Review of the National Ambient Air Quality 

Standards for Lead: 

Policy Assessment of Scientific and Technical 

Information 

OAQPS Staff Paper 
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 
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. 
ACKNOWLEDGEMENTS 

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 leadrelated 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. 
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 DATA.....................................................................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 

i 
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-PM10..............................................................................................................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 

ii 
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 CASAC 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 

iii 
 

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 

 Pb NAAQS.........................................................................................................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 
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 Nonurban 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 Nonurban 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 

v 
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 5A. 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 to Pb 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 
 

 

 

 

 

 

Table 6-1. Models and Measurements Used for Ecological Risk Screening Assessment........... 19 

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

Table 6-7. HQs Calculated for Sediments in Surface Waters for Primary Pb Smelter Case Study.

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 

 . ..................................................................................................................... 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 

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 policyrelevant pathways, heavy arrows indicate the predominant human exposures.........2-2 

Figure 2-7. Airborne Pb -TSP concentrations, averaged across continuously operating 

monitoring sites: 1980-1989 and 1990-2006. (Sources: left plot - AQS data 

Figure 2-8. Distribution of Pb-TSP concentrations (represented by 6 different statistics) at the 

Figure 2-9. Percentages of Pb-TSP monitored populations residing in areas exceeding 

Figure 2-14. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

Figure 2-15. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

Figure 2-16. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

Figure 2-17. Distribution of Pb-TSP annual mean concentrations at different categories 

Figure 2-18. Distribution of Pb-TSP maximum quarterly mean concentrations at different 

Figure 2-19. Distribution of Pb-TSP maximum monthly mean concentrations at different 

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 

extracted 10/10/07; right plot – EPA 2007.)..........................................................2-23 

189 Pb-TSP monitoring sites, 2003-2005..............................................................2-29 

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 

at the source-oriented monitoring sites, 2003-2005............................................. 2-36 

at the non-source-oriented monitoring sites, 2003-2005. .................................... 2-37 

at the nine monitoring sites near previous large emission sources, 2003-2005... 2-38 

of sites, 2003-2005............................................................................................... 2-39 

categories of sites, 2003-2005............................................................................. 2-40 

vii 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 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-PM10 (NATTS) monitoring sites network......................................................2-54 

Figure 2-28. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

at all 28 monitoring sites, 2003-2005. ................................................................. 2-56 

Figure 2-29. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

at the 25 urban monitoring sites, 2003-2005. ...................................................... 2-57 

Figure 2-30. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

 at the urban monitoring sites located in CBSAs of ≥ 1 million population, 

2003-2005. ...........................................................................................................2-58 

Figure 2-31. Pb-PM10 annual means (for all sites), 2003-2005................................................ 2-59 

Figure 2-32. Pb-PM10 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 (Λ).................................................. 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 μm (N = 1466). 

[CD, Figure AX7-2.2.9].................................................................................... 2-84 

Figure 6-1. Overview of Ecological Screening Assessment .................................................. 6-18 

viii 
 

 

 

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. 1 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. 

1-1
 
 

 

 

 

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)(1) 

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.” 

1-2
 
 

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)(1) 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 (μg/m3

), measured as Pb in total suspended particulate matter 

(TSP), not to be exceeded by the maximum arithmetic mean concentration averaged over a 

1-3
 
 

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 μg/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 CASAC 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 

1-4
 
 

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 

1-5
 
 

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 staff’s 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 Pbrelated 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 policyrelevant 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 

1-6
 
 

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. 

1-7
 
 

 

 

 

 

 

 

 

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). 

1-8
 
 

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-RTP Report no. NCEA-R1465. 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/R5/144aF. 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. EPA452/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. 

1-9
 
 

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). 

2-1
 
 

 

 

 

 

Policy-relevant Policy-relevant Sources Policy-relevant
 

Background* Background*
 

Newly emitted Pb Historically emitted Pb 

Air Outdoor Soil, Dusts 

Indoor Dust 

Paint 

Surface Waters 

& Sediments 

Non-air Pb 

releases 

Human Exposures 

-Inhalation & ingestion 

Diet** 

Ecological Exposures 

Drinking 

water 

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

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

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

atmospheric deposition (policy-relevant sources). 

Figure 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 

2-2
 
 

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 

2-3
 
 

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. 

2-4
 
 

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” 

2-5
 
 

 

 

 

 

 

 

 

 

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 

Transportation 64,706 18,973 1,197 564 - 491a
 

Fuel Combustion 4,299 515 500 490 - 377 

Industrial Processes 3,938 2,531 2,474 2,271 - 719 

Solid Waste 1,210 871 804 604 - 110 

Total 74,153 22,890 4,975 3,929 3303 1,697 a

 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/econemissions.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., lowemitting) 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 2A. 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. 

2-6
 
 

Table 2-2. Source categories emitting greater than 5 tpy of Pb in the 2002 NEI. 

Source Category Description Total Emissions (tpy)a

 ALL CATEGORIES b

Mobile sources 

1,697b 

491c 

Industrial/Commercial/ Institutional Boilers & Process Heaters 190 

Utility Boilers 168d 

Iron and Steel Foundries 110 

Primary Lead Smelting 59 

Hazardous Waste Incineration 47 

Secondary Lead Smelting 43 

Military Installations 33 

Municipal Waste Combustors 33 

Integrated Iron & Steel Manufacturing 32 

Pressed and Blown Glass and Glassware Manufacturing 32 

Stainless and Non Stainless Steel Manufacturing: EAF 32 

Mining 31 

Lead Acid Battery Manufacturing 27 

Secondary Nonferrous Metals 24 

Portland Cement Manufacturing 22 

Primary Copper Smelting 22 

Primary Metal Products Manufacturing 21 

Industrial and Commercial Machinery Manufacturing 18 

Fabricated Metal Products Manufacturing 14 

Electrical and Electronics Equipment Manufacturing 12 

Waste Disposal - Solid Waste Disposal 11 

Industrial Inorganic Chemical Manufacturing 10 

Pulp & Paper Production 10 

Sewage Sludge Incineration 10 

Mineral Products Manufacturing 9 

Secondary Aluminum Production 9 

Synthetic Rubber Manufacturing 9 

Secondary Copper Smelting 8 

Transportation Equipment Manufacturing 8 

Ferroalloys Production 7 

Nonferrous Foundries 7 

Stationary Reciprocating Internal Combustion Engines 7 

Commercial and Industrial Solid Waste Incineration 6 

Primary Nonferrous Metals--Zinc, Cadmium and Beryllium 6 

Residential Heating 6 

Asphalt Processing and Asphalt Roofing Manufacturing 5 a

Some 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. b

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. 

2-7
 
 

 

 

 

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, lb/gal) and 0.56 g Pb/L (0.004673 lb/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
 
 

 

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 (TAF) 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 Total 

Range Number of Emissions 

(tpy) Airports (tpy) 

< 0.1 2,104 76.7 

0.1 to 1.0 1,270 367.5 

> 1 36 47.1 

Summary 3,410 491.3 

2-9 
 

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. 

2-10
 
 

Figure 2-2. Emissions density from all Pb sources in the 2002 NEI. 

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). 

2-11
 
 

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. 

2-12
 
 

 

 

 

Table 2-4. Size distribution of point sources within the 2002 NEI and associated estimated 

emissions. 

Average 

Emissions Total Emissions 

Range Number Emissions per Source 

(tpy) of Sources (tpy) (tpy) 

< 0.1 11,800 73 <0.01 

0.1 to 1.0 1,028 326 0.3 

1.0 to 5 210 421 2 

> 5 29 301 10 

Summary 13,067 1121 

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. 

2-13
 
 

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. 

2-14
 
 

Table 2-5. Point Sources with Pb emissions in 2002 NEI greater than or equal to 5 tpy. 

Source Category Name State County Name 

2002 Point 

Emissions (TPY)a 

Primary Lead Smelting MO Jefferson County 58.8 

Military Installation OK Pittsburg County 17.2 

Mining MO Reynolds County 15.4 

Copper Refiningb TX Potter County 13.9 

Primary Copper Smelting AZ Gila County 12.8 

Electric Arc Furnaces IL Peoria County 12.5 

Secondary Lead Smelting MO Iron County 12.4 

Integrated Iron & Steel Manufacturing IN Lake County 11.3 

Pressed and Blown Glass and Glassware Manufacturing TN Madison County 10.9 

Military Installation PA Franklin County 10.4 

Hazardous Waste Incineration AR Union County 10.2c 

Lead Acid Battery Manufacturing KY Madison County 9.9 

Industrial and Commercial Machinery Manufacturing KS Marshall County 8.2 

Synthetic Rubber Products Manufacturing - Fabric 

Coating IN Cass County 7.4 

Commercial and Industrial Solid Waste Incineration AR Clark County 7.3 

Iron and Steel Foundries OH Cuyahoga County 7.3 

Integrated Iron & Steel Manufacturing IN Porter County 7.2 

Integrated Iron & Steel Manufacturing IN Lake County 6.1 

Mineral Products Manufacturing NM Socorro County 6.1 

Commercial and Industrial Solid Waste Incineration CT Windham County 5.8 

Ferroalloys Production OH 

Washington 

County 5.7 

Nonferrous Foundries NE Nemaha County 5.5 

Portland Cement Manufacturing MD Frederick County 5.4 

Hazardous Waste Incineration OH Lorain County 5.4 

Coke Oven VA Buchanan County 5.1 

Iron and Steel Foundries IA Jefferson County 5.1 

Mining MO Reynolds County 5 a

 (USEPA, 2007)

b

 This entry is included in the total provided for “secondary nonferrous metals” in Table 2-2. c

 Following compliance with the MACT standards in 2008, 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. 

2-15
 
 

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. 

2-16
 
 

In summary, generic limitations to the 2002 NEI include the following: 

•	 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. 

•	 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 μm in diameter (PM2.5), and PM less than or equal to 10 μm in diameter 

(PM10). 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/. 

2-17
 
 

 

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 μg/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, PM2.5, PM10. 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 lowvolume sampler. High-volume samplers draw between 70 and 100 m3

/hr 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. 

2-18
 
 

 

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). Semicontinuous 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 inductivelycoupled 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 μg/m3 to as low as 0.00001 μg/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/ 

2-19
 
 

 

 

 

 

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
 
 

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. 

2-21
 
 

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 μg/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
 
 

 

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). 

80 81 82 83 84 85 86 87 88 89 

0 

0.2 

0.4 

0.6 

0.8 

1 

1.2 

1.4 

1.6 

1980 to 1989: 86% decrease 

Concentration (μg/m3 )

National Standard 168 sites 

90% of sites 

below this line 

10% of sites 

below this line 

Average 

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. 

2-23
 
 

 

 

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 μg/m3 for quarterly mean). 

2-24
 
 

CBSA. 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 CBSA 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
 
 

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. 

2-26
 
 

 

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
 
 

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 μg/m3, and the corresponding median annual mean was 0.02 μg/m3. The national composite 

average maximum quarterly mean was 0.17 μg/m3 and the corresponding median maximum 

quarterly mean was 0.03 μg/m3. The national composite average maximum monthly mean was 

0.31 μg/m3 and the median maximum monthly mean was 0.04 μg/m3. The national composite 

average second maximum monthly mean was 0.21 μg/m3 and the median value was 0.03 μg/m3. 

The national composite average of the mean of the three overall highest monthly averages was 

0.31 μg/m3 and the median value was 0.04 μg/m3. The national composite average of the mean 

of the annual highest monthly means was 0.21 μg/m3 and the median value was 0.03 μg/m3. 

2-28
 
 

Annual mean Max quarterly Max monthly 

Concentration (μg/m3 ) 

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 μg/m3

, > 0.05 μg/m3, > 0.20 μg/m3, > 0.50 

μg/m3, and > 1.54 μg/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. 

2-29
 
 

 

 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

percent of monitored population 

a nnua l m e a n m a x q u a r t e r l y m e a n m a x m ont hl y m e a n 2 nd m a x m ont hl y m e a n 

26.9% 

7.7% 

2.2% 

0.7% 

0.1% 

54.4% 

26.0% 

5.1% 

1.6% 

0.2% 

65.8% 

31.3% 

8.7% 

3.3% 

1.1% 

55.6% 

22.4% 

7.3% 

2.1% 

0.3% 

> 0. 02 > 0. 05 > 0. 20 > 0. 50 > 1. 54 

μg/m3 

Figure 2-9. Percentages of Pb-TSP monitored populations residing in areas exceeding 

various concentrations (for 4 different statistics), 2003-2005. 

2-30
 
 

≥ 1.55

0.50 – 1.55

0.20 – 0.50

0.05 – 0.20

< 0.05

Concentration range

(μg/m3)

≥ 1.55 

0.50 – 1.55 

0.20 – 0.50 

0.05 – 0.20 

< 0.05 

Concentration range 

(μg/m3) 

Figure 2-10. Pb-TSP annual means (for all sites), 2003-2005. 

2-31
 
 

 

≥ 1.55

0.50 – 1.55

0.20 – 0.50

0.05 – 0.20

Concentration range

(μg/m3)

≥ 1.55 

0.50 – 1.55 

0.20 – 0.50 

0.05 – 0.20 

Concentration range 

(μg/m3) 

< 0.05 

Figure 2-11. Pb-TSP maximum quarterly means (for all sites), 2003-2005. 

2-32
 
 

≥ 1.55

0.50 – 1.55

0.20 – 0.50

0.05 – 0.20

< 0.05

Concentration range

(μg/m3)

≥ 1.55 

0.50 – 1.55 

0.20 – 0.50 

0.05 – 0.20 

< 0.05 

Concentration range 

(μg/m3) 

Figure 2-12. Maximum monthly Pb-TSP means (all sites), 2003-2005. 

2-33
 
 

≥ 1.55

0.50 – 1.55

0.20 – 0.50

0.05 – 0.20

< 0.05

Concentration range

(μg/m3)

≥ 1.55 

0.50 – 1.55 

0.20 – 0.50 

0.05 – 0.20 

< 0.05 

Concentration range 

(μg/m3) 

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 sitelevel 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
 
 

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-sourceoriented 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 

μg/m3

) is about 14 times greater than the same statistic for non-source-oriented sites (0.02 

μg/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 μg/m3 or more, and over 25 percent had maximum quarterly average of 0.50 μg/m3 or more. 

2-35
 
 

 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean mean mean 

Figure 2-14. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

at the source-oriented monitoring sites, 2003-2005. 

2-36
 
 

 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean mean mean 

Figure 2-15. Distribution of Pb-TSP concentrations (represented by 4 different statistics) 

at the non-source-oriented monitoring sites, 2003-2005. 

2-37
 
 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean 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. 

2-38
 
Concentration (μg/m3 ) 

All sites Source- Not source Previous Urban Urban CBSA Urban CBS

oriented oriented source­ (189 sites) (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
 
Concentration (μg/m3 ) 

All sites Source- Not source Previous Urban Urban CBSA Urban CBS

(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 si

2-40
 
Concentration (μg/m3 ) 

All sites Source- Not source Previous Urban Urban CBSA Urban CBSA 

oriented oriented source­ (189 sites) (140) pop. ≥ 1M pop. < 1M (60) (129) oriented (9) (91) (49) 

Figure 2-19. Distribution of Pb-TSP maximum monthly mean concentrations at different categories of sit

2-41
 
Concentration (μg/m3 ) 

All sites Source- Not source Previous Urban Urban CBSA Urban CBSA 

oriented oriented source­ (189 sites) (140) pop. ≥ 1M pop. < 1M (60) (129) oriented (9) (91) (49) 

Figure 2-20. Distribution of Pb-TSP second maximum monthly mean concentrations at different categorie

2005. 

2-42
 
 

mean

median

population weighted mean

mean

median

population weighted mean

mean

median

population weighted mean

mean

median

population weighted mean

0.000 

0.050 

0.100 

0.150 

0.200 

0.250 

0.300 

all sites sourceoriented 

sites 

non sourceoriented 

sites 

previous 

sourceoriented 

urban sites urban sites 

in CBSAs ≥

1M pop. 

urban sites 

in CBSAs 

<1M pop. 

0.000 

0.100 

0.200 

0.300 

0.400 

0.500 

0.600 

all sites sourceoriented 

sites 

non sourceoriented 

sites 

previous 

sourceoriented 

urban sites urban sites 

in CBSAs ≥

1M pop. 

urban sites 

in CBSAs 

<1M pop. 

0.000 

0.100 

0.200 

0.300 

0.400 

0.500 

0.600 

0.700 

0.800 

0.900 

1.000 

all sites sourceoriented 

sites 

non sourceoriented 

sites 

previous 

sourceoriented 

urban sites urban sites 

in CBSAs ≥

1M pop. 

urban sites 

in CBSAs 

<1M pop. 

0.000 

0.100 

0.200 

0.300 

0.400 

0.500 

0.600 

0.700 

all sites sourceoriented 

sites 

non sourceoriented 

sites 

previous 

sourceoriented 

urban sites urban sites 

in CBSAs ≥

1M pop. 

urban sites 

in CBSAs 

<1M pop. 

Annual Mean Max Quarterly Mean 

Max monthly mean 2nd Max Monthly Mean 

mean 

median 

population weighted mean 

mean 

median 

population weighted mean 

mean 

median 

population weighted mean 

mean 

median 

population weighted mean 

Figure 2-21. Medians, means, and population-weighted means for 4 site-level statistics. (All 

y-axes are in concentration units of μg/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
 
 

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 μg/m3

 is more than five times 

higher than the comparable non-source-oriented level of 0.02 μg/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 CBSA 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 μg/m3, > 0.05 μg/m3, > 0.20 μg/m3, > 0.50 μg/m3, and > 1.54 μg/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
 
 

Concentration (μg/m3) 

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. 

2-45
 
 

Concentration (μg/m3) 

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. 

2-46
 
 

Concentration (μg/m3) 

Annual mean Max quarterly Max monthly 2nd max monthly 

mean mean 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. 

2-47
 
26.2% 

6.7% 1.7% 0.6% 

54.8% 

25.5% 

4.4% 

1.1% 

66.6% 

30.8% 

7.8% 2.6% 

56.0% 

21.8% 

6.6% 1.8% 

0% 

10% 20% 30% 40% 50% 60% 70% 

> 0.02 > 0.05 > 0.20 > 0.50 

μg/m3 

percent of monitored population 

annual mean max quarterly mean max monthly mean 2nd max mont

Figure 2-25. Percentages of Pb-TSP urban monitored populations residing in areas (represented by 4 diff

exceeding various levels. (Note: Site statistics were rounded to 2 decimal places before comparin

2-48
 
 

 

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 μg/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 μg/m3

 (actually 1.54 μg/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 μg/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 μg/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 μg/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 

Maximum Quarterly Mean Maximum Monthly Mean 2nd Maximum Monthly Mean 

(189 in 86 counties) * 

All Sites Urban Sites 

(140 in 73 counties) * 

All Sites 

(189 in 86 counties) * 

Urban Sites 

(140 in 73 counties) * 

All Sites 

(189 in 86 counties) * 

Urban Sites 

(140 in 73 counties) * 

Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties 

0.02 107 47 75 39 127 59 94 50 110 50 78 42 

0.05 74 35 47 29 85 40 55 33 71 34 44 28 

0.10 49 21 26 15 64 31 38 25 54 24 28 17 

0.20 35 15 17 10 49 21 26 16 42 19 22 13 

0.30 25 11 10 6 37 16 17 11 29 12 12 7 

0.40 19 8 7 3 30 12 14 8 26 11 11 6 

0.50 18 8 6 2 27 12 11 7 20 9 9 5 

0.60 17 7 6 2 26 11 10 6 16 7 6 2 

0.70 13 6 5 2 22 10 9 5 15 7 5 2 

0.80 11 6 5 2 19 9 7 3 15 7 5 2 

0.90 9 6 4 2 19 9 7 3 13 5 5 2 

1.00 7 5 4 2 15 7 6 2 9 5 4 2 

1.54 3 3 1 1 11 6 5 2 4 3 2 1 

1-year statistics, 2003 

Level 

Maximum Quarterly Mean Maximum Monthly Mean 2nd Maximum Monthly Mean 

All Sites 

(175 in 83 counties) * 

Urban Sites 

(131 in 70 counties) * (177 in 83 counties) * 

All Sites 

(133 in 71 counties) * 

Urban Sites 

(176 in 83 counties) * 

All Sites Urban Sites 

(132 in 71 counties) * 

Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties 

0.02 90 41 63 35 103 46 76 40 89 42 62 36 

0.05 60 27 35 21 73 34 48 28 56 26 31 20 

0.10 39 15 19 9 50 23 28 17 40 15 19 9 

0.20 28 12 12 7 36 15 17 10 29 11 13 8 

0.30 20 10 7 5 29 12 13 8 24 9 9 5 

0.40 15 7 4 2 25 11 11 6 20 9 7 4 

0.50 15 7 4 2 20 10 7 5 15 7 4 2 

0.60 12 7 2 2 18 8 5 3 14 7 4 2 

0.70 8 6 2 2 16 8 5 3 12 6 3 2 

0.80 6 5 1 1 15 8 4 3 9 5 1 1 

0.90 4 3 1 1 14 7 4 3 7 4 1 1 

1.00 2 2 1 1 11 6 3 2 4 3 1 1 

1.54 1 1 0 0 5 4 1 1 2 2 1 1 

1-year statistics, 2004 

Level 

Maximum Quarterly Mean Maximum Monthly Mean 2nd Maximum Monthly Mean 

All Sites 

(166 in 77 counties) * 

Urban Sites 

(127 in 68 counties) * 

All Sites 

(169 in 77 counties) * 

Urban Sites 

(130 in 68 counties) * 

All Sites 

(168 in 77 counties) * 

Urban Sites 

(129 in 68 counties) * 

Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties 

0.02 82 39 56 32 100 50 73 42 90 44 64 37 

0.05 53 26 31 20 63 30 39 24 54 26 32 20 

0.10 37 18 22 12 46 23 27 17 37 17 21 11 

0.20 21 11 12 6 30 16 18 10 22 10 13 6 

0.30 14 7 8 4 21 11 11 6 14 7 8 4 

0.40 13 6 7 3 15 8 8 4 14 7 8 4 

0.50 12 6 6 2 15 8 8 4 12 6 6 2 

0.60 11 5 6 2 14 7 7 3 12 6 6 2 

0.70 10 5 5 2 13 6 7 3 10 5 5 2 

0.80 8 4 5 2 11 5 6 2 10 5 5 2 

0.90 6 4 4 2 11 5 6 2 10 5 5 2 

1.00 6 4 4 2 10 5 6 2 6 4 4 2 

1.54 1 1 0 0 7 4 5 2 3 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 

Maximum Quarterly Mean Maximum Monthly Mean 2nd Maximum Monthly Mean 

All Sites 

(157 in 76 counties) * 

Urban Sites 

(122 in 67 counties) * 

All Sites 

(157 in 76 counties) * 

Urban Sites 

(122 in 67 counties) * 

All Sites 

(157 in 76 counties) * 

Urban Sites 

(122 in 67 counties) * 

Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties 

0.02 80 39 57 33 93 45 70 39 83 42 60 36 

0.05 52 27 35 23 60 31 40 26 53 27 36 22 

0.10 35 17 23 13 46 23 30 19 34 17 22 12 

0.20 19 10 12 7 32 16 21 13 26 15 17 11 

0.30 15 8 8 4 22 12 14 9 16 8 8 4 

0.40 10 6 5 3 18 9 11 6 13 7 7 4 

0.50 8 6 3 2 16 9 9 5 8 5 4 2 

0.60 7 5 3 2 14 9 7 4 8 5 4 2 

0.70 5 4 3 2 11 7 5 3 8 5 4 2 

0.80 5 4 3 2 8 6 3 2 6 4 3 2 

0.90 5 4 3 2 8 6 3 2 5 4 3 2 

1.00 5 4 3 2 6 5 3 2 5 4 3 2 

1.54 1 1 1 1 3 3 1 1 1 1 1 1 

Average of 3-year statistics, 2003-2005 

Level 

Maximum Quarterly Mean Maximum Monthly Mean 2nd Maximum Monthly Mean 

All Sites Urban Sites All Sites Urban Sites All Sites Urban Sites 

Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties Sites Counties 

0.02 84.0 39.7 58.7 33.3 98.7 47.0 73.0 40.3 87.3 42.7 62.0 36.3 

0.05 55.0 26.7 33.7 21.3 65.3 31.7 42.3 26.0 54.3 26.3 33.0 20.7 

0.10 37.0 16.7 21.3 11.3 47.3 23.0 28.3 17.7 37.0 16.3 20.7 10.7 

0.20 22.7 11.0 12.0 6.7 32.7 15.7 18.7 11.0 25.7 12.0 14.3 8.3 

0.30 16.3 8.3 7.7 4.3 24.0 11.7 12.7 7.7 18.0 8.0 8.3 4.3 

0.40 12.7 6.3 5.3 2.7 19.3 9.3 10.0 5.3 15.7 7.7 7.3 4.0 

0.50 11.7 6.3 4.3 2.0 17.0 9.0 8.0 4.7 11.7 6.0 4.7 2.0 

0.60 10.0 5.7 3.7 2.0 15.3 8.0 6.3 3.3 11.3 6.0 4.7 2.0 

0.70 7.7 5.0 3.3 2.0 13.3 7.0 5.7 3.0 10.0 5.3 4.0 2.0 

0.80 6.3 4.3 3.0 1.7 11.3 6.3 4.3 2.3 8.3 4.7 3.0 1.7 

0.90 5.0 3.7 2.7 1.7 11.0 6.0 4.3 2.3 7.3 4.3 3.0 1.7 

1.00 4.3 3.3 2.7 1.7 9.0 5.3 4.0 2.0 5.0 3.7 2.7 1.7 

1.54 1.0 1.0 0.3 0.3 5.0 3.7 2.3 1.3 2.0 1.7 1.3 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 statistsics. 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 μg/m3; the 3-year maximum monthly averages for 

the two sites that were not source oriented sites were 0.23 and 0.10 μg/m3

. The 3-year maximum 

quarterly averages for the three source-oriented sites were 0.70, 0.35, and 0.21 μg/m3

; the 3-year 

quarterly averages for the two non-source-oriented sites were 0.08 and 0.06 μg/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 of ten 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. 

1.0 

0.8 

0.9 

3 )

2003 

0.7 

/m

0.6 

Concentration (μg

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

jan feb mar apr may jun jul aug sep oct nov dec 

1.0 

0.8 

3 

0.9 ) (μg/m

2004 

0.7 

n 

0.6 

0.5 

tio

0.4 

0.3 

traen

0.2 

Conc

0.1 

0.0 

jan feb mar apr may jun jul aug sep oct nov dec 

1.0 

3 )

0.8 

0.9 

(μg/m

2005 

0.7 

on 

0.6 

ati

0.5 

0.4 

0.3 

ntrce

0.2 

Con

0.1 

0.0 

jan feb mar apr may jun jul 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 μg/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 μg/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 μg/m3; however, the corresponding quarterly average is 0.48 μg/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 PM10 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/airsaqs/. 

2-53
 
 

Figure 2-27. Pb-PM10 (NATTS) monitoring sites network. 

2.3.3.1 Data Analysis Details 

Lead-PM10 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 PbPM10 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 PM10 data were used “as reported”; that is, 1⁄2 

MDL substitutions were not made for reported concentrations less than or equal to MDL. PbPM10 sites were categorized similarly to the Pb-TSP sites. However, no Pb-PM10 sites fell into 

the source-oriented classification. 25 of the 38 Pb-PM10 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-PM10 monitors are listed with various summary and demographic 

data in Appendix 2B, Table 2B-6. 

Three statistical metrics were computed for the Pb-PM10 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-PM10 sites. The national composite average annual 

mean for Pb-PM10 was 0.006 μg/m3 for the 3-year period, 2003-2005; the corresponding median 

annual mean was also 0.006 μg/m3. The national composite average maximum quarterly mean 

was 0.012 μg/m3

 for 2003-2005 and the corresponding median maximum quarterly mean was 

0.009 μg/m3. The national composite average maximum monthly mean was 0.021 μg/m3 and the 

median maximum monthly mean was 0.014 μg/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
 
 Concentration (μg/m3 ) 

Max quarterly Max monthly Annual mean 

mean mean 

Figure 2-28. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

at all 28 monitoring sites, 2003-2005. 

2-56
 
 Concentration (μg/m3 ) 

Max quarterly Max monthly Annual mean 

mean mean 

Figure 2-29. Distribution of Pb-PM10 concentrations (represented by 3 different statistics) 

at the 25 urban monitoring sites, 2003-2005. 

2-57
 
 Concentration (μg/m3 ) 

Max quarterly Max monthly Annual mean 

mean mean 

Figure 2-30. Distribution of Pb-PM10 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
 
 

Concentration range

(μg/m3)

≥ 0.015

0.010 - 0.015

0.005 - 0.010

< 0.005

Concentration range Concentration range 

(μg/m3) 

≥ 0.015 

0.010 - 0.015 

0.005 - 0.010 

< 0.005 

Figure 2-31. Pb-PM10 annual means (for all sites), 2003-2005. 

2-59
 
 

 

Concentration range

(μg/m3)

≥ 0.015

0.010 - 0.015

0.005 - 0.010

< 0.005

Concentration range Concentration range 

(μg/m3) 

≥ 0.015 

0.010 - 0.015 

0.005 - 0.010 

< 0.005 

Figure 2-32. Pb-PM10 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 μm in diameter. Lead is quantified via the XRF method. The standard operating 

procedure for metals by XRF is available at: 

http://www.epa.gov/ttnamti1/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. 

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
 
 

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-PM10 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”; 1⁄2 

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 PbPM2.5 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 CBSAs of 1 million or more population and the other 

117 are located in smaller CBSAs. The 271 Pb-PM2.5 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-PM2.5 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-PM2.5 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 μg/m3 for the 3-year period, 2003-2005; 

the corresponding median annual mean was 0.003 μg/m3. The national composite average 

maximum quarterly mean was 0.008 μg/m3 for 2003-2005 and the corresponding median 

maximum quarterly mean was 0.005 μg/m3

. The national composite average maximum monthly 

mean was 0.013 μg/m3

 and the median maximum monthly mean was 0.007 μg/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 nonsource-oriented sites. Figure 2-36 maps the annual means for Pb-PM2.5 sites. 

2-63
 
 

Concentration (μg/m3 ) 

Max quarterly Max monthly Annual mean 

mean mean 

Figure 2-35. Distribution of Pb-PM2.5 concentrations (represented by 3 different statistics) 

at all 271 monitoring sites, 2003-2005. 

