blood lead. This is based on the changes
in blood lead observed when lead began to be phased-out of gasoline. This analysis relies on the
results of Schwartz and Pitcher (23).
The Schwartz and Pitcher analysis showed that in 1978, the midpoint of the National
Health and Nutrition Examination Survey (NHANES) II, gasoline lead was responsible for 9.1
|ig/dl of blood lead in children. Their estimate is based on their coefficient of 2.14 |ig/dl per 100
metric tons (MT) per day of gasoline lead use, and usage of 426 MT/day in 1976. Between 1976
and when the phase-out of lead from gasoline was completed, air lead concentrations in U.S.
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cities fell a little less than 1 |ig/m3 (24). These two facts imply a ratio of 9-10 jig/dl per |ig/m3
reduction in air lead, taking all pathways into account.
Under this scenario, a decrease of mean air lead concentrations of 0.1 |ig/m3 could be
expected to produce a further decrease in average blood lead concentrations of 0.9-1.0 |ig/dl.
Assuming a slope of three IQ points per |ig/dl reduction in blood lead, which is indicated by the
pooled analysis of low concentration lead exposure (13), this further decrease would be expected
to raise the average IQ of children in U.S. cities by approximately three IQ points — a
significant positive health impact. Put another way, the derivation above empirically justifies the
use of the slope factor of 10 in Approach C, and the resulting estimates that an air quality
standard of 0.11 |ig/m3 (that is, a 13-fold reduction) would be required to keep 99.5% of the
children below a blood lead of 5 |ig/dl.
Approach C
Approach C is more sophisticated, starting with an air lead level and a blood lead level
produced only by airborne lead, and relates that air level to IQ point loss (see Table 2). A linear
model between the ranges of 1-7.5 |ig/dL PbB for both concurrent and lifetime exposures
suggests a three-point decrement in IQ for each unit change in PbB (13). Approach A and C are
in agreement on the relationship between air lead levels associated with PbB, dependent on the
slope factor used. (Approach A does not consider IQ loss or any other health effect.)
These considerations are summarized in Tables 1 and 2, and are also contained in the
individual review comments from various Lead Panel members attached as Appendix F.
Depending on the slope factor selected between 5 and 20 m3/dl, the estimate of blood lead
concentrations from various air lead concentrations varies by a factor of four (Table 1). For
example, using the linear estimate of IQ loss associated with PbB below 7.5 |ig/dl (13), the Lead
Panel estimated that, over the range of PbB from 0.5-4.0 jig/dl (i.e., an eight-fold range), the loss
of IQ would similarly increase from 1.5 to 12 IQ points (Table 2).
Since the Lead Panel considers a population loss of 1-2 IQ points to be highly significant
from a public health perspective, the Lead Panel therefore considers this extent of loss in IQ as a
"change in IQ not to be exceeded." Depending upon the slope factor selected, this results in a
range of 0.025-0.200 |ig/m3 (i.e., about a 7.5- to 60-fold decrease from the current primary Lead
NAAQS) as the estimated air lead concentration to consider under Approach A.
References
23. Schwartz J, Pitcher H. The relationship between gasoline lead and blood lead in the United
States. 1989 J. Official Stat. 5: 421-431.
24. U.S. Environmental Protection Agency. (1986) Air Quality Criteria for Lead. Research
Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria
and Assessment Office; EPA report no. EPA/600/8-83/028aF-dF. 4v. Available from: NTIS,
Springfield, VA; PB87-142378, p. 1-21.
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TABLE 1. Relationship of Blood Lead (PbB) to Air Lead (Pb-Air) by Differing Slope
Factors
Pb-Air (|ig/m3)
0.010
0.025
0.050
0.100
0.200
PbB (|ig/dl)
S.F.* = 5
0.05
0.13
0.25
0.50
1.00
S.F.* = 10
0.10
0.25
0.50
1.00
2.00
S.F.* = 20
0.20
0.50
1.00
2.00
4.00
*S.F. = slope factor (m /dl) = PbB/Pb-Air; S.F. value varies with increasing impact of
indirect Pb-Air pathway (Dust Pb + Soil Pb)
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TABLE 2. Relationship of IQ Point Losses to Increases in Pb-Air and Pb-Air-Based Blood Lead (PbB) Values Above Zero
Pb-Air
(Hg/m3)
0
0.010
0.025
0.050
0.100
S.F. = 5
PbBd
0
0.05
0.13
0.25
0.50
1.00
IQ Loss e'f
0
< 1
<1
< 1
1.5
3.0
S.F. = 10
PbB
0
0.10
0.25
0.50
1.00
2.00
IQ Loss
0
< 1
< 1
1.5
3.0
6.0
S.F. = 20
PbB
0
0.20
0.50
1.00
2.00
IQ Loss
0
< 1
1.5
3.0
6.0
12.0
0.200
a Pb-Air-related increases affecting IQ point loss through calculated PbB values using^ <$ppe factors per Table 1
b IQ vs. PbB dose-response relationships based on Lanphear et a/., 2005 (13): sub-7.5 jig/dl linear segment, average slope = 3.0,
combining slopes of 2.9 and 3.1 for concurrent and lifetime average dose metrics, respectively
c Slope factors as defined in Table 1 and text
d PbB as derived in Table 1
e Rounding for values < 1 IQ point
f Population, not individual, IQ loss/gain projections; U.S. CDC 2007 estimates 23,380,860 U.S. children 0-71 months of age.
Source: U.S. Centers for Disease Control, 2007. CDC Surveillance Data. (Last updated 2/16/2007). URL:
http://www.cdc.gov/nceh/surv/stats.htm [accessed 3/8/2007]
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Appendix E - Issues Related to the Setting of the Secondary Lead NAAQS
Chapters 6 of the Agency's 1st Draft Lead Staff Paper and Chapter 7 of EPA's Draft
(Pilot-Phase) Lead Exposure and Risk Assessments document summarize a very large body of
scientific knowledge about environmental and ecological effects of atmospherically deposited
lead on the biota, soils, sediments, and surface and ground waters of terrestrial and aquatic
ecosystems in various parts of the U.S. and nearby parts of the world. This significant body of
scientific knowledge includes environmental effects of both historically-deposited lead and
continuing air dispersal or reentrainment of lead compounds from primary and secondary lead
smelters, along roadsides, and in ecologically-sensitive areas such the Hubbard Brook
Experimental Forest as described in Chapter 7 of the pilot-phase technical support document.
Although lead is recognized in these two chapters as one among a longer list of heavy
metals in the environment (including cadmium, zinc, and mercury), these two chapters do not
contain adequate discussion of the special characteristics of lead or its ecological effects in the
context of these other metals — or other air-dispersed criteria pollutants. Also, little of the
information about specific environmental effects of lead is presented in a way that is directly
relevant to the issue of whether the EPA Administrator should retain, increase, or decrease the
present primary and secondary National Ambient Air Quality Standard (NAAQS) for lead.
These identical standards were established in 1978 and have been maintained ever since at a
level of 1.5|ig/m3 as a quarterly average (maximum arithmetic mean averaged over a calendar
quarter).
The Lead Panel believes that especially Chapter 6 of the draft Lead Staff Paper (and
perhaps also at least parts of Chapter 7 of the pilot-phase risk-exposure assessment document)
would be much improved in their intended purposes if they were to contain a concise summary
of:
1. The knowledge available and (as best they can discern) the rationale used by the
Administrator in promulgating the original NAAQS for Lead in 1978;
2. The knowledge available and rationale used in the decisions made in 1989 and 1990 to
retain unchanged the identical primary and secondary Lead NAAQS established in 1978;
and
3. The knowledge available and rationale used by the Administrator in establishing and
maintaining identical primary and secondary standards for criteria air pollutant — not
only for lead — but also for most of the other criteria pollutants for NAAQS since 1970.
Despite the limitations mentioned in the last two preceding paragraphs, the Lead Panel
believes that the body of scientific knowledge summarized in the Agency's Final Air Quality
Criteria Document (AQCD) for lead, and further presented in the aforementioned chapters in the
1st Draft Lead Staff Paper and the Draft (Pilot-Phase) Lead Exposure and Risk Assessments
documents, provide compelling scientific justification for both:
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1. The original (1978) decision by EPA to regulate lead as a Criteria Air Pollutant and to
establish what was then considered to be an appropriately-designed primary (public-
health based) NAAQS for lead, with a secondary (public-welfare based) standard set at
the same level and form, and
2. Maintaining for the foreseeable future similarly well designed (but contemporarily
scientifically well-informed) primary and secondary NAAQS for lead — standards with
levels and forms that may be different from, rather than identical to each other.
There are several features of the environmental and ecological effects of lead, and both
the chemical and physical properties of lead in the environment, that make lead distinct from the
other four criteria pollutants for which NAAQS have been developed by the EPA. These
distinctive properties include:
1. The widespread use of lead as an ingredient in decorative paints, lead-acid batteries, as an
additive for gasoline used in motor vehicles, and even in some pesticides used earlier to
protect some horticultural crops from plant pathogens;
2. The persistence of lead in soils, surface and ground waters, sediments, and in both the
structural- and some biologically-active tissues of plants, animals, insects, and
microorganisms;
3. The well-known toxicity and interference in development of cognitive functional capacity
in humans (especially children) and the much less well-known toxicological and other
effects of lead on all the other different types of animals, plants, insects, and
microorganisms in managed and natural terrestrial and aquatic ecosystems of the Earth —
some of which are undoubtedly even more sensitive to lead than human infants;
4. The very substantial decreases in current air concentrations and atmospheric deposition
of lead into the environment that were achieved in recent decades through:
(a) The Phase-out of lead additives in gasoline during the 1970s, '80s, and '90s;
(b) Severe limits on air emissions from lead smelters during earlier decades; and
(c) Decreases in air emissions from lead battery processing facilities in more recent
years.
Thus, most current exposures of living organisms in natural and managed terrestrial and
aquatic ecosystems are caused primarily by redistribution of environmentally persistent
airborne lead compounds deposited in soils, sediments, and surface waters during the
latter earlier decades of the 20th century.
5. The continuing airborne resuspension and dispersal of lead that persists in soils, fugitive
dusts, sediments, and surface waters and are transported and deposited once again from
air in both fine and coarse particulate matter and aerosols — especially along roadways.
These distinctive properties of lead suggest to some policy makers that ecological and
environmental effects of lead might be managed by other means than maintaining both primary
and secondary Lead NAAQS. In the Lead Panel's considered judgment, the limitations of the
other methods of management now available to the EPA are such that none of these alternative
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methods would be anywhere near as effective in protecting public health or welfare as
maintainingybr the foreseeable future as appropriately well-designed (and contemporarily
scientifically well-grounded) primary and secondary NAAQS for lead.
As indicated in the body of the letter to the EPA Administrator, the members of the
CASAC Lead Review Panel have provided a consensus scientific judgment that the present level
(1.5|ig/m3) of the primary Lead NAAQS should be decreased substantially and that appropriate
adjustments probably also should be made in the indicator, averaging time, and statistical form of
the primary NAAQS for lead.
The scientific evidence on ecological and environmental effects of lead summarized in
the Draft Lead Staff Paper and the Draft Lead Exposure and Risk Assessments documents
indicate that any significant decrease in the present level of the primary Lead NAAQS will very
likely have similarly significant beneficial effects on the magnitude of lead exposures in the
environment and lead toxicity impacts on natural and managed terrestrial and aquatic ecosystems
in various regions of the U.S., Canada, Mexico, the Great Lakes, and also in the open-water
regions of the Atlantic Ocean.
Since concentrations of historically deposited lead in soils throughout the United States
(averaging 0.5 to 4 grams/m2 of land area) are changing only slowly — with a half-time
exceeding a century — these concentrated deposits of lead are expected to remain accessible for
exchange with the atmosphere and the rest of the biosphere into the foreseeable future. Fires,
changes in land use, or climatic events such as regional dust storms could mobilize significant
quantities of lead that would be harmful both to human health and ecosystems downwind. This
potential for harm is not adequately recognized in the present Draft Lead Staff Paper and the
Draft Lead Exposure and Risk Assessments documents.
Considering the magnitude of important ecological effects of lead in the environment, as
described in these document, it is very disappointing to note that the EPA apparently lacks (or
chooses not to expend) funds for any additional ecological risk assessment work for this current
(2006-2008) review of the NAAQS for lead. This disappointment also is increased by the very
welcome attention given in the Final Lead AQCD to the alternative concepts of critical loads,
critical limits, target loads, and target times that have been developed in Europe and Canada to
guide the processes of decision making regarding both environmental and public health effects of
airborne chemicals.
Although these alternative concepts and processes of analysis of multiple pollutant/
multiple effects have not been carefully considered for use in the U. S., the CASAC Lead
Review Panel — together with the authors of the National Research Council (NRC)/National
Academy of Sciences (NAS) 2004 report on "Air Quality Management in the United States" —
believes that these alternatives should be considered very carefully in the future as air quality
management tools for use in this country as well as in other countries around the world.
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ATTACHMENT B
Clean Air Scientific Advisory Committee Letter
(September 27, 2007)
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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON D.C. 20460
OFFICE OF THE ADMINISTRATOR
SCIENCE ADVISORY BOARD
September 27, 2007
EPA-CASAC-07-007
Honorable Stephen L. Johnson
Administrator
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, NW
Washington, DC 20460
Subject: Clean Air Scientific Advisory Committee's (CASAC) Review of the 2nd Draft
Lead Human Exposure and Health Risk Assessments Document
Dear Administrator Johnson:
The Clean Air Scientific Advisory Committee (CASAC or Committee), augmented by
subject-matter-expert Panelists — collectively referred to as the CASAC Lead Review Panel
(Lead Panel) — met on August 28-29, 2007, in Durham, NC, at the request of EPA's Office of
Air Quality Planning and Standards (OAQPS) to conduct a peer review of the Agency's Lead
Human Exposure and Health Risk Assessments for Selected Case Studies, Draft Report (2nd Draft
Lead Human Exposure and Health Risk Assessments, July 2007). This letter provides the Lead
Panel's advice and recommendations to you concerning the Agency's exposure/risk assessment
that supports the setting of a primary National Ambient Air Quality Standard (NAAQS) for
Lead, the secondary Lead NAAQS, and implementation issues associated with EPA's revised
NAAQS review process. The CASAC roster is found in Appendix A of this report, and the Lead
Panel roster is attached as Appendix B. The charge questions provided to the Lead Panel by
EPA staff are contained in Appendix C to this report, and Panelists' individual review comments
are provided in Appendix D.
Review of EPA's 2nd Draft Human Lead Exposure and Health Risk Assessments
1. Overall Evaluation
Overall, the CASAC Lead Review Panel judges that the Agency's 2nd Draft Lead Human
Exposure and Health Risk Assessments is not yet a complete, well-documented exposure and risk
assessment that presents the full range of pertinent data and analyses. In particular, in order to
support the establishment of National Ambient Air Quality Standards, it is especially important
that the EPA develop exposure estimates that will have national implications for, and relevance
to, urban areas. Agency staff needs to undertake additional case studies of several urban locales
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with varying lead exposure levels. More generally, in the view of the Committee, the second
draft exposure/risk assessments document was missing certain critical information and is deemed
incomplete, which is described in greater detail below. Therefore, this EPA document, while
representing a worthy effort by Agency staff, is not yet deemed to be adequate for regulatory
decision-making. The CASAC looks forward to reviewing OAQPS' Final Lead Exposure and
Risk Assessments document — and, especially, the Final Staff Paper for Lead — when these are
released by EPA in early November 2007.
2. Reiteration of CASAC Support for Continuing to List Lead as a Criteria Air Pollutant
and the Lead Panel's Preliminary Analyses Concerning the Level of a Primary (Health-
Based) NAAQS for Lead
Before discussing the Lead Panel's review of the EPA's 2nd Draft Lead Exposure and
Risk Assessments document, the Committee wishes to strongly reiterate its opposition to any
considered de-listing of Lead as a criteria air pollutant and its concomitant and unanimous
support for maintaining fully-protective NAAQS. The details of the CASAC's rationale for this
recommendation are contained in the Committee's March 27, 2007 letter to you concerning the
Lead Panel's review of the Agency's 1st Draft Lead Staff Paper and the Draft Lead Exposure and
Risk Assessments documents (EPA-CASAC-07-003).
Furthermore, as described in detail in Appendix D of the Committee's March 2007
letter/report, the Lead Panel previously considered three separate, though related, population-
based analytical approaches aimed at deriving an estimated range of alternative levels for the
primary Lead NAAQS. On the basis of the CASAC's preliminary scientific analyses and risk
management assumptions, EPA needs to substantially lower the level of the primary NAAQS for
Lead, to 0.2 jug/m or less. In the unanimous opinion of the Lead Panel, EPA has not presented
any rigorous analyses or other information in its 2nd Draft Lead Exposure and Risk Assessments
document that leads the CASAC to reconsider its previous recommendation to you that the upper
limit the Agency should consider in revising the Pb NAAQS should be 0.2 fj,g/m3 on a monthly
average.
3. Need Population-Based Risk Assessments of Urban Areas of National Significance
In the CASAC's previous letter to you on this topic (March 2007), the Lead Review
Panel recommended using a "population-based" risk assessment to supplement the case-study
approach used in the "pilot-phase" risk assessment. In addition, the Panel noted that a risk
assessment of this type would typically include two key components:
(1) A quantitative description of the relationship between concentrations of lead in
ambient air in various parts of the U.S. and resulting distributions of blood lead
concentrations; and
(2) A quantitative description of the relationship between blood lead concentrations
levels and impacts on IQ.
The Lead Panel was pleased to see that EPA introduced an urban model in the second
draft of the exposure/risk assessments document, and further recommends that Agency staff
focus on the hybrid urban model, using available information for several urban areas where there
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are multiple monitors. The Committee recognizes that the nature of the national airborne lead
database is limited by both its size (i.e., a total of 189 PM-TSP [total suspended particulates]
sites measuring ambient lead), as well as the selective location of monitors to mostly source-
oriented sites. Some additional PMi0 measurements are available (see the section below on the
use of PMio samplers to monitor lead).
In spite of these limitations, the CAS AC strongly believes that it is important that EPA
staff make estimates of exposure that will have national implications for, and relevance to, urban
areas; and that, significantly, the case studies of both primary lead (Pb) smelter sites as well as
secondary smelter sites, while relevant to a few atypical locations, do not meet the needs of
supporting a Lead NAA Q S. The Agency should also undertake case studies of several urban
areas with varying lead exposure concentrations, based on the prototypic urban risk assessment
that OAQPS produced in the 2nd Draft Lead Human Exposure and Health Risk Assessments. In
order to estimate the magnitude of risk, the Agency should estimate exposures and convert these
exposures to estimates of blood levels and IQ loss for children living in specific urban areas. In
addition, the Agency should make IQ change estimates across the range of exposures to provide
estimates for the change in median, as well as the 5th- to the 95th percentile of the population for
different standards using the hybrid urban model with a geometric standard deviation (GSD) of
2.0 or 2.1.
The Lead Panel recognizes that there are few urban areas with multiple TSP monitors to
estimate distributions of lead exposure. The Panel urges that PMio monitors, with appropriate
adjustments, be used to supplement the data. If necessary, other data with lead concentrations
from special monitoring studies (e.g., speciation studies of particulate matter [PM]) may provide
estimates of the GSD of air lead over urban areas, which could also be used to supplement the
limited TSP data. Discussion of the risk estimates obtained should carry appropriate caveats that
document where estimates fall outside the range of data used to generate the estimates. EPA
should also provide a qualitative discussion of how typical the chosen cities are of the range of
what is seen in a broad spectrum of U.S. urban areas.
4. Completeness of 2nd Draft Lead Exposure and Risk Assessments
In a more general sense, the CAS AC considers that EPA's 2nd Draft Lead Exposure and
Risk Assessments document was missing certain key information and analytical components and
was therefore incomplete. Specifically, the Committee believes that a properly comprehensive
exposure/risk assessment in support of reviewing either primary or secondary NAAQS should be
accompanied by documentation that includes a discussion of the four, policy-relevant elements
of selecting a specific NAAQS — that is, indicator variable, averaging time, statistical form, and
ranges of alternative levels of the standard — along with analyses that model the impact of cur-
rent ("as is") standards and any proposed alternatives (i.e., a truly quantitative risk assessment).
In addition, the Agency needs to provide details on both the scope of the exposure assessment
and the results from the modeling, to include components that would describe: selection of urban
areas to be modeled; the time periods and the populations that were modeled; and the results
from modeling the current lead standard and proposed alternative NAAQS.
Furthermore, with respect to the primary (public-health based) Lead NAAQS, a complete
public-health risk assessment should include documentation that provides appropriate details on
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both the scope of the health risk model and the associated modeling results vis-a-vis: selection of
health endpoint categories; selection of study areas; and air quality considerations for both the
current NAAQS and any proposed alternative levels of the standard. Finally, the exposure/risk
assessment should include a thorough discussion of the key uncertainties.
Accordingly, the CASAC requests that EPA tailor its future exposure/risk assessments to
provide the above documentation and analyses for the CASAC's and the public's review during
the comparable phase of the NAAQS review process — that is, prior to the Agency's issuance of
the Policy Assessment (PA) in the form of an Advance Notice of Proposed Rulemaking (ANPR)
— for any given criteria air pollutant.
5. Choice of Biokinetic Model and Steady-State Dose Metric for Both Dose-Response
Functions and Use in the Full Risk Assessment
Agency staff selected the Integrated Exposure Uptake Biokinetic (IEUBK) model for
lead in children as the preferred method for estimating blood lead as the dose metric in the risk
assessment, based on its overall assessment of blood lead estimating methods. The Lead Panel
concurs in both OAQPS' selection of the IEUBK model as the biokinetic modeling method for
blood lead estimates and its detailed rationale for doing so. The rationale provided by EPA staff
for its choice is, overall, scientifically-sound.
In addition, OAQPS selected concurrent blood lead estimates as the best expression for
the dose parameter used in both the dose-response functions and the risk assessment. The Panel
also concurs in both this selection by Agency staff and the scientific rationales for doing so,
particularly its reliance on the findings of the international pooled analysis of many longitudinal
studies of cognitive deficits at lower exposures (doses) reported in Lanphear et al. (2005).
6. Predicting IQ Changes Based on Concurrent Blood Lead Concentrations
The Panel recommends using the two-piece linear function for relating IQ alterations to
current blood lead levels with a slope change or "hinge" point closer to 7.5 |ig/dL than 10.82
|ig/dL as used by EPA staff in the second draft exposure/risk assessments document. The higher
value used by staff underestimates risk at lower blood Pb levels, where most of the population
will be located. Epidemiologic data indicate that the slope of the line below 7.5 |ig/dL is approx-
imately minus three (-3) IQ decrements per 1 |ig/dL blood lead and the vast majority of children
in the U.S. have maximal baseline Pb blood levels below 7.5 |ig/dL (Lanphear et a/., EHP 2005;
MMWR 2005). On a population level, the mean increase in blood lead concentration from air-
borne lead would generally be up to, but not exceeding, a blood lead concentration of 7.5 |ig/dL.
This approach should also account for sensitive subpopulations of children.
7. Level and Averaging Time for Primary Lead Standard with a Margin of Safety
The most recent epidemiologic studies demonstrate a statistically significant relationship
between blood Pb and IQ loss well below 5 |ig/dL. The CASAC recognizes that lead is a multi-
media pollutant and that most of the country is in compliance with the current Lead NAAQS of
1.5 |ig/m3. However, the risk analysis scenarios presented by EPA for current conditions using
the Agency's hybrid dust model — which the Lead Panel judges to be the most scientifically-
defensible and robust dust model currently available — show that the "recent" air exposure path-
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way contributes anywhere from 28 to 57% of the total amount of ingested lead. Additionally,
recent air exposures still contribute 27% under an alternative primary Pb NAAQS of 0.2 |ig/m3
maximum monthly average, and only fall to 13% under an alternative primary Lead standard of
0.05 |ig/m3 maximum monthly average. Since there is no known threshold in the relationship
between blood Pb andlQ loss, the level of the current primary Lead standard clearly provides
no margin of safety from ambient air lead exposures. However, nor would any lower primary
Lead NAAQS level provide a margin of safety, and hence, the question becomes:
What percentage of the population of children in various parts of the U.S. will
suffer what amount of IQ loss and other harmful effects due to the contribution of
air exposures to the overall toxicity of Pb?
EPA staff should identify what levels of the primary Lead NAAQS would be deemed as
being adequately-protective of human health. As a preliminary target, staff should identify the
level of the standard that would ensure that 95% or more of the children in the U.S. do not
experience decreased IQ from exposure to ambient concentrations of recent airborne lead. As
noted in the Committee's previous letter to you on this subject, target levels of IQ decrements
that would be of great concern would be one to two (1-2) IQ points or more. After identifying
such a level of the standard, Agency staff should investigate alternative levels around this level,
including much lower levels, to provide guidance as to how alternative standards would lead to
changes in health. For example, if the analyses conducted by EPA staff suggest that a 0.1 |ig/m3
standard would lead to a decrease in IQ of one point or less for 95% of the children in the U.S.,
staff should assess other levels of the standard near 0.1 |ig/m3, both above and below, as well as
much lower levels, e.g., on the order of 0.05 and 0.01 |ig/m3. Further, the Agency should pro-
vide additional analyses to adequately inform both the Administrator and CASAC as to how
uncertainties impact the level of protectiveness of the proposed alternative standards.
8. Use of PMio Samplers to Monitor for Airborne Lead
Another recommendation that the CASAC provided in its March 2007 letter was to
consider use of PMio samplers to monitor lead. A substantial reduction in the level of the Pb
NAAQS, combined with a shortening of the averaging time from quarterly to monthly, will
require increases in both the number of lead monitoring sites, as well as the frequency of sample
collection. Improved sampling precision will also be needed as more locations fall closer to
standards and to support future health assessments as ambient lead concentrations are further
reduced. For these and other reasons outlined in our previous advisory letter, the Lead Panel
strongly encourages the Agency to consider revising the Pb reference method to allow sample
collection by PMw, rather than TSP samplers, accompanied by analysis with low-cost multi-
elemental techniques like X-Ray Fluorescence (XRF) or Inductively Coupled Plasma-Mass
Spectroscopy (ICP-MS). (See EPA-CASAC-07-003 for additional details.)
The Lead Panel also recognizes the importance of coarse dust contributions to total Pb
ingestion and acknowledge that TSP sampling is likely to capture additional very coarse particles
which are excluded by PMio samplers. However, the precision of TSP samplers is poor, the
upper particle cut size varies widely as a function of wind speed and direction, and the spatial
non-homogeneity of very coarse particles cannot be efficiently captured by a national monitoring
network. Generally, it can be expected that PMio Pb will represent a large fraction of, and be
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highly correlated with TSP Pb. Ambient Lead data from the (few) collocated TSP and PMio sites
presented in the 1st Draft Pb Staff Paper exhibited a high correlation (r = 0.96), with slopes (PMio
Pb/TSP Pb) ranging from 0.85 to 1. A single quantitative adjustment factor could be developed
from a short period of collocated sampling at multiple sites; or a PMio Pb/TSP Pb "equivalency
ratio" could be determined on a regional or site-specific basis.
9. Other, Non-IQ-Related Effects of Lead in Ambient Air
While the CAS AC agrees with the Agency's choice of IQ alterations in young children as the
priority health effect and population for the risk assessment, the Lead Panel cautions against
focusing only on IQ loss (or gain). There are ramifications of lead exposure on other endpoints
that have societal and individual implications of great importance. Neurological developmental
and functional effects in children exposed to Pb can lead to negative and disruptive behaviors
well into teenage years. Moreover, while the adult nervous system has long been recognized as a
target of Pb toxicity, epidemiologic and experimental toxicology data are emerging that support
the relationship between Pb exposure and increased adverse cardiovascular outcomes, including
increased blood pressure, increased incidence of hypertension, and cardiovascular morbidity and
mortality at lower and lower levels of exposure.
