# Review of the National Ambient Air Quality Standards for Lead:

Policy Assessment of Scientific and Technical Information

OAQPS Staff PaperEPA-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 CarolinaDISCLAIMER

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 th e views of the Agency. Menti on 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 Of fice of Air Quality Planning and Standards (OAQPS). For the chapters on lead -related health effects, exposur e, risk, and primary standards, the principal authors include De irdre 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 chapte r 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 Deve lopment, the Office of General Counsel, the Office of Transportation and Air Quality and Region 7 also provided valuable comments.
An earlier draft of this do cument was formally reviewed by the Clean Air Scientific Advisory Committee (CASAC) and made availabl e for public comment. This document has been informed by the expert advice and comment s received from CASAC, as well as by public comments submitted by independent scientists, o fficials from State and local air pollution organizations, environmental groups, and industry groups and companies.i 

### 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 ORGANI ZATION 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 Di stribution 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 Measur ement 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 ........................................................................2-24ii 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-PM 10 .............................................................................................................. 2-53 2.3.3.1 Data Analysis Details.............................................................................2-54 2.3.3.2 Current Concentrations .......................................................................... 2-55 2.3.4 Pb-PM 2.5 ............................................................................................................. 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 Partic le-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 Ai r Quality Model........................................................2-72
2.5 POLICY-RELEVANT BAC KGROUND IN AIR .................................................... 2-74

2.6 ATMOSPHERIC DEPOSITION............................................................................... 2-75 2.6.1 Temporal Trends................................................................................................2-75 2.6.2 Deposition Flux Estimates si nce 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....................................................................................... 3-12iii 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 Ri sk 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 CU RRENT REVIEW .................................................................5-10 5.4 ADEQUACY OF THE CURRENT STANDARD.................................................... 5-12
5.4.1 Evidence-based Considerations ......................................................................... 5-14iv 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 ............................................................................................................. 5-2 5 5.5.2 Averaging Time and Form.................................................................................5-27 5.5.3 Level ................................................................................................................ 5-3 2 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 a nd Recommendations on the Primary Pb NAAQS......................................................................................................... 5-43
5.6 SUMMARY OF KEY UNCERTA INTIES 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........................................................... 6-17v 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 Co mparison to Screening Value..........................6-27
6.3.5.1 National-scale Surf ace 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 Surf ace 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 -51vi ATTACHMENT A: Clean Air Scientific Advisory Committee Le tter (March 27, 2007)

ATTACHMENT B: Clean Air Scientific A dvisory Committee Letter (September 27, 2007)

APPENDICES

Appendix 2A. Largest Stationary Source Ca tegories 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 pe rcent populations...................................................... 5A-1



### Table of Tables

Table 2-1. Trend in Pb emissi ons (tpy) from 1980 to 2002........................................................2-6 Table 2-2. Source categories em itting greater than 5 tpy of Pb in the 2002 NEI. ......................2-7 Table 2-3. Lead emissions from leaded av iation 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 wi th 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 estimat es for median total blood Pb. ...................................4-26 Table 4-2. Summary of blood Pb estimates for 95 th percentile total blood Pb......................... 4-27 Table 4-3. Summary of risk estimates for medi ans of total-exposure risk distributions. ......... 4-34 Table 4-4. Summary of risk estimates fo r 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. 95 th percentile IQ loss estimates fo r 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.........................................4-38vii Table 6-1. Models and Measurements Used for Ecological Risk Sc reening Assessment........... 19 Table 6-2. Soil Screening Values fo r Pb for Ecological Receptors............................................. 26 Table 6-3. Results of Aquatic Risk Screen - Locations at which Dissolved Pb Measurements Exceed AWQC, Excluding Mining Sites. A ............................................................... 28 Table 6-4. Concentrations of Total Pb in Sedi ments at Locations Near or Matching the 15 Sites at which Dissolved Pb Concentrations Exceeded the AWQC, Excluding Mining Sites . ..................................................................................................................... 30 Table 6-5. HQs for Soils for Primary Pb Smelter Case Study..................................................... 32 Table 6-6. HQs Calculated for Surface Waters for Primary Pb Smelter Case Study. ................. 32 Table 6-7. HQs Calculated for Sediments in Su rface Waters for Primary Pb Smelter Case Study. 33 Table 6-8. HQs Calculated for Soils for Secondary Pb Smelter Case Study. a ............................ 34 Table 6-9. HQs Calculated for Soils Near Roadway Nonurban Case Study............................... 35



### List of Figures

Figure 2-1. Principal pathways of human a nd ecological exposure to Pb. Among the policyrelevant pathways, heavy arrows indicate the predominant human exposures.........2-2 Figure 2-2. Emissions density from all Pb sources in the 2002 NEI........................................ 2-11 Figure 2-3. Emissions density from all st ationary sources of Pb in 2002 NEI......................... 2-12 Figure 2-4. Geographic distributi on of point sources with >1 t py Pb emissions in 2002 NEI. 2-14 Figure 2-5. Pb-TSP monito ring sites: 2003-2005..................................................................... 2-21 Figure 2-6. Change in the number of Pb-TSP monitoring sites from 1980 to 2005.................2-22 Figure 2-7. Airborne Pb -TSP concentrati ons, averaged across continuously operating monitoring sites: 1980-1989 and 1990-2006. (Sources: left plot - AQS data extracted 10/10/07; righ t plot – EPA 2007.).......................................................... 2-23 Figure 2-8. Distribution of Pb-T SP concentrations (represented by 6 different statistics) at the 189 Pb-TSP monitoring sites, 2003-2005.............................................................. 2-29 Figure 2-9. Percentages of Pb-TSP monitore d populations residing in areas exceeding various concentrations (for 4 different statistics), 2003-2005. ..............................2-30 Figure 2-10. Pb-TSP annual means (for all sites), 2003-2005. ................................................2-31 Figure 2-11. Pb-TSP maximum quarterly means (for all sites), 2003-2005. ...........................2-32 Figure 2-12. Maximum monthly Pb-T SP means (all sites), 2003-2005...................................2-33 Figure 2-13. Second maximum monthly Pb -TSP means (all sites), 2003-200 ........................2-34 Figure 2-14. Distribution of Pb -TSP concentrations (represente d by 4 different statistics) at the source-oriented monitoring sites, 2003-2005.............................................2-36 Figure 2-15. Distribution of Pb -TSP concentrations (represente d by 4 different statistics) at the non-source-oriented monitoring sites, 2003-2005. ....................................2-37 Figure 2-16. Distribution of Pb -TSP concentrations (represente d by 4 different statistics) at the nine monitoring sites near pr evious large emission sources, 2003-2005...2-38 Figure 2-17. Distribution of Pb-TSP annual mean concentrations at different categories of sites, 2003-2005............................................................................................... 2-39 Figure 2-18. Distribution of Pb-TSP maximum qu arterly mean concentrations at different categories of sites, 2003-2005.............................................................................2-40 Figure 2-19. Distribution of Pb -TSP maximum monthly mean c oncentrations at differentviii categories of sites, 2003-2005.............................................................................2-41 Figure 2-20. Distribution of Pb-TSP second ma ximum monthly mean concentrations at different categories of sites, 2003-2005..............................................................2-42 Figure 2-21. Medians, means, and population-wei ghted means for 4 site-level statistics.. .....2-43 Figure 2-22. Distribution of Pb -TSP concentrations (represente d by 4 different statistics) at the 140 urban monitoring sites, 2003-2005. ....................................................2-45 Figure 2-23. Distribution of Pb -TSP concentrations (represente d 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 (represente d 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 monito red populations residing in areas (represented by 4 different statistics) exceed ing various levels. ..........................................2-48 Figure 2-26. Pb-TSP monthly means at five si tes located in the Dallas, TX metropolitan area, 2003-2005.................................................................................................... 2-52 Figure 2-27. Pb-PM 10 (NATTS) monitoring sites network......................................................2-54 Figure 2-28. Distribution of Pb-PM 10 concentrations (represented by 3 different statistics) at all 28 monitoring sites, 2003-2005. .................................................................2-56 Figure 2-29. Distribution of Pb-PM 10 concentrations (represente d by 3 different statistics) at the 25 urban monitoring sites, 2003-2005. ......................................................2-57 Figure 2-30. Distribution of Pb-PM 10 concentrations (represente d by 3 different statistics) at the urban monitoring sites located in CBSAs of ≥ 1 million population, 2003-2005. ........................................................................................................... 2-58 Figure 2-31. Pb-PM 10 annual means (for all sites), 2003-2005................................................2-59 Figure 2-32. Pb-PM 10 maximum quarterly means (for all sites), 2003-2005...........................2-60 Figure 2-33. Pb-PM 2.5 (CSN) monitoring sites. .......................................................................2-61 Figure 2-34. Pb-PM 2.5 (IMPROVE) monitoring sites. .............................................................2-62 Figure 2-35. Distribution of Pb-PM 2.5 concentrations (represented by 3 different statistics) at all 271 monitoring sites, 2003-2005. ............................................................... 2-64 Figure 2-36. Pb-PM 2.5 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 dissol ved lead in surface water (N = 3445). [CD, Figure AX7-2.2.7.]...................................................................................... 2-83 Figure 2-41. Spatial distribution of total le ad in bulk sediment <63 μm (N = 1466). [CD, Figure AX7-2.2.9].................................................................................... 2-84