2-64
 
 

Concentration range

(μg/m3)

≥ 0.015

0.010 - 0.015

0.005 - 0.010

< 0.005

Concentration range Concentration range 

(μg/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-PM10 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 PM10; 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 PM10 fraction. On 

2-65
 
 

average, daily Pb-PM10 concentrations were about 83 percent of the level of the Pb-TSP 

concentrations. All sites had an average daily ratio (Pb-PM10 / 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-PM10. 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-PM10 and Pb-PM2.5 during 2003-2005. It appears that the 

relationship between Pb-PM10 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-PM10. 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-PM10 concentrations; all but 3 sites had an average daily ratio (Pb-PM2.5 / PbPM10) 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 sourceoriented PM10 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 PbTSP 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-PM10 “all sites” 

averages (recall that no Pb-PM10 sites were classified as source-oriented); using the national 

medians (right bars), the ratio was closer to 1.8. Restated as a PM10/TSP ratio it is 0.55, (i.e., 55 

percent of TSP Pb is in the PM10 fraction); the collocated sites analysis discussed above has a 

median PM10/TSP ratio of 0.85) The Pb-PM10 “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
 
0.0000 

0.0010 0.0020 0.0030 0.0040 0.0050 0.0060 0.0070 0.0080 0.0090 0.0100 0.0110 0.0120 0.0130 0.0140 0.0150 0.0160 0.0170 0.0180 0.0190 0.0200 

TSP - all 

sit es 

T SP sourceoriented 

sit es 

TSP not 

sourceoriented 

sit es 

TSP 

previous 

sourceorient ed 

sit es 

T SP urban 

sit es 

T SP urban 

sit es in 

CBSA's ≥

1M 

populat ion 

T SP urban 

sit es in 

CBSA's < 

1M 

populat ion 

PM10 - all 

sit es 

PM10 urban 

sit es 

PM10 urban 

sit es in 

CBSA's ≥

1M 

population 

PM10 urban 

sit es in 

CBSA's < 

1M 

population 

PM2.5 CSN 

- all sites 

PM2.5 CSN 

sourceorient ed 

sit es 

PM2.5 CSN 

not sourceoriented 

sit es 

PM2.5 CSN 

urban sit es 

PM2.5 CSN 

urban sit es in 

CBSA's ≥

1M 

populat ion 

mean median 

0.0000 0.0200 0.0400 0.0600 0.0800 0.1000 0.1200 0.1400 0.1600 0.1800 0.2000 0.2200 0.2400 0.2600 0.2800 0.3000 

TSP - all 

sit es 

T SP sourceoriented 

sit es 

TSP not 

sourceoriented 

sit es 

TSP 

previous 

sourceorient ed 

sit es 

T SP urban 

sit es 

T SP urban 

sit es in 

CBSA's ≥

1M 

population 

TSP urban 

sit es in 

CBSA's < 

1M 

population 

PM10 - all 

sit es 

P M10 urban 

sit es 

P M10 urban 

sit es in 

CBSA's ≥

1M 

population 

PM10 urban 

sit es in 

CBSA's < 

1M 

population 

PM2.5 CSN 

- all sites 

PM2.5 CSN 

sourceoriented 

sit es 

PM2.5 CSN 

not sourceoriented 

sit es 

PM2.5 CSN 

urban sit es 

PM2.5 CSN 

urban sit es 

in CBSA's ≥

1M 

populat ion 

mean median 

Figure 2-37. National mean and median monitor level Pb annual means for different size cut PM network

2-67 
 

Table 2-8. Monitoring sites with collocated Pb data in different size fractions 

Site Urban? 

sourceorinted? years 

number of 

collocated 

days 

average 

(larger size 

cut) 

average 

(smaller 

size cut) 

minimum 

ratio of 

smaller size 

cut / larger 

size cut 

maximum 

ratio of 

smaller size 

cut / larger 

size cut 

average 

ratio of 

smaller size 

cut / larger 

size cut 

correlation 

(r) 

Collocated Pb-TSP and Pb-PM10 

060658001 1 1995 - 1996 54 0.031 0.018 0.24 1.17 0.60 0.44 

060374002 1 1995 - 2000 129 0.041 0.022 0.14 1.64 0.60 0.76 

270530053 1 1996 - 2001 13 0.017 0.008 0.33 0.89 0.61 0.93 

Unknown 1 1988 22 2.245 1.121 0.17 1.86 0.65 0.97 

060850004 1 1994 - 1999 23 0.025 0.018 0.38 1.57 0.77 0.72 

261390009 1 2000 - 2001 26 0.013 0.009 0.46 1.07 0.78 0.94 

201730009 1 1993 - 1997 18 0.019 0.015 0.38 1.53 0.81 0.50 

060771002 1 1995 - 2000 53 0.021 0.017 0.39 1.84 0.81 0.82 

202090015 1 1993 - 1997 118 0.028 0.021 0.29 2.79 0.82 0.65 

201730007 1 1993 - 1997 18 0.018 0.013 0.33 1.27 0.82 0.08 

202090020 1 1993 - 1997 107 0.092 0.059 0.04 11.92 0.85 0.99 

060990002 1 1995 - 1998 17 0.024 0.015 0.40 1.19 0.85 0.99 

260770905 1 1993 - 1996 78 0.017 0.014 0.55 1.55 0.85 0.99 

201770007 1 1993 - 1997 19 0.017 0.014 0.50 1.33 0.87 0.67 

060250005 1 1996 - 2001 205 0.031 0.027 0.27 1.55 0.87 0.95 

295100085 1 2004 - 2004 26 0.021 0.017 0.59 1.13 0.89 0.98 

060290014 1 1995 - 2000 32 0.020 0.019 0.65 1.44 0.92 0.98 

261630033 1 2003 - 2006 167 0.031 0.028 0.33 2.69 0.92 0.90 

060190008 1 1995 - 2001 32 0.020 0.018 0.60 1.33 0.92 0.94 

201290003 1993 - 1998 14 0.020 0.017 0.55 1.45 0.93 0.91 

201730008 1 1993 - 1997 16 0.015 0.015 0.50 2.06 0.98 0.05 

490110001 1 2003 - 2003 19 0.024 0.025 0.96 1.10 1.03 1.00 

201731012 1993 - 1997 22 0.022 0.022 0.63 3.33 1.05 0.96 

Collocated Pb-TSP and Pb-PM2.5 

010730023 1 1 2005 14 0.0224 0.0154 0.00 2.04 0.77 0.59 

010731009 2005 15 0.0042 0.0023 0.00 1.84 0.57 0.12 

060250005 1 2003-2005 129 0.0159 0.0122 0.12 1.50 0.71 0.96 

060371103 1 2003-2005 165 0.0228 0.0048 0.00 1.22 0.27 0.11 

060658001 1 2003-2005 148 0.0138 0.0062 0.00 1.39 0.42 0.74 

080010006 1 2003-2005 125 0.0323 0.0080 0.00 3.52 0.43 0.12 

080410011 1 2003-2005 132 0.0176 0.0020 0.00 1.17 0.24 0.07 

100032004 1 2003-2005 50 0.0100 0.0045 0.00 1.94 0.44 0.79 

120571075 1 2003-2005 57 0.0038 0.0020 0.00 8.78 1.12 0.79 

130690002 2003-2005 73 0.0021 0.0015 0.00 17.06 1.01 0.55 

150032004 1 2003-2005 151 0.0015 0.0010 0.00 14.88 0.92 0.18 

170314201 1 2003-2005 115 0.0113 0.0036 0.00 1.40 0.35 0.08 

180970078 1 2003-2005 155 0.0097 0.0050 0.00 3.04 0.66 0.52 

260810020 1 2005 29 0.0076 0.0042 0.00 1.43 0.53 0.59 

261130001 2003-2005 157 0.0032 0.0023 0.00 6.54 0.78 0.59 

261610008 1 2003-2005 69 0.0079 0.0042 0.00 1.82 0.52 0.51 

261630001 1 2003-2005 172 0.0086 0.0042 0.00 1.48 0.46 0.59 

261630033 1 2003-2005 152 0.0239 0.0118 0.00 2.20 0.48 0.71 

270530963 1 2003 54 0.0106 0.0048 0.00 1.83 0.45 0.48 

295100085 1 2004 53 0.0135 0.0077 0.00 7.40 0.94 0.32 

350010023 1 2004 15 0.0029 0.0003 0.00 0.37 0.10 0.50 

410390060 1 2003-2004 56 0.0027 0.0018 0.00 9.32 1.07 0.26 

410510246 1 2003 57 0.0082 0.0058 0.00 3.11 0.74 0.69 

420450002 1 2003-2005 167 0.0374 0.0042 0.00 0.75 0.11 0.04 

450790019 1 2003-2005 54 0.0154 0.0096 0.00 1.81 0.58 0.79 

470370023 1 2003-2004 48 0.0052 0.0041 0.00 8.12 0.79 0.40 

482011034 1 2003-2005 147 0.0087 0.0027 0.00 1.73 0.32 0.42 

490110001 1 2003-2005 23 0.0291 0.0036 0.00 0.29 0.11 0.95 

490110004 1 2003 13 0.0107 0.0017 0.00 0.66 0.22 0.06 

550270007 2004-2005 42 0.0067 0.0044 0.00 1.45 0.58 0.93 

261630001 * 2003-2004 67 0.0083 0.0040 0.11 0.74 0.47 0.83 

Collocated Pb-PM10 and Pb-PM2.5 (CSN) 

490110001 1 2003 20 0.0341 0.0041 0.00 0.28 0.11 0.95 

250250042 1 2003-2005 108 0.0063 0.0026 0.00 2.15 0.46 0.52 

482011039 1 2003-2005 161 0.0057 0.0020 0.00 2.34 0.49 0.43 

080770017 1 2004-2005 108 0.0047 0.0023 0.00 3.24 0.51 0.31 

261630033 1 2003-2005 148 0.0208 0.0113 0.00 1.92 0.54 0.89 

490110004 1 2003 134 0.0060 0.0032 0.00 2.96 0.56 0.62 

440070022 1 2003-2005 151 0.0110 0.0098 0.00 3.65 0.58 1.00 

330150014 1 2003-2005 55 0.0033 0.0022 0.00 2.76 0.60 0.79 

530330080 1 2003-2005 159 0.0047 0.0033 0.00 3.75 0.72 0.86 

550270007 2005 48 0.0057 0.0044 0.00 2.43 0.74 0.93 

330110020 1 2003-2005 59 0.0046 0.0033 0.00 2.79 0.77 0.54 

410610119 2004 95 0.0017 0.0011 0.00 6.86 0.77 0.46 

482011039 * 2004 35 0.0025 0.0017 0.27 2.23 0.78 0.81 

410390060 1 2004 75 0.0024 0.0014 0.00 7.31 0.78 0.63 

530330080 * 2003-2004 99 0.0046 0.0034 0.25 3.60 0.78 0.87 

410510246 1 2003 120 0.0096 0.0084 0.00 4.04 0.83 0.99 

110010043 1 2004-2005 85 0.0049 0.0039 0.00 10.43 0.86 0.39 

450250001 2004 111 0.0029 0.0022 0.00 4.63 0.88 0.52 

530630016 1 2005 54 0.0058 0.0037 0.00 4.23 0.88 0.77 

295100085 1 2003-2005 148 0.0130 0.0096 0.00 7.30 0.89 0.55 

170314201 1 2005 56 0.0059 0.0047 0.00 13.13 1.00 0.63 

130890002 1 2003-2005 158 0.0026 0.0028 0.00 3.86 1.02 0.95 

130890002 * 2004 34 0.0038 0.0049 0.49 1.81 1.09 0.99 

120573002 2004-2005 113 0.0036 0.0025 0.00 19.63 1.16 0.48 

211930003 2003-2005 165 0.0039 0.0042 0.00 33.45 1.42 0.58 

121030026 1 2004-2005 70 0.0025 0.0023 0.00 13.13 1.42 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 μg/m3 and 19.1 

μg/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 

ultrafine fractions of PM10. 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 PM10. In Boston, MA, Pb concentrations of 326 ng/m3

 and 75.6 

ng/m3

 were reported from PM2.5 and PM10-2.5 (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 PM10 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 μg/m3

 NAAQS level. However, when current 

concentrations are compared to alternative NAAQS thresholds (e.g., 0.05 and 0.20 μg/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 μg/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 μg/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 

PM10-sized Pb are fairly well correlated, however, almost all of the study sites were presumably 

not source-oriented. Additional source-oriented Pb-PM10 monitoring, collocated with Pb-TSP, 

would benefit additional evaluations. If further analyses also corroborate a fairly strong TSPPM10 Pb relationship for source-oriented sites, Pb-PM10 measurements may be useful as a PbTSP surrogate. 

2.4 AIR QUALITY MODELING 

2.4.1 National Air Toxics Assessment 

As part of the Agency’s national air toxics assessment (NATA) activities, a national scale 

assessment of hazardous air pollutants including Pb compounds has been performed twice over 

the past few years (USEPA 2006b, 2002c, 2001a). 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, 2001a). 

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, 2001a). 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, 2001a). 

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, 2001a). 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/nata1999/99compare.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 

2-71
 
 

model estimate). For more details on the limitations of the 1999 NATA national scale 

assessment, see http://www.epa.gov/ttn/atw/nata1999/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 gridbased 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 

2-72
 
 

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-PM10 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 

2-73
 
 

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, PbPM2.5 and Pb-PM10. 

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 ultrafine 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 ultrafine 

particles are more numerous because coagulation is too slow to grow ultrafine particles into fine 

particles. Issues related to ultrafine lead emissions pertain to the PM2.5 fraction since ultrafines 

would not be expected to grow into particles larger than PM2.5. PM10 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). 

2-74
 
 

The impact of these sources on current air concentrations is expected to be quite low (relative to 

current concentrations) and has been estimated to fall within the range from 0.00002 μg/m3

 and 

0.00007 μg/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 μg/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 

μg/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 

2-75
 
 

Lewes, Delaware (a small town at mouth of Delaware Bay) have fallen from approximately 3 

μg/L in the early 1980s to less than 1 μg/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/m2

year (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 

μg/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). 

2-76
 
 

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). 

2-78
 
 

Source: Reprinted from Harris and Davidson (2005). Units for resuspension rate (Λ) 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 (Λ). 

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, 2001b, 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 

2-79
 
 

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). Most Pb 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 

2-80
 
 

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. 

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. 

2-81
 
 

2.8.2 Current Concentrations 

An analysis of data from the United States Geological Survey (USGS) National WaterQuality 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 μg/L (range 0.04 to 30 

μg/L), as compared to a mean of 0.52 μg/L (range 0.04 to 8.4 μg/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 μg/g dry weight (range 0.5 to 12,000 μg/g), as 

compared to a mean of 109 μg/g dry weight (range 0.5 to 12,000 μg/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). 

2-82
 
 

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 μm (N = 1466). [CD, 

Figure AX7-2.2.9] 

2-84
 
 

 

 

 

 

 

 

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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 

μg/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 μg/dL, to below 5 μg/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 

3-1
 
 

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 (RBA) 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., 

3-2
 
 

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 

3-3
 
 

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 (halflife 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 μg/dL as a level warranting individual 

intervention (CDC, 1991). In 1985, the CDC recognized a level of 25 μg/dL for individual child 

intervention, and in 1991, they recognized a level of 15 μg/dL for individual intervention and a 

level of 10 μg/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 

3-4
 
 

 

 

 

 

μg/dL and the data demonstrating that no “safe” threshold for blood Pb had been identified, and 

emphasizing the importance of preventive measures (CDC, 2005).1 

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 

1 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 μg/dL, the lack of a 

demonstrated threshold for adverse effects, and concerns for deflecting resources from children with higher blood 

Pb levels (CDC, 2005). 

3-5
 
 

 

 

 

 

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 

2

 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.htm). 

3-6
 
 

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

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

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. 

3-7
 
Table 3-1. Summary of Lowest Observed Effect Levels for Key Lead-Induced Health Effects in Children 

CD, Table 8-5). 

Lowest Observed Effect 

Blood Lead Level Neurological Effects Hematological Effects 

30 μg/dL Increased urinary δaminolevulinic acid 

15 μg/dL Behavioral disturbances 

(e.g., inattention, delinquency) 

Erythrocyte protoporphyrin 

(EP) elevation 

Altered electrophysiological 

responses 

10 μg/dL Effects on neuromotor function 

CNS cognitive effects 

(e.g., IQ deficits) 

Inhibition of δ-aminolevulinic 

acid dehydratase (ALAD) 

│

Pyrimidine-5′-nuclotidase 

(Py5N) activity inhibition 

Effe

a

5 μg/dL 

(???) (???) 

0 μg/dL 

Note: Arrows depict cases where weight of overall evidence strongly substantiates likely occurrence of type of effect in assconcentrations in range of 5-10 μg/dL, or possibly lower, as implied by (???). Although no evident threshold has yet been ceffects, 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). 

3-8
 
Table 3-2. Summary of Lowest Observed Effect Levels for Key Lead-Induced Health Effects in Adults (r

Table 8-6). 

Lowest Observed Effect 

Blood Lead Level Neurological Effects Hematological Effects Cardiovascular Effects 

30 μg/dL Peripheral sensory nerve Erythrocyte Imp

impairment protoporphyrin (EP) 

elevation in males 

20 μg/dL Cognitive impairment 15 μg/dL Postural sway Erythrocyte 

protoporphyrin (EP) 

elevation in females 

Increased urinary 

δ-aminolevulinic acid 

10 μg/dL Inhibition of Elevated blood pressure 

δ-aminolevulinic acid 

dehydratase (ALAD) 

5 μg/dL Ele

(???) (↓ 

0 μg/dL 

Note: Arrows depict cases where weight of overall evidence strongly substantiates likely occurrence of type of effect in association wiconcentrations in range of 5-10 μg/dL, or possibly lower, as implied by (???). Although no evident threshold has yet been clearly estab

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). 

3-9
 
 

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

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

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

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

Cognitive effects associated with Pb exposures that have been observed in 

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

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

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

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

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

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

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

8-30). 

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

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

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

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

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

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

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

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

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

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

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

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

3-10
 
 

 

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

6.2.16). 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3-11
 
 

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 μg/dL being a 

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

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

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

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

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

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

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

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

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

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

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

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 μg/dL). A recent meta-analysis by Nawrot and others 

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

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

3-12
 
 

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

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

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

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

and 8.4.3). 

Multiple studies reporting positive associations of blood pressure and hypertension with 

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

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

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

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

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

124). 

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

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

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

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

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

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

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

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

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

3-13
 
 

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

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

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

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

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

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

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

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

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

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

6.9.2). 

3-14
 
 

 

 

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 cellmediated immunity, and increased risk of autoimmunity (See CD, Figure 5-18, Section 5.9.11). 

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

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

5.9.3.2). These effects 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 μg/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 μg/dL (95% CI: 2.5-3.0) in the 1991-1994 survey period to 

1.7 μg/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 μg/dL reported for U.S. children 

(aged 6 months to 5 years) in 1976-1980 (CD, Section 4.3.1.3). 

3-15
 
 

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

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

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

6.4.7.3 and 6.3.6). 

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 hand3-16
 
 

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. 

3-17
 
 

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 Pbrelated 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 wellbeing, 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). 

3-18
 
 

 

Table 3-3. Population subgroups with characteristics that may contribute to increased 

susceptibility or vulnerability to Pb health effects. 

Childrena 

Living in poverty 

Adultsb

 w. 

hypertensionc 

Adultsb

 w. 

Diabetes 

Adultsb w. chronic 

kidney disease 

Estimated number 

in U.S. populationd 

4.8 million 

(20%)e 

~50 million 

(25.6%)e 

18 million 

(8.7%)e 

19.2 million

 (11%)e 

Year for estimate 2005 1999-2002 2002 1988-1994 

Reference DeNavas-Walt et al., 

2006 NCHS, 2005 CDC, 2003 Coresh et al., 2005 

a

Children less than 6 years of age. 

b

Individuals greater than 20 year of age. 

c

Hypertension, 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). 

d

Note that there may be overlap among some groups (i.e., individuals may be counted in more than one 

subgroup). 

e

Percent 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 

3-19
 
 

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)* 

Number of 

Counties 

Population 

(1,000's) 

Under age 5 

population 

(1,000's) 

≥ 10.0 20 25,756 1,949 

5.0 - 10.0 37 20,180 1,430 

1.0 - 5.0 346 116,496 7,979 

0.5 - 1.0 320 42,995 2,871 

0.1 - 0.5 1,165 56,287 3,687 

≥ 0.1 1,888 261,715 17,915 a

 2002 NEI. 

3-20
 
 

 

 

Table 3-5. Population size in counties with Pb emissions, by emissions density. 

Total Pb Emissions 

Density in County 

(tpy/1000 square miles) 

Number of 

Counties 

Population 

(1,000's) 

Under age 5 population 

(1,000's) 

≥ 10.0 77 32,911 2,185 

5.0 - 10.0 80 39,278 2,888 

1.0 - 5.0 463 108,700 7,377 

0.5 - 1.0 301 32,589 2,200 

0.1 - 0.5 1,105 52,352 3,521 

total ≥ 0.1 2,026 265,829 18,172 a

 2002 NEI. 

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 Pbcontaminated 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, 

4

 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). 

3-21
 
 

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 μg/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 μg/dL, and some analyses appear to show Pb effects on intellectual attainment 

in young children with blood Pb levels ranging from 2 to 8 μg/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 μg/dL and higher. At blood Pb levels on the order 

of 10 μg/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 μg/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 μg/dL, 

with the potential adverse impact of such effects being enhanced for susceptible 

3-22
 
 

subpopulations including those with diabetes, hypertension, and chronic renal 

insufficiency.
 

•	 Other Pb associated effects in adults occurring at or just above 10 μg/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. 

3-23
 
 

 

 

 

 

 

 

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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 fullscale. 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 airrelated 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 

4-1
 
 

 

 

 

this assessment, however, the Pb from these nonair sources is collectively referred to as “policyrelevant background” 1. 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). 

4-2
 
 

μg/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 μg/dL). For example, the overall weight of the available evidence, 

described in the CD, provides clear substantiation of neurocognitive decrements being associated 

in young children with blood Pb levels in the range of 5 to 10 μg/dL, and some analyses indicate 

Pb effects on intellectual attainment of young children ranging from 2 to 8 μg/dL (CD, Sections 

6.2, 8.4.2 and 8.4.2.6). That is, while blood Pb levels in U.S. children ages one to five years 

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

associations of effects on the neurodevelopment of children with these more recent blood Pb 

levels (CD, Chapter 6). 

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

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

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

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

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

4-4
 
 

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 μg/dL increase in blood Pb may have a greater effect 

on IQ at blood Pb levels below 10 μg/dL than at higher levels (CD, pp. 8-63 to 8-64). While this 

may at first seem at odds with certain fundamental toxicological concepts, a number of examples 

of non- or supralinear dose-response relationships exist in toxicology (CD, pp. 6-76 and 8-83 to 

4-5
 
 

 

 

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 concentrationresponse relationship described by these functions is greater at the lower blood Pb levels (e.g., 

less than 10 μg/dL). The impact of the nonlinear slope is illustrated by the estimates of IQ 

decrements associated with increases in blood IQ for different ranges of blood Pb level reported 

for the log-linear model (Lanphear et al., 2005). These estimates were IQ decrements of 3.9 

(with 95% confidence interval, CI, of 2.4-5.3), 1.9 (95% CI, 1.2-2.6) and 1.1 (95% CI, 0.7-1.5), 

for increases in concurrent blood Pb from 2.4 to 10 μg/dL, 10 to 20 μg/dL, and 20 to 30 μg/dL, 

respectively (Lanphear et al., 2005). For an increase in concurrent blood Pb levels from <1 to 10 

ug/dL, the log-linear model estimates a decline of 6.2 points in full scale IQ which is comparable 

to the 7.4 point decrement in IQ for an increase in lifetime mean blood Pb levels up to 10 ug/dL 

observed in the Rochester study (CD, pp 6-30 to 6-31). 

Several studies have examined the relationship of IQ decrement with blood Pb, quantified 

by a variety of metrics, at lower blood Pb levels. On a change in IQ per μg/dL basis, estimates of 

IQ decrement associated with blood Pb levels (concurrent, 24-month, peak, lifetime average or 

lifetime cumulative) below 10 μg/dL range from -0.4 to -1.8 (CD, Table 8.7). At the upper end 

of this range are the slopes derived for the subsets of children in the Rochester and Boston 

cohorts for which peak blood Pb levels were <10 μg/dL; these slopes are -1.8 (for concurrent 

blood Pb influence on IQ) and -1.6 (for 24-month blood Pb influence on IQ), respectively. The 

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). 

4-6
 
 

yielded a slope for the linear function of IQ change associated with concurrent blood Pb of -0.8 

for the subset of the children in the pooled data set for which maximal or peak blood Pb levels 

were below 10 μg/dL. Of the 1333 children in the full pooled dataset, there were 244 in this 

subset. When the full dataset was restricted to a still smaller subset of 103 children for which 

peak blood Pb levels were below 7.5 μg/dL the slope of concurrent blood Pb and IQ was -2.94 

(Lanphear et al., 2005). The analysis of this latter subset supported the authors’ conclusions that 

“for a given increase in blood lead, the lead-associated intellectual decrement for children with a 

maximal blood lead level <7.5 μg/dL was significantly greater than that observed for those with 

a maximal blood lead level > 7.5 μg/dL (p-0.015)” and that “environmental lead exposure in 

children who have maximal blood lead levels < 7.5 μg/dL is associated with intellectual 

deficits”. This subset was composed primarily of children from the Rochester cohort (69 

children), with smaller numbers of children from five of the other 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 μg/dL summarized in the CD (e.g., CD, Table 8-7), providing some uncertainty 

with regard to the precise magnitude of slope for the full range of blood Pb below 7.5 μg/dL. 

As discussed in the CD, threshold blood Pb levels for these effects cannot be discerned 

from the currently available epidemiological studies, and the evidence in the animal Pb 

neurotoxicity literature does not define a threshold for any of the toxic mechanisms of Pb (CD, 

Sections 5.3.7 and 6.2). 

In applying relationships observed with the pooled analysis (Lanphear et al., 2005) to the 

risk assessment, which includes blood Pb levels below the range represented by the pooled 

analysis, several alternative blood Pb concentration-response models were considered in 

recognition of a reduced confidence in our ability to characterize the quantitative blood Pb 

concentration-response relationship at the lowest blood Pb levels represented in the recent 

epidemiological studies. The functions considered and employed in the initial risk analyses for 

this review include the following. 

•	 Log-linear function with low-exposure linearization, for both concurrent and lifetime 

average blood metrics, applies the nonlinear relationship down to the blood Pb 

concentration representing the lower bound of blood Pb levels for that blood metric in 

the pooled analysis and applies the slope of the tangent at that point to blood Pb 

concentrations estimated in the risk assessment to fall below that level. 

•	 Log-linear function with cutpoint, for both concurrent and lifetime average blood 

metrics, also applies the nonlinear relationship at blood Pb concentrations above the 

lower bound of blood Pb concentrations in the pooled analysis dataset for that blood 

4-7
 
 

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 μg/dL and 

•	 Population stratified dual linear function for concurrent blood Pb, derived from the 

pooled dataset stratified at 7.5 μg/dL peak blood Pb. 

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 μg/dL and 10 μg/dL, peak blood Pb, have not undergone 

such careful development. These analyses were primarily done to compare the lead-associated 

decrement at lower blood lead concentrations and higher blood lead concentrations. For these 

analyses, the study population was stratified at the specified cutpoint and separate linear models 

4-8
 
 

were fitted to the data above and below the cutpoint. The fit of the model or sensitivity analyses 

were not conducted (or reported) on these coefficients. While these analyses are quite suitable 

for the purpose of investigating whether the slope at lower concentration levels are greater 

compared to higher concentration levels, use of such coefficients in a risk analysis to assess 

public health impact may be inappropriate. Further, only 103 children had maximal blood lead 

levels less than 7.5 μg/dL and 244 children had maximal blood lead levels less than 10 μg/dL. 

While these children may better represent current blood lead levels, not fitting a single model 

using all available data may lead to bias. Slob et al. (2005) noted that the usual argument for not 

considering data from the high dose range is that different biological mechanisms may play a 

role at higher doses compared to lower doses. However, this does not mean a single curve across 

the entire exposure range cannot describe the relationship. The fitted curve merely assumes that 

the underlying dose-response follows a smooth curve over the whole dose range. If biological 

mechanisms change when going from lower to higher doses, this change will result in a 

gradually changing slope of the dose-response. The major strength of the Lanphear et al. (2005) 

study was the large sample size and the pooled analysis of data from seven different cohorts. In 

the case of the 7.5 μg/dL cutpoint, less than 10% of the available data is used in the analysis, 

with more than half of the data coming from one cohort (Rochester) and the six other cohorts 

contributing zero to 13 children to the analysis. Such an analysis dissipates the strength of the 

Lanphear et al. study. 

In consideration of the preceding discussion, we place greater confidence in the log-linear 

model form compared to the dual-linear stratified models for our purposes in this risk 

assessment. Further, in considering risk estimates derived from the four core functions (loglinear function with low-exposure linearization, log-linear function with cutpoint, dual linear 

function, stratified at 7.5 μg/dL peak blood Pb, and dual linear function, stratified at 10 μg/dL 

peak blood Pb), we have assigned greatest confidence to risk estimates derived using the loglinear function with low-exposure linearization since this function (a) is a nonlinear function that 

describes greater response per unit blood Pb at lower blood Pb levels consistent with multiple 

studies identified in the discussion above, (b) is based on fitting a function to the entire pooled 

dataset (and hence uses all of the data in describing response across the range of exposures), (c) 

is supported by sensitivity analyses showing the model coefficients to be robust, and (d) provides 

an approach for predicting IQ loss at the lowest exposures simulated in the assessment 

(consistent with the lack of evidence for a threshold). Note, however, that risk estimates 

generated using the other three concentration-response functions are also presented to provide 

perspective on the impact of uncertainty in this key modeling step. 

4-9
 
 

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 μg/m3

 (as maximum monthly averages) and 0.2 μg/m3 (as a maximum 

quarterly average). The current NAAQS scenario for the urban case studies involved a "rolling 

4-10
 
 

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 

4-11
 
 

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 policyrelevant 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 of Pb 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 (locationspecific 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 sitespecific 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 (estimating IQ 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 

4-13
 
 

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 locationspecific). 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 

4-14
 
 

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 loglinear 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. 

4-16
 
 

 

•	 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. 

4-17
 
 

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 

4-18 
 

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 

4-19
 
 

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. 