Secondary Lead NAAQS
The "pilot phase" lead exposure and risk assessment document addressed both human
health and environmental effects, but the July 2007 "full-scale" exposure and risk assessment
document is focused entirely on human exposures and health risks. Agency staff made it quite
clear in the 1st Draft Lead Staff Paper that OAQPS did not anticipate having either sufficient
funding or time available to perform additional quantitative ecological risk assessment work
during this current review cycle for the NAAQS for Lead. Thus, EPA staff did not conduct a
full-scale ecological risk assessment for this second draft exposure/risk assessments document.
Nevertheless, the CASAC requests that EPA revise the ecological portion of the "pilot-phase "
lead risk assessment on the basis of Lead Panel members' individual review comments found in
Appendix E of the Committee's March 2007 letter; and that this be reflected in the welfare-
effects sections of both the Final Lead Exposure and Risk Assessments document and the Final
Lead Staff Paper.
With respect to secondary Pb standards, the Lead Panel notes that the secondary Lead
NAAQS was initially set equal to the primary Lead standard in 1978 "due to a lack of relevant
data at that time." Nearly 30 years later, it now appears that the Agency still lacks the relevant
data to provide a clear, quantitative basis for setting a secondary Pb NAAQS that differs from
the primary in indicator, averaging time, level or form. To collect such data for the next Lead
NAAQS review cycle, the EPA needs to initiate new measurement activities in rural areas —
including those that are remote, close to urban and other sources, and located at high elevations
— which quantify and track changes in lead concentrations in the ambient air, soils, deposition,
surface waters, sediments and biota, along with other information as may be needed to calculate
and apply a critical loads approach for assessing environmental lead exposures and risks in the
next review cycle. Depending on the results of these Pb monitoring activities, the Agency may
need to set the level of the secondary Lead NAAQS as a to-be-determined fraction of the level of
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the primary standard, given the likelihood that many of the millions of animal and plant species
are more sensitive to environmental lead pollution than are humans.
Importantly, EPA needs to move away from the traditional practice of simply setting a
secondary Lead NAAQS that is equal to the primary standard — a practice that may technically
meet the Clean Air Act requirements but has no scientific or technical basis. Nevertheless, in the
absence of essential monitoring or other research data, the Lead Panel continues to recommend
that, "at a minimum, the level of the secondary Lead NAAQS should be at least as low as the
lowest-recommended primary lead standard" as the CAS AC wrote in its previous letter to you
on this subject. Furthermore, the Panel also continues to recommend that the Agency "identify
the necessary funds to support needed continuing research on the ecological effects of airborne
lead pollution and to consider developing alternative secondary standards such as critical loads
for lead, which may be different from primary standards in indicator, averaging time, level or
form" (For additional details, see the individual written comments of Drs. Ellis Cowling and
Samuel Luoma found in Appendix D; and also see EPA-CASAC-07-003 and Lead Panelists'
individual written comments found in Appendix E.)
The large environmental burden of historically-deposited lead is currently decreasing.
Accordingly, the goal should be to set the secondary Lead NAAQS such that there is no reversal
of the current downward trend in lead concentrations in the environment. The limited funds
available for monitoring environmental lead should be focused on this critical task.
Comments on Implementation of the Agency's Revised NAAQS Review Process
The review of the NAAQS for ambient Lead has been a hybrid process, which began
under the EPA's long-standing NAAQS review process and has since evolved into the Agency's
new, revised process. Specifically, the Lead Panel in 2006 conducted a peer-review of the 1st
and 2nd Drafts of EPA's Lead Air Quality Criteria Document (AQCD) and, in February 2007,
reviewed OAQPS' 1st Draft Lead Staff Paper and the Draft Lead Exposure and Risk Assessments
documents. The understanding of the CAS AC at the time of the February review was that, since
the Agency was transitioning to the revised review process midway through the current Lead
NAAQS review, this was to be the last version of either of these documents that OAQPS would
develop and that the Lead Panel would have an opportunity to review. Indeed, in the CAS AC's
last letter to you on this subject (March 27, 2007), the Panel expressed its concern that, in the
absence of being given an opportunity to review even a 2nd Draft Lead Staff Paper, or to review a
second draft or a final lead exposure/risk assessment document, prior to EPA's issuance of the
Lead Policy Assessment in the form of an ANPR, the Committee would not have the information
— that is, both the data and the analyses — needed to properly advise you concerning the setting
of NAAQS for Lead that would be adequately-protective of both human health and welfare. In
response to CASAC's concerns, the Agency agreed to produce a second draft of the exposure/
risk assessments document, the peer-review of which is the topic of this letter/report.
However, immediately prior to our August 28-29 public advisory meeting on the 2nd
Draft Lead Human Exposure and Health Risk Assessments, OAQPS staff informed me as the
CASAC Chair that a recent (August 24, 2007) Federal court order was requiring the Agency to
produce a Final Lead Staff Paper by the previously-agreed-upon date of November 1, 2007 —
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and, thus, "the pendulum has swung back" to the former NAAQS review process, at least until
the issuance of the Lead PA by means of an ANPR no later than November 30, 2007. Although
this seesaw process has admittedly been both confusing and vexing, the Committee is looking
forward to reviewing the Final Lead Exposure and Risk Assessments document, the Final Staff
Paper for Lead, and the Lead PA in a meeting to be held in mid-December 2007.
Both these process-related perturbations and, as detailed above, the absence of certain
critical information in the Agency's 2nd Draft Lead Exposure and Risk Assessments document
— which as previously noted was intended to be the CASAC's final source of information from
EPA with respect to the review of the NAAQS for Lead prior to the issuance of a Policy Assess-
ment in an ANPR — underscore the Committee's concerns about the Agency's revised NAAQS
review process as it is presently being implemented and may be implemented in the future. The
CASAC has a statutory mandate to provide the EPA with expert advice and recommendations on
scientifically-appropriate standards for criteria air pollutants. In order to be able to fulfill this, it
is axiomatic that the CASAC must receive, in a timely manner, and be afforded an opportunity to
review and comment on the complete suite of relevant risk- and exposure-related data and anal-
yses that will presumably underpin the Agency's regulatory decisions — not only for the Lead
standards but also for forthcoming risk/exposure assessments associated with the NAAQS
reviews for other criteria pollutants.
In closing, the CASAC is pleased to advise you and OAQPS staff on the 2nd Draft Lead
Human Exposure and Health Risk Assessments document. As both EPA and the Committee
continue to work through the details associated with implementation of the revised NAAQS
review process, we would ask that the Agency ensure that the CASAC receive the full breadth of
information and supporting analyses necessary to provide timely, expert advice and recommen-
dations to the EPA. As always, we wish Agency staff well in this important task.
Sincerely,
/Signed/
Dr. Rogene Henderson, Chair
Clean Air Scientific Advisory Committee
Appendices (A-D)
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NOTICE
This report has been written as part of the activities of the U.S. Environmental
Protection Agency's (EPA) Clean Air Scientific Advisory Committee (CASAC), a
Federal advisory committee administratively-located under the EPA Science
Advisory Board (SAB) Staff Office that is chartered to provide extramural scientific
information and advice to the Administrator and other officials of the EPA. The
CASAC is structured to provide balanced, expert assessment of scientific matters
related to issue and problems facing the Agency. This report has not been reviewed
for approval by the Agency and, hence, the contents of this report do not necessarily
represent the views and policies of the EPA, nor of other agencies in the Executive
Branch of the Federal government, nor does mention of trade names or commercial
products constitute a recommendation for use. CASAC reports are posted on the SAB
Web site at: http://www.epa.gov/sab.
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Appendix A - Roster of the Clean Air Scientific Advisory Committee
U.S. Environmental Protection Agency
Science Advisory Board (SAB) Staff Office
Clean Air Scientific Advisory Committee (CASAC)
CHAIR
Dr. Rogene Henderson, Scientist Emeritus, Lovelace Respiratory Research Institute,
Albuquerque, NM
MEMBERS
Dr. Ellis Cowling, University Distinguished Professor At-Large, North Carolina State
University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina
State University, Raleigh, NC
Dr. James D. Crapo, Professor, Department of Medicine, National Jewish Medical and
Research Center, Denver, CO
Dr. Douglas Crawford-Brown, Director, Carolina Environmental Program; Professor,
Environmental Sciences and Engineering; and Professor, Public Policy, Department of
Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel
Hill, NC
Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of
Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT
Dr. Armistead (Ted) Russell, Georgia Power Distinguished Professor of Environmental
Engineering, Environmental Engineering Group, School of Civil and Environmental
Engineering, Georgia Institute of Technology, Atlanta, GA
Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard
Medical School, Boston, MA
SCIENCE ADVISORY BOARD STAFF
Mr. Fred Butterfield, CASAC Designated Federal Officer, 1200 Pennsylvania Avenue, N.W.,
Washington, DC, 20460, Phone: 202-343-9994, Fax: 202-233-0643 (butterfield.fred@epa.gov)
(Physical/Courier/FedEx Address: Fred A. Butterfield, III, EPA Science Advisory Board Staff
Office (Mail Code 1400F), Woodies Building, 1025 F Street, N.W., Room 3604, Washington,
DC 20004, Telephone: 202-343-9994)
A-l
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Appendix B - Roster of the CASAC Lead Review Panel
U.S. Environmental Protection Agency
Science Advisory Board (SAB) Staff Office
Clean Air Scientific Advisory Committee (CASAC)
CASAC Lead Review Panel
CHAIR
Dr. Rogene Henderson*, Scientist Emeritus, Lovelace Respiratory Research Institute,
Albuquerque, NM
MEMBERS
Dr. Joshua Cohen, Research Associate Professor of Medicine, Tufts University School of
Medicine, Institute for Clinical Research and Health Policy Studies, Center for the Evaluation of
Value and Risk, Tufts New England Medical Center, Boston, MA
Dr. Deborah Cory-Slechta, Professor of Environmental Medicine, Department of Environ-
mental Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY
Dr. Ellis Cowling*, University Distinguished Professor At-Large, North Carolina State
University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina
State University, Raleigh, NC
Dr. James D. Crapo [M.D.]*, Professor, Department of Medicine, National Jewish Medical and
Research Center, Denver, CO
Dr. Douglas Crawford-Brown*, Director, Carolina Environmental Program; Professor,
Environmental Sciences and Engineering; and Professor, Public Policy, Department of
Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel
Hill, NC
Dr. Bruce Fowler, Assistant Director for Science, Division of Toxicology and Environmental
Medicine, Office of the Director, Agency for Toxic Substances and Disease Registry, U.S.
Centers for Disease Control and Prevention (ATSDR/CDC), Chamblee, GA
Dr. Andrew Friedland, Professor and Chair, Environmental Studies Program, Dartmouth
College, Hanover, NH
Dr. Robert Goyer [M.D.], Emeritus Professor of Pathology, Faculty of Medicine, University of
Western Ontario (Canada), Chapel Hill, NC
Mr. Sean Hays, President, Summit Toxicology, Allenspark, CO
B-l
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Dr. Bruce Lanphear [M.D.], Sloan Professor of Children's Environmental Health, and the
Director of the Cincinnati Children's Environmental Health Center at Cincinnati Children's
Hospital Medical Center and the University of Cincinnati, Cincinnati, OH
Dr. Samuel Luoma, Senior Research Hydrologist, U.S. Geological Survey (USGS), Menlo
Park, CA
Dr. Frederick J. Miller, Consultant, Gary, NC
Dr. Paul Mushak, Principal, PB Associates, and Visiting Professor, Albert Einstein College of
Medicine (New York, NY), Durham, NC
Dr. Michael Newman, Professor of Marine Science, School of Marine Sciences, Virginia
Institute of Marine Science, College of William & Mary, Gloucester Point, VA
Mr. Richard L. Poirot*, Environmental Analyst, Air Pollution Control Division, Department of
Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT
Dr. Michael Rabinowitz, Geochemist, Marine Biological Laboratory, Woods Hole, MA
Dr. Armistead (Ted) Russell*, Georgia Power Distinguished Professor of Environmental
Engineering, Environmental Engineering Group, School of Civil and Environmental
Engineering, Georgia Institute of Technology, Atlanta, GA
Dr. Joel Schwartz, Professor, Environmental Health, Harvard University School of Public
Health, Boston, MA
Dr. Frank Speizer [M.D.]*, Edward Kass Professor of Medicine, Channing Laboratory,
Harvard Medical School, Boston, MA
Dr. Ian von Lindern, Senior Scientist, TerraGraphics Environmental Engineering, Inc.,
Moscow, ID
Dr. Barbara Zielinska, Research Professor, Division of Atmospheric Science, Desert Research
Institute, Reno, NV
SCIENCE ADVISORY BOARD STAFF
Mr. Fred Butterfield, CASAC Designated Federal Officer, 1200 Pennsylvania Avenue, N.W.,
Washington, DC, 20460, Phone: 202-343-9994, Fax: 202-233-0643 (butterfield.fred@epa.gov)
* Members of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the EPA
Administrator
B-2
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APPENDIX 2A:
LARGEST STATIONARY SOURCE CATEGORIES FOR Pb IN
THE 2002 NEI
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Appendix 2A. Largest Stationary Source Categories for Pb in the 2002 NEL
Boilers and Process Heaters
Materials including coal, oil, natural gas (or, at times, other substances such as wood and petroleum
coke) are burned in boilers and process heaters to produce steam. With regard to boilers, the steam is
used to produce electricity or provide heat, while process heaters are used in industrial processes. Lead
is present naturally in the fuel and is emitted to air following combustion. The extent of emissions
depends on the concentration of Pb in the fuel, the quantity of fuel burned, and PM control devices
applied.
Industrial, commercial and institutional boilers and process heaters are used at a wide variety of facilities
(e.g., refineries, chemical and manufacturing plants, etc), as well as in a "stand alone" mode to provide
heat for large building complexes. Consequently, there are thousands of these sources throughout the
country, generally located in urban areas, and they range widely in size. Most coal-fired industrial boilers
emit about 0.06 tpy, with the larger ones emitting about 0.07 tpy due to the use of high efficiency
particulate matter (PM) control devices (ERG, 2002a). [
Among utility boilers, coal-fired boilers have the highest Pb emissions, oil-fired utility plants emit
somewhat lower amounts, and gas-fired plants emit very low levels of Pb (USEPA, 1998). There are
approximately 1,300 coal-fired electric utility boilers in the U.S. ranging in size from 25 to approximately
1,400 MWe. Based on emission factor calculations, a 325 MWe coal-fired boiler would be expected to
emit approximately 0.021 tpy Pb, based on the use of an electrostatic precipitator for PM control (USEPA,
1998). Although there are exceptions, coal-fired utility boilers tend to be located in non-urban areas.
Iron and Steel Foundries
Iron and steel foundries melt scrap, ingot, and other forms of iron and steel and pour the molten metal
into molds for particular products. While located in 44 of the lower 48 states (in both cities and rural
areas), the 650 existing foundries in the U.S., are most heavily concentrated in the Midwest (IN, IL, OH,
Ml, Wl, and MN) - roughly 40% of foundries with almost 60% of U.S. production (USEPA, 2002a). Most
are iron foundries operated by manufacturers of automobiles and large industrial equipment and their
suppliers. The largest Pb emission sources at iron foundries are large furnaces, emissions from which
generally range from about 0.3 to 3 tpy (generally released at heights of 25-30 feet), depending on the
throughput of the furnace, the type and operating characteristics of the emission control system, and the
Pb content in the metal charged to the furnace. Regulations promulgated in 2004 are projected to yield
emissions reductions of approximately 25 tpy for this category (USEPA, 2004b).
Hazardous Waste Incineration/ Combustion Facilities
Hazardous waste combustors include hazardous waste incinerators, as well as boilers and industrial
furnaces that burn hazardous waste for energy or material recovery (e.g., production of halogen acid from
the combustion of chlorine-bearing materials). Industrial furnaces burning hazardous waste include
cement kilns, lightweight aggregate kilns, and hydrochloric acid production furnaces. Lead is a trace
contaminant in the hazardous waste, fossil fuels, and raw materials used in the combustors. In 2005,
there were nearly 270 hazardous waste combustor sources in operation in the United States (70 FR at
59530), with approximately 40 percent of them in the states of Texas and Louisiana. As a result of
emissions standards promulgated in 2005, EPA estimates that cumulative Pb emissions from hazardous
waste combustors will be reduced to approximately 4.0 tons per year by the compliance date in 2008
(USEPA, 2005), a 95% reduction from 1990 levels.
Primary Lead Smelting
At primary Pb smelters, Pb-bearing ore concentrates are smelted to produce Pb metal. Lead is emitted
from primary Pb smelters as process emissions, process fugitive emissions, and fugitive dust emissions
(CD, p. 2-21). U.S. EPA promulgated a national emissions standard in 1999 for this category which
includes an emissions limit for Pb (U.S. EPA 1999a). In the 1990s, there were three operating primary Pb
smelters in the U.S: one in Montana and two in Missouri, emitting an estimated total of about 260 tpy Pb.
In 2002, there were two in operation (estimated emissions shown in Table A-1); one of the two had less
than 1 tpy Pb emissions. As of 2004, there was only one operating primary Pb smelter in the U.S.,
2 A-1
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located in Missouri with estimated total emissions of about 28 tpy in year 2005 (CD, p. 2-20). Thus, total
Pb emissions from this category have decreased about 90% since 1990.
Secondary Lead Smelting
Secondary Pb smelters are recycling facilities that use blast, rotary, reverberatory, and/or electric
furnaces to recover Pb metal from Pb-bearing scrap materials, primarily Pb-acid batteries. This category
does not include remelters and refiners or primary Pb smelters. At secondary Pb smelters, Pb may be
emitted from process emissions, process fugitive emissions and fugitive dust emissions from wind or
mechanically induced entrainment of dust from stockpile and plant yards and roadways. In 1995, U.S.
EPA promulgated a national emissions standard for this category which includes an emissions limit for Pb
(USEPA, 1995). In 2002, there were 15 secondary smelters operating in 11 states, most of which are in
the eastern half of the U.S. Estimates of total emissions (process and fugitive) for individual facilities as
of 2002 range between 1 and 4 tpy, with one facility having total lead emissions of about 12 tpy (USEPA,
2007a; EC/R, 2006). Total Pb emissions (tpy) for this category decreased about 60% from 1990 to 2002.
Military Installations
This source category includes sources that are military facilities. The types of sources contributing to Pb
emissions from this category include, among others, rocket and engine test facilities, ammunition
manufacturing, weapons testing, waste combustion and boilers. While there are over 300 military
facilities in the NEI, only 10% emit over 0.1 tpy of Pb and only 3% emit over 1 tpy. The two largest
facilities (listed in Table A-4) are a missile ammunition production plant and a weapons testing facility and
these two facilities account for over 75% of the category emissions.
Mining
This category includes various mining facilities that extract ore from the earth containing Pb, zinc, copper
and/or other non-ferrous metals (such as gold and silver), and/or non-metallic minerals such as talc and
coal. This category does not include the smelting or refining of the metals and minerals. These facilities
produce ore concentrates (such as Pb, zinc, and copper concentrates) that are transported to other
facilities where further processes, such as smelting and refining take place. The 2002 NEI indicates that
there are 3 mining facilities in the U.S. emitting greater than 0.5 tpy Pb, one of which emits more than 5
tpy. This facility is in Missouri and produces Pb, zinc, and copper concentrates that are shipped to
customers for further processing.
Integrated Iron & Steel Manufacturing
Integrated iron and steel manufacturing includes facilities engaged in the production of steel from iron ore.
The processes include sinter plants, blast furnaces that produce iron, and basic oxygen process furnaces
that produce steel, as well as several ancillary processes including hot metal transfer, desulfurization,
slag skimming, and ladle metallurgy. There are currently 17 facilities in this source category each of
whom emit from 2 to 8 tpy of Pb. Stack heights range from 30 - 50 feet. The facilities are located in 9
states, mostly in the Midwest (USEPA, 2003a). EPA promulgated a national emissions standard in 2003
for this category which includes an emissions limit for PM (as a surrogate for metal HAP, including Pb)
(USEPA, 2003b).
Municipal Waste Combustors: Small & Large
Municipal waste combustors (MWCs) incinerate municipal or municipal-type solid waste. The amount of
municipal waste incinerated (about 14% of U.S. municipal waste) has remained stable over the past
decade. The amount of Pb emitted from municipal waste combustors depends on the amount of Pb in
the refuse, with typical sources including paper, inks, cans and other metal scrap and plastics (CD, pp. 2-
35 to 2-36). As of 2005, MACT standards were completed for all existing and new municipal waste
incineration units, resulting in nationwide Pb emissions of less than 10 tons per year, roughly a 97%
reduction since 1990. There are currently 66 large MWC plants and 26 small MWC plants operating
nationally, with individual large MWC plants projected to emit less than 0.1 tpy Pb, and small MWC plants
less than 0.02 tpy Pb (ERG, 2002b,c; Stevenson, 2002). However, there are a few MWC facilities that
emit about 2 tons per year.
Pressed and Blown Glass and Glassware Manufacturing
This category includes manufacturers of flat glass, glass containers, and other pressed and blown glass
and glassware, with Pb emitted primarily from the pressed and blown glass industry sector. Some
container plants also make a leaded-glass product, but this is not typical of container glass plants. Lead
2A-2
-------
may also be added to flat glass for use in microwaves and flat-screen TVs. Emissions from individual
facilities may range from a few pounds per year up to several tons per year depending on Pb content of
their glass and the level of control. Furnace stacks for these facilities are typically of the order of 35-60
feet high. As of 2005, about 22 tons of Pb is emitted from glass manufacturing annually in the U.S. Glass
plants are located in 35 States (RTI, 2006). U.S. EPA is currently developing an emissions regulation for
this category, scheduled for promulgation in December 2007.
Electric Arc Furnace Steelmaking
In the steelmaking process that uses an electric arc furnace (EAF), the primary raw material is scrap
metal, which is melted and refined using electric energy. Since scrap metal is used instead of molten
iron, there are no cokemaking or ironmaking operations associated with steel production that use an EAF.
There are currently 141 EAFs at 93 facilities, with estimated total nationwide Pb and Pb compound
emissions of approximately 80 tons, and the average per facility is approximately 0.75 tpy. Stack heights
range from heights of 30 - 50 feet. The facilities are located in 32 states; mostly in the northeast and
Midwest, with ninety percent of the facilities located in urban areas. This information is drawn from
multiple sources (Lehigh, 1982; Calspan, 1977; RTI, 2005). U.S. EPA is developing a hazardous air
pollutant (HAP) emissions regulation for this category, scheduled for promulgation in December 2007.
Lead Acid Battery Manufacturing
The Pb acid battery manufacturing category includes establishments primarily engaged in manufacturing
storage batteries from Pb alloy ingots and Pb oxide. The Pb oxide may be prepared by the battery
manufacturer or may be purchased from a supplier. There has been a general decline in number of
facilities, with 58 facilities currently in operation (data obtained from the Battery Council International
(BCI)). The estimated range of facility-specific Pb and Pb compound emissions is from 1 x 10~5 to just
below 10 tpy, with an average of about 0.5 tpy. The facilities are located in urban and rural areas of 23
states and Puerto Rico (2002 NEI).
Primary Copper Smelting
This source category includes all industries which refine copper concentrate from mined ore to anode
grade copper, using pyrometallic processes. Seven primary copper smelters are currently operating in
the U.S. Six of these seven smelters use conventional smelter technology which includes batch
converter furnaces for the conversion of matte grade copper to blister copper, while the seventh uses a
continuous flash furnace. Two of the three largest smelters are located in AZ, and the third is in Utah.
The largest facility emitted an estimated 12.8 tons Pb in 2002, while emissions for the other two large
facilities are estimated to be between 0.1 to 5 tpy. No other source in this category emits more than 0.1
tpy. In 2002, U.S. EPA promulgated a national emissions standard, including limits for PM (as a
surrogate for metal HAP, including Pb), for this category (USEPA, 2002c).
Portland Cement Manufacturing
Portland cement manufacturing is an energy intensive process in which cement is made by grinding and
heating a mixture of raw materials such as limestone, clay, sand, and iron ore in a rotary kiln (a large
furnace fueled by coal, oil, gas, coke and/or various waste materials such as tires). Lead, a trace
contaminant both of the raw materials and some fuel materials (e.g., coal, tires), is emitted with particulate
material from the kiln stacks, which range in height from approximately 10 meters to more than 100
meters. Relatively smaller Pb emissions occur from grinding, cooling, and materials handling steps in the
manufacturing process. These facilities are generally located in areas with limestone deposits and in
rural areas or near small towns. The largest numbers of facilities are in Pennsylvania and California,
although a significant percentage of facilities are in the Midwest. As of 2004, there were 107 Portland
cement plants in the U.S. (O'Hare, 2006), with all but three reporting less than 1 tpy of Pb emissions. The
highest estimated Pb emissions for a facility in the 2002 NEI is 5.4 tpy. In 1999, U.S. EPA promulgated a
national emissions standard, including a limit for PM (as a surrogate for metal HAP, including Pb), for this
category (USEPA, 1999b).
2A-3
-------
REFERENCES
Calspan Corporation. (1977) Assessment of Industrial Hazardous Waste Practices in the Metal Smelting and
Refining Industry. Volume III: Ferrous Smelting and Refining. Prepared for EPA's Office of Solid Waste.
No. SW-145c.3
Eastern Research Group. (2002a) Development of Average Emission Factors and Baseline Emission Estimates for
the Industrial, Commercial, and Institutional Boilers and Process Heaters NESHAP. Memorandum to Jim
Eddinger, Office of Air Quality Planning and Standards, U.S. EPA. October, 2002. Docket number - OAR-
2002-0058-0022.
Eastern Research Group. (2002b) National Emission Trends for Large Municipal Waste Combustion Units, (years
1090 to 2005). Memorandum to Walt Stevenson. June 17, 2002, EPA Docket A-90-45 / Item VIII-B-7;
Eastern Research Group. (2002c) National Emission Trends for Small Municipal Waste Combustion Units. Memo
to Walt Stevenson. June 12, 2002, EPA Docket A-98-18 / Item VI-B-2
EC/R Incorporated. (2006) Secondary Lead Smelter Industry - Source Characterization for Residual Risk
Assessment. Prepared for USEPA Office of Air and Radiation, Office of Air Quality Planning and
Standards, Research Triangle Park, NC. November.
Lehigh University. 1982. Characterization, Recovery, and Recycling of Electric Arc Furnace Dust. Final report
prepared for the U.S. Department of Commerce. February 1982.
O'Hare, A. 2006. Email to Michele Price from Andy O'Hare, Portland Cement Association. February 28.
RTI International. (2005) Summary of EPA's 2004 Survey of Minimills. June.
RTI International. (2006) Characterization of the Glass Manufacturing Industry, Glass Manufacturing Area Source
NESHAP. Memorandum to Susan Fairchild, Office of Air Quality Planning and Standards. May 5
Stevenson, W. (2002) Emissions from Large MWCs at MACT Compliance. Memo to Docket from Walt Stevenson.
EPA Docket a-90-45 / Item VIII-B-11.
U.S. Environmental Protection Agency. (1995) National Emission Standards for Hazardous Air Pollutants for
Secondary Lead Smelting. Federal Register, (60FR32587), June 23, 1995. Available at:
http://www.epa.gov/ttn/atw/mactfnlalph.html
U.S. Environmental Protection Agency. (1998) Study of Hazardous Air Pollutant Emissions from Electric Utility
Steam Generating Units - Final Report to Congress. Office of Air Quality Planning and Standards. EPA
453/R-98-004a. February.
U.S. Environmental Protection Agency. (1999a) National Emission Standards for Hazardous Air Pollutants for
Primary Lead Smelters: Final Rule. 4 June 1999. Federal Register, Volume 64, No. 107, page 30194.
Available at: http://www.epa.gov/ttn/atw/mactfnlalph.html
U.S. Environmental Protection Agency. (1999b) National Emission Standards for Hazardous Air Pollutants for
Portland Cement Manufacturing: Final Rule. 14 June 1999. Federal Register, Volume 64, No. 113.