Figure 6-1. Overview of Ecol ogical Screening Assessment ..................................................6-181-1 

### 1 INTRODUCTION

1.1 PURPOSE This OAQPS Staff Paper, prepared by sta ff in the U.S. Environmental Protection Agency’s (EPA) Office of Air Quality Planni ng 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 qua lity standards (NAAQS) that were originally established in 1978. In this document, OAQPS staff evaluates th e 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), prepar ed 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 assessmen ts 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 approp riate to retain or revi se 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: indicator 2 , averaging time,
form 3 , and level. These elements, which together serve to define each standard, must be considered collectively in evaluating the hea lth and welfare protection afforded by the Pb standards. The information, conclusions, and st aff recommendations pres ented in this Staff Paper have been informed by comments and ad vice received from an i ndependent scientific review committee, the Clean Air Scientific Advi sory Committee (CASAC), in their reviews of
an earlier draft of this document and drafts of related te chnical 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.1-2 While this Staff Paper should be of use to a ll parties interested in the Pb NAAQS review, it is written with an expectation that the r eader 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) gove rn the establishment and revision of the NAAQS. Section 108 (42 U.S.C. 7 408) directs the Administrator to identify and list each air pollutant that “in his judgment, cause or contri bute to air pollution which may reasonably be anticipated to endanger public he alth and welfare” and whose “pre sence . . . in the ambient air
results from numerous or diverse mobile or statio nary sources” and to i ssue 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 eff ects 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 fro m 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 li sted under section 10 8. 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 de fined in Section 109(b)(2), must “specify a level of air quality the attainme nt and maintenance of which, in the judgment of the Administrator, based on criteria, is requisite to protect the public we lfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the ambient air.” 5 The requirement that primary standards incl ude 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 repres entative 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, anim als, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to transportation, as well as ef fects on economic values and on personal comfort and well-being.”1-3 information available at the time of standard set ting. It was also intended to provide a reasonable degree of protection against hazards th at 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 select ing primary standards that include an adequate margin of safety, the Administrator is seeking no t only to prevent pollution levels that have been demonstrated to be harmful but also to prev ent 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 safet y, EPA considers such factors as the nature and severity of the health effects involved, the size of the sensit ive population(s) at risk, and the kind and degree
of the uncertainties that must be addressed. The selection of any pa rticular 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 a nd 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 th e 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 th e national ambient air quality st andards . . . and shall make
such revisions in such criteri a and standards and promulgate such new standards as may be appropriate . . . .” Section 109(d)(2) requires th at an independent scie ntific review committee “shall complete a review of th e criteria . . . and the national primary and secondary ambient air quality standards . . . and sha ll recommend to the Administrato r any new . . . standards and revisions of existing criteria and standards as ma y be appropriate . . . .” Since the early 1980's, this independent review function has been pe rformed 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 primar y 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 ( μ g/m 3 ), measured as Pb in tota l suspended particulate matter (TSP), not to be exceeded by the maximum arithmetic mean concentration averaged over a1-4 calendar quarter. This sta ndard 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 1 990 Supplement (USEPA, 1990a). As part of the review, the Agency designed and performe d human exposure and health risk analyses (USEPA, 1989), the results of which were pres ented 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/m 3 , and suggested the second highest monthly average in three years for the form and averaging time of the standard (USEPA, 1990b). After consider ation of the documents developed during the review and the significantly chan ged 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 implemen ting this strategy, the Agency focused efforts primarily on regulatory and remedial cl ean-up actions aimed at reducing Pb exposures from a variety of nonair sources judged to pose more extensiv e 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 informa tion (69 FR 64926). A project work plan (USEPA, 2005a) for the preparation of the CD was rel eased in January 2005 for CASAC and public review. EPA held a series of workshops in August 2005, with invited r ecognized scientific expe rts to discuss initial
draft materials that dealt with various lead-relat ed issues being addresse d 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 CA SAC and public review in December 2005 and discussed at a CASAC meeting he ld on February 28-March 1, 2006. A second draft CD (USEPA, 2006b) was releas ed 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 sc ientific 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 reviewing1-5 the air quality criteria, developing human exposur e and risk assessments and an ecological risk assessment, preparing a policy assessment, and de veloping the proposed and final rulemakings. In May 2006, EPA released for CA SAC 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 disc ussed two assessment phases: a pilot phase and a full-scale phase. The pilot phase of both th e human health and ecological risk assessments was presented in the draft Lead Human Exposure and Health Ri sk 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 releas ed for CASAC and public review in December 2006. The first draft Staff Paper, also rele ased 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 publ ic 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 (Henders on, 2007a) and the public, staff conducted full-scale human exposure and health risk assessment s, although no further work was done on the
environmental assessment due to resource limitati ons. 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 co mments (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 Repor t (USEPA, 2007b) and discussed in this Staff Paper. The schedule for completion of this review is governed by a judici al 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 gove rns 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 revi ew of the lead NAAQS was initiated, EPA has adopted a new process for reviewing NAAQS that eliminates issu ance 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 Pape r consistent with the judicial1-6 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 ORGANI ZATION OF THE DOCUMENT The policy assessment in this Staff Paper doc ument is based on staff’s evaluation of the policy implications of the scientific evidence re viewed 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 presen ted in Chapter 2 and includes information on Pb properties, current Pb air quality patterns, historic tre nds, 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 in cludes information on Pb in medi a other than air including outdoor dust, soil, surface water and sediment. This chap ter 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 co nclusions from the CD, and an examination of issues related to the quantitative assessment of h ealth 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 di scussion 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 eviden ce 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 c onclusions and policy recommendations on the1-7 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.1-8 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 Expo sure and Health Risk Assessments. September 27, 2007.

ICF International. (2006). Lead Human Exposure and Hea lth Risk Assessments and Ecol ogical Risk Assessment for Selected Areas. Pilot Phase. Draft Technical Report. Prepared for the U.S. EP A'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 Tr iangle 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 effe cts on cardiovascular functi on, early development, and stature: an addendum to U.S. EPA Air Quality Criteri a for Lead (1986). In: Air quality criteria for lead, v. 1. Research Triangle Park, NC: Office of Health an d 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).1-9 U.S. Environmental Protection Agency. (2005a) Project Work Plan for Revised Air Quality Criteria for Lead. Research Triangle Park, NC: Nati onal Center for Environmental Assessment-RTP Report no. NCEA-R1465. CASAC Review Draft.

U.S. Environmental Protection Agency. (2005b) Air Qualit y 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 Qu ality Criteria for Lead. Washington, DC, EPA/600/R5/144aF. Available online at: www.epa.gov/ncea/

U.S. Environmental Protection Agency. (2006b) Air Qualit y 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 Re view 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 Huma n Exposure and Health Risk Assessments for Selected Case Studies (Draft Report) Volume I. Human Expo sure and Health Risk A ssessments – Full-Scale and Volume II. Appendices. Office of Air Quality Planni ng and Standards, Resear ch Triangle Park, NC. EPA452/D-07-001a and EPA-452/D-07-001b.