4-20
 
 

 

Limitations in Estimation of IQ Loss and Associated Uncertainty 

•	 Relating blood Pb 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 μg/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 μg/dL and no children 

with levels below 1 μg/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 μg/dL (see Section 

4.3). In recognition of the uncertainty in specifying a quantitative concentrationresponse relationship at such levels, our core modeling approach involves the 

application of four different functions to generate a range of risk estimates (see 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 μg/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 

4-21
 
 

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 μg/m3

 for the study areas in Los 

Angeles, Chicago and Cleveland, respectively. In terms of maximum monthly average 

the values are 0.17 μg/m3, 0.31 μg/m3 and 0.56 μg/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 μg /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 μg/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 μg/m3

 as a maximum quarterly average, the mean for large urban areas, and 0.87 

μg/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 μg/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 μg/dL 

for the mean scenario and 7.2 μg/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 μg/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 μg/dL with the percent 

4-22 
 

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 

μg/dL higher under the current NAAQS scenario compared with the current conditions 

scenario, while 95th percentile levels range from 1.5 to 4 μg/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 μg/dL and a 95th percentile estimate of 10.6 μg/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 highend current conditions scenarios indicate a decrease of about 1 μg/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 μg/dL from the current NAAQS scenario and the 

high-end current conditions scenario differing by 3.4 μg/dL. 

•	 For the primary Pb smelter case study (full study area) the estimate of median total 

blood Pb is 1.5 μg/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 μg/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 

4-23
 
 

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 μg/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 μg/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 μg/m3

 maximum monthly average to 0.5 μg/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 μg/m3

, maximum monthly average and 0.2 μg/m3 maximum 

quarterly average) were simulated, median estimates of total blood Pb levels range 

from 1.7 to 1.8 μg/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 μg/m3

 maximum monthly average) show 

a slight reduction compared with current conditions (i.e., equal to or less than a 0.2 

μg/dL reduction - see Table 4-1). Estimates of 95th percentile total blood Pb levels for 

the higher alternative NAAQS (0.5 and 0.2 μg/m3 maximum monthly average and 0.2 

μg/m3 maximum quarterly average) range from 5.7 to 6.0 μg/dL (see Table 4-2). These 

estimates differ from those for current conditions by about 0.4 μg/dL or less. Estimates 

of 95th percentile total blood Pb levels under the lowest alternative NAAQS (0.02 and 

0.05 μg/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 μg/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 

4-24
 
 

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 μg/m3 maximum monthly average) 

estimated at 4.2 μg/dL and the lowest alternate NAAQS (0.02 μg/m3

 maximum 

monthly average) estimated at 3.8 μg/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 μg/dL for the highest alternative 

NAAQS (0.5 μg/m3

 maximum monthly average), decreasing to 2.3 μg/dL for the 

intermediate alternative NAAQS (0.2 μg/m3 maximum monthly average) and finally 

dropping to 1.6 μg/dL for the lowest alternative NAAQS (0.02 μg/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 μg/dL, dropping to 6.1 μg/dL (for the 0.2 μg/m3 maximum monthly 

average NAAQS) and reaching 4.2 μg/dL with the lowest alternative NAAQS (see 

Table 4-2). 

4-25
 
 

 

 

 

 

 

 

 

Table 4-1. Summary of blood Pb estimates for median total blood Pb. 

Air Quality Scenario 

(and case study) 

Policy-relevant source 

contribution (percent) of 

total blood Pb 

Concurrent 

blood Pb 

concentration 

(total Pb exposure) a Recent 

Airb 

Recent Air 

plus Past Airb 

Location-specific (Chicago) 

Current NAAQS (1.5 μg/m3

, max quarterly) 63% 83% 3.0 

Current conditions 

(0.14 μg/m3 max quarterly; 0.31 μg/m3 max monthly) 22% 67% 1.8 

Alternative NAAQS (0.2 μg/m3

, max monthly) 17% 67% 1.8 

Alternative NAAQS (0.05 μg/m3, max monthly) 6% 69% 1.6 

Alternative NAAQS (0.02 μg/m3

, max monthly) 1% 63% 1.6 

Location-specific (Cleveland) 

Current NAAQS (1.5 μg/m3

, max quarterly) 57% 86% 2.1 

Current conditions 

(0.36 μg/m3 max quarterly; 0.56 μg/m3 max monthly) 17% 67% 1.8 

Alternative NAAQS (0.5 μg/m3

, max monthly) 39% 72% 1.8 

Alternative NAAQS (0.2 μg/m3

, max quarterly) 12% 65% 1.7 

Alternative NAAQS (0.2 μg/m3

, max monthly) 6% 65% 1.7 

Alternative NAAQS (0.05 μg/m3

, max monthly) 1% 63% 1.6 

Alternative NAAQS (0.02 μg/m3

, max monthly) 1% 63% 1.6 

 Location-specific (Los Angeles) 

Current NAAQS (1.5 μg/m3

, max quarterly) 50% 81% 2.6 

Current conditions 

(0.09 μg/m3

 max quarterly; 0.17 μg/m3 max monthly) 18% 71% 1.7 

Alternative NAAQS (0.05 μg/m3

, max monthly) 13% 69% 1.6 

Alternative NAAQS (0.02 μg/m3

, max monthly) 6% 63% 1.6 

General urban 

Current NAAQS (1.5 μg/m3

, max quarterly) 61% 84% 3.1 

Alternative NAAQS (0.5 μg/m3

, max monthly) 41% 73% 2.2 

Current conditions –high-end (0.87 μg/m3

, max quarterly) 38% 76% 2.1 

Alternative NAAQS (0.2 μg/m3

, max quarterly) 35% 75% 2.0 

Current conditions - mean (0.14 μg/m3

, max quarterly) 32% 74% 1.9 

Alternative NAAQS (0.2 μg/m3

, max monthly) 26% 74% 1.9 

Alternative NAAQS (0.05 μg/m3

, max monthly) 12% 65% 1.7 

Alternative NAAQS (0.02 μg/m3

, max monthly) 6% 69% 1.6 

 Primary Pb smelter - full study area 

Current NAAQS (1.5 μg/m3

, max quarterly) 

NA 

53% 1.5 

Alternative NAAQS (0.5 μg/m3

, max monthly) 79% 1.4 

Alternative NAAQS (0.2 μg/m3, max quarterly) 50% 1.4 

Alternative NAAQS (0.2 μg/m3

, max monthly) 36% 1.4 

Alternative NAAQS (0.05 μg/m3

, max monthly) 50% 1.4 

Alternative NAAQS (0.02 μg/m3

, max monthly) 64% 1.4 

Primary Pb smelter - 1.5km study area 

Current NAAQS (1.5 μg/m3

, max quarterly) 

NA 

87% 4.6 

Alternative NAAQS (0.5 μg/m3

, max monthly) 81% 3.2 

Alternative NAAQS (0.2 μg/m3, max quarterly) 72% 2.5 

Alternative NAAQS (0.2 μg/m3

, max monthly) 78% 2.3 

Alternative NAAQS (0.05 μg/m3

, max monthly) 65% 1.7 

Alternative NAAQS (0.02 μg/m3

, max monthly) 69% 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). 

4-26
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 4-2. Summary of blood Pb level estimates for 95th percentile total blood Pb. 

Air Quality Scenario 

(and case study) 

Policy-relevant 

contribution (percent) 

of total blood Pb 

Concurrent blood 

Pb concentration 

(total Pb 

exposure)

Recent a

Airb 

Recent 

plus Past 

Airb 

Location-specific (Chicago) 

Current NAAQS (1.5 μg/m3

, max quarterly) 65% 87% 10.2 

Current conditions 

(0.14 μg/m3

 max quarterly; 0.31 μg/m3 max monthly) 18% 68% 6.0 

Alternative NAAQS (0.2 μg/m3

, max monthly) 25% 70% 6.0 

Alternative NAAQS (0.05 μg/m3

, max monthly) 5% 65% 5.5 

Alternative NAAQS (0.02 μg/m3

, max monthly) 4% 67% 5.4 

Location-specific (Cleveland) 

Current NAAQS (1.5 μg/m3

, max quarterly) 31% 73% 7.4 

Current conditions 

(0.36 μg/m3

 max quarterly; 0.56 μg/m3 max monthly) 15% 67% 6.1 

Alternative NAAQS (0.5 μg/m3

, max monthly) 25% 70% 6.0 

Alternative NAAQS (0.2 μg/m3

, max quarterly) 12% 67% 5.8 

Alternative NAAQS (0.2 μg/m3

, max monthly) 7% 67% 5.7 

Alternative NAAQS (0.05 μg/m3

, max monthly) 2% 65% 5.4 

Alternative NAAQS (0.02 μg/m3

, max monthly) 2% 66% 5.3 

 Location-specific (Los Angeles) 

Current NAAQS (1.5 μg/m3

, max quarterly) 52% 80% 8.9 

Current conditions 

(0.09 μg/m3 max quarterly; 0.17 μg/m3 max monthly) 19% 68% 5.9 

Alternative NAAQS (0.05 μg/m3

, max monthly) 9% 67% 5.5 

Alternative NAAQS (0.02 μg/m3

, max monthly) 4% 65% 5.4 

General urban 

Current NAAQS (1.5 μg/m3

, max quarterly) 60% 83% 10.6 

Alternative NAAQS (0.5 μg/m3

, max monthly) 39% 76% 7.4 

Current conditions –high-end (0.87 μg/m3

, max quarterly) 38% 75% 7.2 

Alternative NAAQS (0.2 μg/m3

, max quarterly) 34% 74% 6.8 

Current conditions - mean (0.14 μg/m3

, max quarterly) 29% 72% 6.5 

Alternative NAAQS (0.2 μg/m3

, max monthly) 27% 72% 6.4 

Alternative NAAQS (0.05 μg/m3

, max monthly) 12% 68% 5.7 

Alternative NAAQS (0.02 μg/m3

, max monthly) 7% 67% 5.5 

 Primary Pb smelter - full study area 

Current NAAQS (1.5 μg/m3

, max quarterly) 

NA 

61% 4.6 

Alternative NAAQS (0.5 μg/m3

, max monthly) 74% 4.2 

Alternative NAAQS (0.2 μg/m3

, max quarterly) 60% 4.0 

Alternative NAAQS (0.2 μg/m3

, max monthly) 63% 4.0 

Alternative NAAQS (0.05 μg/m3

, max monthly) 50% 3.8 

Alternative NAAQS (0.02 μg/m3

, max monthly) 84% 3.8 

Primary Pb smelter - 1.5km study area 

Current NAAQS (1.5 μg/m3

, max quarterly) 

NA 

83% 12.3 

Alternative NAAQS (0.5 μg/m3

, max monthly) 89% 8.5 

Alternative NAAQS (0.2 μg/m3

, max quarterly) 89% 6.6 

Alternative NAAQS (0.2 μg/m3

, max monthly) 80% 6.1 

Alternative NAAQS (0.05 μg/m3

, max monthly) 78% 4.5 

Alternative NAAQS (0.02 μg/m3

, max monthly) 71% 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). 

4-27
 
 

4.4 RISK ASSESSMENT 

Risk results generated for the full-scale analysis are summarized in Tables 4-3 through 4­

8 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 μg/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 lowexposure linearization); and (c) the high C-R function (the dual linear with 

stratification at 7.5 μg/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. 

4-28
 
 

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 

μg/dL (peak blood Pb) yield the lowest risk estimates and the dual-linear function with 

stratification at 7.5 μg/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). 

4-29
 
 

•	 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 μg/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 μg/m3 (maximum monthly average for Los 

Angeles case study) to 0.56 μg/m3

 maximum monthly average for Cleveland case 

study. The estimate for the Chicago case study is between these at 0.31 μg/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 μg/m3 

as a maximum quarterly average) and a high-end ambient air Pb level in large urban 

areas (0.87 μg/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 highend 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 

4-30
 
 

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 

4-31
 
 

exposure for the general urban case study is similar to that estimated for the locationspecific 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 μg/m3 as monthly maximum average and 0.2 μg/m3

as 

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 μg/m3 as a maximum monthly average) show 

a slight reduction in total IQ loss for the median child compared with current 

4-32
 
 

conditions (i.e., equal to or less than a 0.2 IQ point reduction). Estimates of 95th 

percentile IQ loss for the higher alternative NAAQS (0.5 and 0.2 μg/m3 as monthly 

maximum average and 0.2 μg/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 

μg/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 μg/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 μg/m3 

maximum monthly average) estimated at 6.6 points and the lowest alternative NAAQS 

level (0.02 μg/m3 maximum monthly average) estimated at 6.3 μg/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 μg/m3 maximum monthly average), decreasing to 

5.0 points with the intermediate alternative NAAQS (0.2 μg/m3 maximum monthly 

average) and dropping to 4.0 points with the lowest alternative NAAQS level (0.02 

μg/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 μg/m3 

maximum monthly average, and reaching 6.5 points for the lowest alternative NAAQS 

(see Table 4-4). 

4-33
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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)a 

Low C-R 

function 

estimate LLLb 

High C-R 

function 

estimate 

Location-specific (Chicago) 

Current NAAQS (1.5 μg/m3, max quarterly) 2.4 5.6 8.8 

Current conditions (0.14 μg/m3 max quarterly; 0.31 μg/m3 max monthly) 1.4 4.2 5.2 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.4 4.2 5.2 

Alternative NAAQS (0.05 μg/m3, max monthly) 1.3 4.0 4.8 

Alternative NAAQS (0.02 μg/m3, max monthly) 1.3 4.0 4.7 

Location-specific (Cleveland) 

Current NAAQS (1.5 μg/m3, max quarterly) 1.7 4.7 6.3 

Current conditions (0.36 μg/m3 max quarterly; 0.56 μg/m3 max monthly) 1.4 4.2 5.2 

Alternative NAAQS (0.5 μg/m3, max monthly) 1.4 4.2 5.2 

Alternative NAAQS (0.2 μg/m3, max quarterly) 1.4 4.1 5.0 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.3 4.1 4.9 

Alternative NAAQS (0.05 μg/m3, max monthly) 1.3 4.0 4.7 

Alternative NAAQS (0.02 μg/m3, max monthly) 1.2 3.9 4.6 

Location-specific (Los Angeles) 

Current NAAQS (1.5 μg/m3, max quarterly) 2.1 5.3 7.7 

Current conditions (0.09 μg/m3 max quarterly; 0.17 μg/m3 max monthly) 1.4 4.2 5.1 

Alternative NAAQS (0.05 μg/m3, max monthly) 1.3 4.0 4.8 

Alternative NAAQS (0.02 μg/m3, max monthly) 1.3 4.0 4.7 

General Urban 

Current NAAQS (1.5 μg/m3, max quarterly) 2.5 5.8 9.2 

Alternative NAAQS (0.5 μg/m3, max monthly) 1.7 4.8 6.4 

Current conditions – high-end (0.87 μg/m3 max quarterly) 1.7 4.7 6.3 

Alternative NAAQS (0.2 μg/m3, max quarterly) 1.6 4.6 5.9 

Current conditions - mean (0.14 μg/m3 max quarterly) 1.5 4.5 5.6 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.5 4.4 5.6 

Alternative NAAQS (0.05 μg/m3, max monthly) 1.3 4.1 5.0 

Alternative NAAQS (0.02 μg/m3, max monthly) 1.3 4.0 4.8 

Primary Pb smelter - full study area 

Current NAAQS (1.5 μg/m3, max quarterly) 1.2 3.8 4.4 

Alternative NAAQS (0.5 μg/m3, max monthly) 1.0 3.7 4.2 

Alternative NAAQS (0.2 μg/m3, max quarterly) 0.9 3.6 4.2 

Alternative NAAQS (0.2 μg/m3, max monthly) 0.9 3.6 4.1 

Alternative NAAQS (0.05 μg/m3, max monthly) 0.9 3.6 4.0 

Alternative NAAQS (0.02 μg/m3, max monthly) 0.9 3.6 4.1 

Primary Pb smelter - 1.5km subarea 

Current NAAQS (1.5 μg/m3, max quarterly) 3.7 6.8 11.2 

Alternative NAAQS (0.5 μg/m3, max monthly) 2.6 5.8 9.4 

Alternative NAAQS (0.2 μg/m3, max quarterly) 2.0 5.2 7.4 

Alternative NAAQS (0.2 μg/m3, max monthly) 1.9 5.0 6.9 

Alternative NAAQS (0.05 μg/m3, max monthly) 1.4 4.2 5.1 

Alternative NAAQS (0.02 μg/m3, max monthly) 1.3 4.0 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. 

4-34
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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

distributions. 

Points IQ loss 

(total Pb exposure)a 

Case Study and Air Quality Scenario 

Low C-R 

function 

estimate LLLb 

High C-R 

function 

estimate 

Location-specific (Chicago) 

Current NAAQS (1.5 μg/m3, max quarterly) 4.7 9.0 12.1 

Current conditions (0.14 μg/m3 max quarterly; 0.31 μg/m3 max monthly) 4.1 7.5 11.4 

Alternative NAAQS (0.2 μg/m3, max monthly) 4.1 7.5 11.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 4.1 7.3 11.3 

Alternative NAAQS (0.02 μg/m3, max monthly) 4.1 7.3 11.3 

Location-specific (Cleveland) 

Current NAAQS (1.5 μg/m3, max quarterly) 4.3 8.1 11.6 

Current conditions (0.36 μg/m3 max quarterly; 0.56 μg/m3 max monthly) 4.1 7.6 11.4 

Alternative NAAQS (0.5 μg/m3, max monthly) 4.1 7.5 11.4 

Alternative NAAQS (0.2 μg/m3, max quarterly) 4.1 7.4 11.3 

Alternative NAAQS (0.2 μg/m3, max monthly) 4.1 7.4 11.3 

Alternative NAAQS (0.05 μg/m3, max monthly) 4.0 7.2 11.3 

Alternative NAAQS (0.02 μg/m3, max monthly) 4.0 7.2 11.3 

Location-specific (Los Angeles) 

Current NAAQS (1.5 μg/m3, max quarterly) 4.5 8.6 11.8 

Current conditions (0.09 μg/m3 max quarterly; 0.17 μg/m3 max monthly) 4.1 7.5 11.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 4.1 7.3 11.3 

Alternative NAAQS (0.02 μg/m3, max monthly) 4.0 7.2 11.3 

General Urban 

Current NAAQS (1.5 μg/m3, max quarterly) 4.7 9.1 12.1 

Alternative NAAQS (0.5 μg/m3, max monthly) 4.3 8.1 11.6 

Current conditions – high-end (0.87 μg/m3 max quarterly) 4.3 8.0 11.6 

Alternative NAAQS (0.2 μg/m3, max quarterly) 4.2 7.9 11.5 

Current conditions - mean (0.14 μg/m3 max quarterly) 4.2 7.7 11.5 

Alternative NAAQS (0.2 μg/m3, max monthly) 4.2 7.7 11.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 4.1 7.4 11.3 

Alternative NAAQS (0.02 μg/m3, max monthly) 4.1 7.3 11.3 

Primary Pb smelter - full study area 

Current NAAQS (1.5 μg/m3, max quarterly) 3.7 6.8 11.2 

Alternative NAAQS (0.5 μg/m3, max monthly) 3.4 6.6 11.1 

Alternative NAAQS (0.2 μg/m3, max quarterly) 3.2 6.5 11.1 

Alternative NAAQS (0.2 μg/m3, max monthly) 3.2 6.4 11.1 

Alternative NAAQS (0.05 μg/m3, max monthly) 3.1 6.3 11.0 

Alternative NAAQS (0.02 μg/m3, max monthly) 3.1 6.3 11.0 

Primary Pb smelter - 1.5km subarea 

Current NAAQS (1.5 μg/m3, max quarterly) 5.0 9.5 12.4 

Alternative NAAQS (0.5 μg/m3, max monthly) 4.5 8.5 11.8 

Alternative NAAQS (0.2 μg/m3, max quarterly) 4.2 7.8 11.5 

Alternative NAAQS (0.2 μg/m3, max monthly) 4.1 7.6 11.4 

Alternative NAAQS (0.05 μg/m3, max monthly) 3.6 6.8 11.1 

Alternative NAAQS (0.02 μg/m3, max monthly) 3.3 6.5 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. 

4-35
 
 

 

 

 

 

 

 

Table 4-5. Median IQ loss estimates for the current NAAQS scenario. 

Case study 

IQ Loss from policy relevant exposures 

(recent air plus past air) a 

Total IQ loss 

(total Pb exposure) b 

Low C-R function 

estimates 

LLL C-R function 

estimates 

High C-R function 

estimates 

Recent 

air 

Recent air 

 + past air 

Recent 

air 

Recent 

air 

+ past 

air 

Recent 

air 

Recent air 

 + past air 

Low C-R 

function 

estimates 

LLL C-R 

function 

estimates 

High C-R 

function 

estimates 

Location-specific (Chicago) 1.4 2.0 3.4 4.7 5.6 7.4 2.4 5.6 8.8 

Location-specific (Cleveland) 0.6 1.4 2.8 3.9 2.1 4.6 1.7 4.7 6.3 

Location-specific (Los Angeles) 1.1 1.7 2.7 4.2 4.0 6.2 2.1 5.3 7.7 

General urban 1.5 2.1 3.5 4.8 5.6 7.7 2.5 5.8 9.2 

Primary Pb smelter-full area 0.6 c 1.9 2.3 1.2 3.8 4.4 

Primary Pb smelter - subarea 3.2 6.0 9.4 3.7 6.8 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). 

Table 4-6. 95th percentile IQ loss estimates for the current NAAQS scenario 

Case study 

IQ Loss from policy relevant exposures 

(recent air plus past air) a 

Total IQ loss 

(total Pb exposure) b 

Low C-R function 

estimates 

LLL C-R function 

estimates 

High C-R function 

estimates 

Recent 

air 

Recent 

air + 

past air 

Recent 

air 

Recent 

air + 

past air 

Recent 

air 

Recent 

air + 

past air 

Low C-R 

function 

estimates 

LLL C-R 

function 

estimates 

High C-R 

function 

estimates 

Location-specific (Chicago) 3.0 4.0 5.8 7.6 7.7 10.3 4.7 9.0 12.1 

Location-specific (Cleveland) 1.4 3.1 2.6 5.9 3.7 8.5 4.3 8.1 11.6 

Location-specific (Los Angeles) 2.3 3.6 4.4 6.9 6.1 9.5 4.5 8.6 11.8 

General urban 2.9 3.9 5.5 7.6 7.3 10.1 4.7 9.1 12.1 

Primary Pb smelter - full area 2.3 4.2 6.8 3.7 6.8 11.2 

Primary Pb smelter - subarea 4.2 8.0 10.4 5.0 9.5 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 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). 

4-36
 
1 

2 

Table 4-7. Incidence of children with >1 point Pb-related IQ loss. 

Air Quality Scenario 

(for location-specific urban case studies) 

dual linear - stratified at 

7.5 μg/dL peak blood Pb 

log-linear with 

linearization 

dual linear - stratified at 

10 μ/dL peak blood Pb log

Incidence of 

>1 point IQ 

loss 

Delta 

(change in 

incidence 

compared to 

current 

conditions) 

Incidence 

of 

>1 point, 

IQ loss 

Delta 

(change in 

incidence 

compared to 

current 

conditions) 

Incidence 

of >1 

point, IQ 

loss 

Delta 

(change in 

incidence 

compared to 

current 

conditions) 

Inc

>1 

IQ 

Chicago (total modeled child population: 396,511) Chicago Current Conditions (Mean) 391,602 389,754 271,031 

236,257 

Current NAAQS (1.5 μg/m3 Maximum Quarterly) 395,797 4,195 395,528 5,773 347,415 76,384 31

Alternative NAAQS (0.2 μg/m3 Maximum Monthly) 391,158 -444 389,461 -293 271,444 412 23

Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 389,572 -2,030 387,407 -2,347 253,775 -17,256 22

Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 389,176 -2,427 386,630 -3,125 249,865 -21,166 21Cleveland (total modeled child population: 13,990) Cleveland Current Conditions (Mean) 13,809 13,745 

9,526 8,515 

Current NAAQS (1.5 μg/m3 Maximum Quarterly) 13,893 84 13,857 112 10,664 1,137 9

Alternative NAAQS (0.2 μg/m3 Maximum Quarterly) 13,770 -38 13,703 -42 9,221 -305 8

Alternative NAAQS (0.5 μg/m3 Maximum Monthly) 13,789 -20 13,720 -25 9,497 -29 8

Alternative NAAQS (0.2 μg/m3 Maximum Monthly) 13,759 -50 13,694 -51 9,083 -443 8

Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 13,729 -80 13,642 -103 8,785 -741 7

Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 13,720 -88 13,628 -117 8,736 -790 7Los Angeles (total modeled child population: 372,252) Los Angeles Current Conditions (Mean) 282,216 280,711 191,675 

170,474 

Current NAAQS (1.5 μg/m3 Maximum, Quarterly) 285,272 3,056 284,945 4,234 240,988 49,313 22

Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 281,112 -1,104 279,658 -1,053 183,395 -8,280 16

Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 280,740 -1,476 279,057 -1,654 180,745 -10,929 15

4-37
 
1 Table 4-8. Incidence of children with >7 points Pb-related IQ loss. 

Air Quality Scenario 

(location-specific urban case studies) 

dual linear - stratified at 

7.5 ug/dL peak blood Pb 

log-linear with 

linearization 

dual linear - stratified at 

10 ug/dL peak blood Pb log

Incidence of 

> 7 points 

IQ loss 

Delta 

(change in 

incidence 

compared to 

current 

conditions) 

Incidence 

of 

> 7 points 

IQ loss 

Delta 

(change in 

incidence 

compared to 

current 

conditions) 

Incidence 

of 

> 7 points 

IQ loss 

Delta 

(change in 

incidence 

compared to 

current 

conditions) 

Inc

> 7 

IQ 

Chicago (total modeled child population: 396,511) Chicago Current Conditions (Mean) 136,709 33,664 63 1

Current NAAQS (1.5 μg/m3 Maximum Quarterly) 244,401 107,692 100,159 66,495 555 492 5

Alternative NAAQS (0.2 μg/m3 Maximum Monthly) 136,067 -642 32,546 -1,118 48 -16 1

Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 120,706 -16,003 27,367 -6,297 16 -48 

Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 117,819 -18,890 26,027 -7,637 8 -56 Cleveland (total modeled child population: 13,990) Cleveland Current Conditions (Mean) 4,834 1,212 3 

Current NAAQS (1.5 μg/m3 Maximum Quarterly) 6,139 1,305 1,858 647 4 2 

Alternative NAAQS (0.2 μg/m3 Maximum Quarterly) 4,525 -309 1,073 -139 1 -2 

Alternative NAAQS (0.5 μg/m3 Maximum Monthly) 4,806 -28 1,180 -31 1 -2 

Alternative NAAQS (0.2 μg/m3 Maximum Monthly) 4,424 -410 1,026 -186 1 -2 

Alternative NAAQS (0.05 μg/m3 Maximum Monthly) 4,106 -728 886 -326 0 -3 

Alternative NAAQS (0.02 μg/m3 Maximum Monthly) 4,051 -783 866 -345 0 -3 Los Angeles (total modeled child population: 372,252) Los Angeles Current Conditions (Mean) 94,684 22,665 23 

Current NAAQS (1.5 μg/m3 Maximum, Quarterly) 158,171 63,487 57,834 35,168 183 160 3

Alternative NAAQS (0.05 μg/m3 Maximum, Monthly) 87,303 -7,382 19,781 -2884 11 -11 

Alternative NAAQS (0.02 μg/m3 Maximum, Monthly) 83,909 -10,775 17,939 -4726 17 -6 

4-38
 
 

 

 

 

 

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. 

4-39
 
 

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. EPA452/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
 
 

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 CASAC 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 μg/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 μg/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 μg Pb/dl and that 

population blood lead, measured as the geometric mean, must be 15 μg Pb/dl in order 

to place 99.5 percent of children in the United States below 30 μg Pb/dl. 

(3) Attributing the contribution to blood lead from nonair pollution sources. EPA 

concludes that 12 μg 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 μg Pb/dl [the maximum safe level]. Taking into 

account exposure from other sources (12 μg Pb/dl), EPA has designed the standard to 

limit air contribution after achieving the standard to 3 μg 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 μg 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 (δ-ALAD). The 1977 CD reported a threshold for inhibition of this enzyme in 

children at 10 μg Pb/dL. The 1977 CD also reported a threshold of 15-20 μg/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 μg/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 μg/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 μg/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 μg/dL1

. 

1

 The CDC subsequently revised their advisory level for children’s blood Pb to 25 μg/dL in 1985, and to 10 

μg/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 μg/dL would 

have a geometric mean blood level of 15 μg/dL. EPA described 15 μg/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 μg/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 

μg/dL. Thus, 12 μg/dL was identified as the nonair contribution, and subtracted from the 

population mean target level of 15 μg/dL to yield a value of 3 μg/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 μg 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 μg/dL blood Pb concentration to 1 μg/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-1 relationship, EPA concluded that the ambient air standard should be 1.5 

μg 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 μg 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 

5-6
 
 

 

 

 

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. 

CASAC 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 μg/dL as representing a clear pattern of adverse effects 

worthy of avoiding. This is in contrast to EPA’s identification of 30 μg/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 μg/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 

μg/dL and above 15 μg/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 μg/dL to 10 μg/dL. 

5-7 
 

 

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 μg/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 μg/m3 to 0.5 μg/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 μg/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 1-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. 

5-8 
 

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 μg/m3 would have “relatively little, if any, margin of 

safety;” that greater consideration should be given to a standard set below 1.0 μg/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 μg/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. 

5-9
 
 

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 riskbased 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? 

5-10
 
 

•	 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? 

5-11
 
 

 

 

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 technologybased 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 μg/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 “no,” 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. 

5-12
 
 

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. 

5-13
 
 

 

 

 

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 riskbased 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 μg/dl, thus identifying this 

blood Pb level as an approximate threshold for adverse health effects of Pb (USEPA, 1977; 43 

FR 46252).5 

5

 At the time the standard was set, inhibition of delta-aminolevulinic acid dehydratase (δ-ALAD), an 

enzyme integral to hemoglobin synthesis, was demonstrated to occur at a blood Pb level as low as 10 μg/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 μg/dL. 

5-14
 
 

 

 

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 μg/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 μg/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 μg/dL to 5 

μg/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 μg/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 μg/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 μg/dL, EPA did 

not recognize the inhibition of δ-ALAD at lower blood Pb levels as adverse to health. At that time, EPA stated that 

it considered that above blood levels of 30 μg/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 μg/dL and above are clearly 

adverse (43 FR 46251-46253). 

5-15 
 

 

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 μg Pb/day (USEPA 1977, p. 1-2) and the 2006 

CD cites recent studies indicating a dietary intake ranging from 2 to 10 μg 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 μg/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 μg/dL blood Pb per 1 μg/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-toblood 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. 