Available at: http://www.epa.gov/ttn/atw/pcem/pcempg.html
U.S. Environmental Protection Agency. (2002a) PBT national action plan for alkyl-Pb. Washington, DC: Persistent,
Bioaccumulative, and Toxic Pollutants (Pbt) Program. [13 October, 2005] Available:
http://www.epa.gov/opptintr/pbt/cheminfo.htm
2A-4
-------
U.S. Environmental Protection Agency. (2002b) National Emission Standards for Hazardous Air Pollutants
(NESHAP) for Iron and Steel Foundries-Background Information for Proposed Standards. EPA-453/R-02-
013. Office of Air Quality Planning and Standards, Research Triangle Park, NC. December.
U.S. Environmental Protection Agency. (2002c) National Emission Standards for Hazardous Air Pollutants for
Primary Copper Smelters: Final Rule. 12 June 2002. Federal Register, Volume 67, No. 113, page 40478.
Available at: http://www.epa.gov/ttn/atw/mactfnlalph.html
U.S. Environmental Protection Agency. (2003a) Emission estimates for integrated iron and steel plants.
Memorandum to Docket, February 3, 2003. Document no. IV-B-4 in Docket No. OAR-2002-0083
U.S. Environmental Protection Agency. (2003b) National Emission Standards for Hazardous Air Pollutants for
Integrated Iron and Steel Manufacturing: Final Rule. 20 May 2003. Federal Register, Volume 68, No. 97.
Available at: http://www.epa.gov/ttn/atw/iisteel/iisteelpg.html
U.S. Environmental Protection Agency. (2004a) National Emission Standards for Hazardous Air Pollutants for
Industrial/Commercial/Institutional Boilers and Process Heaters: Final Rule. 13 September 2004. Federal
Register, Volume 69, No. 176. Available at: http://www.epa.gov/ttn/atw/boiler/boilerpg.html
U.S. Environmental Protection Agency. (2004b) National Emission Standards for Hazardous Air Pollutants for Iron
and Steel Foundries; Final Rule. Federal Register 69(78): 21906-21940. April 22.
U.S. Environmental Protection Agency. (2004c) Air Quality Criteria for Paniculate Matter. Volume I. EPA 600/P-
99/002aF-bF, Washington, DC. Pages 1-4.
U.S. Environmental Protection Agency. (2005) "Technical Support Document for HWC MACT Replacement
Standards, Volume V: Emission Estimates and Engineering Costs," September 2005, Appendix C.
U.S. Environmental Protection Agency. (2007a) National Emissions Inventory for 2002, version 3. Office of Air
Quality Planning and Standards, Research Triangle Park, NC. September, 2007.
2A-5
-------
APPENDIX 2B:
ADDITIONAL DETAILS OF AIR QUALITY ANALYSES
-------
Appendix 2B
Table 2B-1. Pb-TSP monitoring site information and 3-year statistics
site
011090003
011090006
060250005
060371103
060371301
060371601
060374002
060374004
060375001
060375005
060651003
060658001
060711004
060719004
080010005
080010006
080310002
080310015
080410011
080650001
100010002
100031007
100031008
100032004
100051002
120571065
120571066
120571073
120571075
121030004
121030018
121033005
130890003
132150011
150032004
170310001
170310022
170310026
170310052
170313103
170313301
170314201
170316003
171170002
171190010
171193007
171430037
171630010
180350008
180350009
180890023
180892008
180892011
180930004
poc
2
1
1
2
i
i
2
2
i
i
2
3
1
1
1
1
4
1
1
1
1
1
1
1
1
5
1
1
5
5
5
1
2
1
1
1
2
1
1
1
1
1
1
2
i
2
1
2
i
2
1
1
2
1
lat
31.79056
31.79278
32.67611
34.06659
33.92899
34.01407
33.82376
33.79236
33.92288
33.95080
33.94603
33.99958
34.10374
34.10688
39.79601
39.82574
39.75119
39.70012
38.83139
39.24778
38.98472
39.55111
39.57778
39.73944
38.64444
27.89222
27.96028
27.96583
28.05000
27.94639
27.78556
27.87583
33.69833
32.43083
21.39667
41.67275
41.68920
41.87333
41.96743
41.96528
41.78278
42.14000
41.87194
39.39804
38.69417
38.86056
40.69889
38.61222
40.15806
40.15944
41.65278
41.63944
41.59250
38.88944
long
-85.97917
-85.98056
-115.48333
-118.22688
-118.21071
-118.06056
-118.18921
-118.17533
-118.37026
-118.43043
-117.40063
-117.41601
-117.62914
-117.27411
-104.97754
-104.93699
-104.98762
-104.98714
-104.82778
-106.29139
-75.55556
-75.73083
-75.61111
-75.55806
-75.61306
-82.53861
-82.38250
-82.37944
-82.37806
-82.73194
-82.74000
-82.69639
-84.27333
-84.93167
-157.97167
-87.73246
-87.53932
-87.64507
-87.74982
-87.87639
-87.80528
-87.79917
-87.82611
-89.80975
-90.15361
-90.10583
-89.58474
-90.16028
-85.42111
-85.41556
-87.43944
-87.49361
-87.47194
-86.55194
state
AL
AL
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
CO
CO
CO
CO
DE
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
FL
GA
GA
HI
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IN
IN
IN
IN
IN
IN
county name
Pike
Pike
Imperial
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Riverside
Riverside
San Bernardino
San Bernardino
Adams
Adams
Denver
Denver
El Paso
Lake
Kent
New Castle
New Castle
New Castle
Sussex
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Pinellas
Pmellas
Pinellas
DeKalb
Muscogee
Honolulu
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Macoupin
Madison
Madison
Peoria
St. Clan-
Delaware
Delaware
Lake
Lake
Lake
Lawrence
cbsa name
Troy, AL
Troy, AL
El Centre, CA
Los Angeles-Long Beach-Santa Am
Los Angeles-Long Beach-Santa Ant
Los Angeles-Long Beach-Santa Ant
Los Angeles-Long Beach-Santa Am
Los Angeles-Long Beach-Santa Am
Los Angeles-Long Beach-Santa Ant
Los Angeles-Long Beach-Santa Ant
Riverside-San Bernardino-Ontario, (
Riverside-San Bernardino-Ontario, (
Riverside-San Bernardino-Ontario, (
Riverside-San Bernardino-Ontario, (
Denver-Aurora, CO
Denver-Aurora, CO
Denver-Aurora, CO
Denver-Aurora, CO
Colorado Springs, CO
Edwards, CO
Dover, DE
Philadelphia-Cam den- Wilmington,
Philadelphia-Cam den- Wilmington,
Philadelphia-Cam den- Wilmington,
Seaford, DE
Tampa-St. Petersburg-Clearwa er, F
Tampa-St. Petersburg-Clearwa er, F
Tampa-St. Petersburg-Clearwa er, F
Tampa-St. Petersburg-Clearwa er, F
Tampa-St. Petersburg-Clearwa er, F
Tampa-St. Petersburg-Clearwa er, F
Tampa-St. Petersburg-Clearwa er, F
Atlanta-Sandy Springs-Marietta, G/
Columbus, GA-AL
Honolulu, HI
Chicago-Naperville-Ioliet, IL-IN-W
Chicago-Naperville-Ioliet IL-IN-W
Chicago-Naperville-Ioliet IL-IN-W
Chicago-Naperville-Ioliet, IL-IN-W
Chicago-Naperville-Ioliet IL-IN-W
Chicago-Naperville-Ioliet IL-IN-W
Chicago-Naperville-Ioliet IL-IN-W
Chicago-Naperville-Ioliet IL-IN-W
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
Peoria, IL
St. Louis, MO-IL
Muncie, IN
Muncie, IN
Chicago-Naperville-Ioliet IL-IN-W
Chicago-Naperville-Ioliet IL-IN-W
Chicago-Naperville-Ioliet IL-IN-W
Bedford, IN
cbsa_popOO
29,605
29,605
142,361
12,365,627
12,365,627
12,365,627
12,365,627
12,365,627
12,365,627
12,365,627
3,254,821
3,254,821
3,254,821
3,254,821
2,157,756
2,157,756
2,157,756
2,157,756
537,484
49,471
126,697
5,687,147
5,687,147
5,687,147
156,638
2,395,997
2,395,997
2,395,997
2,395,997
2,395,997
2,395,997
2,395,997
4,247,981
281,768
876,156
9,098,316
9,098,316
9,098,316
9,098,316
9,098,316
9,098,316
9,098,316
9,098,316
2,721,491
2,721,491
2,721,491
366,899
2,721,491
118,769
118,769
9,098,316
9,098,316
9,098,316
45,922
population
near site
(mile radius)
461
461
16,385
29,329
47,423
13,333
20,131
61,497
19,148
33,968
16,320
16,247
18,777
14,861
2,025
3,313
22,019
14,438
10,581
5,903
352
2,041
3,170
34,053
5,450
14,463
5,793
4,541
10,691
13,048
11,289
2,151
7,888
10,871
23,622
13,648
22,040
28,739
42,187
10,302
23,749
6,070
14,862
40
8,014
5,397
12,643
3,512
2,108
980
5,959
7,144
9,815
393
under
age 5
pop.
(mile
radius)
31
31
,290
,633
,066
,066
,232
,697
,680
,358
,278
,678
,578
,755
183
256
974
809
552
361
22
209
160
2,649
390
612
465
340
490
557
571
58
663
1,037
1,207
971
1,708
1,203
2,877
670
1,678
303
1,071
2
529
360
1,109
430
104
82
603
612
729
32
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
sum
point/
nonpt
PbEI
TPY
w/in 1
mile
4.5
4.5
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.3
1.3
0.0
0.0
0.0
0.0
0.0
0.3
0.1
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.3
0.1
0.0
0.3
0.0
0.0
6.5
0.0
0.0
0.0
source
oriented?
1
1
1
1
1
1
1
1
1
1
prev.
source
oriented?
(see end
notes)
1
3-year data capture
(complete periods)
comp.
years
2
2
2
3
3
2
3
2
1
1
3
3
3
3
3
3
3
1
3
2
1
1
1
1
1
1
3
3
1
1
2
3
3
1
3
3
3
3
3
3
3
2
3
3
3
3
3
3
3
1
3
3
3
2
comp.
qtrs
10
10
11
12
12
9
12
10
5
7
12
12
12
12
12
12
12
7
12
11
4
4
4
4
4
4
12
12
4
4
8
12
12
10
12
12
12
12
12
12
12
8
12
12
12
12
12
12
12
6
12
12
12
10
comp.
months
31
31
34
36
34
27
36
28
14
17
36
35
35
36
36
31
34
20
35
28
12
10
9
11
12
12
35
35
12
12
24
36
36
34
35
35
36
34
32
34
35
24
32
36
34
36
35
36
34
13
34
33
34
26
3-year metrics
annual
mean
0.6875
0.3808
0.0175
0.0225
0.0188
0.0186
0.0149
0.0112
0.0222
0.0057
0.0097
0.0121
0.0142
0.0186
0.1697
0.0304
0.0315
0.0153
0.0156
0.0165
0.0033
0.0039
0.0052
0.0097
0.0033
0.0049
0.5835
0.1934
0.0041
0.0028
0.0042
0.0006
0.1000
0.1000
0.0014
0.0143
0.0270
0.0405
0.0214
0.0149
0.0308
0.0113
0.0303
0.0103
0.0768
0.0150
0.0137
0.0433
0.2944
2.6732
0.0389
0.0219
0.0368
0.0270
max
quarterly
mean
1 .9233
0.9100
0.0248
0.0627
0.0313
0.0300
0.0400
0.0938
0.0667
0.0118
0.0114
0.0179
0.0343
0.0773
0.5558
0.0957
0.1780
0.0212
0.0891
0.0224
0.0040
0.0046
0.0063
0.0115
0.0042
0.0062
1 .2600
0.2933
0.0054
0.0041
0.0071
0.0067
0.1000
0.1000
0.0029
0.0229
0.0353
0.0613
0.0260
0.0271
0.0750
0.0133
0.0387
0.0113
0.3280
0.0193
0.0279
0.0707
0.4657
4.0931
0.0691
0.0296
0.1352
0.0270
max
monthly
mean
2.6600
1.6900
0.0404
0.1460
0.0440
0.0480
0.0960
0.1020
0.1700
0.0150
0.0160
0.0220
0.0800
0.1420
1.1037
0.2086
0.2955
0.0305
0.1387
0.0310
0.0051
0.0058
0.0081
0.0163
0.0048
0.0094
1.7400
0.4800
0.0105
0.0067
0.0112
0.0200
0.1000
0.1000
0.0072
0.0360
0.0440
0.0900
0.0400
0.0440
0.1950
0.0175
0.0500
0.0140
0.9100
0.0320
0.0320
0.1050
0.7371
5.775
0.0910
0.0590
0.3050
0.0270
2nd max
monthly
mean
2.4200
1.3400
0.0380
0.0280
0.0360
0.0340
0.0440
0.0840
0.0220
0.0120
0.0140
0.0220
0.0200
0.0680
0.4397
0.0726
0.2297
0.0196
0.1314
0.0310
0.0041
0.0051
0.0058
0.0161
0.0042
0.0080
1.3800
0.4400
0.0072
0.0039
0.0103
0.0000
0.1000
0.1000
0.0025
0.0250
0.0420
0.0860
0.0380
0.0240
0.1140
0.0160
0.0480
0.0140
0.2880
0.0240
0.0300
0.0980
0.5991
5.0220
0.0783
0.0484
0.0778
0.0270
average
of 3
overall
highest
monthly
means
2.2867
1.3233
0.0380
0.0673
0.0380
0.0373
0.0552
0.0673
0.0693
0.0123
0.0147
0.0213
0.0394
0.0873
0.6195
0.1085
0.1906
0.0228
0.0955
0.0305
0.0044
0.0054
0.0065
0.0142
0.0043
0.0082
1.4733
0.4467
0.0075
0.0048
0.0103
0.0067
0.1000
0.1000
0.0040
0.0270
0.0427
0.0820
0.0360
0.0307
0.1263
0.0165
0.0480
0.0133
0.4620
0.0267
0.0300
0.0990
0.6011
4.2890
0.0786
0.0496
0.1522
0.0270
average
of 3
annual
max
monthly
means
1.6852
1.0901
0.0330
0.0663
0.0353
0.0343
0.0427
0.0447
0.0910
0.0135
0.0147
0.0213
0.0387
0.0580
0.5148
0.1085
0.1254
0.0244
0.0551
0.0294
0.0051
0.0058
0.0081
0.0163
0.0048
0.0094
1.4733
0.4133
0.0105
0.0067
0.0107
0.0067
0.1000
0.1000
0.0038
0.0270
0.0407
0.0753
0.0353
0.0280
0.1155
0.0168
0.0480
0.0133
0.4620
0.0262
0.0240
0.0913
0.5585
2.8611
0.0714
0.0496
0.1397
0.0270
2B-1
-------
Appendix 2B
Table 2B-1. Pb-TSP monitoring site information and 3-year statistics
site
180970063
180970076
180970078
181010001
181630006
260490021
261130001
261630001
261630005
261630015
261630019
261630027
261630033
270370001
270370020
270370421
270370423
270370442
270530050
270530963
270530964
270530965
270530966
270530967
270530968
270531007
271231003
271377001
271377555
290930016
290930021
290930023
290930024
290930025
290930026
290930027
290930029
290930030
290990004
290990005
290990008
290990009
290990010
29099001 1
290990013
290990015
291892003
295100085
340231003
360470122
360632008
360713001
360713002
360713004
poc
1
1
1
1
2
4
1
2
i
4
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
6
1
1
1
1
1
1
lat
39.76083
39.75889
39.81110
38.89028
37.97167
43.04722
44.31056
42.22861
42.26722
42.30278
42.43083
42.29222
42.30667
44.83333
44.76535
44.77720
44.77500
44.74036
45.00123
44.95540
44.88855
45.00448
44.98133
44.99646
44.89301
45.04182
44.96322
47.52336
46.73264
37.62528
37.65417
37.50333
37.47972
37.51056
37.45917
37.48611
37.47167
37.46639
38.26330
38.26722
38.26194
38.28444
38.24110
38.26820
38.27361
38.26167
38.64972
38.65630
40.47222
40.71980
43.08216
41.46107
41.45887
41.47633
long
-86.29722
-86.28972
-86.11447
-86.76083
-87.56722
-83.67028
-84.89194
-83.20833
-83.13222
-83.10667
-83.00028
-83.10694
-83.14889
-93.11500
-93.03248
-93.04097
-93.06278
-93.00556
-93.26712
-93.25827
-93.19538
-93.24005
-93.26615
-93.23488
-93.23323
-93.29873
-93.19023
-92.53631
-92.16337
-91.12917
-91.13056
-90.69556
-90.69028
-90.69750
-90.68639
-90.69000
-90.68944
-90.69000
-90.37850
-90.37944
-90.39417
-90.38194
-90.37680
-90.37380
-90.38000
-90.37972
-90.35056
-90.19810
-74.47139
-73.94788
-79.00099
-74.36343
-74.35392
-74.36827
state
IN
IN
IN
IN
IN
MI
MI
MI
MI
MI
MI
MI
MI
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
NI
NY
NY
NY
NY
NY
county name
Marion
Marion
Marion
Martin
Vanderburgh
Genesee
Missaukee
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Dakota
Dakota
Dakota
Dakota
Dakota
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Ramsey
St. Louis
St. Louis
ron
ron
ron
ron
ron
ron
ron
ron
ron
lefferson
lefferson
lefferson
lefferson
lefferson
lefferson
lefferson
lefferson
St. Louis
St. Louis (City)
Middlesex
Kings
Niagara
Orange
Orange
Orange
cbsa name
Indianapolis-Carmel, IN
Indianapolis-Carmel, IN
Indianapolis-Carmel, IN
Evansville, IN-KY
Flint, MI
Cadillac, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Duluth, MN-WI
Duluth, MN-WI
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
New York -Northern New lersey-Lo
New York -Northern New lersey-Lo
Buffalo-Niagra Falls, NY Metropoli
Poughkeepsie-Newburgh-Middletov
Poughkeepsie-Newburgh-Middletov
Poughkeepsie-Newburgh-Middletov
cbsa_popOO
1,525,104
1,525,104
1,525,104
342,815
436,141
44,962
4,452,557
4,452,557
4,452,557
4,452,557
4,452,557
4,452,557
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
275,486
275,486
2,72 ,491
2,72 ,491
2,72 ,491
2,72 ,491
2,72 ,491
2,72 ,491
2,72 ,491
2,72 ,491
2,72 ,491
2,72 ,491
18,323,002
18,323,002
1,170,111
621,517
621,517
621,517
population
near site
(mile radius)
12,176
9,171
14,196
84
13,666
9,889
58
14,329
11,314
17,729
28,362
6,024
17,402
5,074
162
478
886
168
16,318
46,218
209
19,106
17,156
14,621
11,243
14,889
9,247
8,942
4,527
58
58
138
32
138
32
32
32
32
2,418
2,418
2,418
9,804
2,799
2,418
3,570
1,988
12,303
9,140
13,850
92,660
6,795
1,481
1,257
6,816
under
age 5
pop.
(mile
radius)
875
602
1,175
5
817
994
3
798
923
1,771
2,628
516
1,843
404
7
24
83
11
923
3,929
0
1,095
439
580
789
1,118
474
428
287
4
4
7
2
7
2
2
2
2
197
197
197
820
215
197
318
178
512
783
1,124
5,785
386
99
86
434
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
sum
point/
nonpt
PbEI
TPY
w/in 1
mile
1.7
1.7
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.0
1.1
0.5
3.2
0.0
0.0
0.0
0.3
0.0
0.2
0.0
0.4
0.0
0.4
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
58.8
58.8
58.8
0.0
0.0
58.8
58.8
58.8
0.0
0.0
1.7
0.1
0.0
1.8
1.8
0.0
source
oriented?
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
prev.
source
oriented?
(see end
notes)
1 *#
1 *#
1 *#
1 *#
1 *
1 *
1 *#
3-year data capture
(complete periods)
comp.
years
3
3
2
3
3
3
3
3
2
3
3
1
3
2
3
1
3
2
3
3
1
3
3
1
1
3
3
3
3
3
3
1
1
1
1
3
3
1
2
3
1
2
2
3
3
3
2
1
2
2
1
2
2
2
comp.
qtrs
12
12
11
12
12
12
12
12
11
12
12
5
12
8
12
9
12
10
12
12
4
12
12
7
6
12
12
12
12
12
12
6
6
5
5
12
12
6
8
12
10
11
11
12
12
12
11
4
10
9
4
9
9
9
comp.
months
36
35
33
34
33
36
33
35
34
36
34
14
34
24
32
27
34
28
35
36
14
35
35
20
18
35
33
33
34
34
36
18
18
14
15
33
32
18
24
36
31
31
34
36
35
36
34
11
27
22
12
26
26
26
3-year metrics
annual
mean
0.0320
0.0142
0.0108
0.0272
0.0065
0.0100
0.0032
0.0087
0.0166
0.0178
0.0103
0.0256
0.0236
0.0781
0.0051
0.0037
0.0018
0.0027
0.0051
0.0039
0.0045
0.0039
0.0047
0.0075
0.0019
0.0026
0.0065
0.0047
0.0014
0.6918
0.5460
0.2291
0.5898
0.2477
0.2266
0.2678
0.2824
0.1665
1.1300
0.3711
0.0910
0.0957
0.0388
0.4778
0.2633
1.4501
0.0063
0.0134
0.0403
0.0276
0.0054
0.0606
0.1257
0.0305
max
quarterly
mean
0.0770
0.0254
0.0251
0.0299
0.0126
0.0153
0.0056
0.0107
0.0259
0.0252
0.0138
0.0267
0.0410
0.1153
0.0100
0.0069
0.0050
0.0062
0.0093
0.0071
0.0114
0.0080
0.0080
0.0142
0.0033
0.0067
0.0129
0.0362
0.0031
1.3070
0.7187
0.3433
0.6677
0.3263
0.2523
0.8761
0.7148
0.2017
1.4750
0.6779
0.1857
0.1664
0.0813
1 .3047
0.8683
1.9277
0.0500
0.0216
0.1537
0.0333
0.0060
0.0820
0.2417
0.0386
max
monthly
mean
0.1123
0.0360
0.0288
0.0358
0.0286
0.0209
0.0080
0.0124
0.0340
0.0299
0.0149
0.0353
0.0601
0.2300
0.0200
0.0120
0.0100
0.0080
0.0120
0.0100
0.0180
0.0140
0.0120
0.0225
0.0080
0.0080
0.0350
0.0900
0.0050
4.1933
0.9960
0.6320
1.6026
0.6320
0.3555
1.4414
1.4740
0.3330
2.0731
1.0655
0.3700
0.1750
0.1680
2.2070
3.5680
3.2884
0.0500
0.0290
0.1878
0.0360
0.0080
0.1580
0.4025
0.0400
2nd max
monthly
mean
0.0802
0.0346
0.0240
0.0270
0.0170
0.0189
0.0046
0.0115
0.0315
0.0278
0.0141
0.0340
0.0406
0.2100
0.0120
0.0100
0.0060
0.0060
0.0117
0.0080
0.0080
0.0100
0.0100
0.0157
0.0050
0.0067
0.0200
0.0100
0.0040
1.4540
0.9840
0.4275
0.9927
0.4189
0.3370
0.9300
1.1410
0.2797
1.8962
0.9278
0.3100
0.1560
0.1040
1.3510
0.6420
2.2993
0.0500
0.0255
0.1428
0.0350
0.0080
0.1100
0.2400
0.0400
average
of 3
overall
highest
monthly
means
0.0854
0.0317
0.0251
0.0299
0.0181
0.0188
0.0057
0.0116
0.0322
0.0278
0.0144
0.0341
0.0464
0.2107
0.0140
0.0100
0.0073
0.0067
0.0112
0.0085
0.0112
0.0107
0.0107
0.0161
0.0060
0.0069
0.0243
0.0360
0.0043
2.2878
0.9773
0.4865
1 .0864
0.4723
0.3418
1 .0305
1.1597
0.2734
1.8591
0.9277
0.3128
0.1595
0.1207
1.5975
1.6167
2.6139
0.0500
0.0240
0.1496
0.0345
0.0080
0.1207
0.2835
0.0383
average
of 3
annual
max
monthly
means
0.0843
0.0251
0.0184
0.0299
0.0150
0.0185
0.0054
0.0112
0.0308
0.0275
0.0143
0.0296
0.0451
0.2042
0.0133
0.0100
0.0067
0.0067
0.0112
0.0085
0.0110
0.0107
0.0101
0.0163
0.0065
0.0069
0.0210
0.0347
0.0043
2.2878
0.9773
0.3281
0.8292
0.3480
0.2127
0.6387
0.5722
0.1742
1.7524
0.8018
0.2661
0.1583
0.1153
1.3399
1.5650
2.4954
0.0333
0.0290
0.1312
0.0345
0.0080
0.1073
0.2248
0.0351
2B-2
-------
Appendix 2B
Table 2B-1. Pb-TSP monitoring site information and 3-year statistics
site
360850067
390170015
390290019
390290020
390290022
390350038
390350042
390350049
390350050
390350061
390350069
390490025
390510001
390910003
390910005
390910006
390910007
391670008
391670009
401159005
401159006
401159007
401159008
410510246
420030002
420032001
420070505
420110005
420110717
420111717
420210808
420250105
420450002
421010449
421290007
450031001
450130007
450190003
450190046
450190047
450410001
450410002
450430006
450430007
450430009
450430010
450450008
450452002
450470001
450470002
450510002
450630005
450631002
450790006
poc
1
2
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
2
i
i
i
7
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
2
i
i
i
2
2
2
4
lat
40.59733
39.48990
40.63111
40.63972
40.63500
41.47694
41.48222
41.44667
41.44250
41.47506
41.51918
39.92806
41.57528
40.34306
40.34278
40.34111
40.34472
39.43361
39.37696
36.98580
36.98460
36.97190
36.97160
45.56130
40.50056
40.39667
40.68500
40.46630
40.47667
40.37722
40.34806
40.80306
39.83556
39.98250
40.16667
33.43253
32.43654
32.88394
32.94275
32.84461
34.19794
34.16764
33.36378
33.34973
33.37399
33.36960
34.84045
34.94165
34.18111
34.16520
33.70460
33.78560
33.96900
34.00740
long
-74.12619
-84.36407
-80.54694
-80.52389
-80.54667
-81.68194
-81.70889
-81.65111
-81.64917
-81.67596
-81.63794
-82.98111
-83.99639
-83.75500
-83.76028
-83.75778
-83.75444
-81.50250
-81.53730
-94.84920
-94.82490
-94.85180
-94.82500
-122.67878
-80.07194
-79.86361
-80.32500
-75.75890
-75.75917
-75.91444
-78.88278
-75.60833
-75.37250
-75.08306
-79.87500
-81.89233
-80.67785
-79.97754
-79.65718
-79.94804
-79.79885
-79.85040
-79.29426
-79.29821
-79.28570
-79.29840
-82.40291
-82.22961
-82.15224
-82.16048
-78.87745
-81.11978
-81.06533
-81.02329
state
NY
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OK
OK
OK
OK
OR
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
county name
Richmond
Butler
Columbiana
Columbiana
Columbiana
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Franklin
Fulton
Logan
Logan
Logan
Logan
Washington
Washington
Ottawa
Ottawa
Ottawa
Ottawa
Multnomah
Allegheny
Allegheny
Beaver
Berks
Berks
Berks
Cambria
Carbon
Delaware
Philadelphia
Westmoreland
Aiken
Beaufort
Charleston
Charleston
Charleston
Florence
Florence
Georgetown
Georgetown
Georgetown
Georgetown
Greenville
Greenville
Greenwood
Greenwood
Horry
Lexington
Lexington
Richland
cbsa name
New York -Northern New Jersey-Lo
Cincinnati-Middletown, OH-KY-IN
East Liverpool-Salem, OH
East Liverpool-Salem, OH
East Liverpool-Salem, OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyna-Mentor, OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Columbus, OH
Toledo, OH
Bellefontaine, OH
Bellefontame, OH
Bellefontaine, OH
Bellefontaine, OH
Parkersburg-Marietta, WV-OH
Parkersburg-Marietta, WV-OH
Miami, OK
Miami, OK
Miami, OK
Miami, OK
Portland- Vancouver-Beaverton, OR
Pittsburgh, PA
Pittsburgh, PA
Pittsburgh, PA
Reading, PA
Reading, PA
Reading, PA
Johnstown, PA
Allentown-Bethlehem-Easton, PA-I
Philadelphia-Cam den- Wilmington,
Philadelphia-Cam den- Wilmington,
Pittsburgh, PA
Augusta-Richmond County, GA-SC
Hilton Head Island-Beaufort, SC
Charleston-North Charleston, SC
Charleston-North Charleston, SC
Charleston-North Charleston, SC
Florence, SC
Florence, SC
Georgetown, SC
Georgetown, SC
Georgetown, SC
Georgetown, SC
Greenville, SC
Greenville, SC
Greenwood, SC
Greenwood, SC
Myrtle Beach-Conway-North Myrtle
Columbia, SC
Columbia, SC
Columbia, SC
cbsa_popOO
18,323,002
2,009,632
112,075
112,075
112,075
2,148,143
2,148,143
2,148,143
2,148,143
2,148,143
2,148,143
1,612,694
659,188
46,005
46,005
46,005
46,005
164,624
164,624
33,194
33,194
33,194
33,194
1,927,881
2,431,087
2,431,087
2,431,087
373,638
373,638
373,638
152,598
740,395
5,687,147
5,687,147
2,431,087
499,684
141,615
549,033
549,033
549,033
193,155
193,155
55,797
55,797
55,797
55,797
559,940
559,940
66,271
66,271
196,629
647,158
647,158
647,158
population
near site
(mile radius)
21,834
4,668
5,385
6,414
3,318
7,329
18,776
9,720
8,771
6,141
23,566
15,220
1,503
1,536
1,546
1,217
2,156
1,947
314
1,573
1,573
1,573
1,573
24,303
19,559
10,120
6,497
692
575
7,376
2,606
8,477
10,156
8,653
7,739
437
4,928
4,401
63
7,000
3,426
1,795
5,247
1,579
2,447
6,173
7,967
7,266
7,853
1,490
4,510
736
8,086
17,143
under
age 5
pop.