U.S. Environmental Protection Agency. (2007b) Lead: Huma n Exposure and Health Risk Assessments for Selected Case Studies, Volume I. Human Exposure and Hea lth Risk Assessments – Full-Scale and Volume II. Appendices. Office of Air Quality Planning and Stan dards, Research Triangle Park, NC. EPA-452/R-07- 014a and EPA-452/R-07-014b.2-1 

### 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 o ccur 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 concentra tions in ambient air” (CD, p. 3-1). Inhalation exposures can result from Pb emitte d to the ambient air recently or from Pb emitted in the past that has deposited from air to soil or dust and then become resuspended in the ambient air. Further, Pb emitted into the am bient air can contribute to ingestion exposures (associated with indoor dust, outdoor soil/dus t, agricultural products and surface water) of recently deposited Pb and of Pb that was deposited in the past. Consequently, exposure to Pb is multipathway, and we are considering both airborne Pb, as it contributes to exposures through direct inhalation of particles cont aining Pb, and also Pb that has de posited 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 resu spended in the air, co ntributing to inhalation exposures or, upon redeposition, to ingestion expo sures. Thus, as illustrated in Figure 2-1, pathways that are directly relevant to a revi ew 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, whic h are collectively referred to as “policy-relevant sources”. Due to limited data, models, and time availabl e, however, we are not able to fully and completely characterize all of th e 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 syst ems (see CD, Sections 3.3 and 3.5). For purposes of this review, the Pb from these nonair sources is co llectively 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., pol icy-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).2-2 Newly emitted Pb Historically emitted Pb

Air Outdoor Soil, Dusts

Indoor Dust Paint

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

Non-air Pb releases Policy-relevant Background*

Human Exposures -Inhalation & ingestion Diet**

Ecological Exposures Drinking water

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

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

Figure 2-1. Principal pathways of human and eco logical 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 ex ists in the environment predominantly in solid form. Consequently upon emission into the air, Pb deposits onto surf aces 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 in troduced by anthropogenic activities include oxides, chloride s (or other halides), sulfates, a nd sulfides (see CD, Table 2-5). A more complete discussion of th e 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 Sectio n 2.3.1 for discussion of collection and analysis methods). The relative presence of Pb among the va rious environmentally occurring compounds
influences its distribut ion within the environment, and the relative bioavailability of these2-3 compounds has implications for human and ecolo gical exposures and ri sks (CD, Sections 4.2.1, 8.1.3 and 8.2.3). With regard to human exposures and risk, this is refl ected in the exposure modeling described in Chapter 4. Lead speciation and bi oavailability are disc ussed 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 atmo sphere and undergo secondary dispersal via the deposition and resuspension of particles containi ng Pb. As described in the CD (Section 2.3.1), airborne Pb particles generally have a bimodal distribution with the grea ter mass of Pb found in the fine fraction (CD, p. 2-52). Since small part icles 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 ar e, 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 char acteristics and meteor ological conditions. Important meteorological factor s influencing dispersion incl ude wind speed, surface roughness, inversion frequency, inversion dur ation, and temperature. Results are site specific. For long range transport modeling, Lagrangian trajector y 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. Retros pective air mass trajecto ries 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 or iginating from emissions in seve ral 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 ab sence 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 depos ition, it is assumed that the dry deposition flux is directly proportional to the lo cal concentration of the pollutant species, at some reference height above the surface (e.g., 10 m or less), multip lied 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. Estim ates of dry deposition velocity constitute the2-4 primary output of a large number of dry depositi on models that have been developed during the past ten years and most of thes e rely on so-called “resistance sc hemes”. The advantage of this deposition velocity representation is that all th e complexities of the dry deposition process are bundled in a single parameter, but the disadvantag e is that because this parameter addresses a variety of processes, it is difficu lt to specify properly. A large range of Pb deposition velocities (0.05 to 1.3 cm/s) has been reported (C D, pp. 2-55 to 2-57 and Table 2-21). Wet deposition, or the delivery of a pollutant to the ground in precipita tion, is the process by which airborne pollutants are scavenged by pr ecipitation and removed from the atmosphere. The flux of a depositing species can be defined as the product of th e rate of precipitation and the concentration of the chemical species in the pr ecipitation (CD, pp. 2-57 to 2-59). Wet deposition is affected by: 1) nucleation scavenging (rem oval by direct incorporation into new cloud droplets); 2) in-cloud scavenging (removal by incorporation into ex isting cloud droplets); and 3) precipitation washout (removal by ra in 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, beyo nd the influence of individual sources, is found primarily in the submicron size range, and c onsequently 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 was hout 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 particle s 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 resu spended 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 traffi c, construction and agricultural operations, and to a lesser extent, the wind. Understanding the physics of resuspension from natural winds requires analyz ing 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 moveme nt due to three proces ses: 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 comp licate emissions estimates.2-5 2.2 SOURCES AND EMISSIONS TO THE ATMOSPHERE

In this section we describe the available in formation 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 thei r 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 sin ce 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 (regardle ss of the compound in which it may have been emitted) and estimates specific to the Pb compoun ds 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 we ight of Pb (as compared to elements with
which it is associated in Pb compounds), however, reduces th e impact of this reporting inconsistency.
Historical trends in emissions are descri bed in Section 2.2.1, information on the various types of Pb sources is presented in Secti on 2.2.2, the number and geographic distribution of sources is discussed in Section 2.2.3 and the larg er Pb point sources ar e 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 emi ssions from the fuel combustion, industrial process and solid waste sectors from 1980 throug h 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 approximatel y 1,700 tpy) between 1990 and 2002 (USEPA 1990; USEPA, 2007a). The greatest emission reducti ons were from mobile sources, primary and secondary Pb and copper smelting, pulp and paper manufacturing, inorganic paint pigment production and steel wire products. As discusse d in the CD (Section 2.2.4), reductions in Pb
emissions from mobile sources include some asso ciated with the latter pe riod of the “phase-out”2-6 of leaded gasoline. From a national inventory pe rspective, the stationary source categories that have the largest emissions in the 2002 NE I 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

- 491 a 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 NE I (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. Th e main sources of emissions in the 2002 NEI are comprised primarily of combustion-related emissi ons and industrial process-related emissions.
Point source emissions account for about 66% of th e 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 gr ouped into descriptive categories. Presence and relative position of a s ource category on this li st does not necessarily provide an indication of the si gnificance 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 hu man and ecological impacts, are included in the short descriptions of the largest stationary s ource categories presente d in Appendix 2A. The relative sizes of stationary sour ces represented in the NEI and th e geographic distribution of the larger sources are presented in Sections 2.2.2.1 and 2.2.3, respectively.2-7 Table 2-2. Source categories emitting greater th an 5 tpy of Pb in the 2002 NEI.

Source Category Description Total Emissions (tpy) a
ALL CATEGORIES b 1,697 b
Mobile sources 491 c Industrial/Commercial/ Instituti onal Boilers & Process Heaters 190 Utility Boilers 168 d
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 wi ll be reflected in version 4, estimated for 2008 release. d This estimate of 168 tons, which is based on the 2002 NE I, has uncertainties and differs from estimates in some other studies and inventories. For ex ample, the estimated lead emissions r eported 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.2-8 2.2.2.2 Mobile Sources

Thirty-five years ago, combusti on of leaded gasoline was the main contributor of Pb to the air. In the early 1970s, EP A set national regulations to gra dually reduce the Pb content in gasoline. In 1975, unleaded gasoli ne was introduced for motor vehi cles equipped with catalytic converters. EPA banned the use of leaded gaso line 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. Ve hicles 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 Nati onal Association for Stock Car Auto Racing
( NASCAR) with the goal of permanently removing al kyl-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 a nd 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). I nventory 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). Emi ssion 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 lead ed 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), resp ectively (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 calcula ted 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.2-9 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 flig ht (i.e., not limited to the landing and take-off cycle and local operations). EPA is using this a llocation 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 ai rcraft into the NEI. EPA understands that allocating lead emissions to airports from opera tions 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 loss es 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. Le ad emissions from fuel storage and distribution are estimated to total 0.3 tons nationally and are in cluded in the NEI, but not assigned to specific airports. Data regarding military piston engine ai rcraft emissions are supplied to EPA by states. The 2002 version 3 inventory estimates for this ca tegory did not include st ate-submitted data, but future updates to the NEI will include these estimates. These current NEI estimates provide a valuab le comparison with other ambient sources of Pb. Future upgrades to these estimates and assessments specific to in dividual airports could include more refined local data including char acteristics of local operations (e.g., landings and take-offs), Pb retention in piston engine s, and fuel consumption rates. Among the airports in the 2002 NEI where pist on-engine aircraft ope rate, approximately one percent of US airports listed have estimated Pb emissions of greater than one ton per year, a greater percentage has estimated Pb emissions between one ton and 0.1 ton per year, while the majority of airports are estimated to have Pb emissions less than 0.1 ton per year. Table 2-3 below demonstrates these estimated emission ranges.