5-16
 
 

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 μg/dL (CD, pp. 6-31 to 6-32; Lanphear et al., 2000), as 

compared to EPA’s view in 1978 that 30 μg/dL was a maximum safe blood Pb level for an 

individual child, and that 15 μg/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 μg/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 low5-17
 
 

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 policyrelevant 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 

5-18
 
 

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 μg/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 

5-19
 
 

 

 

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 Pbrelated 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 μg/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 μg/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. 

5-20
 
 

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 

5-21
 
 

 

 

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 evidenceand 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 

8

 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 CASAC’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. 

5-22
 
 

 

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, CASAC 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 μg/m3

, specifically recommended analyses of a standard set at 0.25 μg/m3, and also 

recommended a monthly averaging time (CASAC, 1990). In two letters to the Administrator 

during the current review, CASAC has consistently recommended that the primary NAAQS 

should be “substantially lowered” from the current level of 1.5 μg/m3 to a level of “0.2 μg/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 Hyperactivity Disorder (ADHD), delinquency, and criminal behavior (6–10). 

Among U.S. children, eight to fifteen years old, those in the highest quintile (> 2 μg/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 μg/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 μg/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 μg/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 

5-23
 
 

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 

5-24
 
 

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-PM10 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 CASAC recommended consideration of a change in the 

indicator to utilize low-volume PM10 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 

5-25
 
 

particles, and that concurrent Pb-PM10 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 PM10, 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).” 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 PM10 

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-PM10 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-PM10 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 PbTSP as the indicator for the NAAQS at this time. For example, the Administrator might consider 

describing a FEM in terms of PM10 that might be acceptably applied on a site-by-site basis where 

an appropriate relationship between Pb-TSP and Pb-PM10 can be developed based on sitespecific 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 

5-26
 
 

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-PM10 exhibits less spatial variability and that a scaling factor can be 

developed from Pb-PM10 data to level for the standard in terms of Pb-TSP, staff recommends the 

Administrator consider moving to a Pb-PM10 indicator in the future. One of the issues to 

consider when moving to a Pb-PM10 indicator is whether regulating concentrations of Pb-PM10 

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-PM10/Pb-TSP ratios are sensitive to distance from emissions 

sources (due to faster deposition of larger particles). As such, the use of a Pb-PM10 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 PM10 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 PbPM10. 

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 μg/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 μg/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 μg/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 

5-27
 
 

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 toxicologically 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 

5-28
 
 

 

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 policyrelevant 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 

5-29
 
 

 

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 sourceoriented 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). 

5-30
 
 

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 PM10, 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 

5-31
 
 

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 

Staff’s 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 decrementassociated 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 

5-32
 
 

 

 

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 μg/dL to 5 μg/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 μg/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 μg/dL to 

less than 5 μg/dL, with some studies indicating Pb effects on intellectual attainment of young 

children at blood Pb levels ranging from 2 to 8 μg/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 μg/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 

μg/dL or lower as an estimate of the nonair component of blood Pb (Henderson, 2007a, 

Appendix D). The value of 1.4 μg/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 air10 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. 

5-33
 
 

 

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 μg/dL 

for the child population geometric mean with a level of 2 μg/dL reflecting, not a recognized 

“safe” exposure level as was the case with 15 μg/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 μg/dL, representing nonair sources, yields 0.6 to 1 μg/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 μg/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 μg/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 μg/dL (Lanphear et al., 2000). 

5-34
 
 

 

 

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 Pbrelated 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 

5-35
 
 

illustrated by conditions in the three cities chosen for the location-specific urban case studies, 

e.g., 0.09 to 0.36 μg/m3 as a maximum quarterly average or 0.17 to 0.56 μg/m3 as a maximum 

monthly average. Recognizing, as described above, that estimates of IQ loss associated with airrelated 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 μg/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 μg/m3

, and additionally between those levels and the lower 

alternative standard levels assessed. The difference between the alternative standard levels of 

5-36
 
 

 

 

0.5 and 0.2 μg/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 μg/m3 maximum quarterly 

average (0.17 μg/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 μg/m3) are estimated to be reduced from levels in the two current conditions scenarios by 

14% (0.3 μg/dL) and 24% (0.5 μg/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 μg/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 μg/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 μg/m3

 level (either averaging time) 

which are approximately 45-50% for both the median and 95th percentile values. 

5-37
 
 

associated with reduced exposures at the lowest alternative NAAQS level (0.02 μg/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 concentrationresponse 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 μg/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 

μg/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 Pbrelated IQ loss greater than 7.0. 

In summary, in staff’s 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 μg/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 μg/m3

) appear to 

5-38
 
 

 

 

 

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 

CASAC 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 

μg/m3

 or less would provide appreciable improvement in the protection of public health from airrelated ambient Pb relative to that afforded by the current standard. 

In considering standard levels below 0.1 μg/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. 

5-39
 
 

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 μg/m3

 (e.g., the lower levels included in the risk assessment of 0.02 and 0.05 μg/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 μg/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 μg/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 

5-40
 
 

 

 

 

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 μg/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 μg/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 μg/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. 

5-41
 
 

5.5.3.4 Staff Conclusions and Recommendations 

Staff’s 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 μg/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 μg/m3

 Pb with a maximum monthly 

averaging time and form (or levels at and above 0.09 μg/m3

 with a maximum quarterly averaging 

time and form). Thus, the exposure and risk information supports and extends the evidencebased 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 μg/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 

5-42
 
 

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 μg/m3

. 

In conclusion, staff judges that a level for the standard set in the upper part of our 

recommended range (0.1-0.2 μg/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. 

5-43
 
 

(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 PM10 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 μg/m3

 (particularly in 

conjunction with a monthly averaging time) down to the lower levels included in the 

5-44
 
 

 

exposure and risk assessment, 0.02 to 0.05 μg/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 concentrationresponse 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 μg/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. 

5-45
 
 

 

•	 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. 

5-46
 
 

 

 

 

 

 

 

 

 

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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. 

6-1
 
 

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 SO2 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, pl 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 

6-2
 
 

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 

6-3
 
 

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 

6-4
 
 

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 

6-5
 
 

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 μg/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 

6-6
 
 

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). 

6-7
 
 

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 

6-8
 
 

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. 

6-9
 
 

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 CO3

2- 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 50μg/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 (LC50) of 

1140 μg/L at a salinity of 5% and an LC50 of 4274 μg/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. 

6-10
 
 

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 OHand CO3

2- 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 μg/L (ranging from 0.04 to 30) and in bulk 

sediment was 120.11μg/g (ranging from 0.5 to 12,000 μg/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 

6-11
 
 

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). 

6-12
 
 

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 μg/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 μg/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 13,180μg/L. Other species of algae such as 

Synechococcus aeruginosus were much more tolerant and required concentrations in excess of 

82,000 μg/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 

EC50 of 1,100 μg/L was reported for growth inhibition for Azolla pinnata, an aquatic fern, when 

exposed to Pb nitrate for 4 days. Studies with duckweed, Lemna gibba, have reported an EC50 of 

3,750μg/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). 

6-13
 
 

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 CaCO3/L) compared to hard water (124 mg CaCO3/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 μg/L as dissolved Pb and 

466 μg/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 μg/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. 

6-14
 
 

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 μg/L 

Pb and 58 to 118μg/L Pb respectively. A LC50 of 810 μg/L was found using fathead minnows at 

a pH of 6-6.5 while at the same water hardness the LC50 was >5,400 μg/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 μg/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 

6-15
 
 

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 LC50 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. 

6-16
 
 

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
 
 

Figure 6-1. Overview of Ecological Screening Assessment. 

6-18
 
 

 

Table 6-1. Models and Measurements Used for Ecological Risk Screening Assessment. 

Primary Pb Smelter Secondary Pb 

Smelter Near Roadway Non-Urban National-Scale Aquatic Screen 

Location Missouri Alabama Corpus Christi, Texas 

Atlee, Virginia 

Surface water bodies in 

the U.S. 

Spatial extent and resolution Approximately 6 km diameter, 

centered on point source 

U.S. Census 

blocks 

Corpus Christi: single 

transect perpendicular to 

road; 0.5 - 4 m from road 

Atlee: 140 m section of 

road; 2 - 30 m from road 

47 basin study units from 

all regions of U.S., 

covering approx. 50 

percent of U.S. land base 

Exposure Assessment: Estimating Media Concentrations 

Deposition 

to soil 

Models n/a AERMODa n/a n/a 

Measurements n/a n/a n/a n/a 

Soil conc. 

Models n/a MPEb n/a n/a 

Measurements 

Site-specific conc. of total Pb in 

soil samples 

(26 locations) 

n/a 

Site-specific conc. of 

total Pb in soil samples 

(Corpus Christi: 2 

locations; Atlee: 26 

locations) 

n/a 

Surface 

water conc. 

Models n/a n/a n/a n/a 

Measurements 

Site-specific conc. of dissolved 

Pb in water column samples from 

eight water bodies/drainage 

areas (30 locations) 

n/a n/a 

Site-specific conc. of dissolved 

Pb in surface water samples 

(430 samples) 

Sediment 

conc. 

Models n/a n/a n/a n/a 

Measurements 

Site-specific conc. of total Pb in 

sediment samples from five water 

bodies /drainage areas (69 sites) 

n/a n/a 

Site-specific or nearby water 

body conc. of total Pb in 

sediment samples (15 sites) 

Ecological Risk Assessment 

Screening 

ecotoxicity 

benchmarks 

Soil Soil screening values n/a 

Freshwater 

– water 

column 

U.S. EPA Pb freshwater AWQC 

for aquatic life derived based on 

site-specific measured water 

hardness (conc. Of CaCO3)

c 

n/a n/a 

U.S. EPA Pb freshwater 

AWQC for aquatic life derived 

based on site-specific 

measured water hardness 

(conc. of CaCO3)

c 

Freshwater 

– sediment 

Sediment screening values 

based on MacDonald et al. 

(2000) sediment quality 

assessment guidelines 

n/a n/a 

Sediment screening values 

based on MacDonald et al. 

(2000) sediment quality 

assessment guidelines 

a American Meteorological Society/EPA Regulatory Model (AERMOD) (USEPA, 2004) 

b Multiple Pathways of Exposure (MPE) (USEPA, 1998) 

c These screening values are based on measured ecotoxicity data 

6-19
 
 

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) 

6-20
 
 

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
 
 

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. 

6-22
 
 

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 HBEF 

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 

6-23
 
 

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 redtailed 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. 

6-24
 
 

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 

6-25
 
 

 

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 EcoSSL 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 

Soil Concentration 

(mg Pb/kg soil, dry weight) 

Plants 1

 120 

Soil Invertebrates 1

 1700 

Birds 2

 38 

Mammals 2

 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 

6-26
 
 

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 CaCO3) is 1.2 μg/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 μg/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 μg/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 CaCO3 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 μg/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 μg/L co-located measurements of CaCO3 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 

6-27
 
 

 

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 

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). 

Pb Measurements Hazard Quotient 

Basin 

ID State 

Station 

ID 

Land 

Use 

Lead 

CCC 

(ug/L) 

No. 

> CCC 

/ Total 

N 

No. < QL, 

which 

is > CCC 

Mean 

[Pb] / 

CCC 

Max 

[Pb] / 

CCC 

Max 

[Pb] / 

CMC 

45 RIOG NM 8331000 Mixed 2.9 1/12 0 1.03 1.03 0.04 

44 UCOL CO 3.85E+14 Other/Mixed 0.89 1/4 2 1.09 1.09 0.04 

2 CONN CT 1127000 Mixed 0.36 3/22 14 1.13 1.31 0.05 

46 NROK WA 12422500 Urban 0.99 4/28 24 1.14 1.25 0.05 

46 NROK WA 12422000 Urban 0.99 2/20 18 1.17 1.17 0.05 

46 NROK ID 12392155 Forest 0.17 4/17 10 1.32 1.54 0.06 

47 GRSL UT 4.05E+14 Rangeland 5.8 1/2 0 1.45 1.45 0.06 

2 CONN CT 1119375 Mixed 0.18 5/20 13 1.68 2.09 0.08 

31 OZRK MO 7018100 Forest 3.7 1/2 0 1.89 1.89 0.07 

58 OAHU HA 16212700 Mixed 0.17 1/2 1 1.98 1.98 0.08 

1 NECB RI 1112900 Mixed 0.44 3/3 0 2.51 3.53 0.14 

2 CONN CT 1124000 Mixed 0.30 11/23 9 2.53 3.33 0.13 

46 NROK ID 12419000 Mixed 0.37 2/26 16 2.69 4.27 0.17 

31 OZRK AR 7050500 Mixed 2.6 1/8 0 3.46 3.46 0.14 

31 COOK AK 6.01E+14 Reference 0.11 1/1 0 14.91 14.91 0.61 

6-28
 
 

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). 

6-29
 
 

 

 

 

 

 

 

 

 

 

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 RIOG 

2 CONN 

46 NROK 

46 NROK 

47 GRSL

31 OZRK 

58 OAHU 

1 NECB 

2 CONN 

46 NROK 

31 OZRK 

31 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] 

(mg/kg dry 

sediment) 

23 

68 

47.3 

24.9 

2900 

2300 

59 

240 

68 

 1620 

28 

239 

SW

 HQ a

 : 

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 < 

20 km 

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 < 

50 km 

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 TECbased 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. 

6-30
 
 

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 

6-31
 
 

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 3.0μg/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 HQ for 

Plants 

HQ for Soil 

Invertebrates HQ for Birds HQ for Mammals 

1 - West Bank of Joachim Creek 3.55 0.25 11.19 3.80 

2 - Crystal City 1

 0.54 0.04 1.70 0.58 

3 - Near Festus Airport 1

 0.40 0.03 1.28 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 0.39 0.02 

Cluster 2 0.40 0.02 

Cluster 3 0.39 0.02 

Cluster 4 0.41 0.02 

Cluster 5 0.41 0.02 

Mississippi River 

Upstream 0.54 0.02 

Near Facility 0.49 0.02 

Downstream 0.48 0.02 

Emission Deposition 

Cluster 1 0.69 0.03 

CHRDDP 0.24 0.01 

RRDP-02 0.47 0.02 

DAMUP 0.40 0.02 

6-32
 
 

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 1.0 

Cluster 2 1.6 

Cluster 3 2.2 

Cluster 4 0.84 

Cluster 5 0.96 

Mississippi River 

Upstream 0.41 

Near Facility 0.84 

Downstream 0.34 

Pond and Drainage Areas 

U-shaped Pond 

Cluster 4.8 

ED1 3.1 

ED2 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 modelpredicted 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 

6-33
 
 

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. 

Table 6-8. HQs Calculated for Soils for Secondary Pb Smelter Case Study.a 

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. 

6-34
 
 

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 

Sample 

depth 

Total Pb 

concentratio 

n (mg/kg) 

HQ for 

Plants 

HQ for Soil 

Invertebrates 

HQ for 

Birds 

HQ for 

Mammals 

2 m 2.5 cm 340 2.83 0.20 8.95 3.04 

2 m 10 cm 650 5.42 0.38 17.1 5.80 

2 m 20 cm 15 0.13 0.019 0.395 0.13 

4 m 2.5 cm 140 1.17 0.082 3.68 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 

6-35
 
 

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 sitespecific 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). 

6-36
 
 

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 15 m 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. 

6-37
 
 

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 μg/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 μg/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 timespecific CCC values. 

6-38
 
 

•	 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. 

6-39
 
 

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 μg Pb/m3

, 

as a maximum arithmetic mean averaged over a calendar quarter), the basis for which is 

6-40
 
 

 

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 

6-41
 
 

should be given to retaining a secondary standard at or below the level of the then-current 

secondary standards of 1.5 μg/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 bwday) (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 

6-43
 
 

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 

6-44
 
 

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 

6-45
 
 

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 

6-46
 
 

 

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 delisted as 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 

6-47
 
 

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 

6-48
 
 

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 PbTSP 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. 

6-49
 
 

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. 

6-50
 
 

 

 

 

 

 

 

 

 

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VA; PB91-206185. 

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Pathways of Exposure to Combustor Emissions. Cincinnati, OH: Update to EPA/600/6-90/003, 

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oaspub.epa.gov/eims/eimscomm.getfile?p_download_id=427339. 

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Washington, DC: Office of Solid Waste and Emergency Response.OSWER Directive 9285.7-55. 

November. 

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Washington, DC: Office of Solid Waste and Emergency Response. OSWER Directive 9285.7-70. 

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ATTACHMENT A 
 

Clean Air Scientific Advisory Committee Letter 

(March 27, 2007) 
 

 

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/memo_process_for_reviewing_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 CASAC’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. 
 

 

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 PM10 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. 

2
 
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 μg/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 μg/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 μg/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 μg/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 CASAC 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 μg/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 

3
 
 

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 μg/dl. 

However, there remains a significant segment of the population with blood-lead 

concentrations above 5 μg/dl — and some even above 10 μg/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 μg/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 of NAAQS 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 

4
 
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 CASAC Lead 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 humanpopulation 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 leadinduced 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 μg/m3

 to ensure that 99.5% of 

the public did not exceed a blood-lead concentration of 30 μg/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 

5
 
 

 

 

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 μg/m3

 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 

6
 
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 PM10 

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 PM10 

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) PM10 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 PM10 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 PM10 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 PM10 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 PM10 mass; and, if collocated with 

PM2.5 Federal Reference Methods (FRM), could provide needed information on PM10–2.5 

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 PM10 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 PM10 and TSP lead sampling could help develop site-specific scaling 

factors at sites with highest concentrations where long-term historical records are important. 

7
 
Given the advantages of using PM10, the CASAC Lead Review Panel recommends that 

the Agency revise the lead indicator to utilize low-volume PM10 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 PM10 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. 

8
 
 

 

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 

9
 
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 

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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 

Engl J Med 2003;348:1517–1526. 

13. Lanphear BP, Hornung R, Khoury J, et al. Low-level Environmental Lead Exposure and 

Children’s Intellectual Function: An International Pooled Analysis. Environ Health Perspect 

2005;113:894–899. 

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
 
15. Tellez-Rojo MM, Bellinger DC, Arroyo-Quiroz C, et al. Longitudinal associations between 

blood lead concentrations lower than 10 μg/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 

women. JAMA 289:1523–1532. 

18. McDonald JA Potter NU. Lead’s legacy? Early and late mortality of 454 lead- poisoned 

children. Arch Environ Health 1996;51:116–121. 

19. Menke A, Muntner P, Batuman V, Silbergeld EK, Guallar E. Blood Lead Below 0.48 

μmol/L (10 μg/dl) and mortality among U.S. Adults. Circulation 2006;114;1388–1394. 

20. Lin JL, Lin-Tan DT, Hsu KH, Yu CC. 2003. Environmental lead exposure and progression 

of chronic renal diseases in patients without diabetes. N Engl J Med 348:277–286. 

21. Borja-Aburto VH, Hertz-Picciotto I, Rojas Lopez M, Farias P, Rios C, Blanco J. Blood lead 

levels measured prospectively and risk of spontaneous abortion. Am J Epidemiol 

1999;150:590–597. 

22. U.S. Centers for Disease Control. Preventing Lead Poisoning in Young Children. A 

Statement by the Centers for Disease Control (Ch. 1, Introduction), October 1991. Atlanta, 

GA: Department of Health and Human Services. 

11
 
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-1 
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-1
 
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, Cary, 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
 
 

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 staff’s 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 

C-1
 
 

 

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 fullscale 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 fullscale risk assessment given current capabilities? 

C-2
 
 

 

 

 

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? 

C-3
 
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 μg/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 μg/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 μg/dl, with the candidate values considered being 2.0 μg/dl per μg/m3 

(m3/dl) 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 μg/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 

D-1
 
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 μg/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 μg/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 μg/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 μg/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 μg/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 μg/m3

. 

Use of a “not-to-be-exceeded” value of 2.5 μg/dl and the same slope factors produce air lead 

values half as high, i.e., 0.11, 06 and 0.03 μg/m3, respectively. (Note that, for the 1.25 mean PbB 

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 

μg/dl of blood lead in children. Their estimate is based on their coefficient of 2.14 μg/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. 

D-2
 
 

cities fell a little less than 1 μg/m3

 (24). These two facts imply a ratio of 9–10 μg/dl per μg/m3 

reduction in air lead, taking all pathways into account. 

Under this scenario, a decrease of mean air lead concentrations of 0.1 μg/m3 could be 

expected to produce a further decrease in average blood lead concentrations of 0.9–1.0 μg/dl. 

Assuming a slope of three IQ points per μg/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 μg/m3 (that is, a 13-fold reduction) would be required to keep 99.5% of the 

children below a blood lead of 5 μg/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 μg/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 μg/dl (13), the Lead 

Panel estimated that, over the range of PbB from 0.5–4.0 μg/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 μg/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. 

D-3
 
TABLE 1. Relationship of Blood Lead (PbB) to Air Lead (Pb-Air) by Differing Slope 

Factors 

Pb-Air (μg/m3) PbB (μg/dl) 

S.F.* = 5 S.F.* = 10 S.F.* = 20 

0.010 0.05 0.10 0.20 

0.025 0.13 0.25 0.50 

0.050 0.25 0.50 1.00 

0.100 0.50 1.00 2.00 

0.200 1.00 2.00 4.00 

*S.F. = slope factor (m3

/dl) = PbB/Pb-Air; S.F. value varies with increasing impact of 

indirect Pb-Air pathway (Dust Pb + Soil Pb) 

D-4
 
TABLE 2. Relationship of IQ Point Losses to Increases in Pb-Air and Pb-Air-Based Blood Lead (PbB) Valu

Pb-Air 

(μg/m3) 

S.F. = 5 S.F. = 10 S.F. 

PbB d IQ Loss e,f PbB IQ Loss PbB 

0 0 0 0 0 0 

0.010 0.05 < 1 0.10 < 1 0.20 

0.025 0.13 < 1 0.25 < 1 0.50 

0.050 0.25 < 1 0.50 1.5 1.00 

0.100 0.50 1.5 1.00 3.0 2.00 

0.200 

1.00 3.0 2.00 6.0 

4.00 

a Pb-Air-related increases affecting IQ point loss through calculated PbB values using 3 slope factors per T

b IQ vs. PbB dose-response relationships based on Lanphear et al., 2005 (13): sub-7.5 μg/dl linear segment

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

Source: U.S. Centers for Disease Control, 2007. CDC Surveillance Data. (Last updated 2/16/2007). URLhttp://www.cdc.gov/nceh/surv/stats.htm [accessed 3/8/2007] 

D-5
 
 

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 1.5μg/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: 

E-1
 
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 (publichealth 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 

E-2
 
methods would be anywhere near as effective in protecting public health or welfare as 

maintaining for 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 

(1.5μg/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. 

E-3
 
ATTACHMENT B 

Clean Air Scientific Advisory Committee Letter 

(September 27, 2007) 
 

 

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 
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 (HealthBased) 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, populationbased 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 μg/m3 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 μg/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 

2
 
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 sourceoriented sites. Some additional PM10 measurements are available (see the section below on the 

use of PM10 samplers to monitor lead). 

In spite of these limitations, the CASAC 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 NAAQS. 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 PM10 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 CASAC 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 current (“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 

3
 
 

both the scope of the health risk model and the associated modeling results vis-à-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 μg/dL than 10.82 

μg/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 μg/dL is approximately minus three (-3) IQ decrements per 1 μg/dL blood lead and the vast majority of children 

in the U.S. have maximal baseline Pb blood levels below 7.5 μg/dL (Lanphear et al., EHP 2005; 

MMWR 2005). On a population level, the mean increase in blood lead concentration from airborne lead would generally be up to, but not exceeding, a blood lead concentration of 7.5 μg/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 μg/dL. The CASAC recognizes that lead is a multimedia pollutant and that most of the country is in compliance with the current Lead NAAQS of 

1.5 μg/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 scientificallydefensible and robust dust model currently available — show that the “recent” air exposure path4
 
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 μg/m3 

maximum monthly average, and only fall to 13% under an alternative primary Lead standard of 

0.05 μg/m3

 maximum monthly average. Since there is no known threshold in the relationship 

between blood Pb and IQ 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 μg/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 μg/m3, both above and below, as well as 

much lower levels, e.g., on the order of 0.05 and 0.01 μg/m3

. Further, the Agency should provide 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 PM10 Samplers to Monitor for Airborne Lead 

Another recommendation that the CASAC provided in its March 2007 letter was to 

consider use of PM10 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 PM10, rather than TSP samplers, accompanied by analysis with low-cost multielemental 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 PM10 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 PM10 Pb will represent a large fraction of, and be 

5
 
highly correlated with TSP Pb. Ambient Lead data from the (few) collocated TSP and PM10 sites 

presented in the 1st Draft Pb Staff Paper exhibited a high correlation (r = 0.96), with slopes (PM10 

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 PM10 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 CASAC 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 welfareeffects 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 

6
 
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 CASAC 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 CASAC 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 CASAC’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 — 

7
 
 

 

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 Assessment 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 analyses 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 recommendations 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) 

8
 
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. 

9
 
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-1 
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 Environmental 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-1
 
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, Cary, 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
 
APPENDIX 2A: 

LARGEST STATIONARY SOURCE CATEGORIES FOR Pb IN 

THE 2002 NEI 
 

Appendix 2A. Largest Stationary Source Categories for Pb in the 2002 NEI. 

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, 

MI, WI, 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., 

2A-1
 
 

 

 

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 - 0AR2002-0058-0022. 

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 Particulate Matter. Volume I. EPA 600/P99/002aF-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 poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

3-year data capture 

(complete periods) 

comp. 

years 

comp. 

qtrs 

comp. 

months 

annua

mean 

011090003 2 31.79056 -85.97917 AL Pike Troy, AL 29,605 461 31 4.5 1 2 10 31 0.6875 011090006 1 31.79278 -85.98056 AL Pike Troy, AL 29,605 461 31 4.5 1 2 10 31 0.3808 060250005 1 32.67611 -115.48333 CA Imperial El Centro, CA 142,361 16,385 1,290 1 0.0 2 11 34 0.0175 

060371103 2 34.06659 -118.22688 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 29,329 1,633 1 0.3 3 12 36 0.0225 

060371301 1 33.92899 -118.21071 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 47,423 5,066 1 0.0 3 12 34 0.0188 060371601 1 34.01407 -118.06056 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 13,333 1,066 1 0.0 2 9 27 0.0186 060374002 2 33.82376 -118.18921 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 20,131 1,232 1 0.0 3 12 36 0.0149 060374004 2 33.79236 -118.17533 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 61,497 6,697 1 0.0 2 10 28 0.0112 060375001 1 33.92288 -118.37026 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 19,148 1,680 1 0.0 1 5 14 0.0222 060375005 1 33.95080 -118.43043 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 33,968 1,358 1 0.0 1 7 17 0.0057 060651003 2 33.94603 -117.40063 CA Riverside Riverside-San Bernardino-Ontario, C 3,254,821 16,320 1,278 1 0.0 3 12 36 0.0097 060658001 3 33.99958 -117.41601 CA Riverside Riverside-San Bernardino-Ontario, C 3,254,821 16,247 1,678 1 0.0 3 12 35 0.0121 060711004 1 34.10374 -117.62914 CA San Bernardino Riverside-San Bernardino-Ontario, C 3,254,821 18,777 1,578 1 0.0 3 12 35 0.0142 060719004 1 34.10688 -117.27411 CA San Bernardino Riverside-San Bernardino-Ontario, C 3,254,821 14,861 1,755 1 0.0 3 12 36 0.0186 080010005 1 39.79601 -104.97754 CO Adams Denver-Aurora, CO 2,157,756 2,025 183 1.9 1 3 12 36 0.1697 

080010006 1 39.82574 -104.93699 CO Adams Denver-Aurora, CO 2,157,756 3,313 256 1 0.0 3 12 31 0.0304 

080310002 4 39.75119 -104.98762 CO Denver Denver-Aurora, CO 2,157,756 22,019 974 1 0.0 3 12 34 0.0315 

080310015 1 39.70012 -104.98714 CO Denver Denver-Aurora, CO 2,157,756 14,438 809 1 0.0 1 7 20 0.0153 

080410011 1 38.83139 -104.82778 CO El Paso Colorado Springs, CO 537,484 10,581 552 1 0.0 3 12 35 0.0156 080650001 1 39.24778 -106.29139 CO Lake Edwards, CO 49,471 5,903 361 1 0.0 2 11 28 0.0165 

100010002 1 38.98472 -75.55556 DE Kent Dover, DE 126,697 352 22 0.0 1 4 12 0.0033 100031007 1 39.55111 -75.73083 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 2,041 209 0.0 1 4 10 0.0039 100031008 1 39.57778 -75.61111 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 3,170 160 0.0 1 4 9 0.0052 100032004 1 39.73944 -75.55806 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 34,053 2,649 1 0.0 1 4 11 0.0097 100051002 1 38.64444 -75.61306 DE Sussex Seaford, DE 156,638 5,450 390 1 0.0 1 4 12 0.0033 120571065 5 27.89222 -82.53861 FL Hillsborough Tampa-St. Petersburg-Clearwater, FL 2,395,997 14,463 612 1 0.0 1 4 12 0.0049 120571066 1 27.96028 -82.38250 FL Hillsborough Tampa-St. Petersburg-Clearwater, FL 2,395,997 5,793 465 1 1.3 1 3 12 35 0.5835 120571073 1 27.96583 -82.37944 FL Hillsborough Tampa-St. Petersburg-Clearwater, FL 2,395,997 4,541 340 1 1.3 1 3 12 35 0.1934 120571075 5 28.05000 -82.37806 FL Hillsborough Tampa-St. Petersburg-Clearwater, FL 2,395,997 10,691 490 1 0.0 1 4 12 0.0041 121030004 5 27.94639 -82.73194 FL Pinellas Tampa-St. Petersburg-Clearwater, FL 2,395,997 13,048 557 1 0.0 1 4 12 0.0028 121030018 5 27.78556 -82.74000 FL Pinellas Tampa-St. Petersburg-Clearwater, FL 2,395,997 11,289 571 1 0.0 2 8 24 0.0042 121033005 1 27.87583 -82.69639 FL Pinellas Tampa-St. Petersburg-Clearwater, FL 2,395,997 2,151 58 1 0.0 3 12 36 0.0006 130890003 2 33.69833 -84.27333 GA DeKalb Atlanta-Sandy Springs-Marietta, GA 4,247,981 7,888 663 1 0.0 3 12 36 0.1000 132150011 1 32.43083 -84.93167 GA Muscogee Columbus, GA-AL 281,768 10,871 1,037 1 0.3 1 1 1 10 34 0.1000 