(mile
radius)
1,373
373
322
354
202
585
1,575
758
695
444
1,961
1,226
110
108
126
87
185
114
21
117
117
117
117
1,771
1,045
769
218
44
39
390
115
513
859
413
445
24
330
275
4
294
224
106
427
119
185
511
381
494
667
116
227
66
551
574
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
sum
point/
nonpt
PbEI
TPY
w/in 1
mile
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.3
0.1
0.6
0.3
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
4.8
4.8
2.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.3
0.3
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
source
oriented?
1
1
1
1
1
1
1
1
1
1
1
1
prev.
source
oriented?
(see end
notes)
1
1
1
1
3-year data capture
(complete periods)
comp.
years
1
2
3
3
3
3
2
3
3
3
1
3
2
3
3
3
3
3
1
1
1
1
1
1
3
3
2
2
2
3
3
2
3
3
3
1
3
3
3
1
1
2
2
3
3
3
3
3
3
3
3
1
3
1
comp.
qtrs
4
8
12
12
12
12
11
12
12
12
6
12
11
12
12
12
12
12
5
4
4
4
4
4
12
12
11
11
11
12
12
11
12
12
12
4
12
12
12
4
4
8
11
12
12
12
12
12
12
12
12
4
12
4
comp.
months
11
24
36
36
36
36
35
36
36
36
35
36
36
36
36
36
36
36
14
11
11
11
11
11
30
35
31
33
30
33
36
33
35
31
36
12
34
34
33
12
13
24
32
35
35
33
34
32
32
31
35
12
32
12
3-year metrics
annual
mean
0.0059
0.0107
0.0144
0.0158
0.0139
0.0205
0.0169
0.1214
0.0362
0.0477
0.0170
0.0114
0.1332
0.0922
0.1058
0.1578
0.1497
0.0054
0.0073
0.0412
0.0316
0.0505
0.0312
0.0081
0.0096
0.0396
0.0563
0.0618
0.1301
0.2570
0.0383
0.0779
0.0372
0.0203
0.0352
0.0000
0.0006
0.0014
0.0005
0.0022
0.0010
0.0011
0.0072
0.0002
0.0038
0.0049
0.0023
0.0001
0.0028
0.0071
0.0009
0.0018
0.0046
0.0030
max
quarterly
mean
0.0082
0.0248
0.0253
0.0247
0.0367
0.0300
0.0280
0.2367
0.0550
0.3600
0.0233
0.0197
0.2667
0.1467
0.1467
0.2667
0.2200
0.0100
0.0495
0.0613
0.0378
0.1030
0.0408
0.0101
0.0378
0.0567
0.1531
0.0940
0.1800
0.3967
0.0569
0.2493
0.0400
0.0350
0.0400
0.0000
0.0022
0.0041
0.0032
0.0037
0.0026
0.0034
0.0166
0.0017
0.0081
0.0169
0.0071
0.0006
0.0063
0.0163
0.0020
0.0033
0.0179
0.0069
max
monthly
mean
0.0140
0.0650
0.0300
0.0310
0.0800
0.0600
0.0430
0.4500
0.1000
0.5600
0.0470
0.0270
0.6100
0.2700
0.2200
0.3600
0.2600
0.0130
0.0880
0.0927
0.0623
0.1257
0.0708
0.0110
0.0503
0.1140
0.2300
0.1580
0.2820
0.8020
0.0920
0.3560
0.0400
0.0380
0.0400
0.0000
0.0070
0.0104
0.0068
0.0058
0.0063
0.0102
0.0420
0.0054
0.0158
0.0265
0.0125
0.0018
0.0112
0.0320
0.0053
0.0052
0.0356
0.0090
2nd max
monthly
mean
0.0125
0.0160
0.0300
0.0310
0.0300
0.0360
0.0390
0.2600
0.0940
0.4700
0.0370
0.0210
0.5300
0.2000
0.2100
0.3600
0.2500
0.0100
0.0140
0.0630
0.0420
0.1140
0.0363
0.0105
0.0377
0.0660
0.2280
0.1560
0.2740
0.5180
0.0560
0.2980
0.0400
0.0360
0.0400
0.0000
0.0034
0.0078
0.0035
0.0052
0.0023
0.0054
0.0200
0.0016
0.0148
0.0132
0.0066
0.0018
0.0106
0.0272
0.0040
0.0050
0.0125
0.0072
average
of 3
overall
highest
monthly
means
0.0122
0.0320
0.0287
0.0307
0.0433
0.0427
0.0373
0.3233
0.0920
0.3600
0.0377
0.0227
0.5200
0.2233
0.2067
0.3467
0.2500
0.0110
0.0383
0.0677
0.0485
0.1033
0.0474
0.0106
0.0387
0.0811
0.2167
0.1400
0.2737
0.6013
0.0647
0.2924
0.0400
0.0365
0.0400
0.0000
0.0042
0.0077
0.0043
0.0052
0.0035
0.0063
0.0270
0.0023
0.0133
0.0166
0.0086
0.0017
0.0101
0.0279
0.0042
0.0049
0.0192
0.0071
average
of 3
annual
max
monthly
means
0.0140
0.0405
0.0247
0.0307
0.0427
0.0423
0.0373
0.3100
0.0880
0.2090
0.0343
0.0203
0.5067
0.2233
0.2067
0.3467
0.2333
0.0097
0.0510
0.0927
0.0623
0.1257
0.0708
0.0110
0.0338
0.0801
0.1848
0.1380
0.2513
0.6013
0.0647
0.2093
0.0393
0.0344
0.0400
0.0000
0.0035
0.0072
0.0035
0.0058
0.0041
0.0052
0.0252
0.0023
0.0120
0.0153
0.0088
0.0006
0.0082
0.0213
0.0042
0.0052
0.0188
0.0090
2B-3
-------
Appendix 2B
Table 2B-1. Pb-TSP monitoring site information and 3-year statistics
site
450790007
450790019
450790021
450830001
450850001
450910005
470930027
470931017
471570044
471633001
471633002
471633003
471870100
471870102
471871101
480610006
480850003
480850007
480850009
481130018
481130057
481130066
481410033
482011034
484790016
490351001
721270003
poc
2
1
1
2
i
i
i
i
i
i
3
1
2
2
1
1
1
2
1
1
2
2
1
4
1
1
1
lat
34.09584
33.99330
33.81655
34.94774
33.92423
34.96303
35.98306
35.97500
35.08750
36.52556
36.52472
36.52806
35.80222
35.80222
35.79944
25.89251
33.14250
33.14722
33.14472
32.74556
32.77890
32.73972
31.77694
29.76799
27.51083
40.70861
18.44917
long
-80.96230
-81.02414
-80.78114
-81.93255
-80.33774
-81.00085
-83.95222
-83.95444
-90.07250
-82.27333
-82.26806
-82.26833
-86.66028
-86.66028
-86.66500
-97.49382
-96.82472
-96.82556
-96.82889
-96.78250
-96.87306
-96.78278
-106.50167
-95.22058
-99.51972
-112.09472
-66.05306
state
SC
SC
SC
SC
SC
SC
TN
TN
TN
TN
TN
TN
TN
TN
TN
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
UT
PR
county name
Richland
Richland
Richland
Spartanburg
Sumter
York
Knox
Knox
Shelby
Sullivan
Sullivan
Sullivan
Williamson
Williamson
Williamson
Cameron
Coffin
Coffin
Coffin
Dallas
Dallas
Dallas
El Paso
Harris
Webb
Salt Lake
San Juan
cbsa name
Columbia, SC
Columbia, SC
Columbia, SC
Spartanburg, SC
Sumter, SC
Charlotte-Gastonia-Concord, NC-SC
Knoxville, TN
Knoxville, TN
Memphis, TN-MS-AR
Kingsport-Bristol-Bristol, TN-VA
Kingsport-Bristol-Bristol, TN-VA
Kingsport-Bristol-Bristol, TN-VA
Nashville-Davidson— Murfreesboro,
Nashville-Davidson— Murfreesboro,
Nashville-Davidson— Murfreesboro,
Brownsville-Harlingen, TX
Dallas-Fort Worth-Arlington, TX
Dallas-Fort Worth-Arlington, TX
Dallas-Fort Worth-Arlington, TX
Dallas-Fort Worth-Arlington, TX
Dallas-Fort Worth-Arlington, TX
Dallas-Fort Worth-Arlington, TX
El Paso, TX
Houston-Sugar Land-Baytown, TX
Laredo, TX
Salt Lake City, UT
San Juan-Caguas-Guaynabo, PR
cbsa_popOO
647,158
647,158
647,158
253,791
104,646
1,330,448
616,079
616,079
1,205,204
298,484
298,484
298,484
1,311,789
1,311,789
1,311,789
335,227
5,161,544
5,161,544
5,161,544
5,161,544
5,161,544
5,161,544
679,622
4,715,407
193,117
968,858
2,509,007
population
near site
(mile radius)
4,405
15,569
123
7,505
4,990
3,453
8,586
7,817
6,730
942
942
942
165
165
165
14,803
3,837
3,837
3,837
6,451
4,591
8,270
13,680
14,785
14,880
215
319
under
age 5
pop.
(mile
radius)
233
287
10
552
407
221
826
763
548
65
65
65
10
10
10
1,422
415
415
415
491
578
622
1,005
1,770
1,441
23
5
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
sum
point/
nonpt
PbEI
TPY
w/in 1
mile
0.0
0.0
0.0
0.0
0.0
0.0
5.8
5.8
0.0
0.4
0.4
0.4
2.6
2.6
2.6
0.0
3.2
3.2
3.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
source
oriented?
1
prev.
source
oriented?
(see end
notes)
1
1
3-year data capture
(complete periods)
comp.
years
3
3
3
3
3
2
1
1
1
3
3
3
2
2
2
3
3
3
3
3
3
1
1
3
3
2
3
comp.
qtrs
12
12
12
12
12
11
9
9
6
12
12
12
8
8
8
12
12
12
12
12
12
7
6
12
12
11
12
comp.
months
36
35
35
34
35
27
26
26
17
35
36
35
23
23
24
35
35
34
33
34
35
20
17
36
36
32
36
3-year metrics
annual
mean
0.0004
0.0048
0.0001
0.0018
0.0025
0.0021
0.0182
0.0143
0.0100
0.1249
0.0614
0.0651
0.2527
0.2575
0.0811
0.0053
0.2271
0.1186
0.4961
0.0274
0.0362
0.0090
0.0120
0.0081
0.0121
0.0421
0.0014
max
quarterly
mean
0.0014
0.0097
0.0012
0.0035
0.0064
0.0042
0.0233
0.0193
0.0100
0.1959
0.1463
0.1259
0.9867
0.6953
0.3027
0.0085
0.3453
0.2111
0.6982
0.0804
0.0611
0.0209
0.0585
0.0220
0.0163
0.0762
0.0100
max
monthly
mean
0.0042
0.0144
0.0038
0.0062
0.0108
0.0082
0.0400
0.0375
0.0100
0.2843
0.2920
0.2322
1.9120
0.9460
0.7020
0.0090
0.7954
0.4760
0.9692
0.2338
0.1029
0.0420
0.0600
0.0478
0.0230
0.1188
0.0125
2nd max
monthly
mean
0.0030
0.0138
0.0000
0.0060
0.0104
0.0058
0.0400
0.0240
0.0100
0.2360
0.1540
0.1260
0.8200
0.6000
0.1820
0.0090
0.4436
0.3006
0.8914
0.0880
0.1016
0.0280
0.0540
0.0230
0.0214
0.1072
0.0120
average
of 3
overall
highest
monthly
means
0.0031
0.0137
0.0013
0.0060
0.0101
0.0063
0.0387
0.0285
0.0100
0.2501
0.1880
0.1476
1.1579
0.7093
0.3333
0.0089
0.5595
0.3408
0.8710
0.1299
0.0986
0.0320
0.0540
0.0283
0.0217
0.1106
0.0122
average
of 3
annual
max
monthly
means
0.0027
0.0137
0.0013
0.0057
0.0101
0.0052
0.0200
0.0192
0.0100
0.2381
0.1772
0.1476
1.0540
0.5390
0.4090
0.0071
0.5203
0.3040
0.8710
0.1286
0.0947
0.0340
0.0420
0.0260
0.0217
0.1106
0.0082
* These sites were classified as "previous" source-oriented but because production (and related lead emissions) at the associated source was not terminated until December, 2003, only data
for 2004-2005 were considered for the "previous" source oriented characterization.
# Data for 2004-2005 did not meet completeness criteria..
2B-4
-------
Appendix 2B
Table 2B-2. Pb-TSP monitoring site information and 1-year statistics
site
011090003
011090006
060250005
060371103
060371301
060371601
060374002
060374004
060375001
060375005
060651003
060658001
060711004
060719004
080010005
080010006
080310002
080310015
080410011
080650001
100010002
100031007
100031008
100032004
100051002
20571065
20571066
20571073
20571075
21030004
21030018
21033005
130890003
132150011
150032004
170310001
170310022
170310026
170310052
170313103
170313301
170314201
170316003
171170002
171190010
171193007
171430037
171630010
180350008
180350009
180890023
180892008
180892011
180930004
180970063
poc
2
1
1
2
1
1
2
2
1
1
2
3
1
1
1
1
4
1
1
1
1
1
1
1
1
5
1
1
5
5
5
1
2
1
1
1
2
1
1
1
1
1
1
2
1
2
1
2
1
2
1
1
2
1
1
lat
31.79056
31.79278
32.67611
34.06659
33.92899
34.01407
33.82376
33.79236
33.92288
33.95080
33.94603
33.99958
34.10374
34.10688
39.79601
39.82574
39.75119
39.70012
38.83139
39.24778
38.98472
39.55111
39.57778
39.73944
38.64444
27.89222
27.96028
27.96583
28.05000
27.94639
27.78556
27.87583
33.69833
32.43083
21.39667
41.67275
41.68920
41.87333
41.96743
41.96528
41.78278
42.14000
41.87194
39.39804
38.69417
38.86056
40.69889
38.61222
40.15806
40.15944
41.65278
41.63944
41.59250
38.88944
39.76083
long
-85.97917
-85.98056
-115.48333
-118.22688
-118.21071
-118.06056
-118.18921
-118.17533
-118.37026
-118.43043
-117.40063
-117.41601
-117.62914
-117.27411
-104.97754
-104.93699
-104.98762
-104.98714
-104.82778
-106.29139
-75.55556
-75.73083
-75.61111
-75.55806
-75.61306
-82.53861
-82.38250
-82.37944
-82.37806
-82.73194
-82.74000
-82.69639
-84.27333
-84.93167
-157.97167
-87.73246
-87.53932
-87.64507
-87.74982
-87.87639
-87.80528
-87.79917
-87.82611
-89.80975
-90.15361
-90.10583
-89.58474
-90.16028
-85.42111
-85.41556
-87.43944
-87.49361
-87.47194
-86.55194
-86.29722
state
AL
AL
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
CO
CO
CO
CO
DE
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
FL
OA
OA
HI
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IN
IN
IN
IN
IN
IN
IN
county name
Pike
Pike
Imperial
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Riverside
Riverside
San Bernardino
San Bernardino
Adams
Adams
Denver
Denver
El Paso
Lake
Kent
New Castle
New Castle
New Castle
Sussex
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Pinellas
Pinellas
Pinellas
DeKalb
Muscogee
Honolulu
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Macoupin
Madison
Madison
Peoria
St. Clair
Delaware
Delaware
Lake
Lake
Lake
Lawrence
Marion
cbsa name
Troy, AL
Troy, AL
El Centre, CA
Los Angeles-Long Beach-Santa An;
Los Angeles-Long Beach-Santa An;
Los Angeles-Long Beach-Santa An;
Los Angeles-Long Beach-Santa An;
Los Angeles-Long Beach-Santa An;
Los Angeles-Long Beach-Santa An;
Los Angeles-Long Beach-Santa An;
Riverside-San Bernardino-Ontario,
Riverside-San Bernardino-Ontario,
Riverside-San Bernardino-Ontario,
Riverside-San Bernardino-Ontario,
Denver-Aurora, CO
Denver-Aurora, CO
Denver-Aurora, CO
Denver-Aurora, CO
Colorado Springs, CO
Edwards, CO
Dover, DE
Phil adelphia-Camden- Wilmington,
Phil adelphia-Camden- Wilmington,
Phil adelphia-Camden- Wilmington,
Seaford, DE
Tampa-St. Petersburg-Clearwater, F
Tampa-St. Petersburg-Clearwater, F
Tampa-St. Petersburg-Clearwater, F
Tampa-St. Petersburg-Clearwater, F
Tampa-St. Petersburg-Clearwater, F
Tampa-St. Petersburg-Clearwater, F
Tampa-St. Petersburg-Clearwater, F
Atlanta-Sandy Springs-Marietta, G^
Columbus, OA-AL
Honolulu, HI
Chicago-Naperville-Joliet, IL-IN-W
Chicago-Naperville-Joliet, IL-IN-W
Chicago-Naperville-Joliet, IL-IN-W
Chicago-Naperville-Joliet, IL-IN-W
Chicago-Naperville-Joliet, IL-IN-W
Chicago-Naperville-Joliet, IL-IN-W
Chicago-Naperville-Joliet, IL-IN-W
Chicago-Naperville-Joliet, IL-IN-W
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
Peoria, IL
St. Louis, MO-IL
Muncie, IN
Muncie, IN
Chicago-Naperville-Joliet, IL-IN-W
Chicago-Naperville-Joliet, IL-IN-W
Chicago-Naperville-Joliet, IL-IN-W
Bedford, IN
Indianapolis-Carmel, IN
cbsa_popOO
29,605
29,605
142,361
12,365,627
12,365,627
12,365,627
12,365,627
12,365,627
12,365,627
12,365,627
3,254,821
3,254,821
3,254,821
3,254,821
2,157,756
2,157,756
2,157,756
2,157,756
537,484
49,471
126,697
5,687,147
5,687,147
5,687,147
156,638
2,395,997
2,395,997
2,395,997
2,395,997
2,395,997
2,395,997
2,395,997
4,247,981
281,768
876,156
9,098,316
9,098,316
9,098,316
9,098,316
9,098,316
9,098,316
9,098,316
9,098,316
2,721,491
2,721,491
2,721,491
366,899
2,721,491
118,769
118,769
9,098,316
9,098,316
9,098,316
45,922
1,525,104
population
near site
(mile radius)
461
461
16,385
29,329
47,423
13,333
20,131
61,497
19,148
33,968
16,320
16,247
18,777
14,861
2,025
3,313
22,019
14,438
10,581
5,903
352
2,041
3,170
34,053
5,450
14,463
5,793
4,541
10,691
13,048
11,289
2,151
7,888
10,871
23,622
13,648
22,040
28,739
42,187
10,302
23,749
6,070
14,862
40
8,014
5,397
12,643
3,512
2,108
980
5,959
7,144
9,815
393
12,176
under
age 5
pop.
(mile
radius)
31
31
1,290
1,633
5,066
1,066
1,232
6,697
1,680
1,358
1,278
1,678
1,578
1,755
183
256
974
809
552
361
22
209
160
2,649
390
612
465
340
490
557
571
58
663
1,037
1,207
971
1,708
1,203
2,877
670
1,678
303
1,071
2
529
360
1,109
430
104
82
603
612
729
32
875
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
sum
point /
nonpt
PbEI
TPY
w/in 1
mile
4.5
4.5
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.3
1.3
0.0
0.0
0.0
0.0
0.0
0.3
0.1
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.3
0.1
0.0
0.3
0.0
0.0
6.5
0.0
0.0
0.0
1.7
source
oriented?
1
1
1
1
1
1
1
1
1
1
1
prev.
source
oriented?
(see end
notes)
1
1 -year metrics
max
quarterly
mean,
2003
1.9233
0.9100
0.0248
0.0627
0.0300
0.0300
0.0400
0.0938
0.0667
0.0113
0.0179
0.0343
0.0773
0.1739
0.0388
0.0290
0.0212
0.0117
0.0192
0.0040
0.0046
0.0063
0.0115
0.0042
0.0062
0.7400
0.2533
0.0054
0.0041
0.0056
0.0067
0.1000
0.1000
0.0029
0.0157
0.0286
0.0613
0.0250
0.0180
0.0750
0.0133
0.0373
0.0113
0.3280
0.0173
0.0167
0.0563
0.2341
0.8073
0.0435
0.0277
0.0453
0.0270
0.0508
max
monthly
mean,
2003
2.6600
1.6900
0.0404
0.1460
0.0440
0.0480
0.0960
0.1020
0.1700
0.0160
0.0200
0.0800
0.1420
0.2509
0.0443
0.0467
0.0305
0.0165
0.0277
0.0051
0.0058
0.0081
0.0163
0.0048
0.0094
1.3800
0.4800
0.0105
0.0067
0.0103
0.0200
0.1000
0.1000
0.0072
0.0250
0.0360
0.0860
0.0280
0.0240
0.1950
0.0175
0.0480
0.0140
0.9100
0.0320
0.0220
0.0940
0.3394
1.2183
0.0620
0.0413
0.0610
0.0270
0.0812
2nd max
monthly
mean,
2003
2.4200
0.8900
0.0357
0.0260
0.0360
0.0340
0.0440
0.0840
0.0220
0.0120
0.0200
0.0200
0.0680
0.2016
0.0406
0.0284
0.0196
0.0120
0.0209
0.0041
0.0051
0.0058
0.0161
0.0042
0.0080
0.7800
0.4400
0.0072
0.0039
0.0093
0.0000
0.1000
0.1000
0.0021
0.0180
0.0350
0.0620
0.0260
0.0220
0.0360
0.0160
0.0400
0.0100
0.0620
0.0240
0.0180
0.0720
0.3138
0.967273
0.0510
0.0335
0.0420
0.0270
0.0584
max
quarterly
mean,
2004
1.2267
0.8433
0.0179
0.0253
0.0313
0.0215
0.0147
0.0146
0.0107
0.0093
0.0114
0.0144
0.0150
0.0144
0.1384
0.0404
0.0222
0.0151
0.0100
0.0224
1.2600
0.2333
0.0071
0.0000
0.1000
0.1000
0.0015
0.0229
0.0314
0.0557
0.0257
0.0140
0.0520
0.0120
0.0333
0.0113
0.1515
0.0175
0.0129
0.0529
0.4657
4.0931
0.0691
0.0289
0.0358
0.0270
0.0770
max
monthly
mean,
2004
1.7800
1.3400
0.0205
0.0280
0.0320
0.0300
0.0180
0.0160
0.0120
0.0120
0.0140
0.0220
0.0180
0.0160
0.1898
0.0726
0.0339
0.0184
0.0100
0.0310
1.7400
0.3400
0.0112
0.0000
0.1000
0.1000
0.0017
0.0360
0.0420
0.0900
0.0400
0.0160
0.1140
0.0160
0.0460
0.0140
0.2880
0.0240
0.0180
0.0750
0.7371
5.7750
0.0910
0.0590
0.0532
0.0270
0.1123
2nd max
monthly
mean,
2004
1.0000
0.9400
0.0191
0.0280
0.0320
0.0300
0.0160
0.0160
0.0100
0.0100
0.0125
0.0200
0.0160
0.0160
0.1887
0.0346
0.0262
0.0183
0.0100
0.0310
1.0400
0.2800
0.0051
0.0000
0.1000
0.1000
0.0015
0.0200
0.0420
0.0700
0.0300
0.0160
0.0700
0.0140
0.0420
0.0100
0.0900
0.0200
0.0100
0.0520
0.4653
5.0220
0.0783
0.0318
0.0463
0.0270
0.0638
max
quarterly
mean,
2005
0.3948
0.1661
0.0229
0.0179
0.0233
0.0160
0.0125
0.0120
0.0118
0.0113
0.0169
0.0160
0.0133
0.5558
0.0957
0.1780
0.0891
0.0187
1.1188
0.2933
0.0000
0.1000
0.1000
0.0017
0.0167
0.0353
0.0347
0.0260
0.0271
0.0246
0.0387
0.0107
0.1033
0.0193
0.0279
0.0707
0.4642
1.3890
0.0462
0.0296
0.1352
0.0270
0.0329
max
monthly
mean,
2005
0.6156
0.2402
0.0380
0.0250
0.0300
0.0250
0.0140
0.0160
0.0150
0.0140
0.0220
0.0180
0.0160
1.1037
0.2086
0.2955
0.1387
0.0296
1.3000
0.4200
0.0000
0.1000
0.1000
0.0025
0.0200
0.0440
0.0500
0.0380
0.0440
0.0375
0.0500
0.0120
0.1880
0.0225
0.0320
0.1050
0.5991
1.5900
0.0613
0.0484
0.3050
0.0270
0.0594
2nd max
monthly
mean,
2005
0.3346
0.1600
0.0278
0.0200
0.0280
0.0240
0.0140
0.0125
0.0100
0.0100
0.0180
0.0180
0.0150
0.4397
0.0428
0.2297
0.1314
0.0170
1.2000
0.3200
0.0000
0.1000
0.1000
0.0019
0.0180
0.0360
0.0420
0.0280
0.0240
0.0225
0.0360
0.0120
0.0750
0.0200
0.0300
0.0980
0.4671
1.3923
0.0578
0.0363
0.0778
0.0270
0.0380
2B-5
-------
Appendix 2B
Table 2B-2. Pb-TSP monitoring site information and 1-year statistics
site
180970076
180970078
181010001
181630006
260490021
261130001
261630001
261630005
261630015
261630019
261630027
261630033
270370001
270370020
270370421
270370423
270370442
270530050
270530963
270530964
270530965
270530966
270530967
270530968
270531007
271231003
271377001
271377555
290930016
290930021
290930023
290930024
290930025
290930026
290930027
290930029
290930030
290990004
290990005
290990008
290990009
290990010
290990011
290990013
290990015
291892003
295100085
340231003
360470122
360632008
360713001
360713002
360713004
360850067
390170015
poc
1
1
1
2
4
1
2
1
4
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
6
1
1
1
1
1
1
1
2
lat
39.75889
39.81110
38.89028
37.97167
43.04722
44.31056
42.22861
42.26722
42.30278
42.43083
42.29222
42.30667
44.83333
44.76535
44.77720
44.77500
44.74036
45.00123
44.95540
44.88855
45.00448
44.98133
44.99646
44.89301
45.04182
44.96322
47.52336
46.73264
37.62528
37.65417
37.50333
37.47972
37.51056
37.45917
37.48611
37.47167
37.46639
38.26330
38.26722
38.26194
38.28444
38.24110
38.26820
38.27361
38.26167
38.64972
38.65630
40.47222
40.71980
43.08216
41.46107
41.45887
41.47633
40.59733
39.48990
long
-86.28972
-86.11447
-86.76083
-87.56722
-83.67028
-84.89194
-83.20833
-83.13222
-83.10667
-83.00028
-83.10694
-83.14889
-93.11500
-93.03248
-93.04097
-93.06278
-93.00556
-93.26712
-93.25827
-93.19538
-93.24005
-93.26615
-93.23488
-93.23323
-93.29873
-93.19023
-92.53631
-92.16337
-91.12917
-91.13056
-90.69556
-90.69028
-90.69750
-90.68639
-90.69000
-90.68944
-90.69000
-90.37850
-90.37944
-90.39417
-90.38194
-90.37680
-90.37380
-90.38000
-90.37972
-90.35056
-90.19810
-74.47139
-73.94788
-79.00099
-74.36343
-74.35392
-74.36827
-74.12619
-84.36407
state
IN
IN
IN
IN
MI
MI
MI
MI
MI
MI
MI
MI
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
NJ
NY
NY
NY
NY
NY
NY
OH
county name
Marion
Marion
Martin
Vanderburgh
Genes ee
Missaukee
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Dakota
Dakota
Dakota
Dakota
Dakota
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Ramsey
St. Louis
St. Louis
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
St. Louis
St. Louis (City)
Middlesex
Kings
Niagara
Orange
Orange
Orange
Richmond
Butler
cbsa name
Indianapolis-Carmel, IN
Indianapolis-Carmel, IN
Evansville, IN-KY
Flint, MI
Cadillac, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Detroit-Warren-Livonia, MI
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Minneapolis-St. Paul-Bloomington,
Duluth, MN-WI
Duluth, MN-WI
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
St. Louis, MO-IL
New York-Northern New Jersey-Lo
New York-Northern New Jersey-Lo
Buffalo-Niagra Falls, NY Metropol:
Poughkeepsie-Newburgh-Middletoi
Poughkeepsie-Newburgh-Middletoi
Poughkeepsie-Newburgh-Middletoi
New York-Northern New Jersey-Lo
Cincinnati-Middletown, OH-KY-II-.
cbsa_popOO
1,525,104
1,525,104
342,815
436,141
44,962
4,452,557
4,452,557
4,452,557
4,452,557
4,452,557
4,452,557
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
2,968,806
275,486
275,486
2,721,491
2,721,491
2,721,491
2,721,491
2,721,491
2,721,491
2,721,491
2,721,491
2,721,491
2,721,491
18,323,002
18,323,002
1,170,111
621,517
621,517
621,517
18,323,002
2,009,632
population
near site
(mile radius)
9,171
14,196
84
13,666
9,889
58
14,329
11,314
17,729
28,362
6,024
17,402
5,074
162
478
886
168
16,318
46,218
209
19,106
17,156
14,621
11,243
14,889
9,247
8,942
4,527
58
58
138
32
138
32
32
32
32
2,418
2,418
2,418
9,804
2,799
2,418
3,570
1,988
12,303
9,140
13,850
92,660
6,795
1,481
1,257
6,816
21,834
4,668
under
age 5
pop.