Table 2-3. Lead emissions from leaded aviation gas use in the 2002 NEI version 3.

Emissions Range (tpy) Number of Airports Total Emissions (tpy) < 0.1 2,104 76.7 0.1 to 1.0 1,270 367.5 > 1 36 47.1 Summary 3,410 491.32-10 2.2.2.3 Resuspension of Previously Deposited Pb and other Sources

Resuspension of soil-bound Pb particles and c ontaminated 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 in cluded in the 2002 NEI. Studies of emissions
in southern California in dicate 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 gene ral, the main drivers of particle resuspension are typically mechanical stressors such as ve hicular traffic, constr uction 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 no table emissions source if or when such soil is disturbed (e.g., road widening or building construction). Understanding the physics of resuspension fr om natural winds re quires analyzing the wind stresses on individual partic les and although this analysis can be accurate on a small scale, predicting resuspension on a large scale generally focuses on em pirical data for soil movement due to three processes: saltati on, 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 ot her factors conducive to re suspension may dominate annual averages of upward flux (CD, p. 2-65). All of th ese factors complicate emissions estimates (CD, Section 2.2.1) such that quantitat ive estimates for these processes remain an area of significant uncertainty. Other sources not currently included in the NE I are emissions of Pb from natural sources, such as wind-driven resuspension of soil with na turally occurring Pb, s ea 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 rema in 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 em itting counties scattered pr edominantly within a
broad swath from Minnesota to sout hern New England and southward.2-11 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 wi th activities estimated to occur with some frequency within an “area” such as a county (area so urces) or with mobile sources (see Section 2.2.2.2). Emissions from all stationary sources represented in th e NEI are presented in Figure 2-3, in terms of emissions density (tons per ar ea, square mile, per county).2-12 Figure 2-3. Emissions density from all stationary sources of Pb in 2002 NEI.

There are some 13,067 point sources (industrial, commercial or institu tional) 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 ap proximately 1,300 point sources of Pb in the NEI with estimates of emissions greater than or e qual 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.2-13 Table 2-4. Size distribution of point sources with in the 2002 NEI and associated estimated emissions.

Emissions Range (tpy) Number
of Sources Total
Emissions (tpy) Average
Emissions per Source (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 categor ies 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 s ources estimated to emit greater than 1 tpy is presented in Figure 2-4.2-14 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 f acilities with emissions estimated to be greater than or equal to 5 tons per year (see Tabl e 2-4). Most of these sources (Table 2-5) are metallurgical i ndustries, followed by waste dispos al facilities and manufacturing

processes.2-15 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 Refining b 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 M anufacturing TN Madison County 10.9 Military Installation PA Franklin County 10.4 Hazardous Waste Incineration AR Union County 10.2 c 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 Wast e 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 Wast e 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 fo r “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 th e 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 prep aration of technical support information for EPA’s hazardous air poll utant 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/2002in ventory.html). The information presented in this document is based on version 3.2-1 6 The process of identifying sour ces that emit Pb into the ai r has been ongoing since before the Clean Air Act of 1970. The comprehensivene ss 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 informa tion to the inventory. As noted above, the NEI relies on information that is av ailable 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 em issions inventory. First, the categories emitting Pb must be identified. Second, the sources’ pr ocesses and control devices must be known. Third, the activity throughputs and operating schedules of these s ources must be known. Finally,
we must have emission factors to relate em issions to the operati ng throughputs, process conditions and control devices. The proce ss, control device, throughputs and operating schedules are generally available for each sour ce. 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 pr ovides source-specific information. In others,
emissions factors must be estimated from sim ilar sources or source categories or other
information. More information on emission factor s and the estimation of emissions is found in the introduction to EPA’s Compilation of Air Po llutant Emissions Factors (USEPA, 2006a).
Further information on emission factors is avai lable at: http://www.ep a.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 burni ng 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 th e 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 extern al review, includi ng a review of the process for developing the invent ory which includes extensive quality assurance and quality control steps (QA/QC). For example, we creat ed a QA/QC process and tracking database to provide feedback reports to point source data pr oviders at regular intervals during the QA of the data. The feedback report s 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 invent ory 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 fa cilities. 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-2006-0859.2-1 7 In summary, generic limitations to the 2002 NEI include the following:

• Consistency: The 2002 NEI for Pb is a com posite of emissions estimates generated by state and local regulatory agencies, in dustry, 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 so urce types, method of reporting compound classes, level of deta il, and geographic coverage.

• Variability in Quality and Accuracy of Em ission Estimation Methods: The accuracy of emission estimation techniques varies with pol lutants and source categories. In some cases, an estimate may be based on a fe w or only one emission measurement at a similar source. The techniques used and qua lity of the estimates will vary between source categories and between area, major, and mobile source sectors. Generally, the more review and scrutiny given to emissi ons 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 atmo sphere 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. Th e following sections describe the ambient Pb measurement methods, the sites and networks wh ere 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 μ m in diameter (PM 2.5 ), and PM less than or equal to 10 μ m in diameter (PM 10 ). The networks include the Pb TSP network, the PM 2.5 Chemical Speciation Network (CSN), the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, and
the National Air Toxics Trends Stations (NATTS) network. Th e 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 concentratio ns 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/tt n/airs/airsaqs/. In an effort to gather as much air toxics data, including Pb, into one da tabase, the EPA and State and Territorial Air Pollution Program Administrators and the Associa tion of Local Air Pollut ion 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/

.2-18 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 ti me (typically 24 hours) a nd the PM is collected on a suitable filter media. After the sample ha s 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 dete rmine the Pb concentration in the particulate material. The concentration of Pb found in the atmosphere, in μg/m 3 , is calculated based on the
concentration of Pb in the volum e extracted, the size of the collec tion filter, and the volume of air drawn through the filter. The primary factors affecting the measur ements 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 desi gns have been develope d 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 am bient Pb measurements including TSP, PM 2.5 , PM 10 . The TSP inlet is designed to allow as much suspe nded particulate into the sampling device as possible while protecting against precipitation an d 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. Therefor e, 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 propor tional 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 m 3 /hr of air through an 8 inch by 10 inch filter (0.05 m 2 filter area). Low-volume sa mplers typically draw 1 m 3 /hr through a 47 mm diameter filter (0.002 m 2 filter area). Currently all Fe deral Reference Method (FRM) and Federal Equivalence Method (FEM) for Pb -TSP are based on high-volume samplers.2-19 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 ambi ent metals (including Pb) are currently being explored which would allow for more frequent sa mpling (as frequent as 1 sample per hour), but
much more work is needed on these methods befo re they can be deployed in a network setting. More frequent sampling reduces the uncertain ty 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 fr equency. A discussion of the obs erved temporal variation of Pb measurements is given later in this section.

2.3.1.4 Sample Analysis

After the samples have been collected on filte rs 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 fluores cence analysis (XRF), proton-induced x-ray emission (PIXE), neutron activa tion analysis (NAA), atomic ab sorption (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 analyt ical methods. A search conducted on the AQS
database 2 shows that the method detection limits for all of these analytic al methods (coupled with the sampling methods) are very low, ranging from 0.01 μ g/m 3 to as low as 0.00001 μg/m 3

, and are more than adequate for determining compliance with the current NAAQS.

2.3.2 Pb-TSP

This network is comprised of state and lo cally managed Pb monitoring stations which measure Pb in TSP, i.e., part icles up to 25 to 45 microns. Th ese 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, Sec tion 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” sample s by analyzing several consecutive samples together to save costs and/or increase detection limits.

2 EPA’s AQS can be accessed at h ttp://www.epa.gov/ttn /airs/airsaqs/2-20 2.3.2.1 Monitor Locations

The locations of Pb-TSP sites in operati on between 2003 and 2005 are shown in Figure 2-5. State and local agencies are required to ope rate two Pb-TSP monitors in any area which has exceeded the NAAQS in the last two years (40 CF R 58 Appendix D). State and local agencies have the latitude to operate more monitors be yond 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
smelters 3 ) 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 unde restimating the extent of occurrences of relatively hi gher Pb concentrations. Addi tionally, none of the 189 Pb-TSP sites included in the 2003-2005 analysis desc ribed 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.2-21 Figure 2-5. Pb-TSP monitoring sites: 2003-2005.