150032004 1 21.39667 -157.97167 HI Honolulu Honolulu, HI 876,156 23,622 1,207 1 0.1 3 12 35 0.0014 

170310001 1 41.67275 -87.73246 IL Cook Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 13,648 971 1 0.0 3 12 35 0.0143 

170310022 2 41.68920 -87.53932 IL Cook Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 22,040 1,708 1 0.2 3 12 36 0.0270 170310026 1 41.87333 -87.64507 IL Cook Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 28,739 1,203 1 0.0 3 12 34 0.0405 170310052 1 41.96743 -87.74982 IL Cook Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 42,187 2,877 1 0.0 3 12 32 0.0214 170313103 1 41.96528 -87.87639 IL Cook Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 10,302 670 1 0.0 3 12 34 0.0149 170313301 1 41.78278 -87.80528 IL Cook Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 23,749 1,678 1 0.0 3 12 35 0.0308 170314201 1 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 6,070 303 1 0.0 2 8 24 0.0113 170316003 1 41.87194 -87.82611 IL Cook Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 14,862 1,071 1 0.0 3 12 32 0.0303 171170002 2 39.39804 -89.80975 IL Macoupin St. Louis, MO-IL 2,721,491 40 2 0.0 3 12 36 0.0103 

171190010 1 38.69417 -90.15361 IL Madison St. Louis, MO-IL 2,721,491 8,014 529 1 1.3 1 3 12 34 0.0768 

171193007 2 38.86056 -90.10583 IL Madison St. Louis, MO-IL 2,721,491 5,397 360 1 0.1 3 12 36 0.0150 

171430037 1 40.69889 -89.58474 IL Peoria Peoria, IL 366,899 12,643 1,109 1 0.0 3 12 35 0.0137 

171630010 2 38.61222 -90.16028 IL St. Clair St. Louis, MO-IL 2,721,491 3,512 430 1 0.3 3 12 36 0.0433 

180350008 1 40.15806 -85.42111 IN Delaware Muncie, IN 118,769 2,108 104 1 0.0 1 3 12 34 0.2944 

180350009 2 40.15944 -85.41556 IN Delaware Muncie, IN 118,769 980 82 0.0 1 1 6 13 2.6732 180890023 1 41.65278 -87.43944 IN Lake Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 5,959 603 1 6.5 1 3 12 34 0.0389 180892008 1 41.63944 -87.49361 IN Lake Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 7,144 612 1 0.0 3 12 33 0.0219 180892011 2 41.59250 -87.47194 IN Lake Chicago-Naperville-Joliet, IL-IN-WI 9,098,316 9,815 729 1 0.0 3 12 34 0.0368 180930004 1 38.88944 -86.55194 IN Lawrence Bedford, IN 45,922 393 32 0.0 2 10 26 0.0270 

2B- 1 
Appendix 2B

Table 2B-1. Pb-TSP monitoring site information and 3-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

3-year data capture 

(complete periods) 

comp. 

years 

comp. 

qtrs 

comp. 

months 

annua

mean 

180970063 1 39.76083 -86.29722 IN Marion Indianapolis-Carmel, IN 1,525,104 12,176 875 1 1.7 1 3 12 36 0.0320 180970076 1 39.75889 -86.28972 IN Marion Indianapolis-Carmel, IN 1,525,104 9,171 602 1 1.7 1 3 12 35 0.0142 180970078 1 39.81110 -86.11447 IN Marion Indianapolis-Carmel, IN 1,525,104 14,196 1,175 1 0.0 2 11 33 0.0108 

181010001 1 38.89028 -86.76083 IN Martin 84 5 0.0 3 12 34 0.0272 181630006 2 37.97167 -87.56722 IN Vanderburgh Evansville, IN-KY 342,815 13,666 817 1 0.0 3 12 33 0.0065 260490021 4 43.04722 -83.67028 MI Genesee Flint, MI 436,141 9,889 994 1 0.0 3 12 36 0.0100 

261130001 1 44.31056 -84.89194 MI Missaukee Cadillac, MI 44,962 58 3 0.0 3 12 33 0.0032 261630001 2 42.22861 -83.20833 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 14,329 798 1 0.0 3 12 35 0.0087 

261630005 1 42.26722 -83.13222 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 11,314 923 1 0.2 2 11 34 0.0166 

261630015 4 42.30278 -83.10667 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 17,729 1,771 1 0.0 3 12 36 0.0178 261630019 1 42.43083 -83.00028 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 28,362 2,628 1 0.0 3 12 34 0.0103 261630027 1 42.29222 -83.10694 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 6,024 516 1 1.1 1 1 5 14 0.0256 

261630033 2 42.30667 -83.14889 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 17,402 1,843 1 0.5 3 12 34 0.0236 270370001 1 44.83333 -93.11500 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 5,074 404 1 3.2 1 2 8 24 0.0781 270370020 1 44.76535 -93.03248 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 162 7 0.0 3 12 32 0.0051 270370421 1 44.77720 -93.04097 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 478 24 0.0 1 9 27 0.0037 270370423 1 44.77500 -93.06278 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 886 83 0.0 3 12 34 0.0018 270370442 1 44.74036 -93.00556 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 168 11 0.3 2 10 28 0.0027 270530050 1 45.00123 -93.26712 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 16,318 923 1 0.0 3 12 35 0.0051 

270530963 1 44.95540 -93.25827 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 46,218 3,929 1 0.2 3 12 36 0.0039 

270530964 1 44.88855 -93.19538 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 209 0 1 0.0 1 4 14 0.0045 

270530965 1 45.00448 -93.24005 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 19,106 1,095 1 0.4 3 12 35 0.0039 

270530966 1 44.98133 -93.26615 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 17,156 439 1 0.0 3 12 35 0.0047 

270530967 1 44.99646 -93.23488 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 14,621 580 1 0.4 1 1 7 20 0.0075 

270530968 1 44.89301 -93.23323 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 11,243 789 1 0.0 1 6 18 0.0019 

270531007 1 45.04182 -93.29873 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 14,889 1,118 1 0.0 3 12 35 0.0026 

271231003 1 44.96322 -93.19023 MN Ramsey Minneapolis-St. Paul-Bloomington, 2,968,806 9,247 474 1 0.1 3 12 33 0.0065 

271377001 1 47.52336 -92.53631 MN St. Louis Duluth, MN-WI 275,486 8,942 428 1 0.1 3 12 33 0.0047 271377555 1 46.73264 -92.16337 MN St. Louis Duluth, MN-WI 275,486 4,527 287 1 0.0 3 12 34 0.0014 290930016 1 37.62528 -91.12917 MO Iron 58 4 0.0 1 3 12 34 0.6918 

290930021 1 37.65417 -91.13056 MO Iron 58 4 0.0 1 3 12 36 0.5460 

290930023 1 37.50333 -90.69556 MO Iron 138 7 0.0 1 1 *# 1 6 18 0.2291 290930024 1 37.47972 -90.69028 MO Iron 32 2 0.0 1 1 *# 1 6 18 0.5898 290930025 1 37.51056 -90.69750 MO Iron 138 7 0.0 1 1 *# 1 5 14 0.2477 290930026 1 37.45917 -90.68639 MO Iron 32 2 0.0 1 1 *# 1 5 15 0.2266 290930027 1 37.48611 -90.69000 MO Iron 32 2 0.0 1 1 * 3 12 33 0.2678 290930029 1 37.47167 -90.68944 MO Iron 32 2 0.0 1 1 * 3 12 32 0.2824 290930030 1 37.46639 -90.69000 MO Iron 32 2 0.0 1 1 *# 1 6 18 0.1665 

290990004 1 38.26330 -90.37850 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 2 8 24 1.1300 290990005 3 38.26722 -90.37944 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 3 12 36 0.3711 290990008 1 38.26194 -90.39417 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 58.8 1 1 10 31 0.0910 

290990009 1 38.28444 -90.38194 MO Jefferson St. Louis, MO-IL 2,721,491 9,804 820 1 0.0 1 2 11 31 0.0957 290990010 1 38.24110 -90.37680 MO Jefferson St. Louis, MO-IL 2,721,491 2,799 215 1 0.0 1 2 11 34 0.0388 290990011 1 38.26820 -90.37380 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 3 12 36 0.4778 

290990013 1 38.27361 -90.38000 MO Jefferson St. Louis, MO-IL 2,721,491 3,570 318 1 58.8 1 3 12 35 0.2633 

290990015 1 38.26167 -90.37972 MO Jefferson St. Louis, MO-IL 2,721,491 1,988 178 1 58.8 1 3 12 36 1.4501 

291892003 1 38.64972 -90.35056 MO St. Louis St. Louis, MO-IL 2,721,491 12,303 512 1 0.0 2 11 34 0.0063 

295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL 2,721,491 9,140 783 1 0.0 1 4 11 0.0134 

340231003 1 40.47222 -74.47139 NJ Middlesex New York-Northern New Jersey-Lon 18,323,002 13,850 1,124 1 1.7 1 2 10 27 0.0403 

360470122 1 40.71980 -73.94788 NY Kings New York-Northern New Jersey-Lon 18,323,002 92,660 5,785 1 0.1 2 9 22 0.0276 360632008 1 43.08216 -79.00099 NY Niagara Buffalo-Niagra Falls, NY Metropolit 1,170,111 6,795 386 1 0.0 1 4 12 0.0054 360713001 1 41.46107 -74.36343 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 1,481 99 1.8 1 2 9 26 0.0606 

360713002 1 41.45887 -74.35392 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 1,257 86 1.8 1 2 9 26 0.1257 360713004 1 41.47633 -74.36827 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 6,816 434 1 0.0 2 9 26 0.0305 

 2B-2 
Appendix 2B

Table 2B-1. Pb-TSP monitoring site information and 3-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

3-year data capture 

(complete periods) 

comp. 

years 

comp. 

qtrs 

comp. 

months 

annua

mean 

360850067 1 40.59733 -74.12619 NY Richmond New York-Northern New Jersey-Lon 18,323,002 21,834 1,373 0.0 1 4 11 0.0059 

390170015 2 39.48990 -84.36407 OH Butler Cincinnati-Middletown, OH-KY-IN 2,009,632 4,668 373 1 0.0 2 8 24 0.0107 

390290019 1 40.63111 -80.54694 OH Columbiana East Liverpool-Salem, OH 112,075 5,385 322 1 0.0 3 12 36 0.0144 

390290020 1 40.63972 -80.52389 OH Columbiana East Liverpool-Salem, OH 112,075 6,414 354 1 0.0 3 12 36 0.0158 

390290022 1 40.63500 -80.54667 OH Columbiana East Liverpool-Salem, OH 112,075 3,318 202 1 0.0 3 12 36 0.0139 

390350038 1 41.47694 -81.68194 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 7,329 585 1 0.1 3 12 36 0.0205 

390350042 1 41.48222 -81.70889 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 18,776 1,575 1 0.0 2 11 35 0.0169 

390350049 1 41.44667 -81.65111 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 9,720 758 1 0.0 1 1 3 12 36 0.1214 

390350050 1 41.44250 -81.64917 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 8,771 695 1 0.0 1 1 3 12 36 0.0362 390350061 2 41.47506 -81.67596 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 6,141 444 1 0.3 1 1 3 12 36 0.0477 390350069 1 41.51918 -81.63794 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 23,566 1,961 1 0.1 1 6 35 0.0170 390490025 1 39.92806 -82.98111 OH Franklin Columbus, OH 1,612,694 15,220 1,226 1 0.6 1 3 12 36 0.0114 390510001 1 41.57528 -83.99639 OH Fulton Toledo, OH 659,188 1,503 110 1 0.3 1 2 11 36 0.1332 390910003 1 40.34306 -83.75500 OH Logan Bellefontaine, OH 46,005 1,536 108 1 0.1 3 12 36 0.0922 

390910005 1 40.34278 -83.76028 OH Logan Bellefontaine, OH 46,005 1,546 126 1 0.1 1 3 12 36 0.1058 390910006 1 40.34111 -83.75778 OH Logan Bellefontaine, OH 46,005 1,217 87 1 0.1 1 3 12 36 0.1578 

390910007 1 40.34472 -83.75444 OH Logan Bellefontaine, OH 46,005 2,156 185 1 0.1 3 12 36 0.1497 

391670008 1 39.43361 -81.50250 OH Washington Parkersburg-Marietta, WV-OH 164,624 1,947 114 0.0 3 12 36 0.0054 

391670009 1 39.37696 -81.53730 OH Washington Parkersburg-Marietta, WV-OH 164,624 314 21 0.0 1 5 14 0.0073 

401159005 2 36.98580 -94.84920 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0412 

401159006 1 36.98460 -94.82490 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0316 

401159007 1 36.97190 -94.85180 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0505 

401159008 1 36.97160 -94.82500 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0312 

410510246 7 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR- 1,927,881 24,303 1,771 1 0.0 1 4 11 0.0081 

420030002 1 40.50056 -80.07194 PA Allegheny Pittsburgh, PA 2,431,087 19,559 1,045 1 0.0 3 12 30 0.0096 

420032001 1 40.39667 -79.86361 PA Allegheny Pittsburgh, PA 2,431,087 10,120 769 1 0.2 3 12 35 0.0396 

420070505 1 40.68500 -80.32500 PA Beaver Pittsburgh, PA 2,431,087 6,497 218 1 0.0 2 11 31 0.0563 420110005 1 40.46630 -75.75890 PA Berks Reading, PA 373,638 692 44 4.8 1 2 11 33 0.0618 

420110717 1 40.47667 -75.75917 PA Berks Reading, PA 373,638 575 39 1 4.8 1 2 11 30 0.1301 

420111717 1 40.37722 -75.91444 PA Berks Reading, PA 373,638 7,376 390 1 2.1 1 3 12 33 0.2570 

420210808 1 40.34806 -78.88278 PA Cambria Johnstown, PA 152,598 2,606 115 1 0.0 3 12 36 0.0383 420250105 1 40.80306 -75.60833 PA Carbon Allentown-Bethlehem-Easton, PA-N 740,395 8,477 513 1 0.0 2 11 33 0.0779 

420450002 1 39.83556 -75.37250 PA Delaware Philadelphia-Camden-Wilmington, P 5,687,147 10,156 859 1 0.0 3 12 35 0.0372 

421010449 1 39.98250 -75.08306 PA Philadelphia Philadelphia-Camden-Wilmington, P 5,687,147 8,653 413 1 0.0 1 1 3 12 31 0.0203 421290007 1 40.16667 -79.87500 PA Westmoreland Pittsburgh, PA 2,431,087 7,739 445 1 0.0 3 12 36 0.0352 

450031001 1 33.43253 -81.89233 SC Aiken Augusta-Richmond County, GA-SC 499,684 437 24 0.0 1 4 12 0.0000 

450130007 1 32.43654 -80.67785 SC Beaufort Hilton Head Island-Beaufort, SC 141,615 4,928 330 1 0.0 3 12 34 0.0006 

450190003 2 32.88394 -79.97754 SC Charleston Charleston-North Charleston, SC 549,033 4,401 275 1 0.0 3 12 34 0.0014 

450190046 1 32.94275 -79.65718 SC Charleston Charleston-North Charleston, SC 549,033 63 4 0.0 3 12 33 0.0005 

450190047 1 32.84461 -79.94804 SC Charleston Charleston-North Charleston, SC 549,033 7,000 294 1 0.0 1 4 12 0.0022 

450410001 1 34.19794 -79.79885 SC Florence Florence, SC 193,155 3,426 224 1 0.0 1 4 13 0.0010 

450410002 1 34.16764 -79.85040 SC Florence Florence, SC 193,155 1,795 106 1 0.0 2 8 24 0.0011 

450430006 1 33.36378 -79.29426 SC Georgetown Georgetown, SC 55,797 5,247 427 1 0.3 1 2 11 32 0.0072 

450430007 1 33.34973 -79.29821 SC Georgetown Georgetown, SC 55,797 1,579 119 0.3 3 12 35 0.0002 

450430009 1 33.37399 -79.28570 SC Georgetown Georgetown, SC 55,797 2,447 185 1 0.3 3 12 35 0.0038 450430010 1 33.36960 -79.29840 SC Georgetown Georgetown, SC 55,797 6,173 511 1 0.3 3 12 33 0.0049 

450450008 2 34.84045 -82.40291 SC Greenville Greenville, SC 559,940 7,967 381 1 0.0 3 12 34 0.0023 

450452002 1 34.94165 -82.22961 SC Greenville Greenville, SC 559,940 7,266 494 1 0.0 3 12 32 0.0001 

450470001 1 34.18111 -82.15224 SC Greenwood Greenwood, SC 66,271 7,853 667 1 0.0 3 12 32 0.0028 

450470002 1 34.16520 -82.16048 SC Greenwood Greenwood, SC 66,271 1,490 116 0.0 3 12 31 0.0071 

450510002 2 33.70460 -78.87745 SC Horry Myrtle Beach-Conway-North Myrtle 196,629 4,510 227 1 0.0 3 12 35 0.0009 

450630005 2 33.78560 -81.11978 SC Lexington Columbia, SC 647,158 736 66 0.0 1 4 12 0.0018 

450631002 2 33.96900 -81.06533 SC Lexington Columbia, SC 647,158 8,086 551 1 0.0 3 12 32 0.0046 

450790006 4 34.00740 -81.02329 SC Richland Columbia, SC 647,158 17,143 574 1 0.0 1 4 12 0.0030 

 2B-3 
Appendix 2B

Table 2B-1. Pb-TSP monitoring site information and 3-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

3-year data capture 

(complete periods) 

comp. 

years 

comp. 

qtrs 

comp. 

months 

annua

mean 

450790007 2 34.09584 -80.96230 SC Richland Columbia, SC 647,158 4,405 233 1 0.0 3 12 36 0.0004 450790019 1 33.99330 -81.02414 SC Richland Columbia, SC 647,158 15,569 287 1 0.0 3 12 35 0.0048 

450790021 1 33.81655 -80.78114 SC Richland Columbia, SC 647,158 123 10 0.0 3 12 35 0.0001 450830001 2 34.94774 -81.93255 SC Spartanburg Spartanburg, SC 253,791 7,505 552 1 0.0 3 12 34 0.0018 450850001 1 33.92423 -80.33774 SC Sumter Sumter, SC 104,646 4,990 407 1 0.0 3 12 35 0.0025 450910005 1 34.96303 -81.00085 SC York Charlotte-Gastonia-Concord, NC-SC 1,330,448 3,453 221 1 0.0 2 11 27 0.0021 

470930027 1 35.98306 -83.95222 TN Knox Knoxville, TN 616,079 8,586 826 1 5.8 1 1 9 26 0.0182 

470931017 1 35.97500 -83.95444 TN Knox Knoxville, TN 616,079 7,817 763 1 5.8 1 1 9 26 0.0143 

471570044 1 35.08750 -90.07250 TN Shelby Memphis, TN-MS-AR 1,205,204 6,730 548 1 0.0 1 1 1 6 17 0.0100 

471633001 1 36.52556 -82.27333 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 3 12 35 0.1249 

471633002 3 36.52472 -82.26806 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 3 12 36 0.0614 

471633003 1 36.52806 -82.26833 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 3 12 35 0.0651 

471870100 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 2 8 23 0.2527 471870102 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 2 8 23 0.2575 471871101 1 35.79944 -86.66500 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 2 8 24 0.0811 480610006 1 25.89251 -97.49382 TX Cameron Brownsville-Harlingen, TX 335,227 14,803 1,422 1 0.0 3 12 35 0.0053 

480850003 1 33.14250 -96.82472 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 3.2 1 3 12 35 0.2271 

480850007 2 33.14722 -96.82556 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 1 3.2 1 3 12 34 0.1186 

480850009 1 33.14472 -96.82889 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 3.2 1 3 12 33 0.4961 

481130018 1 32.74556 -96.78250 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 6,451 491 1 0.0 3 12 34 0.0274 

481130057 2 32.77890 -96.87306 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 4,591 578 1 0.0 3 12 35 0.0362 481130066 2 32.73972 -96.78278 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 8,270 622 1 0.0 1 1 1 7 20 0.0090 

481410033 1 31.77694 -106.50167 TX El Paso El Paso, TX 679,622 13,680 1,005 1 0.0 1 6 17 0.0120 

482011034 4 29.76799 -95.22058 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 14,785 1,770 1 0.0 3 12 36 0.0081 

484790016 1 27.51083 -99.51972 TX Webb Laredo, TX 193,117 14,880 1,441 1 0.0 3 12 36 0.0121 

490351001 1 40.70861 -112.09472 UT Salt Lake Salt Lake City, UT 968,858 215 23 1 0.0 2 11 32 0.0421 

721270003 1 18.44917 -66.05306 PR San Juan San Juan-Caguas-Guaynabo, PR 2,509,007 319 5 1 3 12 36 0.0014 

 * 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 poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

max 

quarterly 

mean, 

2003 

max 

monthly 

mean, 

2003 

2nd max 

monthly 

mean, 

2003 

ma

quarte

mea

200

011090003 2 31.79056 -85.97917 AL Pike Troy, AL 29,605 461 31 4.5 1 1.9233 2.6600 2.4200 1.22

011090006 1 31.79278 -85.98056 AL Pike Troy, AL 29,605 461 31 4.5 1 0.9100 1.6900 0.8900 0.84

060250005 1 32.67611 -115.48333 CA Imperial El Centro, CA 142,361 16,385 1,290 1 0.0 0.0248 0.0404 0.0357 0.01

060371103 2 34.06659 -118.22688 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 29,329 1,633 1 0.3 0.0627 0.1460 0.0260 0.02

060371301 1 33.92899 -118.21071 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 47,423 5,066 1 0.0 0.0300 0.0440 0.0360 0.03060371601 1 34.01407 -118.06056 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 13,333 1,066 1 0.0 0.0300 0.0480 0.0340 0.02060374002 2 33.82376 -118.18921 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 20,131 1,232 1 0.0 0.0400 0.0960 0.0440 0.014060374004 2 33.79236 -118.17533 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 61,497 6,697 1 0.0 0.0938 0.1020 0.0840 0.014060375001 1 33.92288 -118.37026 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 19,148 1,680 1 0.0 0.0667 0.1700 0.0220 0.01

060375005 1 33.95080 -118.43043 CA Los Angeles Los Angeles-Long Beach-Santa Ana 12,365,627 33,968 1,358 1 0.0 0.00

060651003 2 33.94603 -117.40063 CA Riverside Riverside-San Bernardino-Ontario, C 3,254,821 16,320 1,278 1 0.0 0.0113 0.0160 0.0120 0.01

060658001 3 33.99958 -117.41601 CA Riverside Riverside-San Bernardino-Ontario, C 3,254,821 16,247 1,678 1 0.0 0.0179 0.0200 0.0200 0.014060711004 1 34.10374 -117.62914 CA San Bernardino Riverside-San Bernardino-Ontario, C 3,254,821 18,777 1,578 1 0.0 0.0343 0.0800 0.0200 0.01060719004 1 34.10688 -117.27411 CA San Bernardino Riverside-San Bernardino-Ontario, C 3,254,821 14,861 1,755 1 0.0 0.0773 0.1420 0.0680 0.014080010005 1 39.79601 -104.97754 CO Adams Denver-Aurora, CO 2,157,756 2,025 183 1.9 1 0.1739 0.2509 0.2016 0.13080010006 1 39.82574 -104.93699 CO Adams Denver-Aurora, CO 2,157,756 3,313 256 1 0.0 0.0388 0.0443 0.0406 0.04080310002 4 39.75119 -104.98762 CO Denver Denver-Aurora, CO 2,157,756 22,019 974 1 0.0 0.0290 0.0467 0.0284 0.02080310015 1 39.70012 -104.98714 CO Denver Denver-Aurora, CO 2,157,756 14,438 809 1 0.0 0.0212 0.0305 0.0196 0.01

080410011 1 38.83139 -104.82778 CO El Paso Colorado Springs, CO 537,484 10,581 552 1 0.0 0.0117 0.0165 0.0120 0.01080650001 1 39.24778 -106.29139 CO Lake Edwards, CO 49,471 5,903 361 1 0.0 0.0192 0.0277 0.0209 0.02

100010002 1 38.98472 -75.55556 DE Kent Dover, DE 126,697 352 22 0.0 0.0040 0.0051 0.0041 

100031007 1 39.55111 -75.73083 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 2,041 209 0.0 0.0046 0.0058 0.0051 

100031008 1 39.57778 -75.61111 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 3,170 160 0.0 0.0063 0.0081 0.0058 100032004 1 39.73944 -75.55806 DE New Castle Philadelphia-Camden-Wilmington, P 5,687,147 34,053 2,649 1 0.0 0.0115 0.0163 0.0161 100051002 1 38.64444 -75.61306 DE Sussex Seaford, DE 156,638 5,450 390 1 0.0 0.0042 0.0048 0.0042 120571065 5 27.89222 -82.53861 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 14,463 612 1 0.0 0.0062 0.0094 0.0080 120571066 1 27.96028 -82.38250 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 5,793 465 1 1.3 1 0.7400 1.3800 0.7800 1.26120571073 1 27.96583 -82.37944 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 4,541 340 1 1.3 1 0.2533 0.4800 0.4400 0.23120571075 5 28.05000 -82.37806 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 10,691 490 1 0.0 0.0054 0.0105 0.0072 121030004 5 27.94639 -82.73194 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 13,048 557 1 0.0 0.0041 0.0067 0.0039 121030018 5 27.78556 -82.74000 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 11,289 571 1 0.0 0.0056 0.0103 0.0093 0.00

121033005 1 27.87583 -82.69639 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 2,151 58 1 0.0 0.0067 0.0200 0.0000 0.00

130890003 2 33.69833 -84.27333 GA DeKalb Atlanta-Sandy Springs-Marietta, GA 4,247,981 7,888 663 1 0.0 0.1000 0.1000 0.1000 0.10132150011 1 32.43083 -84.93167 GA Muscogee Columbus, GA-AL 281,768 10,871 1,037 1 0.3 1 1 0.1000 0.1000 0.1000 0.10

150032004 1 21.39667 -157.97167 HI Honolulu Honolulu, HI 876,156 23,622 1,207 1 0.1 0.0029 0.0072 0.0021 0.00

170310001 1 41.67275 -87.73246 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 13,648 971 1 0.0 0.0157 0.0250 0.0180 0.02

170310022 2 41.68920 -87.53932 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 22,040 1,708 1 0.2 0.0286 0.0360 0.0350 0.03170310026 1 41.87333 -87.64507 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 28,739 1,203 1 0.0 0.0613 0.0860 0.0620 0.05170310052 1 41.96743 -87.74982 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 42,187 2,877 1 0.0 0.0250 0.0280 0.0260 0.02170313103 1 41.96528 -87.87639 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 10,302 670 1 0.0 0.0180 0.0240 0.0220 0.014170313301 1 41.78278 -87.80528 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 23,749 1,678 1 0.0 0.0750 0.1950 0.0360 0.05170314201 1 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 6,070 303 1 0.0 0.0133 0.0175 0.0160 0.01

170316003 1 41.87194 -87.82611 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 14,862 1,071 1 0.0 0.0373 0.0480 0.0400 0.03

171170002 2 39.39804 -89.80975 IL Macoupin St. Louis, MO-IL 2,721,491 40 2 0.0 0.0113 0.0140 0.0100 0.01171190010 1 38.69417 -90.15361 IL Madison St. Louis, MO-IL 2,721,491 8,014 529 1 1.3 1 0.3280 0.9100 0.0620 0.15171193007 2 38.86056 -90.10583 IL Madison St. Louis, MO-IL 2,721,491 5,397 360 1 0.1 0.0173 0.0320 0.0240 0.01171430037 1 40.69889 -89.58474 IL Peoria Peoria, IL 366,899 12,643 1,109 1 0.0 0.0167 0.0220 0.0180 0.01171630010 2 38.61222 -90.16028 IL St. Clair St. Louis, MO-IL 2,721,491 3,512 430 1 0.3 0.0563 0.0940 0.0720 0.05180350008 1 40.15806 -85.42111 IN Delaware Muncie, IN 118,769 2,108 104 1 0.0 1 0.2341 0.3394 0.3138 0.46

180350009 2 40.15944 -85.41556 IN Delaware Muncie, IN 118,769 980 82 0.0 1 0.8073 1.2183 0.967273 4.09

180890023 1 41.65278 -87.43944 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 5,959 603 1 6.5 1 0.0435 0.0620 0.0510 0.06

180892008 1 41.63944 -87.49361 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 7,144 612 1 0.0 0.0277 0.0413 0.0335 0.02180892011 2 41.59250 -87.47194 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 9,815 729 1 0.0 0.0453 0.0610 0.0420 0.03180930004 1 38.88944 -86.55194 IN Lawrence Bedford, IN 45,922 393 32 0.0 0.0270 0.0270 0.0270 0.02180970063 1 39.76083 -86.29722 IN Marion Indianapolis-Carmel, IN 1,525,104 12,176 875 1 1.7 1 0.0508 0.0812 0.0584 0.07

2B- 5 
Appendix 2B

Table 2B-2. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

max 

quarterly 

mean, 

2003 

max 

monthly 

mean, 

2003 

2nd max 

monthly 

mean, 

2003 

ma

quarte

mea

200

180970076 1 39.75889 -86.28972 IN Marion Indianapolis-Carmel, IN 1,525,104 9,171 602 1 1.7 1 0.0143 0.0190 0.0178 0.01

180970078 1 39.81110 -86.11447 IN Marion Indianapolis-Carmel, IN 1,525,104 14,196 1,175 1 0.0 0.0057 0.0110 0.0075 0.01

181010001 1 38.89028 -86.76083 IN Martin 84 5 0.0 0.0299 0.0358 0.0270 0.02181630006 2 37.97167 -87.56722 IN Vanderburgh Evansville, IN-KY 342,815 13,666 817 1 0.0 0.0051 0.0075 0.0060 0.01

260490021 4 43.04722 -83.67028 MI Genesee Flint, MI 436,141 9,889 994 1 0.0 0.0153 0.0189 0.0166 0.01

261130001 1 44.31056 -84.89194 MI Missaukee Cadillac, MI 44,962 58 3 0.0 0.0040 0.0042 0.0039 0.00

261630001 2 42.22861 -83.20833 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 14,329 798 1 0.0 0.0096 0.0101 0.0100 0.01

261630005 1 42.26722 -83.13222 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 11,314 923 1 0.2 0.0247 0.0340 0.0208 0.02

261630015 4 42.30278 -83.10667 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 17,729 1,771 1 0.0 0.0207 0.0247 0.0214 0.02261630019 1 42.43083 -83.00028 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 28,362 2,628 1 0.0 0.0136 0.0141 0.0141 0.01261630027 1 42.29222 -83.10694 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 6,024 516 1 1.1 1 0.01