(mile
radius)
602
1,175
5
817
994
3
798
923
1,771
2,628
516
1,843
404
7
24
83
11
923
3,929
0
1,095
439
580
789
1,118
474
428
287
4
4
7
2
7
2
2
2
2
197
197
197
820
215
197
318
178
512
783
1,124
5,785
386
99
86
434
1,373
373
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
sum
point /
nonpt
PbEI
TPY
w/in 1
mile
1.7
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.0
1.1
0.5
3.2
0.0
0.0
0.0
0.3
0.0
0.2
0.0
0.4
0.0
0.4
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
58.8
58.8
58.8
0.0
0.0
58.8
58.8
58.8
0.0
0.0
1.7
0.1
0.0
1.8
1.8
0.0
0.0
0.0
source
oriented?
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
prev.
source
oriented?
(see end
notes)
1 *#
1 *#
1 *#
1 *#
1 *
1 *
1 *#
1-year metrics
max
quarterly
mean,
2003
0.0143
0.0057
0.0299
0.0051
0.0153
0.0040
0.0096
0.0247
0.0207
0.0136
0.0410
0.0086
0.0057
0.0050
0.0062
0.0079
0.0071
0.0114
0.0080
0.0080
0.0067
0.0100
0.0362
0.0020
0.6593
0.5850
0.3433
0.6677
0.3263
0.2523
0.8761
0.7148
0.2017
0.5438
0.1500
0.1664
0.0680
0.5321
0.2717
1.4906
0.0000
0.0419
0.0293
0.0060
0.0820
0.2417
0.0313
0.0082
max
monthly
mean,
2003
0.0190
0.0110
0.0358
0.0075
0.0189
0.0042
0.0101
0.0340
0.0247
0.0141
0.0601
0.2100
0.0200
0.0100
0.0100
0.0080
0.0100
0.0080
0.0180
0.0100
0.0120
0.0080
0.0200
0.0900
0.0040
1.2160
0.9840
0.6320
1.6026
0.6320
0.3555
1.4414
1.4740
0.3330
0.7900
0.2583
0.1440
0.1040
1.0490
0.4850
2.2993
0.0000
0.0875
0.0350
0.0080
0.1580
0.2080
0.0333
0.0140
2nd max
monthly
mean,
2003
0.0178
0.0075
0.0270
0.0060
0.0166
0.0039
0.0100
0.0208
0.0214
0.0141
0.0406
0.0380
0.0100
0.0060
0.0060
0.0020
0.0080
0.0075
0.0080
0.0080
0.0060
0.0060
0.0100
0.0080
0.0020
1.1720
0.7820
0.4275
0.9927
0.4189
0.3370
0.9300
1.1410
0.2797
0.6157
0.1350
0.1100
0.0900
0.7327
0.3355
1.9442
0.0000
0.0360
0.0300
0.0080
0.0940
0.1700
0.0320
0.0125
max
quarterly
mean,
2004
0.0186
0.0103
0.0270
0.0126
0.0121
0.0032
0.0101
0.0259
0.0252
0.0108
0.0173
0.0262
0.1153
0.0060
0.0069
0.0033
0.0027
0.0093
0.0064
0.0047
0.0079
0.0076
0.0033
0.0043
0.0129
0.0021
0.0027
0.7893
0.7187
0.0131
0.0333
0.0642
0.0621
0.1654
0.0893
0.0089
1.4750
0.6779
0.1368
0.1413
0.0700
1.3047
0.8683
1.4760
0.0500
0.0216
0.1146
0.0333
0.0746
0.2369
0.0307
0.0129
max
monthly
mean,
2004
0.0204
0.0154
0.0270
0.0286
0.0156
0.0040
0.0111
0.0315
0.0299
0.0149
0.0240
0.0384
0.2300
0.0080
0.0080
0.0060
0.0060
0.0120
0.0075
0.0040
0.0080
0.0100
0.0100
0.0080
0.0060
0.0350
0.0040
0.0040
1.4540
0.9960
0.0242
0.0558
0.0640
0.0700
0.3080
0.1025
0.0154
2.0731
1.0655
0.1700
0.1750
0.0740
2.2070
3.5680
1.8987
0.0500
0.0290
0.1878
0.0360
0.1100
0.4025
0.0320
0.0160
2nd max
monthly
mean,
2004
0.0190
0.0094
0.0270
0.0170
0.0132
0.0036
0.0105
0.0310
0.0256
0.0124
0.0112
0.0381
0.1920
0.0060
0.0067
0.0040
0.0050
0.0100
0.0060
0.0020
0.0067
0.0100
0.0080
0.0050
0.0050
0.0180
0.0040
0.0020
1.0340
0.9280
0.0136
0.0337
0.0600
0.0660
0.2200
0.0900
0.0125
1.8962
0.9278
0.1570
0.1475
0.0680
1.3510
0.6400
1.8531
0.0500
0.0255
0.1428
0.0325
0.0917
0.2400
0.0320
0.0150
max
quarterly
mean,
2005
0.0254
0.0251
0.0270
0.0083
0.0117
0.0056
0.0107
0.0191
0.0204
0.0138
0.0267
0.0269
0.0979
0.0100
0.0069
0.0029
0.0036
0.0060
0.0050
0.0073
0.0053
0.0142
0.0031
0.0029
0.0067
0.0100
0.0040
1.3070
0.6627
0.1257
0.1027
1.1215
0.3742
0.1857
0.1064
0.0813
0.4200
0.3379
1.9277
0.0500
0.1537
0.0309
0.0453
0.0520
0.0386
0.0248
max
monthly
mean,
2005
0.0360
0.0288
0.0270
0.0088
0.0209
0.0080
0.0124
0.0268
0.0278
0.0140
0.0353
0.0368
0.1725
0.0120
0.0120
0.0040
0.0060
0.0117
0.0100
0.0140
0.0083
0.0225
0.0050
0.0067
0.0080
0.0100
0.0050
4.1933
0.9520
0.1667
0.1400
1.4317
0.5499
0.3700
0.1560
0.1680
0.7638
0.6420
3.2884
0.0500
0.1182
0.0325
0.0540
0.0640
0.0400
0.0650
2nd max
monthly
mean,
2005
0.0346
0.0240
0.0270
0.0088
0.0155
0.0046
0.0115
0.0228
0.0252
0.0125
0.0340
0.0301
0.0900
0.0100
0.0080
0.0040
0.0050
0.0080
0.0067
0.0060
0.0080
0.0157
0.0050
0.0040
0.0080
0.0040
0.0040
1.2120
0.8660
0.1480
0.0980
1.1765
0.4180
0.3100
0.1125
0.0660
0.7153
0.2725
2.2541
0.0500
0.0874
0.0300
0.0460
0.0500
0.0400
0.0130
2B-6
-------
Appendix 2B
Table 2B-2. Pb-TSP monitoring site information and 1-year statistics
site
390290019
390290020
390290022
390350038
390350042
390350049
390350050
390350061
390350069
390490025
390510001
390910003
390910005
390910006
390910007
391670008
391670009
401159005
401159006
401159007
401159008
410510246
420030002
420032001
420070505
420110005
420110717
420111717
420210808
420250105
420450002
421010449
421290007
450031001
450130007
450190003
450190046
450190047
450410001
450410002
450430006
450430007
450430009
450430010
450450008
450452002
450470001
450470002
450510002
450630005
450631002
450790006
450790007
450790019
450790021
poc
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
2
1
1
1
7
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
2
1
1
1
2
2
2
4
2
1
1
lat
40.63111
40.63972
40.63500
41.47694
41.48222
41.44667
41.44250
41.47506
41.51918
39.92806
41.57528
40.34306
40.34278
40.34111
40.34472
39.43361
39.37696
36.98580
36.98460
36.97190
36.97160
45.56130
40.50056
40.39667
40.68500
40.46630
40.47667
40.37722
40.34806
40.80306
39.83556
39.98250
40.16667
33.43253
32.43654
32.88394
32.94275
32.84461
34.19794
34.16764
33.36378
33.34973
33.37399
33.36960
34.84045
34.94165
34.18111
34.16520
33.70460
33.78560
33.96900
34.00740
34.09584
33.99330
33.81655
long
-80.54694
-80.52389
-80.54667
-81.68194
-81.70889
-81.65111
-81.64917
-81.67596
-81.63794
-82.98111
-83.99639
-83.75500
-83.76028
-83.75778
-83.75444
-81.50250
-81.53730
-94.84920
-94.82490
-94.85180
-94.82500
-122.67878
-80.07194
-79.86361
-80.32500
-75.75890
-75.75917
-75.91444
-78.88278
-75.60833
-75.37250
-75.08306
-79.87500
-81.89233
-80.67785
-79.97754
-79.65718
-79.94804
-79.79885
-79.85040
-79.29426
-79.29821
-79.28570
-79.29840
-82.40291
-82.22961
-82.15224
-82.16048
-78.87745
-81.11978
-81.06533
-81.02329
-80.96230
-81.02414
-80.78114
state
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OK
OK
OK
OK
OR
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
county name
Columbiana
Columbiana
Columbiana
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Franklin
Fulton
Logan
Logan
Logan
Logan
Washington
Washington
Ottawa
Ottawa
Ottawa
Ottawa
Multnomah
Allegheny
Allegheny
Beaver
Berks
Berks
Berks
Cambria
Carbon
Delaware
Philadelphia
Westmoreland
Aiken
Beaufort
Charleston
Charleston
Charleston
Florence
Florence
Georgetown
Georgetown
Georgetown
Georgetown
Greenville
Greenville
Greenwood
Greenwood
Horry
Lexington
Lexington
Richland
Richland
Richland
Richland
cbsa name
East Liverpool-Salem, OH
East Liverpool-Salem, OH
East Liverpool-Salem, OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Columbus, OH
Toledo, OH
Bellefontaine, OH
Bellefontaine, OH
Bellefontaine, OH
Bellefontaine, OH
Parkersburg-Marietta, WV-OH
Parkersburg-Marietta, WV-OH
Miami, OK
Miami, OK
Miami, OK
Miami, OK
Portland- Vancouver-Beaverton, OR
Pittsburgh, PA
Pittsburgh, PA
Pittsburgh, PA
Reading, PA
Reading, PA
Reading, PA
Johnstown, PA
Allentown-Bethlehem-Easton, PA-I
Phil adelphia-Camden- Wilmington,
Phil adelphia-Camden- Wilmington,
Pittsburgh, PA
Augusta-Richmond County, GA-SC
Hilton Head Island-Beaufort, SC
Charleston-North Charleston, SC
Charleston-North Charleston, SC
Charleston-North Charleston, SC
Florence, SC
Florence, SC
Georgetown, SC
Georgetown, SC
Georgetown, SC
Georgetown, SC
Greenville, SC
Greenville, SC
Greenwood, SC
Greenwood, SC
Myrtle Beach-Conway-North Myrtl
Columbia, SC
Columbia, SC
Columbia, SC
Columbia, SC
Columbia, SC
Columbia, SC
cbsa_popOO
112,075
112,075
112,075
2,148,143
2,148,143
2,148,143
2,148,143
2,148,143
2,148,143
1,612,694
659,188
46,005
46,005
46,005
46,005
164,624
164,624
33,194
33,194
33,194
33,194
1,927,881
2,431,087
2,431,087
2,431,087
373,638
373,638
373,638
152,598
740,395
5,687,147
5,687,147
2,431,087
499,684
141,615
549,033
549,033
549,033
193,155
193,155
55,797
55,797
55,797
55,797
559,940
559,940
66,271
66,271
196,629
647,158
647,158
647,158
647,158
647,158
647,158
population
near site
(mile radius)
5,385
6,414
3,318
7,329
18,776
9,720
8,771
6,141
23,566
15,220
1,503
1,536
1,546
1,217
2,156
1,947
314
1,573
1,573
1,573
1,573
24,303
19,559
10,120
6,497
692
575
7,376
2,606
8,477
10,156
8,653
7,739
437
4,928
4,401
63
7,000
3,426
1,795
5,247
1,579
2,447
6,173
7,967
7,266
7,853
1,490
4,510
736
8,086
17,143
4,405
15,569
123
under
age 5
pop.
(mile
radius)
322
354
202
585
1,575
758
695
444
1,961
1,226
110
108
126
87
185
114
21
117
117
117
117
1,771
1,045
769
218
44
39
390
115
513
859
413
445
24
330
275
4
294
224
106
427
119
185
511
381
494
667
116
227
66
551
574
233
287
10
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
sum
point /
nonpt
PbEI
TPY
w/in 1
mile
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.3
0.1
0.6
0.3
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
4.8
4.8
2.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.3
0.3
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
source
oriented?
1
1
1
1
1
1
1
1
1
1
1
1
prev.
source
oriented?
(see end
notes)
1
1
1
1
1-year metrics
max
quarterly
mean,
2003
0.0253
0.0247
0.0367
0.0300
0.0233
0.2367
0.0400
0.3600
0.0233
0.0167
0.2667
0.1467
0.1300
0.1967
0.1500
0.0100
0.0101
0.0255
0.0567
0.0913
0.0757
0.1238
0.3860
0.0364
0.0992
0.0364
0.0350
0.0369
0.0000
0.0022
0.0041
0.0032
0.0037
0.0026
0.0166
0.0017
0.0081
0.0102
0.0071
0.0000
0.0053
0.0100
0.0018
0.0033
0.0179
0.0069
0.0006
0.0097
0.0012
max
monthly
mean,
2003
0.0300
0.0300
0.0800
0.0600
0.0300
0.4500
0.0700
0.5600
0.0290
0.0200
0.5300
0.2700
0.1900
0.3200
0.2100
0.0100
0.0110
0.0280
0.1140
0.1920
0.1000
0.1980
0.4840
0.0460
0.1300
0.0380
0.0380
0.0400
0.0000
0.0070
0.0104
0.0068
0.0058
0.0063
0.0000
0.0420
0.0054
0.0158
0.0132
0.0125
0.0000
0.0086
0.0202
0.0032
0.0052
0.0356
0.0090
0.0018
0.0138
0.0038
2nd max
monthly
mean,
2003
0.0300
0.0240
0.0200
0.0300
0.0200
0.2500
0.0500
0.4700
0.0280
0.0200
0.2500
0.2000
0.1100
0.2100
0.1500
0.0100
0.0105
0.0260
0.0660
0.0483
0.0760
0.1580
0.4560
0.0320
0.1240
0.0380
0.0360
0.0400
0.0000
0.0016
0.0048
0.0035
0.0052
0.0023
0.0077
0.0000
0.0094
0.0102
0.0066
0.0000
0.0082
0.0168
0.0020
0.0050
0.0090
0.0072
0.0000
0.0104
0.0000
max
quarterly
mean,
2004
0.0142
0.0190
0.0180
0.0253
0.0230
0.1380
0.0543
0.0257
0.0197
0.2460
0.1337
0.1467
0.2667
0.2200
0.0072
0.0495
0.0115
0.0394
0.0925
0.0940
0.1800
0.3967
0.0453
0.1150
0.0400
0.0269
0.0393
0.0020
0.0026
0.0007
0.0021
0.0123
0.0000
0.0042
0.0169
0.0050
0.0006
0.0063
0.0163
0.0020
0.0067
0.0007
0.0078
0.0000
max
monthly
mean,
2004
0.0220
0.0310
0.0300
0.0360
0.0430
0.2200
0.1000
0.0440
0.0470
0.0270
0.3800
0.2000
0.2100
0.3600
0.2600
0.0130
0.0880
0.0230
0.0630
0.1325
0.1560
0.2820
0.5180
0.0560
0.1420
0.0400
0.0355
0.0400
0.0034
0.0078
0.0020
0.0020
0.0054
0.0200
0.0000
0.0055
0.0265
0.0120
0.0018
0.0112
0.0320
0.0040
0.0125
0.0020
0.0144
0.0000
2nd max
monthly
mean,
2004
0.0140
0.0290
0.0150
0.0320
0.0230
0.1500
0.0500
0.0300
0.0370
0.0210
0.2800
0.1900
0.1300
0.2700
0.2500
0.0100
0.0110
0.0143
0.0525
0.1000
0.1060
0.2650
0.4580
0.0400
0.1225
0.0400
0.0312
0.0400
0.0023
0.0030
0.0020
0.0032
0.0190
0.0000
0.0048
0.0078
0.0060
0.0018
0.0106
0.0272
0.0034
0.0096
0.0020
0.0090
0.0000
max
quarterly
mean,
2005
0.0150
0.0191
0.0142
0.0223
0.0280
0.1503
0.0550
0.0183
0.0210
0.0085
0.1867
0.1070
0.1467
0.2267
0.1700
0.0051
0.0106
0.0613
0.0378
0.1030
0.0408
0.0378
0.0546
0.1531
0.0881
0.1736
0.3907
0.0569
0.2493
0.0400
0.0236
0.0400
0.0000
0.0013
0.0006
0.0034
0.0072
0.0005
0.0069
0.0033
0.0049
0.0000
0.0021
0.0094
0.0020
0.0036
0.0014
0.0096
0.0000
max
monthly
mean,
2005
0.0220
0.0310
0.0180
0.0310
0.0390
0.2600
0.0940
0.0230
0.0270
0.0140
0.6100
0.2000
0.2200
0.3600
0.2300
0.0062
0.0140
0.0927
0.0623
0.1257
0.0708
0.0503
0.0632
0.2300
0.1580
0.2740
0.8020
0.0920
0.3560
0.0400
0.0298
0.0400
0.0000
0.0033
0.0018
0.0102
0.0135
0.0016
0.0148
0.0063
0.0018
0.0000
0.0048
0.0116
0.0053
0.0082
0.0042
0.0128
0.0000
2nd max
monthly
mean,
2005
0.0190
0.0190
0.0170
0.0300
0.0290
0.2600
0.0820
0.0180
0.0200
0.0130
0.4200
0.1700
0.1900
0.2800
0.1900
0.0054
0.0130
0.0630
0.0420
0.1140
0.0363
0.0377
0.0629
0.2280
0.0950
0.2320
0.2400
0.0417
0.2980
0.0400
0.0266
0.0400
0.0000
0.0000
0.0000
0.0000
0.0100
0.0000
0.0072
0.0050
0.0018
0.0000
0.0036
0.0115
0.0016
0.0036
0.0030
0.0122
0.0000
2B-7
-------
Appendix 2B
Table 2B-2. Pb-TSP monitoring site information and 1-year statistics
site
450830001
450850001
450910005
470930027
470931017
471570044
471633001
471633002
471633003
471870100
471870102
471871101
480610006
480850003
480850007
480850009
481130018
481130057
481130066
481410033
482011034
484790016
490351001
721270003
poc
2
1
1
1
1
1
1
3
1
2
2
1
1
1
2
1
1
2
2
1
4
1
1
1
lat
34.94774
33.92423
34.96303
35.98306
35.97500
35.08750
36.52556
36.52472
36.52806
35.80222
35.80222
35.79944
25.89251
33.14250
33.14722
33.14472
32.74556
32.77890
32.73972
31.77694
29.76799
27.51083
40.70861
18.44917
long
-81.93255
-80.33774
-81.00085
-83.95222
-83.95444
-90.07250
-82.27333
-82.26806
-82.26833
-86.66028
-86.66028
-86.66500
-97.49382
-96.82472
-96.82556
-96.82889
-96.78250
-96.87306
-96.78278
-106.50167
-95.22058
-99.51972
-112.09472
-66.05306
state
SC
SC
SC
TN
TN
TN
TN
TN
TN
TN
TN
TN
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
UT
PR
county name
Spartanburg
Sumter
York
Knox
Knox
Shelby
Sullivan
Sullivan
Sullivan
Williamson
Williamson
Williamson
Cameron
Coffin
Coffin
Coffin
Dallas
Dallas
Dallas
El Paso
Harris
Webb
Salt Lake
San Juan
cbsa name
Spartanburg, SC
Sumter, SC
Charlotte-Oastonia-Concord, NC-SI
Knoxville, TN
Knoxville, TN
Memphis, TN-MS-AR
Kingsport-Bristol-Bristol, TN-VA
Kingsport-Bristol-Bristol, TN-VA
Kingsport-Bristol-Bristol, TN-VA
Nashville-Davidson— Murfreesboro,
Nashville-Davidson— Murfreesboro,
Nashville-Davidson— Murfreesboro,
Brownsville-Harlingen, TX
Dallas-Fort Worth-Arlington, TX
Dallas-Fort Worth- Arlington, TX
Dallas-Fort Worth- Arlington, TX
Dallas-Fort Worth- Arlington, TX
Dallas-Fort Worth- Arlington, TX
Dallas-Fort Worth-Arlington, TX
El Paso, TX
Houston-Sugar Land-Baytown, TX
Laredo, TX
Salt Lake City, UT
San Juan-Caguas-Guaynabo, PR
cbsa_popOO
253,791
104,646
1,330,448
616,079
616,079
1,205,204
298,484
298,484
298,484
1,311,789
1,311,789
1,311,789
335,227
5,161,544
5,161,544
5,161,544
5,161,544
5,161,544
5,161,544
679,622
4,715,407
193,117
968,858
2,509,007
population
near site
(mile radius)
7,505
4,990
3,453
8,586
7,817
6,730
942
942
942
165
165
165
14,803
3,837
3,837
3,837
6,451
4,591
8,270
13,680
14,785
14,880
215
319
under
age 5
pop.
(mile
radius)
552
407
221
826
763
548
65
65
65
10
10
10
1,422
415
415
415
491
578
622
1,005
1,770
1,441
23
5
urban
1
1
sum
point /
nonpt
PbEI
TPY
w/in 1
mile
0.0
0.0
0.0
5.8
5.8
0.0
0.4
0.4
0.4
2.6
2.6
2.6
0.0
3.2
3.2
3.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
source
oriented?
1
1
1
1
1
1
1
1
1
1
1
1
1
prev.
source
oriented?
(see end
notes)
1
1
1-year metrics
max
quarterly
mean,
2003
0.0035
0.0064
0.0042
0.0100
0.0100
0.0100
0.1515
0.0719
0.0679
0.9867
0.6953
0.0853
0.0085
0.3006
0.1337
0.6600
0.0318
0.0611
0.0178
0.0585
0.0136
0.0142
0.0628
0.0042
max
monthly
mean,
2003
0.0060
0.0108
0.0082
0.0100
0.0100
0.0100
0.1940
0.0846
0.0844
1.9120
0.9460
0.1160
0.0090
0.4436
0.2458
0.8914
0.0640
0.1016
0.0260
0.0600
0.0230
0.0230
0.1188
0.0125
2nd max
monthly
mean,
2003
0.0058
0.0066
0.0050
0.0100
0.0100
0.0100
0.1718
0.0730
0.0810
0.8200
0.6000
0.1120
0.0090
0.3518
0.2223
0.6658
0.0250
0.0708
0.0217
0.0540
0.0140
0.0147
0.0752
0.0000
max
quarterly
mean,
2004
0.0026
0.0047
0.0021
0.0233
0.0193
0.0100
0.1577
0.1024
0.1259
0.1287
0.0887
0.3027
0.0076
0.2473
0.1241
0.5926
0.0804
0.0447
0.0209
0.0220
0.0156
0.0718
0.0000
max
monthly
mean,
2004
0.0062
0.0104
0.0058
0.0400
0.0375
0.0100
0.2843
0.1550
0.2322
0.1960
0.1320
0.7020
0.0080
0.3220
0.1902
0.7524
0.2338
0.1029
0.0420
0.0478
0.0206
0.1057
0.0000
2nd max
monthly
mean,
2004
0.0054
0.0060
0.0032
0.0400
0.0240
0.0100
0.1488
0.0840
0.0750
0.1800
0.1100
0.1820
0.0078
0.2854
0.1728
0.6670
0.0500
0.0913
0.0280
0.0104
0.0202
0.1016
0.0000
max
quarterly
mean,
2005
0.0021
0.0044
0.0008
0.0100
0.0100
0.1959
0.1463
0.0739
0.0040
0.3453
0.2111
0.6982
0.0467
0.0563
0.0147
0.0054
0.0163
0.0762
0.0100
max
monthly
mean,
2005
0.0048
0.0090
0.0017
0.0100
0.0100
0.2360
0.2920
0.1260
0.0042
0.7954
0.4760
0.9692
0.0880
0.0796
0.0240
0.0073
0.0214
0.1072
0.0120
2nd max
monthly
mean,
2005
0.0022
0.0034
0.0016
0.0100
0.0100
0.2300
0.0933
0.0830
0.0040
0.4396
0.3006
0.7368
0.0680
0.0700
0.0160
0.0058
0.0196
0.1032
0.0120
* These sites were classified as "previous" source-oriented but because production (and related lead emissions) at the associated source was not terminated until December, 2003, only
data for 2004-2005 were considered for the "previous" source oriented characterization.