The number of sites in the Pb-TSP network has decreased signifi cantly since the 1980s (see Figure 2-6). The number of sites in th e network reached its highest point in 1981 (946 sites). About 250 sampling sites operated duri ng 2005. This decline in the number of Pb-TSP
sites is attributable to the dramatic decrease in Pb concentrations obser ved since the 1980s and
the need to fund new mon itoring objectives (e.g., PM 2.5 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 PM 2.5 and ozone.2-22 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 St ates have fallen dramatically over the last 30 years due largely to the phase -out of leaded gasoline additiv es. Figure 2-7 shows national
trends in airborne TSP Pb concentrations fo r 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 th e second for 1990 through 2006 (re presenting 44 sites). Two separate graphs were used to characterize the overall (1980 th rough 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 curr ent NAAQS of 1.5 μ g/m 3 (maximum quarterly average). The monitors used in this analysis are typica lly population-oriented urban monitors that are not source-oriented. The left plot shows an 86 percent decrease in na tional average maximum quarterly means from 1980 to 1989 and the right plot shows a 54 percent de crease in the same statistic from 1990 to 2006. The combination of these equate s to an overall declin e 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 952-23 percent decrease in the same metric from 1980 through 2006. Since the early 1980’s, major declines over several orders of magnitude have be en 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% decreaseConcentration (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 (param eter code 12128, durations ‘7’ and ‘C’ ) were extracted from EPA’s AQS on May 22, 2007. Mo st 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) a nd 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 incl usion in this national Pb-TSP characterization. The non-FRM/FEM Pb-TSP methods typically have lower reported uncerta inties 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, sandblas ting); 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.2-24 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 nati onal characterization. Th e completeness criteria employed for this national charact erization were: 1) a minimum of 10 observations per quarter, 2) for at least one full year (a ll 4 quarters), and 3) at least 9 months with 4 observations each 5 ; all
three criteria had to be met for inclusion. 209 mo nitors met the three-pron ged criteria; of these 209 monitors, 20 were collocated with another comp lete monitor. Only one monitor from each collocated pair (i.e., from each site location) was kept in the analysis, sp ecifically the one with highest 3-year maximum quarterly mean. Thus, da ta from 189 monitors at 189 distinct locations were actually used; 109 of these monitors/sites ha d 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 we re used, even if they did not correspond to the complete years. The 18 9 sites have an average of about 10 complete quarters and 28 complete months. The 189 utiliz ed monitors are listed along with various summary and demographic data in Appe ndix 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 Co re Based Statistical Area (CBSA) per 2003 CBSA geographic definitions. CBSA is a collectiv e term for both metropolitan and micropolitan
statistical areas. A metro area contains a co re urban area of 50,000 or more population, and a
micro area contains an urban core of at le ast 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 co unties that have a high degree of social and economic integration with the urban core. Th e monitors in the anal ysis map to 65 unique CBSA’s. Only 10 of the 189 monitors are not lo cated 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) ar e found in counties not defined as (or part of) a CBSA, all of the 140 urba n 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/m 3 for quarterly mean).2-25 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 m onitoring sites are gene rally 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 percen t 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 “sour ce oriented” with regard to sources of Pb emissions if: 1) they met a graduated (or slid ing 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-ori ented sites, 40
met the first criterion and 51 met the second. The graduated cumulative emission ton per ye ar to distance crit erion (criterion #1) utilized the 2002 (version 3) nati onal emission inventory (NEI) for Pb point sources and Pb area nonpoint sources. The Pb point so urce emissions were assigned to the specific facility point locations (longitude/latitude coordinates), and the area nonpoin t inventory was allocated to
Census tracts and based on an assumed uniform di stribution 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 cumu lative 0.3 tpy, ..., or within 1.0 miles of a cumulative 1.0 tpy). The area nonpoint contributi on to the comparison cu mulative inventory was based on the composite emission densities of the Ce nsus tract in which a s ite was located and all other tracts with population centroids with in 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 lis t has been utilized in recent EPA Trends Report analysis. The list encompa sses 114 sites. Many of the monitoring sites on
this list did not have data that met the da ta completeness criteria for 2003–2005 because they have permanently discontinued Pb monitoring, most ostensibly because the associated nearby Pb emission source(s) has implement ed controls, closed operations , and/or reduced production. Some ambient monitoring sites continue monitori ng even after significan t reported reductions in nearby new Pb emissions. Sites were not screen ed out of the source-orie nted classification in those instances. In addition to including such sites in the source-orient ed category, these sites were separately reviewed to see if they sti ll had higher concentrati ons than nonsource sites because of previously emitted Pb becoming resuspended into the air and/or possible emission2-2 6 estimate errors. These sites are termed, “previous source-oriented sites” in relevant figures and
tables. There are only nine sites that were categor ized 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 st atus 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 b een undertaken. For the monitoring sites associated with this source, two sets of sta tistics were generated (or attempted). Statistics representing the entire 3-year period were calcu lated and used everywhere applicable except for the “previous” category, and st atistics representing the post- production period (2004-2005) were generated and used for the “previous” classificatio n. 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 si tes included in the “previous” source oriented classification and the uncertainty in the emission s ource status, results for this category should be
viewed with caution.

2.3.2.3.4 Population Associations

Two population statistics were su mmarized 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 assi gned to sites were based on Census block group population densities, specifically the density of the bloc k 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 gr oup definitions utilized are from
the 2000 Census. The median size of populations a ssociated with the Pb-TSP mon itors in this analysis is about 6,200 and the corresponding u nder age 5 median population is around 420. These median populations are slightly smaller than the overall U. S. block group median ra dial mile populations (19 percent smaller for total and 7 percent sma ller for under age 5). Appendix 2B, Table 2B-1 shows the assigned site-level popul ations; CBSA information for each site is also shown. Based on the radial mile population association (des cribed above) approximately 1.73 million people (0.125 million under the age of 5) are in prox imity of a 2003-2005 Pb-TSP monitor included in this analysis.2-2 7 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 m onthly 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 overa ll 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 m ean statistic is actually the av erage 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 mea n. Annual means were computed from quarterly means. The 3-year maximum quarterly mean sta tistic represents the highest quarterly mean of the complete ones (sites have from four to 12 complete quarters), a nd 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 sec ond 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 th ree overall highest monthly means for the 3-year period (year nonspecific), and 2) the averag e of the annual maximum monthly means. Population weighted means were also calcula ted for the four basic metrics for various aggregation levels. The site-l evel means were weighted by to tal 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 th e 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 m onitor are uniform over the entire radial mile.

2.3.2.4 Current Concentrations

In the following subsections, analyses are pres ented for the different categorizations of Pb-TSP monitoring sites described above. Thes e categories include “al l Pb-TSP sites meeting screening criteria”, and the following subsets: sites in urban areas, si tes in urban areas of population greater than 1 million, sites that are so urce-oriented, sites that are not known to be source-oriented, and sites that we re 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 mo nthly mean, and second maximum monthly2-28 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 5 th and 95 th percentiles, and asterisks identify composite averages. Additional points on the distributions for these statistics are given in Appendix 2B, Table 2B-3. For example, th e national composite average annual mean was
0.09 μ g/m 3 , and the corresponding median annual mean was 0.02 μ g/m 3 . The national composite average maximum quarterly mean was 0.17 μ g/m 3 and the corresponding median maximum quarterly mean was 0.03 μ g/m 3 . The national composite aver age maximum monthly mean was
0.31 μ g/m 3 and the median maximum monthly mean was 0.04 μ g/m 3 . The national composite average second maximum monthly mean was 0.21 μ g/m3 and the median value was 0.03 μ g/m 3

. The national composite average of the mean of the three overall highest monthly averages was
0.31 μ g/m 3 and the median value was 0.04 μ g/m 3 . The national composite average of the mean of the annual highest monthly means was 0.21 μ g/m 3 and the median value was 0.03 μ g/m 3

.2-29 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

2 nd max monthly

mean Average of 3 overall max monthly means Average of annual max monthly

means

Figure 2-8. Distribution of Pb-TSP concentrations (r epresented by 6 different statistics) at the 189 Pb-TSP monitoring sites, 2003-2005.