261630033 2 42.30667 -83.14889 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 17,402 1,843 1 0.5 0.0410 0.0601 0.0406 0.02

270370001 1 44.83333 -93.11500 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 5,074 404 1 3.2 1 0.2100 0.0380 0.11

270370020 1 44.76535 -93.03248 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 162 7 0.0 0.0086 0.0200 0.0100 0.00

270370421 1 44.77720 -93.04097 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 478 24 0.0 0.0057 0.0100 0.0060 0.00270370423 1 44.77500 -93.06278 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 886 83 0.0 0.0050 0.0100 0.0060 0.00270370442 1 44.74036 -93.00556 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 168 11 0.3 0.0062 0.0080 0.0020 0.00270530050 1 45.00123 -93.26712 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 16,318 923 1 0.0 0.0079 0.0100 0.0080 0.00

270530963 1 44.95540 -93.25827 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 46,218 3,929 1 0.2 0.0071 0.0080 0.0075 0.00

270530964 1 44.88855 -93.19538 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 209 0 1 0.0 0.0114 0.0180 0.0080 270530965 1 45.00448 -93.24005 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 19,106 1,095 1 0.4 0.0080 0.0100 0.0080 0.00270530966 1 44.98133 -93.26615 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 17,156 439 1 0.0 0.0080 0.0120 0.0060 0.00

270530967 1 44.99646 -93.23488 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 14,621 580 1 0.4 1 0.00270530968 1 44.89301 -93.23323 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 11,243 789 1 0.0 0.00

270531007 1 45.04182 -93.29873 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 14,889 1,118 1 0.0 0.0067 0.0080 0.0060 0.00271231003 1 44.96322 -93.19023 MN Ramsey Minneapolis-St. Paul-Bloomington, 2,968,806 9,247 474 1 0.1 0.0100 0.0200 0.0100 0.01

271377001 1 47.52336 -92.53631 MN St. Louis Duluth, MN-WI 275,486 8,942 428 1 0.1 0.0362 0.0900 0.0080 0.00

271377555 1 46.73264 -92.16337 MN St. Louis Duluth, MN-WI 275,486 4,527 287 1 0.0 0.0020 0.0040 0.0020 0.00

290930016 1 37.62528 -91.12917 MO Iron 58 4 0.0 1 0.6593 1.2160 1.1720 0.78

290930021 1 37.65417 -91.13056 MO Iron 58 4 0.0 1 0.5850 0.9840 0.7820 0.71

290930023 1 37.50333 -90.69556 MO Iron 138 7 0.0 1 1 *# 0.3433 0.6320 0.4275 0.01290930024 1 37.47972 -90.69028 MO Iron 32 2 0.0 1 1 *# 0.6677 1.6026 0.9927 0.03290930025 1 37.51056 -90.69750 MO Iron 138 7 0.0 1 1 *# 0.3263 0.6320 0.4189 0.06

290930026 1 37.45917 -90.68639 MO Iron 32 2 0.0 1 1 *# 0.2523 0.3555 0.3370 0.06290930027 1 37.48611 -90.69000 MO Iron 32 2 0.0 1 1 * 0.8761 1.4414 0.9300 0.16

290930029 1 37.47167 -90.68944 MO Iron 32 2 0.0 1 1 * 0.7148 1.4740 1.1410 0.08

290930030 1 37.46639 -90.69000 MO Iron 32 2 0.0 1 1 *# 0.2017 0.3330 0.2797 0.00290990004 1 38.26330 -90.37850 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 1.47

290990005 3 38.26722 -90.37944 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 0.5438 0.7900 0.6157 0.67

290990008 1 38.26194 -90.39417 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 58.8 1 0.1500 0.2583 0.1350 0.13

290990009 1 38.28444 -90.38194 MO Jefferson St. Louis, MO-IL 2,721,491 9,804 820 1 0.0 1 0.1664 0.1440 0.1100 0.14

290990010 1 38.24110 -90.37680 MO Jefferson St. Louis, MO-IL 2,721,491 2,799 215 1 0.0 1 0.0680 0.1040 0.0900 0.07

290990011 1 38.26820 -90.37380 MO Jefferson St. Louis, MO-IL 2,721,491 2,418 197 1 58.8 1 0.5321 1.0490 0.7327 1.304

290990013 1 38.27361 -90.38000 MO Jefferson St. Louis, MO-IL 2,721,491 3,570 318 1 58.8 1 0.2717 0.4850 0.3355 0.86

290990015 1 38.26167 -90.37972 MO Jefferson St. Louis, MO-IL 2,721,491 1,988 178 1 58.8 1 1.4906 2.2993 1.9442 1.47

291892003 1 38.64972 -90.35056 MO St. Louis St. Louis, MO-IL 2,721,491 12,303 512 1 0.0 0.0000 0.0000 0.0000 0.05

295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL 2,721,491 9,140 783 1 0.0 0.02340231003 1 40.47222 -74.47139 NJ Middlesex New York-Northern New Jersey-Lo 18,323,002 13,850 1,124 1 1.7 1 0.0419 0.0875 0.0360 0.114360470122 1 40.71980 -73.94788 NY Kings New York-Northern New Jersey-Lo 18,323,002 92,660 5,785 1 0.1 0.0293 0.0350 0.0300 0.03

360632008 1 43.08216 -79.00099 NY Niagara Buffalo-Niagra Falls, NY Metropoli 1,170,111 6,795 386 1 0.0 0.0060 0.0080 0.0080 

360713001 1 41.46107 -74.36343 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 1,481 99 1.8 1 0.0820 0.1580 0.0940 0.074

360713002 1 41.45887 -74.35392 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 1,257 86 1.8 1 0.2417 0.2080 0.1700 0.23360713004 1 41.47633 -74.36827 NY Orange Poughkeepsie-Newburgh-Middletow 621,517 6,816 434 1 0.0 0.0313 0.0333 0.0320 0.03360850067 1 40.59733 -74.12619 NY Richmond New York-Northern New Jersey-Lo 18,323,002 21,834 1,373 0.0 0.0082 0.0140 0.0125 390170015 2 39.48990 -84.36407 OH Butler Cincinnati-Middletown, OH-KY-IN 2,009,632 4,668 373 1 0.0 0.01

 2B-6 
Appendix 2B

Table 2B-2. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

max 

quarterly 

mean, 

2003 

max 

monthly 

mean, 

2003 

2nd max 

monthly 

mean, 

2003 

ma

quarte

mea

200

390290019 1 40.63111 -80.54694 OH Columbiana East Liverpool-Salem, OH 112,075 5,385 322 1 0.0 0.0253 0.0300 0.0300 0.014

390290020 1 40.63972 -80.52389 OH Columbiana East Liverpool-Salem, OH 112,075 6,414 354 1 0.0 0.0247 0.0300 0.0240 0.01

390290022 1 40.63500 -80.54667 OH Columbiana East Liverpool-Salem, OH 112,075 3,318 202 1 0.0 0.0367 0.0800 0.0200 0.01

390350038 1 41.47694 -81.68194 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 7,329 585 1 0.1 0.0300 0.0600 0.0300 0.02390350042 1 41.48222 -81.70889 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 18,776 1,575 1 0.0 0.0233 0.0300 0.0200 0.02

390350049 1 41.44667 -81.65111 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 9,720 758 1 0.0 1 1 0.2367 0.4500 0.2500 0.13

390350050 1 41.44250 -81.64917 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 8,771 695 1 0.0 1 1 0.0400 0.0700 0.0500 0.054

390350061 2 41.47506 -81.67596 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 6,141 444 1 0.3 1 1 0.3600 0.5600 0.4700 0.02

390350069 1 41.51918 -81.63794 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 23,566 1,961 1 0.1 0.0233 0.0290 0.0280 

390490025 1 39.92806 -82.98111 OH Franklin Columbus, OH 1,612,694 15,220 1,226 1 0.6 1 0.0167 0.0200 0.0200 0.01

390510001 1 41.57528 -83.99639 OH Fulton Toledo, OH 659,188 1,503 110 1 0.3 1 0.2667 0.5300 0.2500 0.24

390910003 1 40.34306 -83.75500 OH Logan Bellefontaine, OH 46,005 1,536 108 1 0.1 0.1467 0.2700 0.2000 0.13

390910005 1 40.34278 -83.76028 OH Logan Bellefontaine, OH 46,005 1,546 126 1 0.1 1 0.1300 0.1900 0.1100 0.14

390910006 1 40.34111 -83.75778 OH Logan Bellefontaine, OH 46,005 1,217 87 1 0.1 1 0.1967 0.3200 0.2100 0.26390910007 1 40.34472 -83.75444 OH Logan Bellefontaine, OH 46,005 2,156 185 1 0.1 0.1500 0.2100 0.1500 0.22

391670008 1 39.43361 -81.50250 OH Washington Parkersburg-Marietta, WV-OH 164,624 1,947 114 0.0 0.0100 0.0100 0.0100 0.00

391670009 1 39.37696 -81.53730 OH Washington Parkersburg-Marietta, WV-OH 164,624 314 21 0.0 0.04

401159005 2 36.98580 -94.84920 OK Ottawa Miami, OK 33,194 1,573 117 0.0 

401159006 1 36.98460 -94.82490 OK Ottawa Miami, OK 33,194 1,573 117 0.0 

401159007 1 36.97190 -94.85180 OK Ottawa Miami, OK 33,194 1,573 117 0.0 

401159008 1 36.97160 -94.82500 OK Ottawa Miami, OK 33,194 1,573 117 0.0 

410510246 7 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 24,303 1,771 1 0.0 0.0101 0.0110 0.0105 

420030002 1 40.50056 -80.07194 PA Allegheny Pittsburgh, PA 2,431,087 19,559 1,045 1 0.0 0.0255 0.0280 0.0260 0.01

420032001 1 40.39667 -79.86361 PA Allegheny Pittsburgh, PA 2,431,087 10,120 769 1 0.2 0.0567 0.1140 0.0660 0.03

420070505 1 40.68500 -80.32500 PA Beaver Pittsburgh, PA 2,431,087 6,497 218 1 0.0 0.0913 0.1920 0.0483 0.09

420110005 1 40.46630 -75.75890 PA Berks Reading, PA 373,638 692 44 4.8 1 0.0757 0.1000 0.0760 0.094

420110717 1 40.47667 -75.75917 PA Berks Reading, PA 373,638 575 39 1 4.8 1 0.1238 0.1980 0.1580 0.18420111717 1 40.37722 -75.91444 PA Berks Reading, PA 373,638 7,376 390 1 2.1 1 0.3860 0.4840 0.4560 0.39

420210808 1 40.34806 -78.88278 PA Cambria Johnstown, PA 152,598 2,606 115 1 0.0 0.0364 0.0460 0.0320 0.04

420250105 1 40.80306 -75.60833 PA Carbon Allentown-Bethlehem-Easton, PA-N 740,395 8,477 513 1 0.0 0.0992 0.1300 0.1240 0.11

420450002 1 39.83556 -75.37250 PA Delaware Philadelphia-Camden-Wilmington, P 5,687,147 10,156 859 1 0.0 0.0364 0.0380 0.0380 0.04

421010449 1 39.98250 -75.08306 PA Philadelphia Philadelphia-Camden-Wilmington, P 5,687,147 8,653 413 1 0.0 1 1 0.0350 0.0380 0.0360 0.02421290007 1 40.16667 -79.87500 PA Westmoreland Pittsburgh, PA 2,431,087 7,739 445 1 0.0 0.0369 0.0400 0.0400 0.03

450031001 1 33.43253 -81.89233 SC Aiken Augusta-Richmond County, GA-SC 499,684 437 24 0.0 0.0000 0.0000 0.0000 

450130007 1 32.43654 -80.67785 SC Beaufort Hilton Head Island-Beaufort, SC 141,615 4,928 330 1 0.0 0.0022 0.0070 0.0016 0.00

450190003 2 32.88394 -79.97754 SC Charleston Charleston-North Charleston, SC 549,033 4,401 275 1 0.0 0.0041 0.0104 0.0048 0.00450190046 1 32.94275 -79.65718 SC Charleston Charleston-North Charleston, SC 549,033 63 4 0.0 0.0032 0.0068 0.0035 0.00450190047 1 32.84461 -79.94804 SC Charleston Charleston-North Charleston, SC 549,033 7,000 294 1 0.0 0.0037 0.0058 0.0052 

450410001 1 34.19794 -79.79885 SC Florence Florence, SC 193,155 3,426 224 1 0.0 0.0026 0.0063 0.0023 

450410002 1 34.16764 -79.85040 SC Florence Florence, SC 193,155 1,795 106 1 0.0 0.0000 0.00450430006 1 33.36378 -79.29426 SC Georgetown Georgetown, SC 55,797 5,247 427 1 0.3 1 0.0166 0.0420 0.0077 0.01

450430007 1 33.34973 -79.29821 SC Georgetown Georgetown, SC 55,797 1,579 119 0.3 0.0017 0.0054 0.0000 0.00

450430009 1 33.37399 -79.28570 SC Georgetown Georgetown, SC 55,797 2,447 185 1 0.3 0.0081 0.0158 0.0094 0.004

450430010 1 33.36960 -79.29840 SC Georgetown Georgetown, SC 55,797 6,173 511 1 0.3 0.0102 0.0132 0.0102 0.01

450450008 2 34.84045 -82.40291 SC Greenville Greenville, SC 559,940 7,967 381 1 0.0 0.0071 0.0125 0.0066 0.00

450452002 1 34.94165 -82.22961 SC Greenville Greenville, SC 559,940 7,266 494 1 0.0 0.0000 0.0000 0.0000 0.00

450470001 1 34.18111 -82.15224 SC Greenwood Greenwood, SC 66,271 7,853 667 1 0.0 0.0053 0.0086 0.0082 0.00450470002 1 34.16520 -82.16048 SC Greenwood Greenwood, SC 66,271 1,490 116 0.0 0.0100 0.0202 0.0168 0.01

450510002 2 33.70460 -78.87745 SC Horry Myrtle Beach-Conway-North Myrtle 196,629 4,510 227 1 0.0 0.0018 0.0032 0.0020 0.00

450630005 2 33.78560 -81.11978 SC Lexington Columbia, SC 647,158 736 66 0.0 0.0033 0.0052 0.0050 450631002 2 33.96900 -81.06533 SC Lexington Columbia, SC 647,158 8,086 551 1 0.0 0.0179 0.0356 0.0090 0.00

450790006 4 34.00740 -81.02329 SC Richland Columbia, SC 647,158 17,143 574 1 0.0 0.0069 0.0090 0.0072 

450790007 2 34.09584 -80.96230 SC Richland Columbia, SC 647,158 4,405 233 1 0.0 0.0006 0.0018 0.0000 0.00

450790019 1 33.99330 -81.02414 SC Richland Columbia, SC 647,158 15,569 287 1 0.0 0.0097 0.0138 0.0104 0.00450790021 1 33.81655 -80.78114 SC Richland Columbia, SC 647,158 123 10 0.0 0.0012 0.0038 0.0000 0.00

 2B-7 
Appendix 2B

Table 2B-2. Pb-TSP monitoring site information and 1-year statistics 

site poc lat long state county_name cbsa_name cbsa_pop00 

population 

near site 

(mile radius) 

under 

age 5 

pop. 

(mile 

radius) 

urban 

sum 

point / 

nonpt 

Pb EI 

TPY 

w/in 1 

mile 

source 

oriented? 

prev. 

source 

oriented? 

(see end 

notes) 

max 

quarterly 

mean, 

2003 

max 

monthly 

mean, 

2003 

2nd max 

monthly 

mean, 

2003 

ma

quarte

mea

200

450830001 2 34.94774 -81.93255 SC Spartanburg Spartanburg, SC 253,791 7,505 552 1 0.0 0.0035 0.0060 0.0058 0.00

450850001 1 33.92423 -80.33774 SC Sumter Sumter, SC 104,646 4,990 407 1 0.0 0.0064 0.0108 0.0066 0.004

450910005 1 34.96303 -81.00085 SC York Charlotte-Gastonia-Concord, NC-SC 1,330,448 3,453 221 1 0.0 0.0042 0.0082 0.0050 0.00

470930027 1 35.98306 -83.95222 TN Knox Knoxville, TN 616,079 8,586 826 1 5.8 1 0.0100 0.0100 0.0100 0.02470931017 1 35.97500 -83.95444 TN Knox Knoxville, TN 616,079 7,817 763 1 5.8 1 0.0100 0.0100 0.0100 0.01471570044 1 35.08750 -90.07250 TN Shelby Memphis, TN-MS-AR 1,205,204 6,730 548 1 0.0 1 1 0.0100 0.0100 0.0100 0.01

471633001 1 36.52556 -82.27333 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 0.1515 0.1940 0.1718 0.15471633002 3 36.52472 -82.26806 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 0.0719 0.0846 0.0730 0.10471633003 1 36.52806 -82.26833 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 942 65 0.4 1 0.0679 0.0844 0.0810 0.12

471870100 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 0.9867 1.9120 0.8200 0.12471870102 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 0.6953 0.9460 0.6000 0.08471871101 1 35.79944 -86.66500 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 10 2.6 1 0.0853 0.1160 0.1120 0.30480610006 1 25.89251 -97.49382 TX Cameron Brownsville-Harlingen, TX 335,227 14,803 1,422 1 0.0 0.0085 0.0090 0.0090 0.00

480850003 1 33.14250 -96.82472 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 3.2 1 0.3006 0.4436 0.3518 0.24

480850007 2 33.14722 -96.82556 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 1 3.2 1 0.1337 0.2458 0.2223 0.124480850009 1 33.14472 -96.82889 TX Collin Dallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 3.2 1 0.6600 0.8914 0.6658 0.59

481130018 1 32.74556 -96.78250 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 6,451 491 1 0.0 0.0318 0.0640 0.0250 0.08

481130057 2 32.77890 -96.87306 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 4,591 578 1 0.0 0.0611 0.1016 0.0708 0.044

481130066 2 32.73972 -96.78278 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 8,270 622 1 0.0 1 1 0.0178 0.0260 0.0217 0.02481410033 1 31.77694 -106.50167 TX El Paso El Paso, TX 679,622 13,680 1,005 1 0.0 0.0585 0.0600 0.0540 

482011034 4 29.76799 -95.22058 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 14,785 1,770 1 0.0 0.0136 0.0230 0.0140 0.02

484790016 1 27.51083 -99.51972 TX Webb Laredo, TX 193,117 14,880 1,441 1 0.0 0.0142 0.0230 0.0147 0.01

490351001 1 40.70861 -112.09472 UT Salt Lake Salt Lake City, UT 968,858 215 23 1 0.0 0.0628 0.1188 0.0752 0.07

721270003 1 18.44917 -66.05306 PR San Juan San Juan-Caguas-Guaynabo, PR 2,509,007 319 5 1 0.0042 0.0125 0.0000 0.00

 * 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-8 
Appendix 2B

Table 2B-3. Pb-TSP monitoring site distribution statistics 

All sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 189 0.0000 0.0010 0.0019 0.0032 0.0042 0.0052 0.0071 0.0097 0.0114 0.0143 0.0166 0.0934 0.0203 0.0272 0.0316 0.0396 0.0606 0.09

max quarter mean 189 0.0000 0.0031 0.0041 0.0063 0.0071 0.0100 0.0126 0.0179 0.0224 0.0254 0.0299 0.1738 0.0367 0.0495 0.0627 0.0820 0.1259 0.18

max monthly mean 189 0.0000 0.0054 0.0080 0.0100 0.0112 0.0140 0.0200 0.0288 0.0320 0.0380 0.0430 0.3079 0.0503 0.0880 0.1000 0.1460 0.2200 0.29

2nd max monthly mean 189 0.0000 0.0035 0.0051 0.0072 0.0100 0.0117 0.0140 0.0196 0.0240 0.0280 0.0340 0.2066 0.0380 0.0440 0.0726 0.1000 0.1428 0.23

average of 3 overall highest monthly means 189 0.0000 0.0043 0.0060 0.0075 0.0101 0.0122 0.0147 0.0228 0.0279 0.0320 0.0373 0.2253 0.0400 0.0500 0.0811 0.1033 0.1496 0.25average of 3 annual max monthly means 189 0.0000 0.0042 0.0058 0.0080 0.0100 0.0112 0.0150 0.0203 0.0252 0.0299 0.0344 0.1942 0.0400 0.0496 0.0753 0.1073 0.1380 0.20

Source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 60 0.0072 0.0095 0.0142 0.0229 0.0375 0.0440 0.0616 0.0775 0.0933 0.1122 0.1253 0.2596 0.1455 0.1815 0.2281 0.2549 0.2655 0.33

max quarter mean 60 0.0100 0.0180 0.0221 0.0309 0.0731 0.0880 0.1206 0.1502 0.1829 0.2064 0.2470 0.4781 0.2800 0.3272 0.3526 0.5107 0.6866 0.71

max monthly mean 60 0.0100 0.0311 0.0378 0.0420 0.1000 0.1580 0.1814 0.2311 0.2881 0.3577 0.4263 0.8572 0.5200 0.6320 0.7663 0.9280 1.0307 1.45

2nd max monthly mean 60 0.0100 0.0205 0.0310 0.0380 0.0871 0.1070 0.1484 0.1690 0.2230 0.2670 0.2943 0.5738 0.3485 0.4336 0.4568 0.5645 0.7310 0.92

Not source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 129 0.0000 0.0006 0.0014 0.0021 0.0028 0.0038 0.0045 0.0051 0.0057 0.0081 0.0100 0.0162 0.0113 0.0142 0.0153 0.0175 0.0214 0.02

max quarter mean 129 0.0000 0.0022 0.0033 0.0042 0.0060 0.0067 0.0080 0.0100 0.0114 0.0138 0.0179 0.0322 0.0229 0.0253 0.0280 0.0343 0.0386 0.04

max monthly mean 129 0.0000 0.0051 0.0062 0.0080 0.0090 0.0105 0.0120 0.0140 0.0160 0.0220 0.0290 0.0525 0.0320 0.0360 0.0404 0.0480 0.0600 0.08

2nd max monthly mean 129 0.0000 0.0025 0.0040 0.0052 0.0060 0.0080 0.0100 0.0115 0.0125 0.0148 0.0196 0.0358 0.0230 0.0270 0.0300 0.0360 0.0390 0.04

Previous source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 9 0.0090 0.0090 0.0090 0.0100 0.0100 0.0203 0.0203 0.0362 0.0362 0.0477 0.0477 0.0595 0.0477 0.0799 0.0799 0.1000 0.1000 0.11

max quarter mean 9 0.0100 0.0100 0.0100 0.0209 0.0209 0.0350 0.0350 0.0550 0.0550 0.1000 0.1000 0.1206 0.1000 0.1027 0.1027 0.1654 0.1654 0.23

max monthly mean 9 0.0100 0.0100 0.0100 0.0380 0.0380 0.0420 0.0420 0.1000 0.1000 0.1000 0.1000 0.1942 0.1000 0.1400 0.1400 0.3080 0.3080 0.45

2nd max monthly mean 9 0.0100 0.0100 0.0100 0.0280 0.0280 0.0360 0.0360 0.0940 0.0940 0.1000 0.1000 0.1467 0.1000 0.1025 0.1025 0.2200 0.2200 0.26

Urban sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 140 0.0001 0.0012 0.0021 0.0032 0.0045 0.0052 0.0074 0.0097 0.0112 0.0138 0.0149 0.0594 0.0168 0.0187 0.0230 0.0304 0.0365 0.04

max quarter mean 140 0.0006 0.0032 0.0042 0.0067 0.0080 0.0104 0.0131 0.0174 0.0214 0.0247 0.0260 0.1100 0.0300 0.0364 0.0405 0.0612 0.0766 0.09max monthly mean 140 0.0018 0.0062 0.0081 0.0103 0.0120 0.0149 0.0204 0.0287 0.0315 0.0360 0.0400 0.1958 0.0440 0.0502 0.0800 0.1000 0.1164 0.18

2nd max monthly mean 140 0.0000 0.0040 0.0059 0.0080 0.0100 0.0118 0.0145 0.0192 0.0220 0.0253 0.0305 0.1295 0.0355 0.0385 0.0430 0.0670 0.0870 0.10Urban sites, located in MSA's ≥ 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 91 0.0006 0.0026 0.0042 0.0051 0.0075 0.0090 0.0103 0.0113 0.0142 0.0150 0.0178 0.0711 0.0205 0.0225 0.0276 0.0315 0.0368 0.03

max quarter mean 91 0.0033 0.0060 0.0071 0.0100 0.0114 0.0133 0.0197 0.0220 0.0252 0.0267 0.0300 0.1343 0.0353 0.0400 0.0567 0.0667 0.0773 0.09

max monthly mean 91 0.0067 0.0082 0.0110 0.0124 0.0160 0.0200 0.0290 0.0340 0.0360 0.0400 0.0440 0.2442 0.0500 0.0601 0.0960 0.1029 0.1460 0.18

2nd max monthly mean 91 0.0000 0.0067 0.0080 0.0103 0.0120 0.0160 0.0200 0.0230 0.0255 0.0315 0.0360 0.1530 0.0380 0.0406 0.0500 0.0778 0.0880 0.10

Urban sites, located in MSA's < 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct8

annual mean 49 0.0001 0.0006 0.0010 0.0014 0.0018 0.0025 0.0030 0.0046 0.0048 0.0065 0.0100 0.0378 0.0121 0.0143 0.0156 0.0175 0.0305 0.07

max quarter mean 49 0.0006 0.0020 0.0026 0.0034 0.0037 0.0063 0.0069 0.0085 0.0126 0.0166 0.0179 0.0649 0.0224 0.0248 0.0279 0.0386 0.0585 0.10

max monthly mean 49 0.0018 0.0048 0.0053 0.0063 0.0072 0.0102 0.0108 0.0144 0.0209 0.0286 0.0310 0.1060 0.0320 0.0400 0.0404 0.0800 0.0920 0.13

2nd max monthly mean 49 0.0018 0.0025 0.0034 0.0042 0.0054 0.0072 0.0090 0.0106 0.0132 0.1700 0.0200 0.0861 0.0240 0.0300 0.0310 0.0400 0.0560 0.13

 2B-9 
Appendix 2B

Table 2B-4. Pb-TSP metric correlations 

Statistic (Q = quarterly, M = monthly) 

All sites 

annual 

mean, 2003-

2005 

max Q 

mean, 2003-

2005 

max M 

mean, 2003-

2005 

2nd max M 

mean, 2003-

2005 

avg. of 3 

overall 

highest M 

means, 

2003-2005 

avg. of 3 

annual max 

M means, 

2003-2005 

number of sites 189 189 189 189 189 189 

mean (μg/m3) 0.0934 0.1738 0.3079 0.2066 0.2253 0.1942 

Correlation coeeficients annual mean, 2003-2005 1.00 0.96 0.88 0.97 0.94 0.90 max Q mean, 2003-2005 1.00 0.94 0.99 0.98 0.94 max M mean, 2003-2005 1.00 0.92 0.98 0.97 2nd max M mean, 2003-2005 1.00 0.97 0.93 average of 3 overall highest M means, 2003-2005 1.00 0.98 average of 3 annual max M means, 2003-2005 1.00 

Statistic (Q = quarterly, M = monthly) 

annual 

mean, 2003-

2005 

max Q 

mean, 2003-

2005 

ma

mean

20

number of sites 140 140 14

mean (μg/m3) 0.0594 0.1100 0.1

Correlation coeeficients annual mean, 2003-2005 1.00 0.95 0.

max Q mean, 2003-2005 1.00 0.

max M mean, 2003-2005 1.

2nd max M mean, 2003-2005 

average of 3 overall highest M means, 2003-2005 

average of 3 annual max M means, 2003-2005 

Statistic (Q = quarterly, M = monthly) 

Source-oriented sites 

annual 

mean, 2003-

2005 

max Q 

mean, 2003-

2005 

max M 

mean, 2003-

2005 

2nd max M 

mean, 2003-

2005 

avg. of 3 

overall 

highest M 

means, 

2003-2005 

avg. of 3 

annual max 

M means, 

2003-2005 

number of sites 60 60 60 60 60 60 

mean (μg/m3) 0.2596 0.4781 0.8572 0.5738 0.6259 0.5333 

Correlation coeeficients annual mean, 2003-2005 1.00 0.95 0.85 0.96 0.93 0.88 max Q mean, 2003-2005 1.00 0.93 0.99 0.98 0.92 max M mean, 2003-2005 1.00 0.89 0.97 0.96 2nd max M mean, 2003-2005 1.00 0.97 0.90 average of 3 overall highest M means, 2003-2005 1.00 0.97 average of 3 annual max M means, 2003-2005 1.00 

Statistic (Q = quarterly, M = monthly) 

Urban sites in 

annual 

mean, 2003-

2005 

max Q 

mean, 2003-

2005 

ma

mean

20

number of sites 91 91 9

mean (μg/m3) 0.0711 0.1343 0.2

Correlation coeeficientsannual mean, 2003-2005 1.00 0.95 0.

max Q mean, 2003-2005 1.00 0.

max M mean, 2003-2005 1.