# Data for 2004-2005 did not meet completeness criteria..
-------
Appendix 2B
Table 2B-3. Pb-TSP monitoring site distribution statistics
All sites
annual mean
max quarter mean
max monthly mean
2nd max monthly mean
average of 3 overall highest
monthly me am
average of 3 annual max monthly
means
n
189
189
189
189
189
189
min
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
pct5
0.0010
0.0031
0.0054
0.0035
0.0043
0.0042
pctlO
0.0019
0.0041
0.0080
0.0051
0.0060
0.0058
pct!5
0.0032
0.0063
0.0100
0.0072
0.0075
0.0080
pct20
0.0042
0.0071
0.0112
0.0100
0.0101
0.0100
pct25
0.0052
0.0100
0.0140
0.0117
0.0122
0.0112
pct30
0.0071
0.0126
0.0200
0.0140
0.0147
0.0150
pct35
0.0097
0.0179
0.0288
0.0196
0.0228
0.0203
pct40
0.0114
0.0224
0.0320
0.0240
0.0279
0.0252
pct45
0.0143
0.0254
0.0380
0.0280
0.0320
0.0299
median
0.0166
0.0299
0.0430
0.0340
0.0373
0.0344
mean
0.0934
0.1738
0.3079
0.2066
0.2253
0.1942
pct55
0.0203
0.0367
0.0503
0.0380
0.0400
0.0400
pcteo
0.0272
0.0495
0.0880
0.0440
0.0500
0.0496
pct65
0.0316
0.0627
0.1000
0.0726
0.0811
0.0753
pct70
0.0396
0.0820
0.1460
0.1000
0.1033
0.1073
pct75
0.0606
0.1259
0.2200
0.1428
0.1496
0.1380
pctSO
0.0957
0.1857
0.2955
0.2360
0.2501
0.2093
pct85
0.1332
0.2667
0.4760
0.3100
0.3418
0.3100
pct90
0.2527
0.4657
0.9100
0.5300
0.6013
0.5390
pct95
0.4778
0.8761
1.6900
0.9927
1.1597
1.0540
max
2.6732
4.0931
5.7750
5.0220
4.2890
2.8611
Source-oriented sites
annual mean
max quarter mean
max monthly mean
2nd max monthly mean
n
60
60
60
60
min
0.0072
0.0100
0.0100
0.0100
pct5
0.0095
0.0180
0.0311
0.0205
pctlO
0.0142
0.0221
0.0378
0.0310
pct!5
0.0229
0.0309
0.0420
0.0380
pct20
0.0375
0.0731
0.1000
0.0871
pct25
0.0440
0.0880
0.1580
0.1070
pct30
0.0616
0.1206
0.1814
0.1484
pct35
0.0775
0.1502
0.2311
0.1690
pct40
0.0933
0.1829
0.2881
0.2230
pct45
0.1122
0.2064
0.3577
0.2670
median
0.1253
0.2470
0.4263
0.2943
mean
0.2596
0.4781
0.8572
0.5738
pct55
0.1455
0.2800
0.5200
0.3485
pct60
0.1815
0.3272
0.6320
0.4336
pct65
0.2281
0.3526
0.7663
0.4568
pct70
0.2549
0.5107
0.9280
0.5645
pct75
0.2655
0.6866
1.0307
0.7310
pctSO
0.3327
0.7167
1.4577
0.9289
pct85
0.4869
0.8930
1.7150
1.0669
pct90
0.5866
1.2823
2.1401
1.3655
pct95
0.9109
1.6992
3.4282
2.0977
max
2.6732
4.0931
5.7750
5.0220
Not source-oriented sites
annual mean
max quarter mean
max monthly mean
2nd max monthly mean
n
129
129
129
129
min
0.0000
0.0000
0.0000
0.0000
pct5
0.0006
0.0022
0.0051
0.0025
pctlO
0.0014
0.0033
0.0062
0.0040
pct!5
0.0021
0.0042
0.0080
0.0052
pct20
0.0028
0.0060
0.0090
0.0060
pct25
0.0038
0.0067
0.0105
0.0080
pct30
0.0045
0.0080
0.0120
0.0100
pct35
0.0051
0.0100
0.0140
0.0115
pct40
0.0057
0.0114
0.0160
0.0125
pct45
0.0081
0.0138
0.0220
0.0148
median
0.0100
0.0179
0.0290
0.0196
mean
0.0162
0.0322
0.0525
0.0358
pct55
0.0113
0.0229
0.0320
0.0230
pcteo
0.0142
0.0253
0.0360
0.0270
pct65
0.0153
0.0280
0.0404
0.0300
pct70
0.0175
0.0343
0.0480
0.0360
pct75
0.0214
0.0386
0.0600
0.0390
pctSO
0.0272
0.0495
0.0880
0.0440
pct85
0.0308
0.0613
0.1000
0.0630
pct90
0.0372
0.0773
0.1387
0.0880
pct95
0.0433
0.1030
0.2300
0.1140
max
0.1497
0.2493
0.3560
0.2980
Previous source-oriented sites
annual mean
max quarter mean
max monthly mean
2nd max monthly mean
n
9
9
9
9
min
0.0090
0.0100
0.0100
0.0100
pct5
0.0090
0.0100
0.0100
0.0100
pctlO
0.0090
0.0100
0.0100
0.0100
pct!5
0.0100
0.0209
0.0380
0.0280
pct20
0.0100
0.0209
0.0380
0.0280
pct25
0.0203
0.0350
0.0420
0.0360
pct30
0.0203
0.0350
0.0420
0.0360
pct35
0.0362
0.0550
0.1000
0.0940
pct40
0.0362
0.0550
0.1000
0.0940
pct45
0.0477
0.1000
0.1000
0.1000
median
0.0477
0.1000
0.1000
0.1000
mean
0.0595
0.1206
0.1942
0.1467
pct55
0.0477
0.1000
0.1000
0.1000
pcteo
0.0799
0.1027
0.1400
0.1025
pct65
0.0799
0.1027
0.1400
0.1025
pct70
0.1000
0.1654
0.3080
0.2200
pct75
0.1000
0.1654
0.3080
0.2200
pctSO
0.1105
0.2367
0.4500
0.2600
pct85
0.1105
0.2367
0.4500
0.2600
pct90
0.1214
0.3600
0.5600
0.4700
pct95
0.1214
0.3600
0.5600
0.4700
max
0.1214
0.3600
0.5600
0.4700
Urban sites
annual mean
max quarter mean
max monthly mean
2nd max monthly mean
n
140
140
140
140
min
0.0001
0.0006
0.0018
0.0000
pct5
0.0012
0.0032
0.0062
0.0040
pctlO
0.0021
0.0042
0.0081
0.0059
pct!5
0.0032
0.0067
0.0103
0.0080
pct20
0.0045
0.0080
0.0120
0.0100
pct25
0.0052
0.0104
0.0149
0.0118
pct30
0.0074
0.0131
0.0204
0.0145
pct35
0.0097
0.0174
0.0287
0.0192
pct40
0.0112
0.0214
0.0315
0.0220
pct45
0.0138
0.0247
0.0360
0.0253
median
0.0149
0.0260
0.0400
0.0305
mean
0.0594
0.1100
0.1958
0.1295
pct55
0.0168
0.0300
0.0440
0.0355
pcteo
0.0187
0.0364
0.0502
0.0385
pct65
0.0230
0.0405
0.0800
0.0430
pct70
0.0304
0.0612
0.1000
0.0670
pct75
0.0365
0.0766
0.1164
0.0870
pctSO
0.0404
0.0979
0.1814
0.1056
pct85
0.0780
0.1534
0.2469
0.2100
pct90
0.1200
0.2430
0.4050
0.2930
pct95
0.2601
0.4312
0.8560
0.5645
max
1.4501
1.9277
3.5680
2.2993
Urban sites, located in MSA's > 1 million population
annual mean
max quarter mean
max monthly mean
2nd max monthly mean
n
91
91
91
91
min
0.0006
0.0033
0.0067
0.0000
pct5
0.0026
0.0060
0.0082
0.0067
pctlO
0.0042
0.0071
0.0110
0.0080
pct!5
0.0051
0.0100
0.0124
0.0103
pct20
0.0075
0.0114
0.0160
0.0120
pct25
0.0090
0.0133
0.0200
0.0160
pct30
0.0103
0.0197
0.0290
0.0200
pct35
0.0113
0.0220
0.0340
0.0230
pct40
0.0142
0.0252
0.0360
0.0255
pct45
0.0150
0.0267
0.0400
0.0315
median
0.0178
0.0300
0.0440
0.0360
mean
0.0711
0.1343
0.2442
0.1530
pct55
0.0205
0.0353
0.0500
0.0380
pct60
0.0225
0.0400
0.0601
0.0406
pct65
0.0276
0.0567
0.0960
0.0500
pct70
0.0315
0.0667
0.1029
0.0778
pct75
0.0368
0.0773
0.1460
0.0880
pctSO
0.0396
0.0957
0.1878
0.1016
pct85
0.0563
0.1537
0.2338
0.2100
pct90
0.1000
0.2367
0.4760
0.2880
pct95
0.3711
0.8683
1.7400
0.9278
max
1.4501
1.9277
3.5680
2.2993
Urban sites, located in MSA's < 1 mi
annual mean
max quarter mean
max monthly mean
2nd max monthly mean
n
49
49
49
49
lion DODI
min
0.0001
0.0006
0.0018
0.0018
ilation
pct5
0.0006
0.0020
0.0048
0.0025
pctlO
0.0010
0.0026
0.0053
0.0034
pct!5
0.0014
0.0034
0.0063
0.0042
pct20
0.0018
0.0037
0.0072
0.0054
pct25
0.0025
0.0063
0.0102
0.0072
pct30
0.0030
0.0069
0.0108
0.0090
pct35
0.0046
0.0085
0.0144
0.0106
pct40
0.0048
0.0126
0.0209
0.0132
pct45
0.0065
0.0166
0.0286
0.1700
median
0.0100
0.0179
0.0310
0.0200
mean
0.0378
0.0649
0.1060
0.0861
pct55
0.0121
0.0224
0.0320
0.0240
pcteo
0.0143
0.0248
0.0400
0.0300
pct65
0.0156
0.0279
0.0404
0.0310
pct70
0.0175
0.0386
0.0800
0.0400
pct75
0.0305
0.0585
0.0920
0.0560
pctSO
0.0779
0.1000
0.1387
0.1314
pct85
0.1000
0.1467
0.2600
0.2100
pct90
0.1332
0.2493
0.3560
0.2980
pct95
0.1578
0.2667
0.6100
0.5180
max
0.2944
0.4657
0.8020
0.5991
2B-9
-------
Appendix 2B
Table 2B-4. Pb-TSP metric correlations
Statistic (Q = quarterly, M = monthly)
number of sites
mean (ug/m3)
ifl
g
fi
o
ta
g
o
o
annual mean, 2003-2005
max Q mean, 2003-2005
max M mean, 2003-2005
2nd max M mean, 2003-2005
average of 3 overall highest M
means, 2003-2005
average of 3 annual max M means,
2003-2005
All sites
annual
mean, 2003
2005
189
0.0934
1.00
maxQ
mean, 2003
2005
189
0.1738
0.96
1.00
maxM
mean, 2003
2005
189
0.3079
0.88
0.94
1.00
2nd max M
mean, 2003
2005
189
0.2066
0.97
0.99
0.92
1.00
avg. of 3
overall
highest M
means,
2003-2005
189
0.2253
0.94
0.98
0.98
0.97
1.00
avg. of 3
annual max
189
0.1942
0.90
0.94
0.97
0.93
0.98
1.00
Statistic (Q = quarterly, M = monthly)
number of sites
mean (ug/m3)
Correlation coeeficients
annual mean, 2003-2005
max Q mean, 2003-2005
max M mean, 2003-2005
2nd max M mean, 2003-2005
average of 3 overall highest M
means, 2003-2005
average of 3 annual max M means,
2003-2005
Urban sites
annual
mean, 2003
2005
140
0.0594
1.00
maxQ
mean, 2003
2005
140
0.1100
0.95
1.00
maxM
mean, 2003
2005
140
0.1958
0.83
0.93
1.00
2nd max M
mean, 2003
2005
140
0.1295
0.97
0.99
0.88
1.00
avg. of 3
overall
highest M
means,
2003-2005
140
0.1455
0.94
0.99
0.97
0.97
1.00
1.00
avg. of 3
annual max
M means,
2003-2005
140
0.1350
0.94
0.98
0.96
0.97
1.00
1.00
Statistic (Q = quarterly, M = monthly)
number of sites
mean (ug/m3)
-S3
_OJ
ifl
O
g
ta
b
s
annual mean, 2003-2005
max Q mean, 2003-2005
max M mean, 2003-2005
2nd max M mean, 2003-2005
average of 3 overall highest M
means, 2003-2005
average of 3 annual max M means,
2003-2005
Source-oriented sites
annual
mean, 2003
2005
60
0.2596
1.00
maxQ
mean, 2003
2005
60
0.4781
0.95
1.00
maxM
mean, 2003
2005
60
0.8572
0.85
0.93
1.00
2nd max M
mean, 2003
2005
60
0.5738
0.96
0.99
0.89
1.00
avg. of 3
overall
highest M
means,
2003-2005
60
0.6259
0.93
0.98
0.97
0.97
1.00
of 3
annual max
M means,
60
0.5333
0.88
0.92
0.96
0.90
0.97
1.00
Statistic (Q = quarterly, M = monthly)
number of sites
mean (ug/m3)
-S3
_OJ
ifl
O
g
ta
b
S
annual mean, 2003-2005
max Q mean, 2003-2005
max M mean, 2003-2005
2nd max M mean, 2003-2005
average of 3 overall highest M
means, 2003-2005
average of 3 annual max M means,
2003-2005
Urban sites in CBSA's > 1M po
annual
mean, 2003
2005
91
0.0711
1.00
maxQ
mean, 2003
2005
91
0.1343
0.95
1.00
maxM
mean, 2003
2005
91
0.2442
0.82
0.93
1.00
2nd max M
mean, 2003
2005
91
0.1530
0.97
0.99
0.87
1.00
pulation
avg. of 3
overall
highest M
means,
2003-2005
91
0.1762
0.93
0.99
0.96
0.97
1.00
of 3
annual max
M means,
91
0.1634
0.94
0.99
0.96
0.96
1.00
1.00
2B-10
-------
Appendix 2B
Table 2B-5. Pb-TSP metric ratio distribution statistics
TSP Category
All sites
Source-oriented sites
Non-source-oriented sites
Previous source-oriented
sites
Urban sites
Urban sites in CBSAs >.
1M population
Urban sites in CBSAs <
1M population
Ratio
ratio of max quarterly mean to annual
mean
ratio of max monthly mean to annual
mean
ratio of 2nd max monthly mean to
annual mean
ratio of max quarterly mean to annual
mean
ratio of max monthly mean to annual
mean
ratio of 2nd max monthly mean to
annual mean
ratio of max quarterly mean to annual
mean
ratio of max monthly mean to annual
mean
ratio of 2nd max monthly mean to
annual mean
ratio of max quarterly mean to annual
mean
ratio of max monthly mean to annual
mean
ratio of 2nd max monthly mean to
annual mean
ratio of max quarterly mean to annual
mean
ratio of max monthly mean to annual
mean
ratio of 2nd max monthly mean to
annual mean
ratio of max quarterly mean to annual
mean
ratio of max monthly mean to annual
mean
ratio of 2nd max monthly mean to
annual mean
ratio of max quarterly mean to annual
mean
ratio of max monthly mean to annual
mean
ratio of 2nd max monthly mean to
annual mean
Sites
189
60
129
13
140
91
49
mm
1.0000
1.0000
0.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.0000
1.0000
1.0000
0.0000
1.0000
1.0000
1.0000
pct5
1.1135
1.3553
1.1211
1.0787
1.4735
1.4088
1.1368
1.3140
1.0746
1.0000
1.0000
1.0000
1.1605
1.3391
1.1889
1.1077
1.3046
1.0746
1.2648
1.4392
1.3116
pctlO
1.2080
1.5556
1.3057
1.2484
1.8318
1.6543
1.2034
1.4769
1 .2445
1.0000
1.0000
1.0000
1.2504
1.5925
1.3271
1.2080
1.4769
1.3057
1.3522
1.7365
1.6698
pctlS
1.2852
1.8176
1.4296
1.3167
1.9768
1.6815
1.2648
1.6578
1.3294
1.0000
1.0000
1.0000
1.3070
1.7771
1.4700
1.2745
1.6578
1.4089
1.3838
1.9615
1.7451
pct20
1.3433
1.9537
1.5822
1.3529
2.1641
1.7864
1.3095
1.8680
1.4296
1.0000
1.0000
1.0000
1.3536
1.9220
1.6053
1.3095
1.8346
1.4965
1.4152
2.0898
1.8787
pct25
1.3848
2.1475
1.6784
1.3966
2.2716
1.8292
1.3826
2.0164
1.5582
1.2844
1.7515
1.7759
1.4227
2.1133
1.6784
1.3759
1.9910
1.5855
1.5299
2.2033
1.9843
pctSO
1.4837
2.2817
1.7597
1.5077
2.3528
1.8838
1.4753
2.2496
1.6657
1.2844
1.7515
1.7759
1.5010
2.2936
1.7660
1.4753
2.2676
1.6553
1.5608
2.3056
2.0346
pct35
1.5299
2.4036
1.8311
1.5205
2.5202
1.9438
1.5591
2.3671
1.7556
1.4961
1.8746
2.1414
1.5712
2.4105
1.8606
1.5167
2.3690
1.7491
1.6127
2.5035
2.1235
pct40
1.6127
2.5471
1.9092
1.5559
2.5824
2.0124
1.6332
2.5200
1.8372
1.4961
1.8746
2.1414
1.6427
2.5423
1.9967
1.6157
2.5370
1.7890
1.6901
2.6417
2.1673
pct45
1.7079
2.6265
2.0346
1.7079
2.7379
2.1000
1.7079
2.5851
1.9200
1.5195
2.7626
2.5969
1.7167
2.6341
2.1038
1.6829
2.5619
1.8960
1.9505
2.9528
2.2033
median
1.7846
2.8310
2.1246
1.7571
2.9086
2.1723
1.8151
2.7967
2.1185
1.5195
2.7626
2.5969
1.7773
2.8310
2.1650
1.7366
2.6726
2.0982
2.0025
3.1207
2.3435
mean
2.3541
4.4159
25728
2.0471
3.7485
2.4356
2.4980
4.7287
2.6371
2.2049
3.4751
4.9066
2.3313
4.2395
26532
2.3159
4.0747
2.4155
2.3597
4.5456
3.0946
pct55
1.8893
2.9634
2.2033
1.8085
3.0625
2.2784
1.9665
2.9508
2.1818
1.5195
2.7626
2.5969
1.8683
2.9518
2.2452
1.7900
2.8714
2.1414
2.0091
3.5286
2.3684
pct60
2.0025
3.5018
2.3015
1.9070
3.5073
2.4017
2.0293
3.4823
2.2633
1.7266
2.7863
3.0986
2.0072
3.4976
2.3637
1.8451
2.9508
2.2633
2.1715
4.4055
2.6953
pct65
2.1421
4.0128
2.4453
1.9759
3.6900
2.5034
2.2498
4.0858
2.3912
1.7266
2.7863
3.0986
2.1508
4.0307
2.5051
2.0496
3.5868
2.3912
2.2498
4.5723
2.7642
pct70
2.3164
4.4273
25908
2.1285
4.0401
2.5293
2.4153
4.5723
2.6316
1.9492
3.7062
8.4131
2.3167
4.4998
2.6213
2.3164
4.0253
2.5004
2.6223
5.0000
2.9302
pct75
2.5474
4.9871
2.7650
2.3486
4.5092
2.6395
2.6899
5.5927
2.8245
1.9492
3.7062
8.4131
2.5890
5.1122
2.7859
2.4317
4.6478
2.5969
2.7241
5.5497
3.8199
pctSO
2.7853
5.7675
3.0986
2.4706
4.6536
2.7960
2.9390
6.3223
3.2174
2.3170
4.6478
9.8590
2.7581
5.8364
3.0213
2.7033
5.6357
2.7659
3.1395
5.8680
3.9800
pct85
3.2023
6.5038
3.5200
2.7642
5.3012
3.3263
3.4555
7.6473
3.8274
2.3170
4.6478
9.8590
3.1770
6.6889
3.7220
3.0063
6.4892
3.0986
3.4555
7.3340
4.4010
pct90
3.9233
8.5462
3.9800
3.2865
6.2826
3.5193
4.1647
9.1396
4.5000
7.5516
11.7469
14.2747
3.9279
8.4122
4.1463
3.8141
8.0000
3.6621
3.9762
9.3326
5.5005
pct95
5.9868
11.8424
6.6439
3.8592
10.2029
3.8639
7.3577
12.1935
7.5267
7.5516
11.7469
14.2747
5.6878
11.7723
7.4078
7.3577
9.3770
7.2888
4.8718
12.1935
8.4269
max
12.0000
39.0000
12.1935
7.5516
13.5518
9.8590
12.0000
39.0000
12.1935
7.5516
11.7469
14.2747
12.0000
36.0000
12.1935
12.0000
36.0000
9.8590
7.6772
19.1113
12.1935
2B-1
-------
Appendix 2B
Table 2B-6. Pb-PMlO monitoring site information
site
080770017
110010043
120571065
120573002
121030018
121030026
130890002
170314201
211930003
250250042
261630033
295100085
360850106
360850111
360850131
360850132
410390060
410510030
410510080
410510244
410510246
410610119
410670111
440070022
440070029
450250001
481390017
481410041
482011035
482011039
490110004
530110030
530330080
530630016
530630050
530630052
530630053
550270007
poc
1
1
5
5
5
5
1
6
1
6
1
6
1
1
1
1
7
7
7
8
7
7
7
1
1
2
1
1
1
1
1
7
1
1
1
1
1
1
lat
39.06363
38.91889
27.89222
27.96565
27.78556
27.85004
33.68801
42.14000
37.28306
42.32944
42.30667
38.65630
40.57811
40.57997
40.58806
40.58061
44.02631
45.49742
45.49667
45.53500
45.56130
45.33897
45.47020
41.80795
41.81644
34.61537
32.47361
31.76054
29.73371
29.67005
40.90297
45.64168
47.56833
47.66083
47.69545
47.66512
47.68220
43.43500
long
-108.56102
-77.01250
-82.53861
-82.23040
-82.74000
-82.71459
-84.29033
-87.79917
-83.22028
-71.08278
-83.14889
-90.19810
-74.18430
-74.19872
-74.16882
-74.15158
-123.08374
-122.67467
-122.60222
-122.69889
-122.67878
-117.90480
-122.81585
-71.41500
-71.43790
-80.19879
-97.04250
-106.50045
-95.25759
-95.12849
-111.88447
-122.68123
-122.30806
-117.35722
-117.37030
-117.42909
-117.30480
-88.52778
state
CO
DC
FL
FL
FL
FL
GA
IL
KY
MA
MI
MO
NY
NY
NY
NY
OR
OR
OR
OR
OR
OR
OR
RI
RI
SC
TX
TX
TX
TX
UT
WA
WA
WA
WA
WA
WA
WI
county name
Mesa
District of Columbia
Hillsborough
Hillsborough
Pinellas
Pinellas
DeKalb
Cook
Perry
Suffolk
Wayne
St. Louis (City)
Richmond
Richmond
Richmond
Richmond
Lane
Multnomah
Multnomah
Multnomah
Multnomah
Union
Washington
Providence
Providence
Chesterfield
Ellis
El Paso
Harris
Harris
Davis
Clark
King
Spokane
Spokane
Spokane
Spokane
Dodge
cbsa name
Grand Junction, CO
Washington-Arlington-Alexandria,
Tampa-St. Petersburg-Clearwater,
Tampa-St. Petersburg-Clearwater,
Tampa-St. Petersburg-Clearwater,
Tampa-St. Petersburg-Clearwater,
Atlanta-Sandy Springs-Marietta, G
Chicago-Naperville-Joliet, IL-IN-V
Boston-Cambridge-Quincy, MA-N
Detroit- Warren-Livonia, MI
St. Louis, MO-IL
New York-Northern New Jersey-L
New York-Northern New Jersey-L
New York-Northern New Jersey-L
New York-Northern New Jersey-L
Eugene-Springfield, OR
Portland- Vancouver-Beaverton, Ol
Portland- Vancouver-Beaverton, Ol
Portland- Vancouver-Beaverton, Ol
Portland- Vancouver-Beaverton, Ol
La Grande, OR
Portland- Vancouver-Beaverton, Ol
Providence-New Bedford-Fall Rivs
Providence-New Bedford-Fall Riv«
Dallas-Fort Worth-Arlington, TX
El Paso, TX
Houston-Sugar Land-Baytown, TX
Houston-Sugar Land-Baytown, TX
Ogden-Clearfield, UT
Portland- Vancouver-Beaverton, Ol
Seattle-Tacoma-Bellevue, WA
Spokane, WA
Spokane, WA
Spokane, WA
Spokane, WA
Beaver Dam, WI
cbsa_popOO
116,255
4,796,183
2,395,997
2,395,997
2,395,997
2,395,997
4,247,981
9,098,316
4,391,344
4,452,557
2,721,491
18,323,002
18,323,002
18,323,002
18,323,002
322,959
1,927,881
1,927,881
1,927,881
1,927,881
24,530
1,927,881
1,582,997
1,582,997
5,161,544
679,622
4,715,407
4,715,407
442,656
1,927,881
3,043,878
417,939
417,939
417,939
417,939
85,897
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
data completeness
(complete periods)
qtrs
1
2
2
2
1
1
3
1
3
1
3
2
1
1
1
1
1
1
2
2
2
2
1
3
1
2
1
1
3
3
2
1
3
1
1
1
1
1
years
4
7
8
8
4
6
12
4
12
6
12
10
4
4
4
4
3
4
7
7
8
7
3
12
4
8
6
4
12
12
10
3
11
4
4
4
4
4
months
13
20
23
24
12
17
34
12
34
22
35
30
11
11
10
11
9
11
22
21
23
20
11
36
13
20
17
12
36
31
29
11
32
12
12
12
12
10
3-year metrics
annual
mean
0.0049
0.0048
0.0062
0.0035
0.0022
0.0023
0.0026
0.0060
0.0040
0.0049
0.0212
0.0127
0.0071
0.0074
0.0069
0.0095
0.0023
0.0056
0.0055
0.0065
0.0097
0.0016
0.0025
0.0098
0.0061
0.0029
0.0151
0.0118
0.0077
0.0056
0.0059
0.0032
0.0046
0.0059
0.0049
0.0037
0.0051
0.0054
max
quarterly
mean
0.0056
0.0085
0.0207
0.0048
0.0030
0.0034
0.0046
0.0076
0.0066
0.0085
0.0390
0.0170
0.0117
0.0123
0.0115
0.0223
0.0032
0.0104
0.0088
0.0098
0.0273
0.0027
0.0032
0.0547
0.0092
0.0049
0.0211
0.0167
0.0106
0.0113
0.0081
0.0051
0.0085
0.0108
0.0090
0.0055
0.0078
0.0082
max
monthly
mean
0.0085
0.0097
0.0469
0.0075
0.0047
0.0045
0.0106
0.0094
0.0078
0.0151
0.0667
0.0256
0.0150
0.0160
0.0120
0.0300
0.0040
0.0123
0.0144
0.0190
0.0608
0.0030
0.0051
0.1529
0.0142
0.0071
0.0370
0.0253
0.0116
0.0136
0.0111
0.0061
0.0146
0.0211
0.0168
0.0088
0.0134
0.0153
2B-12
-------
Appendix 2B
Table 2B-7. Pb-PMlO monitoring site distribution statistics
All sites
annual mean
max quarter mean
max monthly mean
n
38
38
38
min
0.0016
0.0027
0.0030
pct5
0.0022
0.0030
0.0040
pctlO
0.0023
0.0032
0.0047
pct!5
0.0026
0.0046
0.0061
pct20
0.0032
0.0049
0.0075
pct25
0.0037
0.0055
0.0085
pct30
0.0046
0.0066
0.0094
pct35
0.0049
0.0078
0.0106
pct40
0.0049
0.0082
0.0116
pct45
0.0054
0.0085
0.0123
median
0.0056
0.0087
0.0135
mean
0.0063
0.0117
0.0205
pctSS
0.0056
0.0090
0.0142
pct60
0.0059
0.0098
0.0146
petes
0.0061
0.0106
0.0151
pct70
0.0065
0.0113
0.0160
pct75
0.0071
0.0117
0.0190
pctSO
0.0077
0.0167
0.0253
pct8 5
0.0097
0.0207
0.0300
pct90
0.0118
0.0223
0.0469
pct95
0.0151
0.0390
0.0667
max
0.0212
0.0547
0.1529
Urban sites
annual mean
max quarter mean
max monthly mean
n
25
25
25
min
0.0022
0.0030
0.0040
pctS
0.0023
0.0032
0.0045
pctlO
0.0023
0.0032
0.0047
pctlS
0.0025
0.0034
0.0051
pct20
0.0029
0.0049
0.0073
pct25
0.0046