Figure 2-9 shows cumulative percentages of to tal monitored populations associated with each of the four Pb metrics for various levels [> 0.02 μg/m 3 , > 0.05 μg/m 3 , > 0.20 μg/m 3 , > 0.50 μg/m 3 , and > 1.54 μg/m 3 ]. Note that site statistics were rounded to 2 decimal places before comparing to stated levels. The phrase “monito red 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 (annua l average, maximum quarterly mean, maximum quarterly mean, and second maxi mum 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 gene rally the same. In fact, there are significant correlations among all four 3-ye ar (2003-2005) summary metrics; see Appendix 2B, Table 2B-4.2-30 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% 10% 20% 30% 40% 50% 60% 70%

> 0.02 > 0.05 > 0.20 > 0.50 > 1.54
μg/m 3percent of monitored population annual mean max quarterly mean max monthly mean 2nd max monthly mean

Figure 2-9. Percentages of Pb-TSP monitored popul ations residing in areas exceeding various concentrations (for 4 di fferent statistics), 2003-2005.2-31 

### ≥ 1.55 0.50 – 1.55 0.20 – 0.50 0.05 – 0.20 < 0.05 Concentration range (μg/m 3 )



### ≥ 1.55 0.50 – 1.55 0.20 – 0.50 0.05 – 0.20 < 0.05 Concentration range (μg/m 3 )

Figure 2-10. Pb-TSP annual 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/m 3 )



### ≥ 1.55 0.50 – 1.55 0.20 – 0.50 0.05 – 0.20 < 0.05 Concentration range (μg/m 3 )

Figure 2-11. Pb-TSP maximum quarterly me ans (for 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/m 3 )



### ≥ 1.55 0.50 – 1.55 0.20 – 0.50 0.05 – 0.20 < 0.05 Concentration range (μg/m 3 )

Figure 2-12. Maximum monthly Pb-TSP means (all sites), 2003-2005.2-34 

### ≥ 1.55 0.50 – 1.55 0.20 – 0.50 0.05 – 0.20 < 0.05 Concentration range (μg/m 3 )



### ≥ 1.55 0.50 – 1.55 0.20 – 0.50 0.05 – 0.20 < 0.05 Concentration range (μg/m 3 )

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 ma ximum monthly mean to annual mean are presented in Appendix 2B, Table 2B-5. For al l TSP-Pb sites included in the analysis, the
national median for the ratio of site-level maxi mum 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 th e ratio of site-level second maximum monthly mean to site-l evel annual mean was about 2.1.

2.3.2.4.1 Source-oriented Sites

As seen in the previously discussed Figur e 2-8, the national (“all sites”) means are substantially higher than the na tional medians for all four statistical metrics (annual mean, maximum quarterly mean, maximum monthly me an, 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.2-35 Eliminating the source-oriented monitors from the national aggregations lowers most of the corresponding distribution statistics and makes th e means more comparable to the medians.
The distributions of th e site-level metrics for the sour ce-oriented sites, the non-sourceoriented sites, and the “previous ” source-oriented sites, are pr esented in Figures 2-14, 2-15, and 2-16, respectively. For comparison purposes, Fi gures 2-17 through 2-20 present the categorical
data distributions for each of the four statisti cal metrics on the same scales. In all of these figures, the boxes depict inter-quartile range s and medians, whiskers depict the 5 th and 95 th percentiles, and asterisks identify composite aver ages. Additional points on the distributions of these statistical metrics for these three categorie s of monitoring sites ar e 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 pr esented in Figure 2-21 for the so urce-oriented and other groupings of monitoring sites. Per Figure 2-18, the median maximum quarter ly mean for source-oriented sites (0.25 μg/m 3 ) is about 14 times greater than the same statistic for non-sour ce-oriented sites (0.02 μg/m 3 ); in fact, the median (50 th percentile) maximum quarterly mean for non-source-oriented sites is approximately the same value as the 5 th percentile for source-oriented sites. Almost 95 percent of all monitors identifie d as being source-oriented had a maximum quarterly average of
0.02 μ g/m 3 or more, and over 25 percent had maximum quarterly average of 0.50 μ g/m 3 or more.2-3 6 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

2 nd max monthly

mean

Figure 2-14. Distribution of Pb-TSP concentrations (r epresented by 4 different statistics) at the source-oriented monitoring sites, 2003-2005.2-3 7 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

2 nd max monthly

mean

Figure 2-15. Distribution of Pb-TSP concentrations (r epresented by 4 different statistics) at the non-source-oriented monitoring sites, 2003-2005.2-38 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

2 nd max monthly

mean

Figure 2-16. Distribution of Pb-TSP concentrations (r epresented by 4 different statistics) at the nine monitoring sites near p revious large emissi on sources, 2003-2005.2-39

Concentration (μg/m 3 )All sites(189 sites)Sourceoriented(60)Not sourceoriented(129)Previoussourceoriented (9)Urban(140)Urban CBSApop.≥1M(91)Urban CBSApop. < 1M(49)Figure 2-17.Distribution of Pb-TSP annual mean concentrations at different categories of sites, 2003-2005.2-40

Concentration (μg/m 3 )All sites(189 sites)Sourceoriented(60)Not sourceoriented(129)Previoussourceoriented (9)Urban(140)Urban CBSApop.≥1M(91)Urban CBSApop. < 1M(49)Figure 2-18.Distribution of Pb-TSP maximum quarterly mean concentrations at different categories of sites, 2003-2005.2-41

Concentration (μg/m 3 )All sites(189 sites)Sourceoriented(60)Not sourceoriented(129)Previoussourceoriented (9)Urban(140)Urban CBSApop.≥1M(91)Urban CBSApop. < 1M(49)Figure 2-19.Distribution of Pb-TSP maximum monthly mean concentrations at different categories of sites, 2003-2005.2-42

Concentration (μg/m 3 )All sites(189 sites)Sourceoriented(60)Not sourceoriented(129)Previoussourceoriented (9)Urban(140)Urban CBSApop.≥1M(91)Urban CBSApop. < 1M(49)Figure 2-20.Distribution of Pb-TSP second maximum monthly mean concentrations atdifferent categories of sites, 2003-2005.2-43 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 2 nd 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/m 3 ).

Although 60 Pb-TSP monitoring sites met the sour ce oriented classification criteria, that number does not correspond to the number of re presented or “covered” sources of significant emissions. Recall that the emissions sliding sc ale 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 coul d actually have several nearby moderate-sized emission sources and/or many near by small sources. However, the majority of
the source-oriented sites in this national anal ysis do have just one nearby significant emission source. Furthermore, many of these significant emission sources have multiple Pb-TSP monitors2-44 in the vicinity. For example, the Herculan eum primary Pb smelter has 7 nearby Pb-TSP monitoring sites that are included in this national characterization (a s well as others that operated during 2003-2005 but that did not m eet the screening criteria). T hus, the 60 source-oriented sites really represent fewer than 60 significant emissi on 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 circumstan ces, the distribution stat istics for that category (for all three metrics) are generally much higher than the non-source-oriented levels; for example, the “previous” median ma ximum quarterly mean of 0.10 μg/m 3 is more than five times higher than the comparable non-source-oriented level of 0.02 μg/m 3

.
2.3.2.4.2 Urban Sites

The distributions of th e site-level values for the four st atistical metrics for the set of 140
sites classified as “urb an” are presented in Figure 2-22. The di stributions 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 5 th and 95 th percentiles, and asterisks identify composite averages. Additional points on the
distributions for these statisti cs 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 ur ban sites. The median and mean values for all
three concentration metrics are lo wer for sites in less populated CBSA’ s than they are for sites in high population CBSA’s. Figure 2-25 shows cu mulative percentages of urban monitored populations (“total) associated w ith each of the three Pb metric s for various concentration ranges [> 0.02 μg/m 3 , > 0.05 μg/m 3 , > 0.20 μg/m 3 , > 0.50 μg/m 3 , and > 1.54 μg/m 3 ]. 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 populat ions) because the large majori ty of the monitored population
resides in urban areas.2-45 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

2 nd max monthly

mean

Figure 2-22. Distribution of Pb-TSP concentrations (r epresented by 4 different statistics) at the 140 urban monitoring sites, 2003-2005.2-4 6 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