2nd max M mean, 2003-2005 

average of 3 overall highest M means, 2003-2005 

average of 3 annual max M means, 2003-2005 

2B-1 0 
Appendix 2B

Table 2B-5. Pb-TSP metric ratio distribution statistics 

TSP Category Ratio Sites min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 

ratio of max quarterly mean to annual 

mean 1.0000 1.1135 1.2080 1.2852 1.3433 1.3848 1.4837 1.5299 1.6127 1.7079 1.7846 2.3541 1.8893 2.0025 2.1421 2.3164 2.547

All sites ratio of max monthly mean to annual 

mean 

189 1.0000 1.3553 1.5556 1.8176 1.9537 2.1475 2.2817 2.4036 2.5471 2.6265 2.8310 4.4159 2.9634 3.5018 4.0128 4.4273 4.987

ratio of 2nd max monthly mean to 

annual mean 0.0000 1.1211 1.3057 1.4296 1.5822 1.6784 1.7597 1.8311 1.9092 2.0346 2.1246 2.5728 2.2033 2.3015 2.4453 2.5908 2.765

ratio of max quarterly mean to annual 

mean 1.0000 1.0787 1.2484 1.3167 1.3529 1.3966 1.5077 1.5205 1.5559 1.7079 1.7571 2.0471 1.8085 1.9070 1.9759 2.1285 2.348

Source-oriented sites ratio of max monthly mean to annual 

mean 

60 1.0000 1.4735 1.8318 1.9768 2.1641 2.2716 2.3528 2.5202 2.5824 2.7379 2.9086 3.7485 3.0625 3.5073 3.6900 4.0401 4.509

ratio of 2nd max monthly mean to 

annual mean 1.0000 1.4088 1.6543 1.6815 1.7864 1.8292 1.8838 1.9438 2.0124 2.1000 2.1723 2.4356 2.2784 2.4017 2.5034 2.5293 2.639

ratio of max quarterly mean to annual 

mean 1.0000 1.1368 1.2034 1.2648 1.3095 1.3826 1.4753 1.5591 1.6332 1.7079 1.8151 2.4980 1.9665 2.0293 2.2498 2.4153 2.689

Non-source-oriented sites ratio of max monthly mean to annual 

mean 

129 1.0000 1.3140 1.4769 1.6578 1.8680 2.0164 2.2496 2.3671 2.5200 2.5851 2.7967 4.7287 2.9508 3.4823 4.0858 4.5723 5.592

ratio of 2nd max monthly mean to 

annual mean 0.0000 1.0746 1.2445 1.3294 1.4296 1.5582 1.6657 1.7556 1.8372 1.9200 2.1185 2.6371 2.1818 2.2633 2.3912 2.6316 2.824

ratio of max quarterly mean to annual 

mean 1.0000 1.0000 1.0000 1.0000 1.0000 1.2844 1.2844 1.4961 1.4961 1.5195 1.5195 2.2049 1.5195 1.7266 1.7266 1.9492 1.949

Previous source-oriented 

sites

ratio of max monthly mean to annual 

mean 

13 1.0000 1.0000 1.0000 1.0000 1.0000 1.7515 1.7515 1.8746 1.8746 2.7626 2.7626 3.4751 2.7626 2.7863 2.7863 3.7062 3.706

ratio of 2nd max monthly mean to 

annual mean 1.0000 1.0000 1.0000 1.0000 1.0000 1.7759 1.7759 2.1414 2.1414 2.5969 2.5969 4.9066 2.5969 3.0986 3.0986 8.4131 8.413

ratio of max quarterly mean to annual 

mean 1.0000 1.1605 1.2504 1.3070 1.3536 1.4227 1.5010 1.5712 1.6427 1.7167 1.7773 2.3313 1.8683 2.0072 2.1508 2.3167 2.589

Urban sites ratio of max monthly mean to annual 

mean 

140 1.0000 1.3391 1.5925 1.7771 1.9220 2.1133 2.2936 2.4105 2.5423 2.6341 2.8310 4.2395 2.9518 3.4976 4.0307 4.4998 5.112

ratio of 2nd max monthly mean to 

annual mean 0.0000 1.1889 1.3271 1.4700 1.6053 1.6784 1.7660 1.8606 1.9967 2.1038 2.1650 2.6532 2.2452 2.3637 2.5051 2.6213 2.785

ratio of max quarterly mean to annual 

mean 1.0000 1.1077 1.2080 1.2745 1.3095 1.3759 1.4753 1.5167 1.6157 1.6829 1.7366 2.3159 1.7900 1.8451 2.0496 2.3164 2.431

Urban sites in CBSAs > 

1M population 

ratio of max monthly mean to annual 

mean 

91 1.0000 1.3046 1.4769 1.6578 1.8346 1.9910 2.2676 2.3690 2.5370 2.5619 2.6726 4.0747 2.8714 2.9508 3.5868 4.0253 4.647

ratio of 2nd max monthly mean to 

annual mean 0.0000 1.0746 1.3057 1.4089 1.4965 1.5855 1.6553 1.7491 1.7890 1.8960 2.0982 2.4155 2.1414 2.2633 2.3912 2.5004 2.596

ratio of max quarterly mean to annual 

mean 1.0000 1.2648 1.3522 1.3838 1.4152 1.5299 1.5608 1.6127 1.6901 1.9505 2.0025 2.3597 2.0091 2.1715 2.2498 2.6223 2.724

Urban sites in CBSAs < 

1M population 

ratio of max monthly mean to annual 

mean 

49 1.0000 1.4392 1.7365 1.9615 2.0898 2.2033 2.3056 2.5035 2.6417 2.9528 3.1207 4.5456 3.5286 4.4055 4.5723 5.0000 5.549

ratio of 2nd max monthly mean to 

annual mean 1.0000 1.3116 1.6698 1.7451 1.8787 1.9843 2.0346 2.1235 2.1673 2.2033 2.3435 3.0946 2.3684 2.6953 2.7642 2.9302 3.819

2B-1 1 
Appendix 2B

Table 2B-6. Pb-PM10 monitoring site information 

site poc lat long state county_name cbsa_name cbsa_pop00 urban 

data completeness 

(complete periods) 

qtrs years months annual mean 

080770017 1 39.06363 -108.56102 CO Mesa Grand Junction, CO 116,255 1 1 4 13 0.0049 

110010043 1 38.91889 -77.01250 DC District of Columbia Washington-Arlington-Alexandria, 4,796,183 1 2 7 20 0.0048 

120571065 5 27.89222 -82.53861 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 1 2 8 23 0.0062 

120573002 5 27.96565 -82.23040 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 2 8 24 0.0035 121030018 5 27.78556 -82.74000 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 1 1 4 12 0.0022 121030026 5 27.85004 -82.71459 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 1 1 6 17 0.0023 130890002 1 33.68801 -84.29033 GA DeKalb Atlanta-Sandy Springs-Marietta, GA 4,247,981 1 3 12 34 0.0026 170314201 6 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 1 4 12 0.0060 211930003 1 37.28306 -83.22028 KY Perry 3 12 34 0.0040 250250042 6 42.32944 -71.08278 MA Suffolk Boston-Cambridge-Quincy, MA-NH 4,391,344 1 1 6 22 0.0049 261630033 1 42.30667 -83.14889 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 1 3 12 35 0.0212 

295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL 2,721,491 1 2 10 30 0.0127 

360850106 1 40.57811 -74.18430 NY Richmond New York-Northern New Jersey-Lo 18,323,002 1 4 11 0.0071 

360850111 1 40.57997 -74.19872 NY Richmond New York-Northern New Jersey-Lo 18,323,002 1 4 11 0.0074 360850131 1 40.58806 -74.16882 NY Richmond New York-Northern New Jersey-Lo 18,323,002 1 4 10 0.0069 360850132 1 40.58061 -74.15158 NY Richmond New York-Northern New Jersey-Lo 18,323,002 1 4 11 0.0095 410390060 7 44.02631 -123.08374 OR Lane Eugene-Springfield, OR 322,959 1 1 3 9 0.0023 410510030 7 45.49742 -122.67467 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 1 4 11 0.0056 

410510080 7 45.49667 -122.60222 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 2 7 22 0.0055 410510244 8 45.53500 -122.69889 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 2 7 21 0.0065 410510246 7 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 2 8 23 0.0097 410610119 7 45.33897 -117.90480 OR Union La Grande, OR 24,530 2 7 20 0.0016 410670111 7 45.47020 -122.81585 OR Washington Portland-Vancouver-Beaverton, OR 1,927,881 1 1 3 11 0.0025 440070022 1 41.80795 -71.41500 RI Providence Providence-New Bedford-Fall Rive 1,582,997 1 3 12 36 0.0098 440070029 1 41.81644 -71.43790 RI Providence Providence-New Bedford-Fall Rive 1,582,997 1 1 4 13 0.0061 450250001 2 34.61537 -80.19879 SC Chesterfield 2 8 20 0.0029 481390017 1 32.47361 -97.04250 TX Ellis Dallas-Fort Worth-Arlington, TX 5,161,544 1 6 17 0.0151 481410041 1 31.76054 -106.50045 TX El Paso El Paso, TX 679,622 1 1 4 12 0.0118 482011035 1 29.73371 -95.25759 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 12 36 0.0077 482011039 1 29.67005 -95.12849 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 12 31 0.0056 490110004 1 40.90297 -111.88447 UT Davis Ogden-Clearfield, UT 442,656 1 2 10 29 0.0059 530110030 7 45.64168 -122.68123 WA Clark Portland-Vancouver-Beaverton, OR 1,927,881 1 1 3 11 0.0032 530330080 1 47.56833 -122.30806 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 11 32 0.0046 530630016 1 47.66083 -117.35722 WA Spokane Spokane, WA 417,939 1 1 4 12 0.0059 530630050 1 47.69545 -117.37030 WA Spokane Spokane, WA 417,939 1 4 12 0.0049 530630052 1 47.66512 -117.42909 WA Spokane Spokane, WA 417,939 1 4 12 0.0037 530630053 1 47.68220 -117.30480 WA Spokane Spokane, WA 417,939 1 4 12 0.0051 550270007 1 43.43500 -88.52778 WI Dodge Beaver Dam, WI 85,897 1 4 10 0.0054 

2B-1 2 
Appendix 2B

Table 2B-7. Pb-PM10 monitoring site distribution statistics 

All sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 38 0.0016 0.0022 0.0023 0.0026 0.0032 0.0037 0.0046 0.0049 0.0049 0.0054 0.0056 0.0063 0.0056 0.0059 0.0061 0.0065 0.0071 0

max quarter mean 38 0.0027 0.0030 0.0032 0.0046 0.0049 0.0055 0.0066 0.0078 0.0082 0.0085 0.0087 0.0117 0.0090 0.0098 0.0106 0.0113 0.0117 0

max monthly mean 38 0.0030 0.0040 0.0047 0.0061 0.0075 0.0085 0.0094 0.0106 0.0116 0.0123 0.0135 0.0205 0.0142 0.0146 0.0151 0.0160 0.0190 0

Urban sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 25 0.0022 0.0023 0.0023 0.0025 0.0029 0.0046 0.0048 0.0049 0.0052 0.0056 0.0056 0.0064 0.0059 0.0060 0.0061 0.0062 0.0065 0

max quarter mean 25 0.0030 0.0032 0.0032 0.0034 0.0049 0.0056 0.0076 0.0081 0.0085 0.0085 0.0088 0.0126 0.0092 0.0101 0.0106 0.0108 0.0113 0

max monthly mean 25 0.0040 0.0045 0.0047 0.0051 0.0073 0.0094 0.0097 0.0106 0.0114 0.0123 0.0136 0.0235 0.0142 0.0145 0.0151 0.0190 0.0211 0

Urban sites, located in MSA's ≥ 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 20 0.0022 0.0022 0.0024 0.0025 0.0029 0.0039 0.0047 0.0048 0.0052 0.0056 0.0056 0.0065 0.0058 0.0061 0.0061 0.0063 0.0071 0

max quarter mean 20 0.0030 0.0031 0.0033 0.0040 0.0049 0.0064 0.0080 0.0085 0.0085 0.0087 0.0090 0.0136 0.0095 0.0101 0.0105 0.0109 0.0142 0

max monthly mean 20 0.0045 0.0046 0.0049 0.0056 0.0077 0.0095 0.0101 0.0111 0.0120 0.0130 0.0139 0.0259 0.0143 0.0145 0.0148 0.0170 0.0223 0

Urban sites, located in MSA's < 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 5 0.0023 0.0023 0.0023 0.0023 0.0036 0.0049 0.0049 0.0049 0.0054 0.0059 0.0059 0.0061 0.0059 0.0059 0.0059 0.0059 0.0059 0max quarter mean 5 0.0032 0.0032 0.0032 0.0032 0.0044 0.0056 0.0056 0.0056 0.0069 0.0081 0.0081 0.0089 0.0081 0.0095 0.0108 0.0108 0.0108 0max monthly mean 5 0.0040 0.0040 0.0040 0.0040 0.0062 0.0085 0.0085 0.0085 0.0098 0.0111 0.0111 0.0140 0.0111 0.0161 0.0211 0.0211 0.0211 0

 2B-13 
Appendix 2B

Table 2B-8. Pb-PM2.5 monitoring site information 

site poc lat long state county_name cbsa_name cbsa_pop00 urban 

data completeness 

(complete periods) 3-year metrics 

qtrs years months annual mean max quarterly 

mean 

max 

monthly 

mean 

010050002 5 31.66414 -85.60623 AL Barbour Eufaula, AL-GA 31,636 2 8 25 0.0026 0.0033 0.0053 

010730023 5 33.55306 -86.81500 AL Jefferson Birmingham-Hoover, AL 1,052,238 1 3 12 36 0.0180 0.0296 0.0475 

010731009 5 33.45972 -87.30556 AL Jefferson Birmingham-Hoover, AL 1,052,238 3 12 34 0.0021 0.0032 0.0044 

010732003 5 33.49972 -86.92417 AL Jefferson Birmingham-Hoover, AL 1,052,238 1 3 12 36 0.0450 0.0967 0.2091 

010890014 5 34.69083 -86.58306 AL Madison Huntsville, AL 342,376 1 3 12 34 0.0024 0.0040 0.0057 

010970003 5 30.76972 -88.08750 AL Mobile Mobile, AL 399,843 1 3 12 36 0.0038 0.0060 0.0096 

011011002 5 32.40694 -86.25639 AL Montgomery Montgomery, AL 346,528 1 3 12 34 0.0045 0.0083 0.0115 

011030011 5 34.51861 -86.97694 AL Morgan Decatur, AL 145,867 1 3 11 30 0.0029 0.0042 0.0060 

011130001 5 32.47639 -84.99917 AL Russell Columbus, GA-AL 281,768 1 1 3 9 0.0030 0.0037 0.0063 

020200018 5 61.20667 -149.82083 AK Anchorage Municipa Anchorage, AK 319,605 1 1 6 17 0.0043 0.0067 0.0101 

020900010 6 64.84111 -147.72000 AK Fairbanks North Star Fairbanks, AK 82,840 1 1 3 8 0.0034 0.0053 0.0070 

040130019 5 33.48385 -112.14257 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 2 7 20 0.0030 0.0057 0.0100 

040134009 5 33.40642 -112.14434 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 4 9 0.0062 0.0123 0.0228 

040137003 5 33.28936 -112.15732 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 4 10 0.0027 0.0049 0.0067 

040137020 5 33.47333 -111.85418 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 4 11 0.0026 0.0038 0.0058 

040138006 5 33.43671 -112.09141 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 1 5 13 0.0042 0.0067 0.0084 

040139997 7 33.50364 -112.09500 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 3 12 36 0.0027 0.0047 0.0069 

040139998 5 33.45513 -111.99610 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 1 6 18 0.0033 0.0047 0.0075 

040191028 5 32.29515 -110.98230 AZ Pima Tucson, AZ 843,746 1 3 12 35 0.0017 0.0022 0.0035 

050030005 5 33.13944 -91.95000 AR Ashley 1 2 9 21 0.0027 0.0055 0.0082 051190007 5 34.75611 -92.27583 AR Pulaski Little Rock-North Little Rock, AR 610,518 1 3 12 33 0.0029 0.0042 0.0061 051450001 5 35.24861 -91.71528 AR White Searcy, AR 67,165 1 2 9 22 0.0026 0.0046 0.0063 060070002 5 39.75750 -121.84222 CA Butte Chico, CA 203,171 1 3 12 36 0.0026 0.0039 0.0054 060190008 5 36.78139 -119.77222 CA Fresno Fresno, CA 799,407 1 3 12 36 0.0030 0.0050 0.0066 060250005 5 32.67611 -115.48333 CA Imperial El Centro, CA 142,361 1 3 12 36 0.0119 0.0172 0.0342 060290014 5 35.35611 -119.04028 CA Kern Bakersfield, CA 661,645 1 3 11 32 0.0026 0.0046 0.0061 060371103 5 34.06659 -118.22688 CA Los Angeles Los Angeles-Long Beach-Santa An 12,365,627 1 3 12 36 0.0053 0.0098 0.0228 

060631009 5 39.80833 -120.47167 CA Plumas 1 3 12 36 0.0025 0.0041 0.0054 

060658001 5 33.99958 -117.41601 CA Riverside Riverside-San Bernardino-Ontario, 3,254,821 1 3 12 36 0.0058 0.0088 0.0151 

060670006 5 38.61417 -121.36694 CA Sacramento Sacramento--Arden-Arcade--Rosev 1,796,857 1 3 12 36 0.0022 0.0031 0.0047 

060670010 5 38.55833 -121.49194 CA Sacramento Sacramento--Arden-Arcade--Rosev 1,796,857 1 3 12 36 0.0029 0.0037 0.0052 060730003 5 32.79139 -116.94167 CA San Diego San Diego-Carlsbad-San Marcos, C 2,813,833 1 3 12 36 0.0039 0.0059 0.0078 060731002 5 33.12778 -117.07417 CA San Diego San Diego-Carlsbad-San Marcos, C 2,813,833 1 3 12 36 0.0035 0.0050 0.0064 060850005 5 37.34850 -121.89500 CA Santa Clara San Jose-Sunnyvale-Santa Clara, C 1,735,819 1 3 12 36 0.0026 0.0063 0.0138 060990005 5 37.64167 -120.99361 CA Stanislaus Modesto, CA 446,997 1 3 12 36 0.0033 0.0065 0.0090 

061072002 5 36.33222 -119.29028 CA Tulare Visalia-Porterville, CA 368,021 1 3 12 36 0.0034 0.0046 0.0060 

061112002 5 34.27750 -118.68472 CA Ventura Oxnard-Thousand Oaks-Ventura, C 753,197 1 3 12 33 0.0020 0.0032 0.0042 

080010006 5 39.82574 -104.93699 CO Adams Denver-Aurora, CO 2,157,756 1 3 12 36 0.0077 0.0163 0.0185 

080410011 5 38.83139 -104.82778 CO El Paso Colorado Springs, CO 537,484 1 3 12 34 0.0019 0.0028 0.0048 

080670008 5 37.26861 -107.87500 CO La Plata Durango, CO 43,941 1 1 3 8 0.0014 0.0016 0.0024 

080770003 5 39.09083 -108.56389 CO Mesa Grand Junction, CO 116,255 1 1 3 8 0.0015 0.0021 0.0031 

080770017 5 39.06363 -108.56102 CO Mesa Grand Junction, CO 116,255 1 2 9 25 0.0023 0.0035 0.0056 

081230008 5 40.20917 -104.82306 CO Weld Greeley, CO 180,936 3 12 36 0.0020 0.0034 0.0054 

090090027 5 41.30111 -72.90278 CT New Haven New Haven-Milford, CT 824,008 1 2 8 20 0.0029 0.0043 0.0066 

100010003 5 39.15500 -75.51806 DE Kent Dover, DE 126,697 1 3 12 34 0.0024 0.0038 0.0051 

100032004 5 39.73944 -75.55806 DE New Castle Philadelphia-Camden-Wilmington, 5,687,147 1 3 12 32 0.0042 0.0084 0.0114 

110010042 6 38.88083 -77.03250 DC District of Columbia Washington-Arlington-Alexandria, 4,796,183 1 1 6 18 0.0037 0.0058 0.0075 110010043 5 38.91889 -77.01250 DC District of Columbia Washington-Arlington-Alexandria, 4,796,183 1 3 12 36 0.0035 0.0063 0.0093 120330004 6 30.52500 -87.20417 FL Escambia Pensacola-Ferry Pass-Brent, FL 412,153 1 3 12 36 0.0019 0.0026 0.0042 

120571075 5 28.05000 -82.37806 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 1 1 4 12 0.0023 0.0034 0.0052 

120573002 5 27.96565 -82.23040 FL Hillsborough Tampa-St. Petersburg-Clearwater, F 2,395,997 2 8 24 0.0027 0.0042 0.0069 120730012 5 30.43972 -84.34833 FL Leon Tallahassee, FL 320,304 1 3 12 36 0.0020 0.0034 0.0049 

120861016 5 25.79417 -80.20611 FL Miami-Dade Miami-Fort Lauderdale-Miami Bea 5,007,564 1 3 12 36 0.0020 0.0068 0.0163 

121030026 5 27.85004 -82.71459 FL Pinellas Tampa-St. Petersburg-Clearwater, F 2,395,997 1 1 5 16 0.0025 0.0039 0.0088 130210007 5 32.77944 -83.64694 GA Bibb Macon, GA 222,368 3 12 34 0.0029 0.0069 0.0147 

2B-1 4 
Appendix 2B

Table 2B-8. Pb-PM2.5 monitoring site information 

site poc lat long state county_name cbsa_name cbsa_pop00 urban 

data completeness 

(complete periods) 3-year metrics 

qtrs years months annual mean max quarterly 

mean 

max 

monthly 

mean 

130510017 5 32.09278 -81.14417 GA Chatham Savannah, GA 293,000 1 2 8 22 0.0017 0.0029 0.0041 130590001 5 33.94583 -83.37222 GA Clarke Athens-Clarke County, GA 166,079 1 3 12 29 0.0021 0.0029 0.0041 130690002 5 31.52430 -82.76510 GA Coffee Douglas, GA 45,022 3 12 30 0.0013 0.0022 0.0032 

130890002 5 33.68750 -84.29028 GA DeKalb Atlanta-Sandy Springs-Marietta, G 4,247,981 1 3 12 36 0.0027 0.0042 0.0077 

131150005 5 34.26333 -85.27250 GA Floyd Rome, GA 90,565 3 12 33 0.0023 0.0030 0.0040 

132150011 5 32.43083 -84.93167 GA Muscogee Columbus, GA-AL 281,768 1 3 12 32 0.0036 0.0101 0.0086 

132450091 5 33.43333 -82.02194 GA Richmond Augusta-Richmond County, GA-SC 499,684 1 3 12 32 0.0025 0.0038 0.0067 

132950002 5 34.96611 -85.29750 GA Walker Chattanooga, TN-GA 476,531 1 1 3 9 0.0033 0.0040 0.0051 

150032004 5 21.39667 -157.97167 HI Honolulu Honolulu, HI 876,156 1 3 12 34 0.0010 0.0021 0.0031 

160270004 5 43.56240 -116.56323 ID Canyon Boise City-Nampa, ID 464,840 1 3 12 36 0.0022 0.0046 0.0096 

170310057 5 41.91473 -87.72273 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 3 12 35 0.0071 0.0115 0.0172 

170310076 5 41.75137 -87.71375 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 3 12 36 0.0054 0.0063 0.0087 170314201 5 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 3 12 36 0.0040 0.0054 0.0085 170434002 5 41.77120 -88.15250 IL DuPage Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 2 8 23 0.0047 0.0063 0.0072 171150013 5 39.86694 -88.92556 IL Macon Decatur, IL 114,706 3 12 35 0.0067 0.0142 0.0228 

171192009 5 38.90278 -90.14306 IL Madison St. Louis, MO-IL 2,721,491 1 3 12 32 0.0090 0.0208 0.0413 

180030004 5 41.09472 -85.10194 IN Allen Fort Wayne, IN 390,156 1 2 7 20 0.0257 0.1674 0.3091 180372001 5 38.39139 -86.92917 IN Dubois Jasper, IN 52,511 1 1 4 12 0.0042 0.0051 0.0063 

180390003 5 41.66778 -85.96944 IN Elkhart Elkhart-Goshen, IN 182,791 1 1 4 12 0.0044 0.0048 0.0056 

180650003 5 40.01167 -85.52361 IN Henry New Castle, IN 48,508 3 12 36 0.0037 0.0055 0.0074 

180890022 5 41.60667 -87.30472 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 3 11 32 0.0097 0.0128 0.0204 

180892004 5 41.58528 -87.47444 IN Lake Chicago-Naperville-Joliet, IL-IN-W 9,098,316 1 2 8 24 0.0090 0.0120 0.0244 180970078 5 39.81110 -86.11447 IN Marion Indianapolis-Carmel, IN 1,525,104 1 3 12 36 0.0048 0.0071 0.0087 

181411008 5 41.69361 -86.23667 IN St. Joseph South Bend-Mishawaka, IN-MI 316,663 1 1 4 12 0.0042 0.0054 0.0072 

181630012 5 38.02167 -87.56944 IN Vanderburgh Evansville, IN-KY 342,815 1 3 12 34 0.0031 0.0057 0.0080 

191130037 5 42.00833 -91.67861 IA Linn Cedar Rapids, IA 237,230 1 3 12 35 0.0033 0.0044 0.0071 191530030 5 41.60306 -93.64306 IA Polk Des Moines-West Des Moines, IA 481,394 1 3 12 33 0.0027 0.0037 0.0058 

191630015 5 41.53000 -90.58750 IA Scott Davenport-Moline-Rock Island, IA 376,019 1 3 12 33 0.0065 0.0084 0.0118 

201730010 5 37.70111 -97.31389 KS Sedgwick Wichita, KS 571,166 1 2 9 18 0.0021 0.0032 0.0053 

202090021 5 39.11750 -94.63556 KS Wyandotte Kansas City, MO-KS 1,836,038 1 3 12 36 0.0048 0.0066 0.0100 

210190017 5 38.45917 -82.64056 KY Boyd Huntington-Ashland, WV-KY-OH 288,649 1 3 12 35 0.0043 0.0060 0.0096 210590005 5 37.78083 -87.07556 KY Daviess Owensboro, KY 109,875 1 1 4 12 0.0037 0.0044 0.0061 

210590014 5 37.74111 -87.11806 KY Daviess Owensboro, KY 109,875 1 2 8 19 0.0023 0.0038 0.0036 

210670012 5 38.06500 -84.50000 KY Fayette Lexington-Fayette, KY 408,326 1 3 12 36 0.0038 0.0066 0.0101 

211110043 5 38.23222 -85.82528 KY Jefferson Louisville-Jefferson County, KY-IN 1,161,975 1 3 12 36 0.0042 0.0070 0.0100 

211110048 5 38.24056 -85.73167 KY Jefferson Louisville-Jefferson County, KY-IN 1,161,975 1 3 12 36 0.0048 0.0071 0.0133 211170007 5 39.07250 -84.52500 KY Kenton Cincinnati-Middletown, OH-KY-IN 2,009,632 1 3 12 36 0.0048 0.0106 0.0170 211250004 5 37.08722 -84.06333 KY Laurel London, KY 52,715 1 3 12 36 0.0037 0.0048 0.0095 

211451004 5 37.06556 -88.63778 KY McCracken Paducah, KY-IL 98,765 1 3 12 36 0.0029 0.0043 0.0059 

211930003 5 37.28306 -83.22028 KY Perry 3 12 34 0.0041 0.0059 0.0079 

212270007 5 36.99333 -86.41833 KY Warren Bowling Green, KY 104,166 1 3 12 35 0.0033 0.0056 0.0098 

220150008 5 32.53417 -93.74972 LA Bossier Shreveport-Bossier City, LA 375,965 3 12 32 0.0046 0.0089 0.0147 

220330009 5 30.46111 -91.17694 LA East Baton Rouge Baton Rouge, LA 705,973 1 3 11 31 0.0051 0.0101 0.0198 

240030019 5 39.10111 -76.72944 MD Anne Arundel Baltimore-Towson, MD 2,552,994 1 2 7 17 0.0033 0.0061 0.0087 

240053001 5 39.31083 -76.47444 MD Baltimore Baltimore-Towson, MD 2,552,994 1 2 9 26 0.0054 0.0080 0.0101 

240330030 5 39.05528 -76.87833 MD Prince George's Washington-Arlington-Alexandria, 4,796,183 1 1 4 12 0.0039 0.0069 0.0099 

250130008 5 42.19446 -72.55571 MA Hampden Springfield, MA 680,014 2 10 25 0.0025 0.0035 0.0045 

250250042 6 42.32944 -71.08278 MA Suffolk Boston-Cambridge-Quincy, MA-N 4,391,344 1 3 12 35 0.0027 0.0039 0.0056 

260050003 5 42.76778 -86.14861 MI Allegan Allegan, MI 105,665 1 3 11 35 0.0035 0.0055 0.0079 

260330901 5 46.49361 -84.36417 MI Chippewa Sault Ste. Marie, MI 38,543 1 3 12 36 0.0023 0.0038 0.0046 

260770008 5 42.27806 -85.54194 MI Kalamazoo Kalamazoo-Portage, MI 314,866 1 3 11 33 0.0050 0.0068 0.0097 

260810020 5 42.98417 -85.67139 MI Kent Grand Rapids-Wyoming, MI 740,482 1 3 12 35 0.0048 0.0083 0.0104 

261130001 5 44.31056 -84.89194 MI Missaukee Cadillac, MI 44,962 3 12 33 0.0022 0.0057 0.0102 

261150005 5 41.76389 -83.47194 MI Monroe Monroe, MI 145,945 3 12 32 0.0042 0.0050 0.0074 

261610008 5 42.24056 -83.59972 MI Washtenaw Ann Arbor, MI 322,895 1 2 10 30 0.0038 0.0060 0.0087

 2B-15 
Appendix 2B

Table 2B-8. Pb-PM2.5 monitoring site information 

site poc lat long state county_name cbsa_name cbsa_pop00 urban 

data completeness 

(complete periods) 3-year metrics 

qtrs years months annual mean max quarterly 

mean 

max 

monthly 

mean 

261630001 5 42.22861 -83.20833 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 1 3 12 36 0.0042 0.0051 0.0063 

261630033 5 42.30667 -83.14889 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 1 3 12 33 0.0118 0.0182 0.0329 

270530963 5 44.95540 -93.25827 MN Hennepin Minneapolis-St. Paul-Bloomington 2,968,806 1 3 12 36 0.0031 0.0041 0.0072 

270953051 5 46.20703 -93.75941 MN Mille Lacs 3 11 31 0.0017 0.0023 0.0036 

271095008 5 43.99691 -92.45037 MN Olmsted Rochester, MN 163,618 1 3 12 35 0.0027 0.0043 0.0067 

271230871 5 44.96145 -93.03589 MN Ramsey Minneapolis-St. Paul-Bloomington 2,968,806 1 2 9 27 0.0042 0.0073 0.0084 

280350004 5 31.32364 -89.28717 MS Forrest Hattiesburg, MS 123,812 1 3 12 35 0.0048 0.0128 0.0302 

280430001 5 33.83611 -89.79722 MS Grenada Grenada, MS 23,263 3 11 31 0.0017 0.0032 0.0056 

280470008 5 30.39014 -89.04972 MS Harrison Gulfport-Biloxi, MS 246,190 1 3 11 32 0.0023 0.0034 0.0062 

280490018 5 32.29681 -90.18831 MS Hinds Jackson, MS 497,197 1 3 12 31 0.0046 0.0071 0.0112 

280670002 5 31.68844 -89.13506 MS Jones Laurel, MS 83,107 1 3 12 35 0.0030 0.0073 0.0180 

290470005 5 39.30306 -94.37639 MO Clay Kansas City, MO-KS 1,836,038 3 12 36 0.0026 0.0040 0.0050 

290530001 5 38.79500 -92.91806 MO Cooper 3 12 33 0.0020 0.0028 0.0041 

290990012 5 38.43778 -90.36139 MO Jefferson St. Louis, MO-IL 2,721,491 1 3 12 36 0.0089 0.0126 0.0191 