0.0056
0.0094
pct30
0.0048
0.0076
0.0097
pct35
0.0049
0.0081
0.0106
pct40
0.0052
0.0085
0.0114
pct45
0.0056
0.0085
0.0123
median
0.0056
0.0088
0.0136
mean
0.0064
0.0126
0.0235
pctSS
0.0059
0.0092
0.0142
pct60
0.0060
0.0101
0.0145
pct65
0.0061
0.0106
0.0151
pct70
0.0062
0.0108
0.0190
pct75
0.0065
0.0113
0.0211
pctSO
0.0087
0.0169
0.0254
pctS 5
0.0098
0.0207
0.0469
pct90
0.0118
0.0273
0.0608
pct95
0.0127
0.0390
0.0667
max
0.0212
0.0547
0.1529
Urban sites, located in MSA's > 1 m
annual mean
max quarter mean
max monthly mean
n
20
20
20
Minn DODulation
min
0.0022
0.0030
0.0045
pctS
0.0022
0.0031
0.0046
pctlO
0.0024
0.0033
0.0049
pctlS
0.0025
0.0040
0.0056
pct20
0.0029
0.0049
0.0077
pct25
0.0039
0.0064
0.0095
pct30
0.0047
0.0080
0.0101
pct35
0.0048
0.0085
0.0111
pct40
0.0052
0.0085
0.0120
pct45
0.0056
0.0087
0.0130
median
0.0056
0.0090
0.0139
mean
0.0065
0.0136
0.0259
pctS 5
0.0058
0.0095
0.0143
pct60
0.0061
0.0101
0.0145
pct65
0.0061
0.0105
0.0148
pct70
0.0063
0.0109
0.0170
pct75
0.0071
0.0142
0.0223
pctSO
0.0087
0.0189
0.0363
pctS 5
0.0097
0.0240
0.0539
pct90
0.0113
0.0332
0.0637
pct95
0.0170
0.0469
0.1098
max
0.0212
0.0547
0.1529
Urban sites, located in MSA's < 1 million population
annual mean
max quarter mean
max monthly mean
n
5
5
5
min
0.0023
0.0032
0.0040
pctS
0.0023
0.0032
0.0040
pctlO
0.0023
0.0032
0.0040
pctlS
0.0023
0.0032
0.0040
pct20
0.0036
0.0044
0.0062
pct25
0.0049
0.0056
0.0085
pct30
0.0049
0.0056
0.0085
pct35
0.0049
0.0056
0.0085
pct40
0.0054
0.0069
0.0098
pct45
0.0059
0.0081
0.0111
median
0.0059
0.0081
0.0111
mean
0.0061
0.0089
0.0140
pctS 5
0.0059
0.0081
0.0111
pct60
0.0059
0.0095
0.0161
pct65
0.0059
0.0108
0.0211
pct70
0.0059
0.0108
0.0211
pct75
0.0059
0.0108
0.0211
pctSO
0.0089
0.0137
0.0232
pctS 5
0.0118
0.0167
0.0253
pct90
0.0118
0.0167
0.0253
pct95
0.0118
0.0167
0.0253
max
0.0118
0.0167
0.0253
2B-13
-------
Appendix 2B
Table 2B-8. Pb-PM2.5 monitoring site information
site
010050002
010730023
010731009
010732003
010890014
010970003
011011002
011030011
011130001
020200018
020900010
040130019
040134009
040137003
040137020
040138006
040139997
040139998
040191028
050030005
051190007
051450001
060070002
060190008
060250005
060290014
060371103
060631009
060658001
060670006
060670010
060730003
060731002
060850005
060990005
061072002
061112002
080010006
080410011
080670008
080770003
080770017
081230008
090090027
100010003
100032004
110010042
110010043
120330004
120571075
120573002
120730012
120861016
121030026
130210007
poc
5
5
5
5
5
5
5
5
5
5
6
5
5
5
5
5
7
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
5
6
5
5
5
5
5
5
lat
31.66414
33.55306
33.45972
33.49972
34.69083
30.76972
32.40694
34.51861
32.47639
61.20667
64.84111
33.48385
33.40642
33.28936
33.47333
33.43671
33.50364
33.45513
32.29515
33.13944
34.75611
35.24861
39.75750
36.78139
32.67611
35.35611
34.06659
39.80833
33.99958
38.61417
38.55833
32.79139
33.12778
37.34850
37.64167
36.33222
34.27750
39.82574
38.83139
37.26861
39.09083
39.06363
40.20917
41.30111
39.15500
39.73944
38.88083
38.91889
30.52500
28.05000
27.96565
30.43972
25.79417
27.85004
32.77944
long
-85.60623
-86.81500
-87.30556
-86.92417
-86.58306
-88.08750
-86.25639
-86.97694
-84.99917
-149.82083
-147.72000
-112.14257
-112.14434
-112.15732
-111.85418
-112.09141
-112.09500
-111.99610
-110.98230
-91.95000
-92.27583
-91.71528
-121.84222
-119.77222
-115.48333
-119.04028
-118.22688
-120.47167
-117.41601
-121.36694
-121.49194
-116.94167
-117.07417
-121.89500
-120.99361
-119.29028
-118.68472
-104.93699
-104.82778
-107.87500
-108.56389
-108.56102
-104.82306
-72.90278
-75.51806
-75.55806
-77.03250
-77.01250
-87.20417
-82.37806
-82.23040
-84.34833
-80.20611
-82.71459
-83.64694
state
AL
AL
AL
AL
AL
AL
AL
AL
AL
AK
AK
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AR
AR
AR
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
CO
CO
CO
CO
CT
DE
DE
DC
DC
FL
FL
FL
FL
FL
FL
OA
county name
Harbour
Jefferson
Jefferson
Jefferson
Madison
Mobile
Montgomery
Morgan
Russell
Anchorage MunicipE
Fairbanks North Star
Maricopa
Maricopa
Maricopa
Maricopa
Maricopa
Maricopa
Maricopa
Pima
Ashley
Pulaski
White
Butte
Fresno
Imperial
Kern
Los Angeles
Plumas
Riverside
Sacramento
Sacramento
San Diego
San Diego
Santa Clara
Stanislaus
Tulare
Ventura
Adams
El Paso
La Plata
Mesa
Mesa
Weld
New Haven
Kent
New Castle
District of Columbia
District of Columbia
Escambia
Hillsborough
Hillsborough
Leon
Miami-Dade
Pinellas
Bibb
cbsa name
Eufaula, AL-OA
Birmingham-Hoover, AL
Birmingham-Hoover, AL
Birmingham-Hoover, AL
Huntsville, AL
Mobile, AL
Montgomery, AL
Decatur, AL
Columbus, GA-AL
Anchorage, AK
Fairbanks, AK
Phoenix-Mesa-Scottsdale, AZ
Phoenix-Mesa-Scottsdale, AZ
Phoenix-Mesa-Scottsdale, AZ
Phoenix-Mesa-Scottsdale, AZ
Phoenix-Mesa-Scottsdale, AZ
Phoenix-Mesa-Scottsdale, AZ
Phoenix-Mesa-Scottsdale, AZ
Tucson, AZ
Little Rock-North Little Rock, AR
Searcy, AR
Chico, CA
Fresno, CA
El Centro, CA
Bakersfield, CA
Los Angeles-Long Beach-Santa Ai
Riverside-San Bernardino-Ontario
Sacramento-Arden- Arcade—Rose
Sacramento-Arden- Arcade— Rose
San Diego-Carlsbad-San Marcos, (
San Diego-Carlsbad-San Marcos, (
San Jose-Sunnyvale-Santa Clara, C
Modesto, CA
Visalia-Porterville, CA
Oxnard- Thousand Oaks-Ventura, C
Denver-Aurora, CO
Colorado Springs, CO
Durango, CO
Grand Junction, CO
Grand Junction, CO
Greeley, CO
New Haven-Milford, CT
Dover, DE
Phil adelphia-Camden- Wilmington
Washington- Arlington- Alexandria
Washington- Arlington- Alexandria
Pensacola-Ferry Pass-Brent, FL
Tampa-St. Petersburg-Clearwater,
Tampa-St. Petersburg-Clearwater,
Tallahassee, FL
Miami-Fort Lauderdale- Miami Be;
Tampa-St. Petersburg-Clearwater,
Macon, GA
cbsa_popOO
31,636
1,052,238
1,052,238
1,052,238
342,376
399,843
346,528
145,867
281,768
319,605
82,840
3,251,876
3,251,876
3,251,876
3,251,876
3,251,876
3,251,876
3,251,876
843,746
610,518
67,165
203,171
799,407
142,361
661,645
12,365,627
3,254,821
1,796,857
1,796,857
2,813,833
2,813,833
1,735,819
446,997
368,021
753,197
2,157,756
537,484
43,941
116,255
116,255
180,936
824,008
126,697
5,687,147
4,796,183
4,796,183
412,153
2,395,997
2,395,997
320,304
5,007,564
2,395,997
222,368
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
data completeness
(complete periods)
qtrs
2
3
3
3
3
3
3
3
1
1
1
2
1
1
1
1
3
1
3
2
3
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
1
1
2
3
2
3
3
1
3
3
1
2
3
3
1
3
years
8
12
12
12
12
12
12
11
3
6
3
7
4
4
4
5
12
6
12
9
12
9
12
12
12
11
12
12
12
12
12
12
12
12
12
12
12
12
12
3
3
9
12
8
12
12
6
12
12
4
8
12
12
5
12
months
25
36
34
36
34
36
34
30
9
17
8
20
9
10
11
13
36
18
35
21
33
22
36
36
36
32
36
36
36
36
36
36
36
36
36
36
33
36
34
8
8
25
36
20
34
32
18
36
36
12
24
36
36
16
34
3-year metrics
annual
mean
0.0026
0.0180
0.0021
0.0450
0.0024
0.0038
0.0045
0.0029
0.0030
0.0043
0.0034
0.0030
0.0062
0.0027
0.0026
0.0042
0.0027
0.0033
0.0017
0.0027
0.0029
0.0026
0.0026
0.0030
0.0119
0.0026
0.0053
0.0025
0.0058
0.0022
0.0029
0.0039
0.0035
0.0026
0.0033
0.0034
0.0020
0.0077
0.0019
0.0014
0.0015
0.0023
0.0020
0.0029
0.0024
0.0042
0.0037
0.0035
0.0019
0.0023
0.0027
0.0020
0.0020
0.0025
0.0029
max
quarterly
mean
0.0033
0.0296
0.0032
0.0967
0.0040
0.0060
0.0083
0.0042
0.0037
0.0067
0.0053
0.0057
0.0123
0.0049
0.0038
0.0067
0.0047
0.0047
0.0022
0.0055
0.0042
0.0046
0.0039
0.0050
0.0172
0.0046
0.0098
0.0041
0.0088
0.0031
0.0037
0.0059
0.0050
0.0063
0.0065
0.0046
0.0032
0.0163
0.0028
0.0016
0.0021
0.0035
0.0034
0.0043
0.0038
0.0084
0.0058
0.0063
0.0026
0.0034
0.0042
0.0034
0.0068
0.0039
0.0069
max
monthly
mean
0.0053
0.0475
0.0044
0.2091
0.0057
0.0096
0.0115
0.0060
0.0063
0.0101
0.0070
0.0100
0.0228
0.0067
0.0058
0.0084
0.0069
0.0075
0.0035
0.0082
0.0061
0.0063
0.0054
0.0066
0.0342
0.0061
0.0228
0.0054
0.0151
0.0047
0.0052
0.0078
0.0064
0.0138
0.0090
0.0060
0.0042
0.0185
0.0048
0.0024
0.0031
0.0056
0.0054
0.0066
0.0051
0.0114
0.0075
0.0093
0.0042
0.0052
0.0069
0.0049
0.0163
0.0088
0.0147
2B-14
-------
Appendix 2B
Table 2B-8. Pb-PM2.5 monitoring site information
site
130510017
130590001
130690002
130890002
131150005
132150011
132450091
132950002
150032004
160270004
170310057
170310076
170314201
170434002
171150013
171192009
180030004
180372001
180390003
180650003
180890022
180892004
180970078
181411008
181630012
191130037
191530030
191630015
201730010
202090021
210190017
210590005
210590014
210670012
211110043
211110048
211170007
211250004
211451004
211930003
212270007
220150008
220330009
240030019
240053001
240330030
250130008
250250042
260050003
260330901
260770008
260810020
261130001
261150005
261610008
poc
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
5
5
5
5
5
5
5
lat
32.09278
33.94583
31.52430
33.68750
34.26333
32.43083
33.43333
34.96611
21.39667
43.56240
41.91473
41.75137
42.14000
41.77120
39.86694
38.90278
41.09472
38.39139
41.66778
40.01167
41.60667
41.58528
39.81110
41.69361
38.02167
42.00833
41.60306
41.53000
37.70111
39.11750
38.45917
37.78083
37.74111
38.06500
38.23222
38.24056
39.07250
37.08722
37.06556
37.28306
36.99333
32.53417
30.46111
39.10111
39.31083
39.05528
42.19446
42.32944
42.76778
46.49361
42.27806
42.98417
44.31056
41.76389
42.24056
long
-81.14417
-83.37222
-82.76510
-84.29028
-85.27250
-84.93167
-82.02194
-85.29750
-157.97167
-116.56323
-87.72273
-87.71375
-87.79917
-88.15250
-88.92556
-90.14306
-85.10194
-86.92917
-85.96944
-85.52361
-87.30472
-87.47444
-86.11447
-86.23667
-87.56944
-91.67861
-93.64306
-90.58750
-97.31389
-94.63556
-82.64056
-87.07556
-87.11806
-84.50000
-85.82528
-85.73167
-84.52500
-84.06333
-88.63778
-83.22028
-86.41833
-93.74972
-91.17694
-76.72944
-76.47444
-76.87833
-72.55571
-71.08278
-86.14861
-84.36417
-85.54194
-85.67139
-84.89194
-83.47194
-83.59972
state
OA
OA
OA
OA
OA
OA
OA
OA
HI
ID
IL
IL
IL
IL
IL
IL
IN
IN
IN
IN
IN
IN
IN
IN
IN
IA
IA
IA
KS
KS
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
LA
LA
MD
MD
MD
MA
MA
MI
MI
MI
MI
MI
MI
MI
county name
Chatham
Clarke
Coffee
DeKalb
Floyd
Muscogee
Richmond
Walker
Honolulu
Canyon
Cook
Cook
Cook
DuPage
Macon
Madison
Allen
Dubois
Elkhart
Henry
Lake
Lake
Marion
St. Joseph
Vanderburgh
Linn
Polk
Scott
Sedgwick
Wyandotte
Boyd
Daviess
Daviess
Fayette
Jefferson
Jefferson
Kenton
Laurel
McCracken
Perry
Warren
Bossier
East Baton Rouge
Anne Anindel
Baltimore
Prince George's
Hampden
Suffolk
Allegan
Chippewa
Kalamazoo
Kent
Missaukee
Monroe
Washtenaw
cbsa name
Savannah, GA
Athens-Clarke County, OA
Douglas, GA
Atlanta-Sandy Springs-Marietta, G
Rome, GA
Columbus, GA-AL
Augusta-Richmond County, GA-S
Chattanooga, TN-OA
Honolulu, HI
Boise City-Nampa, ID
Chicago-Naperville-Joliet, IL-IN-A
Chicago-Naperville-Joliet, IL-IN-A
Chicago-Naperville-Joliet, IL-IN-A
Chicago-Naperville-Joliet, IL-IN-A
Decatur, IL
St. Louis, MO-IL
Fort Wayne, IN
Jasper, IN
Elkhart-Goshen, IN
New Castle, IN
Chicago-Naperville-Joliet, IL-IN-A
Chicago-Naperville-Joliet, IL-IN-A
Indianapolis-Carmel, IN
South Bend-Mishawaka, IN-MI
Evansville, IN-KY
Cedar Rapids, IA
Des Moines-West Des Moines, IA
Davenport-Moline-Rock Island, IA
Wichita, KS
Kansas City, MO-KS
Huntington-Ashland, WV-KY-OH
Owensboro, KY
Owensboro, KY
Lexington-Fayette, KY
Louisville- Jefferson County, KY-I
Louisville- Jefferson County, KY-I
Cincinnati-Middletown, OH-KY-I
London, KY
Paducah, KY-IL
Bowling Green, KY
Shreveport-Bossier City, LA
Baton Rouge, LA
Baltimore- Towson, MD
Baltimore- Towson, MD
Washington- Arlington- Alexandria
Springfield, MA
Boston-Cambridge-Quincy, MA-Is
Allegan, MI
Sault Ste. Marie, MI
Kalamazoo-Portage, MI
Grand Rapids-Wyoming, MI
Cadillac, MI
Monroe, MI
Ann Arbor, MI
cbsa_popOO
293,000
166,079
45,022
4,247,981
90,565
281,768
499,684
476,531
876,156
464,840
9,098,316
9,098,316
9,098,316
9,098,316
114,706
2,721,491
390,156
52,511
182,791
48,508
9,098,316
9,098,316
1,525,104
316,663
342,815
237,230
481,394
376,019
571,166
1,836,038
288,649
109,875
109,875
408,326
1,161,975
1,161,975
2,009,632
52,715
98,765
104,166
375,965
705,973
2,552,994
2,552,994
4,796,183
680,014
4,391,344
105,665
38,543
314,866
740,482
44,962
145,945
322,895
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
data completeness
(complete periods)
qtrs
2
3
3
3
3
3
3
1
3
3
3
3
3
2
3
3
2
1
1
3
3
2
3
1
3
3
3
3
2
3
3
1
2
3
3
3
3
3
3
3
3
3
3
2
2
1
2
3
3
3
3
3
3
3
2
years
8
12
12
12
12
12
12
3
12
12
12
12
12
8
12
12
7
4
4
12
11
8
12
4
12
12
12
12
9
12
12
4
8
12
12
12
12
12
12
12
12
12
11
7
9
4
10
12
11
12
11
12
12
12
10
months
22
29
30
36
33
32
32
9
34
36
35
36
36
23
35
32
20
12
12
36
32
24
36
12
34
35
33
33
18
36
35
12
19
36
36
36
36
36
36
34
35
32
31
17
26
12
25
35
35
36
33
35
33
32
30
3-year metrics
annual
mean
0.0017
0.0021
0.0013
0.0027
0.0023
0.0036
0.0025
0.0033
0.0010
0.0022
0.0071
0.0054
0.0040
0.0047
0.0067
0.0090
0.0257
0.0042
0.0044
0.0037
0.0097
0.0090
0.0048
0.0042
0.0031
0.0033
0.0027
0.0065
0.0021
0.0048
0.0043
0.0037
0.0023
0.0038
0.0042
0.0048
0.0048
0.0037
0.0029
0.0041
0.0033
0.0046
0.0051
0.0033
0.0054
0.0039
0.0025
0.0027
0.0035
0.0023
0.0050
0.0048
0.0022
0.0042
0.0038
max
quarterly
mean
0.0029
0.0029
0.0022
0.0042
0.0030
0.0101
0.0038
0.0040
0.0021
0.0046
0.0115
0.0063
0.0054
0.0063
0.0142
0.0208
0.1674
0.0051
0.0048
0.0055
0.0128
0.0120
0.0071
0.0054
0.0057
0.0044
0.0037
0.0084
0.0032
0.0066
0.0060
0.0044
0.0038
0.0066
0.0070
0.0071
0.0106
0.0048
0.0043
0.0059
0.0056
0.0089
0.0101
0.0061
0.0080
0.0069
0.0035
0.0039
0.0055
0.0038
0.0068
0.0083
0.0057
0.0050
0.0060
max
monthly
mean
0.0041
0.0041
0.0032
0.0077
0.0040
0.0086
0.0067
0.0051
0.0031
0.0096
0.0172
0.0087
0.0085
0.0072
0.0228
0.0413
0.3091
0.0063
0.0056
0.0074
0.0204
0.0244
0.0087
0.0072
0.0080
0.0071
0.0058
0.0118
0.0053
0.0100
0.0096
0.0061
0.0036
0.0101
0.0100
0.0133
0.0170
0.0095
0.0059
0.0079
0.0098
0.0147
0.0198
0.0087
0.0101
0.0099
0.0045
0.0056
0.0079
0.0046
0.0097
0.0104
0.0102
0.0074
0.0087
2B-15
-------
Appendix 2B
Table 2B-8. Pb-PM2.5 monitoring site information
site
261630001
261630033
270530963
270953051
271095008
271230871
280350004
280430001
280470008
280490018
280670002
290470005
290530001
290990012
291860005
292070001
295100085
300530018
300630031
310550019
320030560
320030561
320310016
330110020
330150014
340070003
340230006
340273001
340390004
350010023
360050083
360050110
360290005
360310003
360551007
360556001
360610062
360632008
360710002
360810124
361010003
361030001
370210034
370350004
370510009
370570002
370670022
370810013
371070004
371190041
371590021
371830014
380150003
380171004
380530002
poc
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
5
5
5
5
5
5
5
5
5
6
5
5
5
6
5
6
5
5
5
i
i
i
6
5
i
5
5
5
5
5
5
5
5
5
5
5
5
5
lat
42.22861
42.30667
44.95540
46.20703
43.99691
44.96145
31.32364
33.83611
30.39014
32.29681
31.68844
39.30306
38.79500
38.43778
37.89694
36.97000
38.65630
48.38417
46.87491
41.24722
36.15861
36.16399
39.52508
43.00056
43.07528
39.92304
40.47279
40.78763
40.64144
35.13426
40.86586
40.81616
42.87684
44.39309
43.14620
43.16100
40.72052
43.08216
41.49947
40.73620
42.09071
40.74583
35.60972
35.72889
35.04142
35.81444
36.11056
36.10917
35.23146
35.24028
35.55187
35.85611
46.82543
46.93375
47.58120
long
-83.20833
-83.14889
-93.25827
-93.75941
-92.45037
-93.03589
-89.28717
-89.79722
-89.04972
-90.18831
-89.13506
-94.37639
-92.91806
-90.36139
-90.42222
-90.14000
-90.19810
-115.54806
-113.99525
-95.97556
-115.11083
-115.11393
-119.80772
-71.46806
-70.74806
-75.09762
-74.42251
-74.67630
-74.20836
-106.58551
-73.88075
-73.90207
-78.80988
-73.85892
-77.54813
-77.60357
-74.00409
-79.00099
-74.00973
-73.82317
-77.21025
-73.42028
-82.35083
-81.36556
-78.95311
-80.26250
-80.22667
-79.80111
-77.56879
-80.78556
-80.39504
-78.57417
-100.76821
-96.85535
-103.29950
state
MI
MI
MN
MN
MN
MN
MS
MS
MS
MS
MS
MO
MO
MO
MO
MO
MO
MT
MT
NE
NV
NV
NV
NH
NH
NJ
NJ
NJ
NJ
NM
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
ND
ND
ND
county name
Wayne
Wayne
Hennepin
Mille Lacs
Olmsted
Ramsey
Forrest
Grenada
Harrison
Hinds
Jones
Clay
Cooper
Jefferson
Ste Genevieve
Stoddard
St. Louis (City)
Lincoln
Missoula
Douglas
Clark
Clark
Washoe
Hillsborough
Rockingham
Camden
Middlesex
Morris
Union
Bernalillo
Bronx
Bronx
Erie
Essex
Monroe
Monroe
New York
Niagara
Orange
Queens
Steuben
Suffolk
Buncombe
Catawba
Cumberland
Davidson
Forsyth
Guilford
Lenoir
Mecklenburg
Rowan
Wake
Burleigh
Cass
Me Kenzie
cbsa name
Detroit- Warren-Livonia, MI
Detroit- Warren-Livonia, MI
Minneapolis-St. Paul-Bloomingtor
Rochester, MN
Minneapolis-St. Paul-Bloomingtor
Hattiesburg, MS
Grenada, MS
Gulfport-Biloxi, MS
Jackson, MS
Laurel, MS
Kansas City, MO-KS
St. Louis, MO-IL
St. Louis, MO-IL
Missoula, MT
Omaha-Council Bluffs, NE-IA
Las Vegas-Paradise, NV
Las Vegas-Paradise, NV
Reno-Sparks, NV
Manchester-Nashua, NH
Boston-Cambridge-Quincy, MA-Is
Philadelphia-Camden- Wilmington
New York-Northern New Jersey-L
New York-Northern New Jersey-L
New York-Northern New Jersey-L
Albuquerque, NM
New York-Northern New Jersey-L
New York-Northern New Jersey-L
Buffalo-Niagra Falls, NY Metropo
Rochester, NY
Rochester, NY
New York-Northern New Jersey-L
Buffalo-Niagra Falls, NY Metropo
Poughkeepsie-Newburgh-Middletc
New York-Northern New Jersey-L
Corning, NY
New York-Northern New Jersey-L
Asheville, NC
Hickory-Lenoir-Morganton, NC
Fayetteville, NC
Thomasville-Lexington, NC
Winston-Salem, NC
Greensboro-High Point, NC
Kinston, NC
Charlotte-Gastonia-Concord, NC-'
Salisbury, NC
Raleigh-Cary, NC
Bismarck, ND
Fargo, ND-MN
cbsa_popOO
4,452,557
4,452,557
2,968,806
163,618
2,968,806
123,812
23,263
246,190
497,197
83,107
1,836,038
2,721,491
2,721,491
95,802
767,041
1,375,765
1,375,765
342,885
380,841
4,391,344
5,687,147
18,323,002
18,323,002
18,323,002
729,649
18,323,002
18,323,002
1,170,111
1,037,831
1,037,831
18,323,002
1,170,111
621,517
18,323,002
98,726
18,323,002
369,171
341,851
336,609
147,246
421,961
643,430
59,648
1,330,448
130,340
797,071
94,719
174,367
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
data completeness
(complete periods)
qtrs
3
3
3
3
3
2
3
3
3
3
3
3
3
3
3
1
3
3
3
3
1
2
3
3
3
3
3
3
3
2
3
3
3
3
2
1
1
1
1
3
3
1
3
3
2
2
3
2
3
3
1
3
3
3
3
years
12
12
12
11
12
9
12
11
11
12
12
12
12
12
12
4
12
12
12
12
5
7
12
12
12
11
12
12
12
8
12
12
12
12
7
5
4
4
4
12
12
4
12
12
8
8
12
9
12
12
4
12
12
12
12
months
36
33
36
31
35
27
35
31
32
31
35
36
33
36
34
11
36
35
36
35
15
20
36
34
36
33
24
35
36
22
36
36
36
34
20
15
12
11
11
36
36
11
36
35
22
23
36
23
34
36
11
34
36
36
35
3-year metrics
annual
mean
0.0042
0.0118
0.0031
0.0017
0.0027
0.0042
0.0048
0.0017
0.0023
0.0046
0.0030
0.0026
0.0020
0.0089
0.0045
0.0034
0.0095
0.0017
0.0020
0.0030
0.0025
0.0025
0.0024
0.0034
0.0024
0.0042
0.0045
0.0027
0.0044
0.0013
0.0040
0.0047
0.0106
0.0015
0.0031
0.0031
0.0070
0.0052
0.0034
0.0038
0.0028
0.0032
0.0019
0.0025
0.0021
0.0032
0.0026
0.0028
0.0026
0.0029
0.0032
0.0021
0.0012
0.0019
0.0012
max
quarterly
mean
0.0051
0.0182
0.0041
0.0023
0.0043
0.0073
0.0128
0.0032
0.0034
0.0071
0.0073
0.0040
0.0028
0.0126
0.0084
0.0044
0.0140
0.0029
0.0035
0.0042
0.0039
0.0044
0.0040
0.0053
0.0028
0.0052
0.0063
0.0038
0.0059
0.0020
0.0059
0.0064
0.0157
0.0021
0.0040
0.0037
0.0092
0.0063
0.0040
0.0055
0.0034
0.0039
0.0031
0.0036
0.0037
0.0047
0.0036
0.0043
0.0046
0.0042
0.0040