2 nd max monthly

mean

Figure 2-23. Distribution of Pb-TSP concentrations (r epresented by 4 different statistics) at the 91 urban monitoring sites located in metropolitan areas (CBSAs) with 1 million or more population, 2003-2005.2-4 7 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

2 nd max monthly

mean

Figure 2-24. Distribution of Pb-TSP concentrations (r epresented by 4 different statistics) at the 49 urban monitoring sites locate d in CBSA’s with less than 1 million population, 2003-2005.2-48

26.2% 6.7% 1.7% 0 . 6 % 0.0%

54.8% 25 . 5% 4 . 4%

1. 1 % 0.1%

66 . 6% 3 0. 8 % 7 .8 %

2.6% 1 . 0 %

5 6 .0% 21.8% 6. 6 %

1.8% 0.3%0%10%20%30%40%50%60%70%> 0.02 > 0.05 > 0.20 > 0.50 > 1.54μg/m3

percent of monitored populationannual meanmax quarterly meanmax monthly mean2nd max monthly meanFigure 2-25.Percentages of Pb-TSP urban monitored populations residing in areas (represented by 4 different statistics)exceeding various levels.(Note: Site statistics were rounded to 2 decimal places beforecomparing to stated levels.)2-49 2.3.2.5 Variability

Some seasonal variability related to meteor ology is common for air Pb concentrations. The extent to which seasonal variability associat ed with meteorological conditions is present for a given area depends on precipita tion trends, wind direction patterns, and mixing height
fluctuations. For monitors situated near Pb point sources, aspects rela ted to the facilities’ operations also contribute to temporal variabi lity. These same factors, weather and emissions (location and magnitude), also co ntribute to spatial variability, as do other features such as topography. Many monitors that ar e not source oriented exhibit no discernable seasonal pattern
because their concentration levels are so lo w; over 57 percent of th e 129 non-source-oriented sites had over 50 percent of thei r 2003-2005 raw concentration data at levels equal to or less than
the associated MDL. Note that some data re porting agencies erroneously submit substituted values (e.g., zero or half the minimum detecti on 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” data base and the urban site subset, that exceeded
various concentration leve ls between 0.02 and 1.5 μg/m 3 with averaging times or forms of maximum quarterly, maximum monthly, and sec ond maximum monthly For example, with a stated level equal to the current standard of 1.5 μg/m 3 (actually 1.54 μg/m 3 per rounding protocol) and a 3-year evaluation window (i.e., the first table subs et), 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/m 3

, 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 va riability. For example, at the 1.55 μg/m 3 concentration level with a maximum quart erly 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/m 3 concentration level and same st atistic, 107 sites exceeded using the 3-year window but only 84 sites on aver age exceeded with a 1-year window.2-50 Table 2-6. Comparison of number of sites that exceed various Pb-TSP levels using different averaging times or forms, 2003-2005

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

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

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 2nd Maximum Monthly Mean Urban Sites All Sites Urban Sites All Sites Urban Sites Level (189 in 86 counties) * Maximum Quarterly Mean Maximum Monthly Mean

(189 in 86 counties) * All Sites (189 in 86 counties) * (140 in 73 counties) * (140 in 73 counties) * (140 in 73 counties) * 3-year statistics, 2003-2005

1-year statistics, 2003

1-year statistics, 2004 Level Maximum Quarterly Mean Maximum Monthly Mean 2nd Maximum Monthly Mean All Sites Urban Sites Urban Sites (175 in 83 counties) * (131 in 70 counties) * (177 in 83 counties) * (133 in 71 counties) * (176 in 83 counties) * (132 in 71 counti es) * All Sites Urban Sites All Sites

Level Maximum Quarterly Mean Maximum Monthly Mean 2nd Maximum Monthly Mean All Sites Urban Sites All Sites Urban Sites All Sites Urban Sites (166 in 77 counties) * (127 in 68 counties) * (169 in 77 counties) * (130 in 68 counties) * (168 in 77 counties) * (129 in 68 counti es) *2-51 Table 2-7. Comparison of number of sites that exceed various Pb-TSP levels using different averaging times or forms, 2003-2005 – continued.

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

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 1-year statistics, 2005

Level Maximum Quarterly Mean Maximum Monthly Mean 2nd Maximum Monthly Mean All Sites Urban Sites All Sites Urban Sites All Sites Urban Sites (157 in 76 counties) * (122 in 67 counties) * (157 in 76 counties) * (122 in 67 counties) * (157 in 76 counties) * (122 in 67 counti es) *

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

* 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 an d/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 represen ted.. [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%.] Urban Sites

To further illustrate temporal variability a nd also spatial variability, Figure 2-26 graphs 2003-2005 Pb-TSP monthly averages for several si tes in the Dallas, TX metropolitan area (CBSA). Three of the plotted sites (the ones us ing circle, square, and diamonds symbols) were classified as source-oriented; they are all located within one mile (genera lly 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/m 3 ; the 3-year maximum monthly averages for the two sites that were not source oriented sites were 0.23 and 0.10 μg/m 3 . The 3-year maximum quarterly averages for the three source -oriented sites were 0.70, 0.35, and 0.21 μg/m 3 ; the 3-year quarterly averages for th e two non-source-oriented sites were 0.08 and 0.06 μg/m 3 . Thus, the2-52 highest source-related values a nd lowest non-source- related valu es 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 lowe st 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-s ource-oriented Dallas site s shown in Figure 2-26 recorded a large number of 24-hour Pb-TSP concen tration values less th an or equal to the corresponding minimum detection limit; these lo w 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.

jan feb mar apr may jun jul aug sep oct nov dec 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

jan feb mar apr may jun jul aug sep oct nov dec 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 jan feb mar apr may jun jul aug sep oct nov dec 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 

### 2003

2004

2005Concentration (μg/m3) Concentration (μg/m3)Concentration (μg/m3)

Figure 2-26. Pb-TSP monthly means at five sites lo cated 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/m 3 using a 3-year2-53 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 ma ximum monthly average level of 0.50 μg/m 3 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 th e top plot provides good examples of the inconsistencies between maxi mum monthly averages and maximum quarterly averages. The highest concentration month for th at site in 2003 was Apr il with an average of 0.89 μg/m 3 ; however, the corresponding qua rterly average is 0.48 μg/m 3 which is the only third highest of the year. Note that the quarter (second) with the high est monthly average (April) also contains a month with the lowest average (June) . Thus, the low month o ffset 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 da ta. 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 larges t quarter at only six percent of
the sites.

2.3.3 Pb-PM 10

The NATTS network operated in 2003-2005 incl uded 23 sites in mostly urban, but some rural, areas (Figure 2-27). These sites are opera ted by 21 state or local hos t agencies. All collect particulate matter as PM 10 for toxic metals analysis, typicall y on a 1 in 6 day sampling schedule. Lead in the collected sample is generally qua ntified 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. Thes e 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/

.2-54 Figure 2-27. Pb-PM 10 (NATTS) monitoring sites network.

2.3.3.1 Data Analysis Details

Lead-PM 10 data collected in 2003-2005 (param eter 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 sc reening criteria utilized for Pb-TSP were implemented for PbPM 10 with one variation; because of the limite d 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 obser vations each; all three criteria had to be met for inclusion. Forty monitors met the three-part criteria. Of thes e 40 monitors, two were collocated with another complete monitor. Only one monitor from each co llocated 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 ac tually used. Seven of the 38 sites had complete data (i.e., 3 or 4 valid quarters) for each of th e three years (2003-2005), 10 sites had only two years of complete data; and 21 sites had only one co mplete year of data. Complete quarters that were not part of a complete year were used. Li kewise, all complete months were used, even if they did not correspond to the complete years. Th e 38 sites have an average of about 7 complete quarters and 19 complete months.2-55 As with the Pb-TSP data processing, the PM 10 data were used “as reported”; that is, 1⁄2 MDL substitutions were not made for reported con centrations less than or equal to MDL. PbPM 10 sites were categorized similarly to the Pb-TSP sites. However, no Pb-PM 10 sites fell into
the source-oriented classifi cation. 25 of the 38 Pb-PM 10 sites were classifi ed 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-PM 10 monitors are listed with va rious summary and demographic data in Appendix 2B, Table 2B-6. Three statistical metrics were computed for the Pb-PM 10 data: annual means, maximum quarterly means, and maximum monthly means. Thes e metrics were calculated at the site level. They were calculated only for th e overall 3-year period (2003-2005). Note that the 3-year annual mean statistic is actually the av erage of the annual means for the co mplete 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 quar terly 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 hi ghest 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 monthl y 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-PM 10 sites. The national composite average annual
mean for Pb-PM 10 was 0.006 μ g/m 3 for the 3-year period, 2003- 2005; the corresponding median
annual mean was also 0.006 μ g/m 3 . The national composite average maximum quarterly mean
was 0.012 μ g/m 3 for 2003-2005 and the corresponding me dian maximum quarterly mean was
0.009 μ g/m 3 . The national composite average maximum monthly mean was 0.021 μ g/m 3 and the median maximum monthly mean was 0.014 μ g/m 3 . Figure 2-29 shows di stribution boxplots for the 25 urban sites and Figure 2-30 shows distribu tion 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 a nd medians, whiskers depict the 5 th and 95 th percentiles, and asterisks identify composite averages. Additional points on the distribution for these statistics are given in Appendix 2B, Table 2B-7.2-56 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