291860005 5 37.89694 -90.42222 MO Ste Genevieve 3 12 34 0.0045 0.0084 0.0095 

292070001 5 36.97000 -90.14000 MO Stoddard 1 4 11 0.0034 0.0044 0.0068 

295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL 2,721,491 1 3 12 36 0.0095 0.0140 0.0192 

300530018 5 48.38417 -115.54806 MT Lincoln 1 3 12 35 0.0017 0.0029 0.0039 

300630031 5 46.87491 -113.99525 MT Missoula Missoula, MT 95,802 1 3 12 36 0.0020 0.0035 0.0051 

310550019 5 41.24722 -95.97556 NE Douglas Omaha-Council Bluffs, NE-IA 767,041 1 3 12 35 0.0030 0.0042 0.0055 

320030560 5 36.15861 -115.11083 NV Clark Las Vegas-Paradise, NV 1,375,765 1 1 5 15 0.0025 0.0039 0.0061 

320030561 5 36.16399 -115.11393 NV Clark Las Vegas-Paradise, NV 1,375,765 1 2 7 20 0.0025 0.0044 0.0086 

320310016 5 39.52508 -119.80772 NV Washoe Reno-Sparks, NV 342,885 1 3 12 36 0.0024 0.0040 0.0060 

330110020 5 43.00056 -71.46806 NH Hillsborough Manchester-Nashua, NH 380,841 1 3 12 34 0.0034 0.0053 0.0062 

330150014 5 43.07528 -70.74806 NH Rockingham Boston-Cambridge-Quincy, MA-N 4,391,344 1 3 12 36 0.0024 0.0028 0.0036 

340070003 5 39.92304 -75.09762 NJ Camden Philadelphia-Camden-Wilmington, 5,687,147 1 3 11 33 0.0042 0.0052 0.0069 340230006 6 40.47279 -74.42251 NJ Middlesex New York-Northern New Jersey-Lo 18,323,002 1 3 12 24 0.0045 0.0063 0.0114 

340273001 5 40.78763 -74.67630 NJ Morris New York-Northern New Jersey-Lo 18,323,002 1 3 12 35 0.0027 0.0038 0.0059 340390004 5 40.64144 -74.20836 NJ Union New York-Northern New Jersey-Lo 18,323,002 1 3 12 36 0.0044 0.0059 0.0067 350010023 5 35.13426 -106.58551 NM Bernalillo Albuquerque, NM 729,649 1 2 8 22 0.0013 0.0020 0.0027 

360050083 6 40.86586 -73.88075 NY Bronx New York-Northern New Jersey-Lo 18,323,002 1 3 12 36 0.0040 0.0059 0.0067 

360050110 5 40.81616 -73.90207 NY Bronx New York-Northern New Jersey-Lo 18,323,002 1 3 12 36 0.0047 0.0064 0.0079 360290005 6 42.87684 -78.80988 NY Erie Buffalo-Niagra Falls, NY Metropol 1,170,111 1 3 12 36 0.0106 0.0157 0.0192 

360310003 5 44.39309 -73.85892 NY Essex 3 12 34 0.0015 0.0021 0.0028 

360551007 5 43.14620 -77.54813 NY Monroe Rochester, NY 1,037,831 1 2 7 20 0.0031 0.0040 0.0048 

360556001 5 43.16100 -77.60357 NY Monroe Rochester, NY 1,037,831 1 1 5 15 0.0031 0.0037 0.0045 

360610062 1 40.72052 -74.00409 NY New York New York-Northern New Jersey-Lo 18,323,002 1 1 4 12 0.0070 0.0092 0.0190 

360632008 1 43.08216 -79.00099 NY Niagara Buffalo-Niagra Falls, NY Metropol 1,170,111 1 1 4 11 0.0052 0.0063 0.0065 

360710002 1 41.49947 -74.00973 NY Orange Poughkeepsie-Newburgh-Middleto 621,517 1 1 4 11 0.0034 0.0040 0.0053 

360810124 6 40.73620 -73.82317 NY Queens New York-Northern New Jersey-Lo 18,323,002 1 3 12 36 0.0038 0.0055 0.0068 

361010003 5 42.09071 -77.21025 NY Steuben Corning, NY 98,726 3 12 36 0.0028 0.0034 0.0042 

361030001 1 40.74583 -73.42028 NY Suffolk New York-Northern New Jersey-Lo 18,323,002 1 1 4 11 0.0032 0.0039 0.0051 

370210034 5 35.60972 -82.35083 NC Buncombe Asheville, NC 369,171 1 3 12 36 0.0019 0.0031 0.0052 

370350004 5 35.72889 -81.36556 NC Catawba Hickory-Lenoir-Morganton, NC 341,851 1 3 12 35 0.0025 0.0036 0.0060 370510009 5 35.04142 -78.95311 NC Cumberland Fayetteville, NC 336,609 1 2 8 22 0.0021 0.0037 0.0057 

370570002 5 35.81444 -80.26250 NC Davidson Thomasville-Lexington, NC 147,246 1 2 8 23 0.0032 0.0047 0.0087 

370670022 5 36.11056 -80.22667 NC Forsyth Winston-Salem, NC 421,961 1 3 12 36 0.0026 0.0036 0.0063 

370810013 5 36.10917 -79.80111 NC Guilford Greensboro-High Point, NC 643,430 1 2 9 23 0.0028 0.0043 0.0064 

371070004 5 35.23146 -77.56879 NC Lenoir Kinston, NC 59,648 3 12 34 0.0026 0.0046 0.0062 

371190041 5 35.24028 -80.78556 NC Mecklenburg Charlotte-Gastonia-Concord, NC-S 1,330,448 1 3 12 36 0.0029 0.0042 0.0052 

371590021 5 35.55187 -80.39504 NC Rowan Salisbury, NC 130,340 1 1 4 11 0.0032 0.0040 0.0057 

371830014 5 35.85611 -78.57417 NC Wake Raleigh-Cary, NC 797,071 1 3 12 34 0.0021 0.0038 0.0041 

380150003 5 46.82543 -100.76821 ND Burleigh Bismarck, ND 94,719 1 3 12 36 0.0012 0.0023 0.0036 

380171004 5 46.93375 -96.85535 ND Cass Fargo, ND-MN 174,367 3 12 36 0.0019 0.0027 0.0038 380530002 5 47.58120 -103.29950 ND Mc Kenzie 3 12 35 0.0012 0.0026 0.0040

 2B-16 
Appendix 2B

Table 2B-8. Pb-PM2.5 monitoring site information 

site poc lat long state county_name cbsa_name cbsa_pop00 urban 

data completeness 

(complete periods) 3-year metrics 

qtrs years months annual mean max quarterly 

mean 

max 

monthly 

mean 

390171004 5 39.53000 -84.39250 OH Butler Cincinnati-Middletown, OH-KY-IN 2,009,632 1 3 12 36 0.0092 0.0147 0.0273 

390350038 6 41.47694 -81.68194 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 1 3 12 35 0.0120 0.0163 0.0282 

390350060 5 41.49396 -81.67854 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 1 3 12 36 0.0123 0.0207 0.0270 

390490081 6 40.08778 -82.95972 OH Franklin Columbus, OH 1,612,694 1 3 12 34 0.0038 0.0052 0.0073 390530003 5 38.94996 -82.10910 OH Gallia Point Pleasant, WV-OH 57,026 1 6 16 0.0043 0.0072 0.0085 

390610040 5 39.12861 -84.50417 OH Hamilton Cincinnati-Middletown, OH-KY-IN 2,009,632 1 2 8 25 0.0056 0.0069 0.0113 

390610042 5 39.10500 -84.55111 OH Hamilton Cincinnati-Middletown, OH-KY-IN 2,009,632 1 1 4 11 0.0079 0.0114 0.0286 390810017 5 40.36610 -80.61500 OH Jefferson Weirton-Steubenville, WV-OH 132,008 1 1 5 11 0.0127 0.0150 0.0193 

390870010 5 38.51972 -82.66556 OH Lawrence Huntington-Ashland, WV-KY-OH 288,649 1 3 12 33 0.0059 0.0095 0.0137 

390930016 5 41.43944 -82.16167 OH Lorain Cleveland-Elyria-Mentor, OH 2,148,143 1 1 4 10 0.0157 0.0244 0.0450 

390933002 5 41.46306 -82.11444 OH Lorain Cleveland-Elyria-Mentor, OH 2,148,143 2 8 20 0.0238 0.0337 0.0465 

390950026 5 41.62056 -83.64139 OH Lucas Toledo, OH 659,188 1 3 12 36 0.0035 0.0053 0.0069 

390990014 5 41.09587 -80.65843 OH Mahoning Youngstown-Warren-Boardman, O 602,964 1 3 12 35 0.0131 0.0253 0.0382 

391130031 5 39.75944 -84.14444 OH Montgomery Dayton, OH 848,153 1 3 11 25 0.0042 0.0079 0.0085 

391510017 5 40.78667 -81.39444 OH Stark Canton-Massillon, OH 406,934 1 1 4 10 0.0114 0.0148 0.0186 

391510020 5 40.80056 -81.37333 OH Stark Canton-Massillon, OH 406,934 1 2 8 24 0.0060 0.0082 0.0157 391530023 5 41.08806 -81.54167 OH Summit Akron, OH 694,960 1 3 11 29 0.0050 0.0069 0.0098 

400450890 5 36.08518 -99.93494 OK Ellis 3 12 34 0.0012 0.0019 0.0027 

401091037 5 35.61278 -97.47222 OK Oklahoma Oklahoma City, OK 1,095,421 1 3 12 36 0.0022 0.0033 0.0046 

401431127 5 36.20490 -95.97654 OK Tulsa Tulsa, OK 859,532 1 3 12 36 0.0031 0.0045 0.0056 

410170120 5 44.06390 -121.31258 OR Deschutes Bend, OR 115,367 1 1 4 11 0.0014 0.0018 0.0021 

410290133 5 42.31408 -122.87924 OR Jackson Medford, OR 181,269 1 3 12 35 0.0019 0.0029 0.0035 

410390060 5 44.02631 -123.08374 OR Lane Eugene-Springfield, OR 322,959 1 3 12 35 0.0015 0.0025 0.0041 

410510246 6 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR 1,927,881 1 3 12 36 0.0075 0.0182 0.0398 

410610119 5 45.33897 -117.90480 OR Union La Grande, OR 24,530 2 8 20 0.0012 0.0020 0.0026 

420010001 5 39.92000 -77.31000 PA Adams Gettysburg, PA 91,292 3 12 35 0.0037 0.0070 0.0082 

420030008 6 40.46556 -79.96111 PA Allegheny Pittsburgh, PA 2,431,087 1 3 12 36 0.0112 0.0141 0.0252 420030021 5 40.41361 -79.94139 PA Allegheny Pittsburgh, PA 2,431,087 1 1 3 9 0.0073 0.0083 0.0129 

420030064 6 40.32361 -79.86833 PA Allegheny Pittsburgh, PA 2,431,087 1 2 9 23 0.0143 0.0239 0.0356 

420270100 5 40.81139 -77.87703 PA Centre State College, PA 135,758 1 3 12 35 0.0032 0.0043 0.0061 

420290100 5 39.83444 -75.76861 PA Chester Philadelphia-Camden-Wilmington, 5,687,147 3 12 32 0.0046 0.0086 0.0105 

420430401 5 40.24500 -76.84472 PA Dauphin Harrisburg-Carlisle, PA 509,074 1 3 12 34 0.0063 0.0122 0.0190 

420450002 5 39.83556 -75.37250 PA Delaware Philadelphia-Camden-Wilmington, 5,687,147 1 3 12 34 0.0042 0.0057 0.0073 

420490003 5 42.14175 -80.03861 PA Erie Erie, PA 280,843 1 3 12 34 0.0057 0.0153 0.0323 

420692006 5 41.44278 -75.62306 PA Lackawanna Scranton--Wilkes-Barre, PA 560,625 1 3 12 33 0.0054 0.0087 0.0115 

420710007 5 40.04667 -76.28333 PA Lancaster Lancaster, PA 470,658 1 3 12 35 0.0073 0.0175 0.0231 

420950025 5 40.62806 -75.34111 PA Northampton Allentown-Bethlehem-Easton, PA- 740,395 1 3 12 36 0.0065 0.0095 0.0152 

420990301 5 40.45694 -77.16556 PA Perry Harrisburg-Carlisle, PA 509,074 3 12 36 0.0035 0.0056 0.0084 

421010004 7 40.00889 -75.09778 PA Philadelphia Philadelphia-Camden-Wilmington, 5,687,147 1 3 12 36 0.0052 0.0071 0.0090 

421010136 5 39.92750 -75.22278 PA Philadelphia Philadelphia-Camden-Wilmington, 5,687,147 1 3 12 34 0.0038 0.0061 0.0104 421255001 5 40.44528 -80.42083 PA Washington Pittsburgh, PA 2,431,087 3 12 36 0.0050 0.0067 0.0084 

421290008 5 40.30469 -79.50567 PA Westmoreland Pittsburgh, PA 2,431,087 1 3 12 35 0.0051 0.0070 0.0097 

421330008 5 39.96528 -76.69944 PA York York-Hanover, PA 381,751 1 3 12 34 0.0058 0.0112 0.0169 

440070022 5 41.80795 -71.41500 RI Providence Providence-New Bedford-Fall Rive 1,582,997 1 3 12 36 0.0065 0.0432 0.1103 

440071010 5 41.84092 -71.36094 RI Providence Providence-New Bedford-Fall Rive 1,582,997 1 1 5 14 0.0030 0.0037 0.0051 450190046 5 32.94275 -79.65718 SC Charleston Charleston-North Charleston, SC 549,033 2 6 17 0.0019 0.0026 0.0039 

450190049 5 32.79098 -79.95869 SC Charleston Charleston-North Charleston, SC 549,033 1 3 12 36 0.0022 0.0035 0.0048 

450250001 5 34.61712 -80.19879 SC Chesterfield 3 12 36 0.0021 0.0035 0.0044 

450450009 5 34.90105 -82.31307 SC Greenville Greenville, SC 559,940 1 3 12 36 0.0026 0.0050 0.0060 

450790019 5 33.99330 -81.02414 SC Richland Columbia, SC 647,158 1 3 12 34 0.0048 0.0092 0.0122 

460990006 5 43.54429 -96.72644 SD Minnehaha Sioux Falls, SD 187,093 1 3 12 36 0.0022 0.0031 0.0052 

470370023 5 36.17633 -86.73890 TN Davidson Nashville-Davidson--Murfreesboro 1,311,789 1 3 12 34 0.0038 0.0065 0.0107 

470654002 5 35.05093 -85.12631 TN Hamilton Chattanooga, TN-GA 476,531 1 3 12 34 0.0038 0.0050 0.0071 

470931020 5 36.01944 -83.87361 TN Knox Knoxville, TN 616,079 1 3 11 29 0.0040 0.0052 0.0059 

470990002 5 35.11611 -87.47000 TN Lawrence Lawrenceburg, TN 39,926 3 12 35 0.0021 0.0030 0.0040

 2B-17 
Appendix 2B

Table 2B-8. Pb-PM2.5 monitoring site information 

site poc lat long state county_name cbsa_name cbsa_pop00 urban 

data completeness 

(complete periods) 3-year metrics 

qtrs years months annual mean max quarterly 

mean 

max 

monthly 

mean 

471570047 5 35.16895 -90.02157 TN Shelby Memphis, TN-MS-AR 1,205,204 1 3 12 36 0.0033 0.0045 0.0076 

471631007 5 36.54065 -82.52167 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 1 3 12 33 0.0031 0.0049 0.0086 

471650007 5 36.29778 -86.65278 TN Sumner Nashville-Davidson--Murfreesboro 1,311,789 1 3 12 32 0.0027 0.0051 0.0068 

480430002 5 30.36580 -103.64910 TX Brewster 3 11 20 0.0014 0.0025 0.0043 

480430101 5 29.30250 -103.16782 TX Brewster 3 12 25 0.0009 0.0018 0.0028 

481130050 5 32.77417 -96.79778 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 1 3 12 34 0.0027 0.0041 0.0055 

481130069 5 32.81995 -96.86008 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 1 3 12 36 0.0036 0.0077 0.0169 

481390015 5 32.43694 -97.02500 TX Ellis Dallas-Fort Worth-Arlington, TX 5,161,544 3 11 31 0.0029 0.0057 0.0085 

481410044 5 31.76567 -106.45523 TX El Paso El Paso, TX 679,622 1 3 12 34 0.0036 0.0060 0.0090 

481410053 5 31.75852 -106.50105 TX El Paso El Paso, TX 679,622 1 3 12 34 0.0078 0.0148 0.0236 

481670014 5 29.26332 -94.85657 TX Galveston Houston-Sugar Land-Baytown, TX 4,715,407 3 11 32 0.0021 0.0028 0.0041 

482010024 5 29.90111 -95.32694 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 12 35 0.0041 0.0066 0.0087 482010026 5 29.80250 -95.12555 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 11 31 0.0028 0.0038 0.0056 482010055 5 29.69574 -95.49924 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 11 32 0.0020 0.0026 0.0037 482011034 5 29.76799 -95.22058 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 3 11 31 0.0026 0.0073 0.0160 482011039 7 29.67005 -95.12849 TX Harris Houston-Sugar Land-Baytown, TX 4,715,407 1 2 10 30 0.0023 0.0042 0.0072 482030002 5 32.66900 -94.16745 TX Harrison Marshall, TX 62,110 3 11 32 0.0019 0.0027 0.0035 

482430004 5 30.66938 -104.02463 TX Jeff Davis 3 11 25 0.0008 0.0014 0.0028 

482450022 5 29.86395 -94.31776 TX Jefferson Beaumont-Port Arthur, TX 385,090 3 11 32 0.0019 0.0030 0.0049 

482570005 5 32.56917 -96.31583 TX Kaufman Dallas-Fort Worth-Arlington, TX 5,161,544 1 3 11 30 0.0024 0.0063 0.0128 

482730314 5 27.42694 -97.29861 TX Kleberg Kingsville, TX 31,963 3 11 29 0.0010 0.0017 0.0024 

483030001 5 33.59085 -101.84759 TX Lubbock Lubbock, TX 249,700 1 2 10 28 0.0010 0.0024 0.0062 

483390078 5 30.35030 -95.42514 TX Montgomery Houston-Sugar Land-Baytown, TX 4,715,407 3 11 32 0.0031 0.0042 0.0058 

483550034 5 27.81180 -97.46563 TX Nueces Corpus Christi, TX 403,280 1 3 12 36 0.0013 0.0021 0.0033 

483611100 5 30.19417 -93.86694 TX Orange Beaumont-Port Arthur, TX 385,090 3 11 32 0.0020 0.0028 0.0041 

490110004 5 40.90297 -111.88447 UT Davis Ogden-Clearfield, UT 442,656 1 2 10 29 0.0035 0.0059 0.0071 

490353006 5 40.73639 -111.87222 UT Salt Lake Salt Lake City, UT 968,858 1 3 12 36 0.0042 0.0077 0.0131 

490494001 5 40.34139 -111.71361 UT Utah Provo-Orem, UT 376,774 1 3 12 36 0.0034 0.0072 0.0095 

500070012 5 44.48028 -73.21444 VT Chittenden Burlington-South Burlington, VT 198,889 1 3 12 35 0.0023 0.0029 0.0037 

510870014 5 37.55833 -77.40028 VA Henrico Richmond, VA 1,096,957 1 2 8 24 0.0030 0.0042 0.0064 

511390004 5 38.66333 -78.50472 VA Page 2 8 24 0.0027 0.0045 0.0081 515200006 5 36.60778 -82.16444 VA Bristol (City) Kingsport-Bristol-Bristol, TN-VA 298,484 1 3 12 35 0.0036 0.0057 0.0083 

517600020 5 37.51056 -77.49833 VA Richmond (City) Richmond, VA 1,096,957 1 1 4 12 0.0027 0.0033 0.0064 

517700014 5 37.25611 -79.98500 VA Roanoke (City) Roanoke, VA 288,309 1 2 8 24 0.0074 0.0140 0.0283 

530330024 6 47.75333 -122.27722 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 35 0.0030 0.0046 0.0073 

530330032 6 47.54556 -122.32222 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 2 7 22 0.0078 0.0134 0.0201 

530330048 6 47.61846 -122.32972 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 33 0.0032 0.0052 0.0089 

530330057 6 47.56333 -122.33833 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 33 0.0074 0.0150 0.0260 

530330080 6 47.57027 -122.30860 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 36 0.0034 0.0055 0.0075 

530630016 5 47.66083 -117.35722 WA Spokane Spokane, WA 417,939 1 1 4 11 0.0038 0.0062 0.0087 

540390011 5 38.44861 -81.68389 WV Kanawha Charleston, WV 309,635 2 9 25 0.0026 0.0043 0.0048 

540391005 5 38.36806 -81.69361 WV Kanawha Charleston, WV 309,635 1 2 8 25 0.0043 0.0067 0.0077 

540511002 5 39.91597 -80.73406 WV Marshall Wheeling, WV-OH 153,172 1 1 6 19 0.0065 0.0081 0.0124 

550270007 5 43.43500 -88.52778 WI Dodge Beaver Dam, WI 85,897 3 12 36 0.0036 0.0059 0.0083 

550590019 5 42.50472 -87.80930 WI Kenosha Chicago-Naperville-Joliet, IL-IN-W 9,098,316 3 12 36 0.0038 0.0057 0.0073 

550710007 5 44.13861 -87.61611 WI Manitowoc Manitowoc, WI 82,887 2 8 25 0.0039 0.0060 0.0113 

550790026 5 43.06111 -87.91250 WI Milwaukee Milwaukee-Waukesha-West Allis, 1,500,741 1 3 12 36 0.0058 0.0115 0.0245 

551198001 5 45.20389 -90.60000 WI Taylor 3 12 36 0.0020 0.0030 0.0050 

551330027 5 43.02028 -88.21500 WI Waukesha Milwaukee-Waukesha-West Allis, 1,500,741 1 3 12 36 0.0097 0.0185 0.0217 

720610001 5 18.42472 -66.11639 PR Guaynabo San Juan-Caguas-Guaynabo, PR 2,509,007 1 3 12 36 0.0018 0.0026 0.0058 

780100012 5 17.71444 -64.78528 VI St Croix 1 1 5 12 0.0003 0.0007 0.0009

 2B-18 
Appendix 2B

Table 2B-9. Pb_PM2.5 monitoring site distribution statistics 

All sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 271 0.0003 0.0014 0.0019 0.0020 0.0022 0.0024 0.0026 0.0027 0.0029 0.0031 0.0033 0.0043 0.0035 0.0038 0.0040 0.0042 0.0046 0

max quarter mean 271 0.0007 0.0022 0.0028 0.0030 0.0034 0.0037 0.0039 0.0042 0.0044 0.0047 0.0052 0.0076 0.0056 0.0059 0.0063 0.0067 0.0072 0

max monthly mean 271 0.0009 0.0033 0.0040 0.0044 0.0050 0.0053 0.0057 0.0061 0.0064 0.0068 0.0073 0.0123 0.0079 0.0085 0.0089 0.0098 0.0112 0

Source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 8 0.0036 0.0036 0.0036 0.0043 0.0043 0.0053 0.0063 0.0063 0.0067 0.0067 0.0073 0.0086 0.0079 0.0079 0.0106 0.0106 0.0110 0

max quarter mean 8 0.0067 0.0067 0.0067 0.0101 0.0101 0.0107 0.0114 0.0114 0.0122 0.0122 0.0132 0.0143 0.0142 0.0142 0.0148 0.0148 0.0153 0

max monthly mean 8 0.0077 0.0077 0.0077 0.0086 0.0086 0.0136 0.0186 0.0186 0.0190 0.0190 0.0191 0.0215 0.0192 0.0192 0.0228 0.0228 0.0257 0

Not source-oriented sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 263 0.0003 0.0014 0.0019 0.0020 0.0022 0.0024 0.0026 0.0027 0.0029 0.0030 0.0032 0.0042 0.0034 0.0037 0.0038 0.0042 0.0045 0

max quarter mean 263 0.0007 0.0022 0.0027 0.0030 0.0034 0.0037 0.0039 0.0042 0.0043 0.0046 0.0050 0.0073 0.0055 0.0057 0.0061 0.0066 0.0070 0max monthly mean 263 0.0009 0.0033 0.0040 0.0043 0.0049 0.0053 0.0056 0.0060 0.0063 0.0067 0.0072 0.0120 0.0076 0.0084 0.0087 0.0096 0.0104 0

Urban sites 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 216 0.0003 0.0017 0.0020 0.0023 0.0024 0.0026 0.0027 0.0030 0.0031 0.0033 0.0035 0.0046 0.0037 0.0039 0.0042 0.0045 0.0049 0max quarter mean 216 0.0007 0.0025 0.0031 0.0035 0.0038 0.0040 0.0042 0.0044 0.0047 0.0051 0.0055 0.0082 0.0059 0.0063 0.0066 0.0071 0.0079 0

max monthly mean 216 0.0009 0.0036 0.0045 0.0051 0.0055 0.0058 0.0061 0.0063 0.0067 0.0072 0.0077 0.0135 0.0085 0.0088 0.0097 0.0104 0.0123 0

Urban sites, located in MSA's > 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 99 0.0018 0.0022 0.0025 0.0027 0.0027 0.0030 0.0031 0.0033 0.0036 0.0038 0.0041 0.0055 0.0042 0.0047 0.0048 0.0054 0.0065 0max quarter mean 99 0.0026 0.0033 0.0037 0.0039 0.0042 0.0045 0.0051 0.0054 0.0058 0.0061 0.0063 0.0093 0.0066 0.0069 0.0071 0.0083 0.0106 0max monthly mean 99 0.0036 0.0047 0.0052 0.0058 0.0064 0.0068 0.0072 0.0075 0.0078 0.0086 0.0088 0.0160 0.0099 0.0104 0.0128 0.0160 0.0185 0Urban sites, located in MSA's < 1 million population 

n min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 p

annual mean 117 0.0003 0.0013 0.0017 0.0020 0.0021 0.0023 0.0025 0.0026 0.0028 0.0030 0.0032 0.0038 0.0033 0.0035 0.0037 0.0038 0.0042 0max quarter mean 117 0.0007 0.0021 0.0026 0.0029 0.0034 0.0037 0.0038 0.0040 0.0042 0.0044 0.0046 0.0072 0.0050 0.0053 0.0057 0.0060 0.0068 0

max monthly mean 117 0.0009 0.0031 0.0037 0.0042 0.0051 0.0053 0.0056 0.0059 0.0060 0.0062 0.0064 0.0114 0.0070 0.0079 0.0086 0.0090 0.0097 0

 2B-19 
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 & asso

population. The values below were derived from the Pb-TSP dataset described in Chapter 2 and in Appendix 2.B. 

Total Northeast Southeast 

Industrial 

Midwest 

Upper 

Midwest Southwest Northwest 

S

C

Number of counties 

with monitors 

(population in those 

counties) 

► 125 (58,035) 14 (7,285) 43 (13,298) 43 (17,166) 7 (2,184) 3 (1,015) 7 (3,002) 6 

level / form 

▼

Percent of counties with a monitor not likely to meet stated standard and leve

(percent of total or regional population in counties with monitors that reside in non-meeting 

0.02, max quarterly 53% (55%) 64% (83%) 35% (29%) 72% (41%) 29% (25%) 33% (67%) 57% (78%) 67

0.02, max monthly 66% (75%) 64% (83%) 51% (58%) 86% (83%) 43% (49%) 67% (81%) 86% (78%) 67

0.02, 2nd max monthly 55% (62%) 64% (83%) 40% (32%) 74% (74%) 14% (16%) 67% (81%) 71% (78%) 50

0.05, max quarterly 39% (36%) 43% (21%) 35% (29%) 44% (15%) 14% (16%) 33% (67%) 57% (78%) 50

0.05, max monthly 45% (42%) 43% (21%) 35% (29%) 56% (25%) 29% (25%) 33% (67%) 57% (78%) 67

0.05, 2nd max monthly 38% (18%) 43% (21%) 35% (29%) 44% (15%) 14% (16%) 0% (0%) 57% (78%) 30.10, max quarterly 23% (6%) 29% (11%) 21% (6%) 30% (5%) 14% (16%) 0% (0%) 29% (31%) 0

0.10, max monthly 34% (32%) 43% (21%) 26% (23%) 40% (11%) 14% (16%) 0% (0%) 57% (78%) 50

0.10, 2nd max monthly 27% (10%) 43% (21%) 23% (6%) 30% (5%) 14% (16%) 0% (0%) 57% (78%) 0

0.20, max quarterly 17% (3%) 21% (1%) 16% (5%) 23% (4%) 0% (0%) 0% (0%) 14% (12%) 0

0.20, max monthly 23% (9%) 21% (1%) 23% (23%) 30% (5%) 14% (16%) 0% (0%) 29% (31%) 0

0.20, 2nd max monthly 20% (5%) 21% (1%) 19% (6%) 26% (5%) 14% (16%) 0% (0%) 29% (31%) 0

0.3, max quarterly 14% (3%) 7% (0%) 16% (5%) 19% (3%) 0% (0%) 0% (0%) 14% (12%) 00.3, max monthly 18% (3%) 21% (1%) 16% (5%) 26% (4%) 0% (0%) 0% (0%) 14% (12%) 0

0.3, 2nd max monthly 14% (2%) 7% (0%) 16% (5%) 21% (2%) 0% (0%) 0% (0%) 14% (12%) 00.4, max quarterly 10% (1%) 0% (0%) 14% (1%) 14% (2%) 0% (0%) 0% (0%) 14% (12%) 00.4, max monthly 14% (3%) 7% (0%) 16% (5%) 21% (4%) 0% (0%) 0% (0%) 14% (12%) 00.4, 2nd max monthly 14% (2%) 7% (0%) 16% (5%) 19% (2%) 0% (0%) 0% (0%) 14% (12%) 00.50, max quarterly 10% (1%) 0% (0%) 14% (1%) 12% (1%) 0% (0%) 0% (0%) 14% (12%) 0

0.50, max monthly 14% (3%) 7% (0%) 16% (5%) 21% (4%) 0% (0%) 0% (0%) 14% (12%) 00.50, 2nd max monthly 11% (1%) 7% (0%) 14% (1%) 16% (2%) 0% (0%) 0% (0%) 0% (0%) 0
United States Office of Air Quality Planning and Standards Publication N

Environmental Protection Air Quality Strategies and Standards Division 

Agency Research Triangle Park, NC