0.0038
0.0023
0.0027
0.0026
max
monthly
mean
0.0063
0.0329
0.0072
0.0036
0.0067
0.0084
0.0302
0.0056
0.0062
0.0112
0.0180
0.0050
0.0041
0.0191
0.0095
0.0068
0.0192
0.0039
0.0051
0.0055
0.0061
0.0086
0.0060
0.0062
0.0036
0.0069
0.0114
0.0059
0.0067
0.0027
0.0067
0.0079
0.0192
0.0028
0.0048
0.0045
0.0190
0.0065
0.0053
0.0068
0.0042
0.0051
0.0052
0.0060
0.0057
0.0087
0.0063
0.0064
0.0062
0.0052
0.0057
0.0041
0.0036
0.0038
0.0040
2B-16
-------
Appendix 2B
Table 2B-8. Pb-PM2.5 monitoring site information
site
390171004
390350038
390350060
390490081
390530003
390610040
390610042
390810017
390870010
390930016
390933002
390950026
390990014
391130031
391510017
391510020
391530023
400450890
401091037
401431127
410170120
410290133
410390060
410510246
410610119
420010001
420030008
420030021
420030064
420270100
420290100
420430401
420450002
420490003
420692006
420710007
420950025
420990301
421010004
421010136
421255001
421290008
421330008
440070022
440071010
450190046
450190049
450250001
450450009
450790019
460990006
470370023
470654002
470931020
470990002
poc
5
6
5
6
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
5
5
6
5
6
5
5
5
5
5
5
5
5
5
7
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
lat
39.53000
41.47694
41.49396
40.08778
38.94996
39.12861
39.10500
40.36610
38.51972
41.43944
41.46306
41.62056
41.09587
39.75944
40.78667
40.80056
41.08806
36.08518
35.61278
36.20490
44.06390
42.31408
44.02631
45.56130
45.33897
39.92000
40.46556
40.41361
40.32361
40.81139
39.83444
40.24500
39.83556
42.14175
41.44278
40.04667
40.62806
40.45694
40.00889
39.92750
40.44528
40.30469
39.96528
41.80795
41.84092
32.94275
32.79098
34.61712
34.90105
33.99330
43.54429
36.17633
35.05093
36.01944
35.11611
long
-84.39250
-81.68194
-81.67854
-82.95972
-82.10910
-84.50417
-84.55111
-80.61500
-82.66556
-82.16167
-82.11444
-83.64139
-80.65843
-84.14444
-81.39444
-81.37333
-81.54167
-99.93494
-97.47222
-95.97654
-121.31258
-122.87924
-123.08374
-122.67878
-117.90480
-77.31000
-79.96111
-79.94139
-79.86833
-77.87703
-75.76861
-76.84472
-75.37250
-80.03861
-75.62306
-76.28333
-75.34111
-77.16556
-75.09778
-75.22278
-80.42083
-79.50567
-76.69944
-71.41500
-71.36094
-79.65718
-79.95869
-80.19879
-82.31307
-81.02414
-96.72644
-86.73890
-85.12631
-83.87361
-87.47000
state
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OK
OK
OK
OR
OR
OR
OR
OR
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
RI
RI
SC
SC
SC
SC
SC
SD
TN
TN
TN
TN
county name
Butler
Cuyahoga
Cuyahoga
Franklin
Oallia
Hamilton
Hamilton
Jefferson
Lawrence
Lorain
Lorain
Lucas
Mahoning
Montgomery
Stark
Stark
Summit
Ellis
Oklahoma
Tulsa
Deschutes
Jackson
Lane
Multnomah
Union
Adams
Allegheny
Allegheny
Allegheny
Centre
Chester
Dauphin
Delaware
Erie
Lackawanna
Lancaster
Northampton
Perry
Philadelphia
Philadelphia
Washington
Westmoreland
York
Providence
Providence
Charleston
Charleston
Chesterfield
Greenville
Richland
Minnehaha
Davidson
Hamilton
Knox
Lawrence
cbsa name
Cincinnati-Middletown, OH-KY-I
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Columbus, OH
Point Pleasant, WV-OH
Cincinnati-Middletown, OH-KY-I
Cincinnati-Middletown, OH-KY-I
Weirton-Steubenville, WV-OH
Huntington-Ashland, WV-KY-OH
Cleveland-Elyria-Mentor, OH
Cleveland-Elyria-Mentor, OH
Toledo, OH
Youngstown-Warren-Boardman, C
Dayton, OH
Canton-Massillon, OH
Canton-Massillon, OH
Akron, OH
Oklahoma City, OK
Tulsa, OK
Bend, OR
Medford, OR
Eugene-Springfield, OR
Portland- Vancouver-Beaverton, O
La Grande, OR
Gettysburg, PA
Pittsburgh, PA
Pittsburgh, PA
Pittsburgh, PA
State College, PA
Phil adelphia-Camden- Wilmington
Harrisburg-Carlisle, PA
Phil adelphia-Camden- Wilmington
Erie, PA
Scranton-Wilkes-Barre, PA
Lancaster, PA
Allentown-Bethlehem-Easton, PA-
Harrisburg-Carlisle, PA
Phil adelphia-Camden- Wilmington
Phil adelphia-Camden- Wilmington
Pittsburgh, PA
Pittsburgh, PA
York-Hanover, PA
Providence-New Bedford-Fall Riv
Providence-New Bedford-Fall Riv
Charleston-North Charleston, SC
Charleston-North Charleston, SC
Greenville, SC
Columbia, SC
Sioux Falls, SD
Nashville-Davidson— Murfrees bore
Chattanooga, TN-GA
Knoxville, TN
Lawrenceburg, TN
cbsa_popOO
2,009,632
2,148,143
2,148,143
1,612,694
57,026
2,009,632
2,009,632
132,008
288,649
2,148,143
2,148,143
659,188
602,964
848,153
406,934
406,934
694,960
1,095,421
859,532
115,367
181,269
322,959
1,927,881
24,530
91,292
2,431,087
2,431,087
2,431,087
135,758
5,687,147
509,074
5,687,147
280,843
560,625
470,658
740,395
509,074
5,687,147
5,687,147
2,431,087
2,431,087
381,751
1,582,997
1,582,997
549,033
549,033
559,940
647,158
187,093
1,311,789
476,531
616,079
39,926
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
data completeness
(complete periods)
qtrs
3
3
3
3
1
2
1
1
3
1
2
3
3
3
1
2
3
3
3
3
1
3
3
3
2
3
3
1
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
1
2
3
3
3
3
3
3
3
3
3
years
12
12
12
12
6
8
4
5
12
4
8
12
12
11
4
8
11
12
12
12
4
12
12
12
8
12
12
3
9
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
5
6
12
12
12
12
12
12
12
11
12
months
36
35
36
34
16
25
11
11
33
10
20
36
35
25
10
24
29
34
36
36
11
35
35
36
20
35
36
9
23
35
32
34
34
34
33
35
36
36
36
34
36
35
34
36
14
17
36
36
36
34
36
34
34
29
35
3-year metrics
annual
mean
0.0092
0.0120
0.0123
0.0038
0.0043
0.0056
0.0079
0.0127
0.0059
0.0157
0.0238
0.0035
0.0131
0.0042
0.0114
0.0060
0.0050
0.0012
0.0022
0.0031
0.0014
0.0019
0.0015
0.0075
0.0012
0.0037
0.0112
0.0073
0.0143
0.0032
0.0046
0.0063
0.0042
0.0057
0.0054
0.0073
0.0065
0.0035
0.0052
0.0038
0.0050
0.0051
0.0058
0.0065
0.0030
0.0019
0.0022
0.0021
0.0026
0.0048
0.0022
0.0038
0.0038
0.0040
0.0021
max
quarterly
mean
0.0147
0.0163
0.0207
0.0052
0.0072
0.0069
0.0114
0.0150
0.0095
0.0244
0.0337
0.0053
0.0253
0.0079
0.0148
0.0082
0.0069
0.0019
0.0033
0.0045
0.0018
0.0029
0.0025
0.0182
0.0020
0.0070
0.0141
0.0083
0.0239
0.0043
0.0086
0.0122
0.0057
0.0153
0.0087
0.0175
0.0095
0.0056
0.0071
0.0061
0.0067
0.0070
0.0112
0.0432
0.0037
0.0026
0.0035
0.0035
0.0050
0.0092
0.0031
0.0065
0.0050
0.0052
0.0030
max
monthly
mean
0.0273
0.0282
0.0270
0.0073
0.0085
0.0113
0.0286
0.0193
0.0137
0.0450
0.0465
0.0069
0.0382
0.0085
0.0186
0.0157
0.0098
0.0027
0.0046
0.0056
0.0021
0.0035
0.0041
0.0398
0.0026
0.0082
0.0252
0.0129
0.0356
0.0061
0.0105
0.0190
0.0073
0.0323
0.0115
0.0231
0.0152
0.0084
0.0090
0.0104
0.0084
0.0097
0.0169
0.1103
0.0051
0.0039
0.0048
0.0044
0.0060
0.0122
0.0052
0.0107
0.0071
0.0059
0.0040
2B-17
-------
Appendix 2B
Table 2B-8. Pb-PM2.5 monitoring site information
site
471570047
471631007
471650007
480430002
480430101
481130050
481130069
481390015
481410044
481410053
481670014
482010024
482010026
482010055
482011034
482011039
482030002
482430004
482450022
482570005
482730314
483030001
483390078
483550034
483611100
490110004
490353006
490494001
500070012
510870014
511390004
515200006
517600020
517700014
530330024
530330032
530330048
530330057
530330080
530630016
540390011
540391005
540511002
550270007
550590019
550710007
550790026
551198001
551330027
720610001
780100012
poc
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
7
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
5
5
5
5
5
5
5
5
5
5
5
5
lat
35.16895
36.54065
36.29778
30.36580
29.30250
32.77417
32.81995
32.43694
31.76567
31.75852
29.26332
29.90111
29.80250
29.69574
29.76799
29.67005
32.66900
30.66938
29.86395
32.56917
27.42694
33.59085
30.35030
27.81180
30.19417
40.90297
40.73639
40.34139
44.48028
37.55833
38.66333
36.60778
37.51056
37.25611
47.75333
47.54556
47.61846
47.56333
47.57027
47.66083
38.44861
38.36806
39.91597
43.43500
42.50472
44.13861
43.06111
45.20389
43.02028
18.42472
17.71444
long
-90.02157
-82.52167
-86.65278
-103.64910
-103.16782
-96.79778
-96.86008
-97.02500
-106.45523
-106.50105
-94.85657
-95.32694
-95.12555
-95.49924
-95.22058
-95.12849
-94.16745
-104.02463
-94.31776
-96.31583
-97.29861
-101.84759
-95.42514
-97.46563
-93.86694
-111.88447
-111.87222
-111.71361
-73.21444
-77.40028
-78.50472
-82.16444
-77.49833
-79.98500
-122.27722
-122.32222
-122.32972
-122.33833
-122.30860
-117.35722
-81.68389
-81.69361
-80.73406
-88.52778
-87.80930
-87.61611
-87.91250
-90.60000
-88.21500
-66.11639
-64.78528
state
TN
TN
TN
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
UT
UT
UT
VT
VA
VA
VA
VA
VA
WA
WA
WA
WA
WA
WA
WV
WV
WV
WI
WI
WI
WI
WI
WI
PR
VI
county name
Shelby
Sullivan
Sumner
Brewster
Brewster
Dallas
Dallas
Ellis
El Paso
El Paso
Galveston
Harris
Harris
Harris
Harris
Harris
Harrison
Jeff Davis
Jefferson
Kaufman
Kleberg
Lubbock
Montgomery
Nueces
Orange
Davis
Salt Lake
Utah
Chittenden
Henri co
Page
Bristol (City)
Richmond (City)
Roanoke (City)
King
King
King
King
King
Spokane
Kanawha
Kanawha
Marshall
Dodge
Kenosha
Manitowoc
Milwaukee
Taylor
Waukesha
Guaynabo
St Croix
cbsa name
Memphis, TN-MS-AR
Kingsport-Bristol-Bristol, TN-VA
Nashville-Davidson— Murfrees bore
Dallas-Fort Worth-Arlington, TX
Dallas-Fort Worth-Arlington, TX
Dallas-Fort Worth-Arlington, TX
El Paso, TX
El Paso, TX
Houston-Sugar Land-Baytown, T>
Houston-Sugar Land-Baytown, T>
Houston-Sugar Land-Baytown, T>
Houston-Sugar Land-Baytown, T>
Houston-Sugar Land-Baytown, T>
Houston-Sugar Land-Baytown, T>
Marshall, TX
Beaumont-Port Arthur, TX
Dallas-Fort Worth-Arlington, TX
Kingsville, TX
Lubbock, TX
Houston-Sugar Land-Baytown, T>
Corpus Christi, TX
Beaumont-Port Arthur, TX
Ogden-Clearfield, UT
Salt Lake City, UT
Provo-Orem, UT
Burlington-South Burlington, VT
Richmond, VA
Kingsport-Bristol-Bristol, TN-VA
Richmond, VA
Roanoke, VA
Seattle-Tacoma-Bellevue, WA
Seattle-Tacoma-Bellevue, WA
Seattle-Tacoma-Bellevue, WA
Seattle-Tacoma-Bellevue, WA
Seattle-Tacoma-Bellevue, WA
Spokane, WA
Charleston, WV
Charleston, WV
Wheeling, WV-OH
Beaver Dam, WI
Chicago-Naperville-Joliet, IL-IN-A
Manitowoc, WI
Milwaukee-Waukesha-West Allis,
Milwaukee-Waukesha-West Allis,
San Juan-Caguas-Guaynabo, PR
cbsa_popOO
1,205,204
298,484
1,311,789
5,161,544
5,161,544
5,161,544
679,622
679,622
4,715,407
4,715,407
4,715,407
4,715,407
4,715,407
4,715,407
62,110
385,090
5,161,544
31,963
249,700
4,715,407
403,280
385,090
442,656
968,858
376,774
198,889
1,096,957
298,484
1,096,957
288,309
3,043,878
3,043,878
3,043,878
3,043,878
3,043,878
417,939
309,635
309,635
153,172
85,897
9,098,316
82,887
1,500,741
1,500,741
2,509,007
urban
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
data completeness
(complete periods)
qtrs
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
3
3
3
3
3
2
3
3
3
2
3
3
3
2
2
3
1
2
3
2
3
3
3
1
2
2
1
3
3
2
3
3
3
3
1
years
12
12
12
11
12
12
12
11
12
12
11
12
11
11
11
10
11
11
11
11
11
10
11
12
11
10
12
12
12
8
8
12
4
8
12
7
12
12
12
4
9
8
6
12
12
8
12
12
12
12
5
months
36
33
32
20
25
34
36
31
34
34
32
35
31
32
31
30
32
25
32
30
29
28
32
36
32
29
36
36
35
24
24
35
12
24
35
22
33
33
36
11
25
25
19
36
36
25
36
36
36
36
12
3-year metrics
annual
mean
0.0033
0.0031
0.0027
0.0014
0.0009
0.0027
0.0036
0.0029
0.0036
0.0078
0.0021
0.0041
0.0028
0.0020
0.0026
0.0023
0.0019
0.0008
0.0019
0.0024
0.0010
0.0010
0.0031
0.0013
0.0020
0.0035
0.0042
0.0034
0.0023
0.0030
0.0027
0.0036
0.0027
0.0074
0.0030
0.0078
0.0032
0.0074
0.0034
0.0038
0.0026
0.0043
0.0065
0.0036
0.0038
0.0039
0.0058
0.0020
0.0097
0.0018
0.0003
max
quarterly
mean
0.0045
0.0049
0.0051
0.0025
0.0018
0.0041
0.0077
0.0057
0.0060
0.0148
0.0028
0.0066
0.0038
0.0026
0.0073
0.0042
0.0027
0.0014
0.0030
0.0063
0.0017
0.0024
0.0042
0.0021
0.0028
0.0059
0.0077
0.0072
0.0029
0.0042
0.0045
0.0057
0.0033
0.0140
0.0046
0.0134
0.0052
0.0150
0.0055
0.0062
0.0043
0.0067
0.0081
0.0059
0.0057
0.0060
0.0115
0.0030
0.0185
0.0026
0.0007
max
monthly
mean
0.0076
0.0086
0.0068
0.0043
0.0028
0.0055
0.0169
0.0085
0.0090
0.0236
0.0041
0.0087
0.0056
0.0037
0.0160
0.0072
0.0035
0.0028
0.0049
0.0128
0.0024
0.0062
0.0058
0.0033
0.0041
0.0071
0.0131
0.0095
0.0037
0.0064
0.0081
0.0083
0.0064
0.0283
0.0073
0.0201
0.0089
0.0260
0.0075
0.0087
0.0048
0.0077
0.0124
0.0083
0.0073
0.0113
0.0245
0.0050
0.0217
0.0058
0.0009
2B-18
-------
Appendix 2B
Table 2B-9. Pb_PM2.5 monitoring site distribution statistics
All sites
annual mean
max quarter mean
max monthly mean
n
271
271
271
min
0.0003
0.0007
0.0009
pct5
0.0014
0.0022
0.0033
pctlO
0.0019
0.0028
0.0040
pct!5
0.0020
0.0030
0.0044
pct20
0.0022
0.0034
0.0050
pct25
0.0024
0.0037
0.0053
pct30
0.0026
0.0039
0.0057
pct35
0.0027
0.0042
0.0061
pct40
0.0029
0.0044
0.0064
pct45
0.0031
0.0047
0.0068
median
0.0033
0.0052
0.0073
mean
0.0043
0.0076
0.0123
pctSS
0.0035
0.0056
0.0079
pct60
0.0038
0.0059
0.0085
petes
0.0040
0.0063
0.0089
pct70
0.0042
0.0067
0.0098
pct75
0.0046
0.0072
0.0112
pctSO
0.0051
0.0083
0.0133
pct8 5
0.0060
0.0101
0.0180
pct90
0.0074
0.0140
0.0228
pct95
0.0112
0.0175
0.0302
max
0.0450
0.1674
0.3091
Source-oriented sites
annual mean
max quarter mean
max monthly mean
n
8
8
8
min
0.0036
0.0067
0.0077
pctS
0.0036
0.0067
0.0077
pctlO
0.0036
0.0067
0.0077
pctlS
0.0043
0.0101
0.0086
pct20
0.0043
0.0101
0.0086
pct25
0.0053
0.0107
0.0136
pct30
0.0063
0.0114
0.0186
pct35
0.0063
0.0114
0.0186
pct40
0.0067
0.0122
0.0190
pct45
0.0067
0.0122
0.0190
median
0.0073
0.0132
0.0191
mean
0.0086
0.0143
0.0215
pctSS
0.0079
0.0142
0.0192
pct60
0.0079
0.0142
0.0192
pct65
0.0106
0.0148
0.0228
pct70
0.0106
0.0148
0.0228
pct75
0.0110
0.0153
0.0257
pctSO
0.0114
0.0157
0.0286
pctS 5
0.0114
0.0157
0.0286
pct90
0.0180
0.0296
0.0475
pct95
0.0180
0.0296
0.0475
max
0.0180
0.0296
0.0475
Not source-oriented sites
annual mean
max quarter mean
max monthly mean
n
263
263
263
min
0.0003
0.0007
0.0009
pctS
0.0014
0.0022
0.0033
pctlO
0.0019
0.0027
0.0040
pctlS
0.0020
0.0030
0.0043
pct20
0.0022
0.0034
0.0049
pct25
0.0024
0.0037
0.0053
pct30
0.0026
0.0039
0.0056
pct35
0.0027
0.0042
0.0060
pct40
0.0029
0.0043
0.0063
pct45
0.0030
0.0046
0.0067
median
0.0032
0.0050
0.0072
mean
0.0042
0.0073
0.0120
pctS 5
0.0034
0.0055
0.0076
pct60
0.0037
0.0057
0.0084
pct65
0.0038
0.0061
0.0087
pct70
0.0042
0.0066
0.0096
pct75
0.0045
0.0070
0.0104
pctSO
0.0048
0.0080
0.0124
pctS 5
0.0057
0.0092
0.0163
pct90
0.0073
0.0128
0.0204
pct95
0.0097
0.0172
0.0283
max
0.0450
0.1674
0.3091
Urban sites
annual mean
max quarter mean
max monthly mean
n
216
216
216
min
0.0003
0.0007
0.0009
pctS
0.0017
0.0025
0.0036
pctlO
0.0020
0.0031
0.0045
pctlS
0.0023
0.0035
0.0051
pct20
0.0024
0.0038
0.0055
pct25
0.0026
0.0040
0.0058
pct30
0.0027
0.0042
0.0061
pct35
0.0030
0.0044
0.0063
pct40
0.0031
0.0047
0.0067
pct45
0.0033
0.0051
0.0072
median
0.0035
0.0055
0.0077
mean
0.0046
0.0082
0.0135
pctS 5
0.0037
0.0059
0.0085
pct60
0.0039
0.0063
0.0088
pct65
0.0042
0.0066
0.0097
pct70
0.0045
0.0071
0.0104
pct75
0.0049
0.0079
0.0123
pctSO
0.0056
0.0092
0.0160
pctS 5
0.0065
0.0115
0.0191
pct90
0.0079
0.0148
0.0244
pct95
0.0118
0.0182
0.0329
max
0.0450
0.1674
0.3091
Urban sites, located in MSA's > 1 million population
annual mean
max quarter mean
max monthly mean
n
99
99
99
min
0.0018
0.0026
0.0036
pctS
0.0022
0.0033
0.0047
pctlO
0.0025
0.0037
0.0052
pctlS
0.0027
0.0039
0.0058
pct20
0.0027
0.0042
0.0064
pct25
0.0030
0.0045
0.0068
pct30
0.0031
0.0051
0.0072
pct35
0.0033
0.0054
0.0075
pct40
0.0036
0.0058
0.0078
pct45
0.0038
0.0061
0.0086
median
0.0041
0.0063
0.0088
mean
0.0055
0.0093
0.0160
pctSS
0.0042
0.0066
0.0099
pct60
0.0047
0.0069
0.0104
pct65
0.0048
0.0071
0.0128
pct70
0.0054
0.0083
0.0160
pct75
0.0065
0.0106
0.0185
pctSO
0.0075
0.0126
0.0201
pctS 5
0.0090
0.0147
0.0245
pct90
0.0097
0.0182
0.0282
pct95
0.0123
0.0239
0.0413
max
0.0450
0.0967
0.2091
Urban sites, located in MSA's < 1 million population
annual mean
max quarter mean
max monthly mean
n
117
117
117
min
0.0003
0.0007
0.0009
pctS
0.0013
0.0021
0.0031
pctlO
0.0017
0.0026
0.0037
pctlS
0.0020
0.0029
0.0042
pct20
0.0021
0.0034
0.0051
pct25
0.0023
0.0037
0.0053
pct30
0.0025
0.0038
0.0056
pct35
0.0026
0.0040
0.0059
pct40
0.0028
0.0042
0.0060
pct45
0.0030
0.0044
0.0062
median
0.0032
0.0046
0.0064
mean
0.0038
0.0072
0.0114
pctSS
0.0033
0.0050
0.0070
pct60
0.0035
0.0053
0.0079
pct65
0.0037
0.0057
0.0086
pct70
0.0038
0.0060
0.0090
pct75
0.0042
0.0068
0.0097
pctSO
0.0046
0.0079
0.0112
pctS 5
0.0051
0.0087
0.0131
pct90
0.0063
0.0112
0.0186
pct95
0.0078
0.0150
0.0283
max
0.0257
0.1674
0.3091
2B-19
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APPENDIX 5A:
PREDICTED PERCENT OF COUNTIES WITH A MONITOR NOT
LIKELY TO MEET ALTERNATIVE STANDARDS AND
ASSOCIATED PERCENT POPULATION
-------
Appendix 5.A. Predicted percent of counties with a monitor not likely to meet alternative standards & associated percent
population.
The values below were derived from the Pb-TSP dataset described in Chapter 2 and in Appendix 2.B.
Number of counties
with monitors
(population in those
counties) ^
level / form T
0.02, max quarterly
0.02, max monthly
0.02, 2nd max monthly
0.05, max quarterly
0.05, max monthly
0.05, 2nd max monthly
0.10, max quarterly
0.10, max monthly
0.10, 2nd max monthly
0.20, max quarterly
0.20, max monthly
0.20, 2nd max monthly
0.3, max quarterly
0.3, max monthly
0.3, 2nd max monthly
0.4, max quarterly
0.4, max monthly
0.4, 2nd max monthly
0.50, max quarterly
0.50, max monthly
0.50, 2nd max monthly
Total
125(58,035)
Northeast
14(7,285)
Southeast
43(13,298)
Industrial
Midwest
43(17,166)
Upper
Midwest
7(2,184)
Southwest
3(1,015)
Northwest
7 (3,002)
Southern
California
6(12,774)
Outlying
areas
2(1,311)
Percent of counties with a monitor not likely to meet stated standard and level
(percent of total or regional population in counties with monitors that reside in non-meeting counties)
53% (55%)
66% (75%)
55% (62%)
39% (36%)
45% (42%)
38% (18%)
23% (6%)
34% (32%)
27% (10%)
17% (3%)
23% (9%)
20% (5%)
14% (3%)
18% (3%)
14% (2%)
10% (1%)
14% (3%)
14% (2%)
10% (1%)
14% (3%)
11% (1%)
64% (83%)
64% (83%)
64% (83%)
43% (21%)
43% (21%)
43% (21%)
29% (11%)
43% (21%)
43% (21%)
21% (1%)
21% (1%)
21% (1%)
7% (0%)
21% (1%)
7% (0%)
0% (0%)
7% (0%)
7% (0%)
0% (0%)
7% (0%)
7% (0%)
35% (29%)
51% (58%)
40% (32%)
35% (29%)
35% (29%)
35% (29%)
21% (6%)
26% (23%)
23% (6%)
16% (5%)
23% (23%)
19% (6%)
16% (5%)
16% (5%)
16% (5%)
14% (1%)
16% (5%)
16% (5%)
14% (1%)
16% (5%)
14% (1%)
72% (41%)
86% (83%)
74% (74%)
44% (15%)
56% (25%)
44% (15%)
30% (5%)
40% (11%)
30% (5%)
23% (4%)
30% (5%)
26% (5%)
19% (3%)
26% (4%)
21% (2%)
14% (2%)
21% (4%)
19% (2%)
12% (1%)
21% (4%)
16% (2%)
29% (25%)
43% (49%)
14% (16%)
14% (16%)
29% (25%)
14% (16%)
14% (16%)
14% (16%)
14% (16%)
0% (0%)
14% (16%)
14% (16%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
33% (67%)
67% (81%)
67% (81%)
33% (67%)
33% (67%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
57% (78%)
86% (78%)
71% (78%)
57% (78%)
57% (78%)
57% (78%)
29% (31%)
57% (78%)
57% (78%)
14% (12%)
29% (31%)
29% (31%)
14% (12%)
14% (12%)
14% (12%)
14% (12%)
14% (12%)
14% (12%)
14% (12%)
14% (12%)
0% (0%)
67% (88%)
67% (88%)
50% (75%)
50% (75%)
67% (88%)
33% (0%)
0% (0%)
50% (75%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
0% (0%)
-------
United States Office of Air Quality Planning and Standards Publication No. EPA 452/R-07-013
Environmental Protection Air Quality Strategies and Standards Division November 2007
Agency Research Triangle Park, NC
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