Figure 2-28. Distribution of Pb-PM 10 concentrations (represented by 3 different statistics) at all 28 monitoring sites, 2003-2005.2-57 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

Figure 2-29. Distribution of Pb-PM 10 concentrations (represented by 3 different statistics) at the 25 urban monitoring sites, 2003-2005.2-58 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

Figure 2-30. Distribution of Pb-PM 10 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 2-32.2-59 Concentration range (μg/m 3 )

≥ 0.015 0.010 - 0.015 0.005 - 0.010 < 0.005 Concentration range (μg/m 3 )

≥ 0.015 0.010 - 0.015 0.005 - 0.010 < 0.005

Figure 2-31. Pb-PM 10 annual means (for all sites), 2003-2005.2-60 Concentration range (μg/m 3 )

≥ 0.015 0.010 - 0.015 0.005 - 0.010 < 0.005 Concentration range (μg/m 3 )

≥ 0.015 0.010 - 0.015 0.005 - 0.010 < 0.005

Figure 2-32. Pb-PM 10 maximum quarterly mean s (for all sites), 2003-2005

2.3.4 Pb-PM 2.5

Two networks measure Pb in PM 2.5 , the EPA CSN and the IMPROVE network. The CSN consists of 54 long-term trends sites (co mmonly referred to as the Speciation Trends Network or STN sites) and about 150 supplem ental sites, all opera ted 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 samp ling schedule. All sites in the CSN network
determine the Pb concentrations in PM 2.5 samples and, as such, do not measure Pb in the size fraction >2.5 μm in diameter. Lead is quant ified via the XRF method. The standard operating procedure for metals by XRF is available at: http://www.epa.gov/ttnamti1/files/ambient/pm25/sp ec/xrfsop.pdf. Data are managed through the AQS.2-61 The IMPROVE network is administered by th e National Park Service, largely with funding by EPA, on behalf of federal land manageme nt agencies and state air agencies that use
the data to track trends in ru ral visibility. Lead in the PM 2.5 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/vi ews/), but also are available vi a 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 PM 2.5 concentrations. The first CSN sites generally began operation around 2000.

Figure 2-33. Pb-PM 2.5 (CSN) monitoring sites.

In the IMPROVE network, PM 2.5 monitors are placed in “Class I” areas (including National Parks and wilderness areas) and are mostly in rural locations (Fig ure 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 locat ed in or near nationa l parks and other Class I visibility areas, virtually all of these being ru ral. 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.2-62 Figure 2-34. Pb-PM 2.5 (IMPROVE) monitoring sites.

2.3.4.1 Data Analysis Details

2003-2005 Pb-PM 2.5 data (parameter code 88128, dur ation ‘7’) were extracted from EPA’s AQS on May 22, 2007. Data generated w ith IMPROVE collection/analysis methods
were excluded from the central focus of this na tional characterization on the basis that most of the monitors utilizing those met hods are located in rural or remo te areas distant from both Pb sources and large populations. Most remaining da ta are associated with EPA’s CSN program. The same screening criteria utilized for Pb-PM 10 were also implemented for Pb-PM 2.5 : 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 obser vations each; all three criteria had to be met for inclusion. 278 monitors met the data complete ness criteria. Of these 278 mon itors, 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 actu ally used. 192 of the 271 sites had complete data (i.e., 3 or 4 valid quarters) for each of th e three years (2003-2005), 40 sites had only two years of complete data; and 39 sites had only one complete year of data. Complete2-63 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-PM 2.5 data were used “as reported”; 1⁄2 MDL substitutions were not made for reported concentrations less than or equal MDL.

PM 2.5 sites were categorized similarly to the si tes in the other size cuts. Only eight PbPM 2.5 sites were classified as sour ce-oriented. 216 of the 271 Pb-PM 2.5 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-PM 2.5 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-PM 2.5 concentrations for each of the three statistics (annual mean, maximum quarterly mean, and maximum monthl y mean) during the three-year period, 2003- 2005, are shown in Appendix 2B, Table 2B-8. Figur e 2-35 shows the distributions of the three
statistical metrics for the 271 Pb-PM 2.5 sites; the boxes depict inte r-quartile ranges and medians, whiskers depict the 5 th and 95 th percentiles, and asterisks identify composite averages. Additional points on the distributi on for these statistics are give n in Appendix 2B, Table 2B-9. The national composite average annual mean was 0.004 μ g/m 3 for the 3-year period, 2003-2005; the corresponding median annual mean was 0.003 μ g/m 3 . The national composite average maximum quarterly mean was 0.008 μ g/m 3 for 2003-2005 and the corresponding median maximum quarterly mean was 0.005 μ g/m 3 . The national composite average maximum monthly
mean was 0.013 μ g/m 3 and the median maximum monthly mean was 0.007 μ g/m 3 . 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 si tes were approximately double those for the nonsource-oriented sites. Figure 2-36 maps the annual means for Pb-PM 2.5 sites.2-64 Annual mean Max quarterly

mean Max monthly

meanConcentration (μg/m3)

Figure 2-35. Distribution of Pb-PM 2.5 concentrations (represented by 3 different statistics) at all 271 monitoring sites, 2003-2005.2-65 Concentration range (μg/m 3 )

≥ 0.015 0.010 - 0.015 0.005 - 0.010 < 0.005 Concentration range (μg/m 3 )

≥ 0.015 0.010 - 0.015 0.005 - 0.010 < 0.005

Figure 2-36. Pb-PM 2.5 annual means (for all sites), 2003-2005.

2.3.5 Relationships among Different Part icle-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 wher e Pb values exceed minimum detection limits). Very few locations in the United States have m easured 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-PM 10 measurements. For this particular analysis, data prior to 2003 (and in one case, data not from AQS) we re utilized. In general, there is typically good correla tion between Pb measurements in TSP and PM 10 ; the average site level correlation coefficient (r) is 0.79 for the 23 listed s ites. Although site-lev el 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 PM 10 fraction. On2-66 average, daily Pb-PM 10 concentrations were about 83 pe rcent of the leve l of the Pb-TSP
concentrations. All sites had an average daily ratio (Pb-PM 10 / 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-PM 2.5 during 2003-2005. The relationship between these tw o size fractions is not nearly as strong as
for Pb-TSP and Pb-PM 10 . 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-PM 2.5 concentrations were about 55 pe rcent of the level of Pb-TSP concentrations; however, the percenta ge varied significantly by site. The last section of the table
lists the sites with co-located Pb-PM 10 and Pb-PM 2.5 during 2003-2005. It appears that the relationship between Pb-PM 10 and Pb-PM 2.5 is stronger than that for Pb-TSP and Pb-PM 2.5 but not as strong as between Pb-TSP and Pb-PM 10 . The average r for the 28 sites is 0.69; ten of the 26 sites (38 percent) have r’s (cor relation coefficients) of 0.80 or above. Site-level correlations ranged from 0.31 to 1.00. On average, daily Pb-PM 2.5 concentrations were about 79 percent of
the level of Pb-PM 10 concentrations; all but 3 sites had an average daily ratio (Pb-PM 2.5 / PbPM 10 ) 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 re lationships described can only be presumed to exist at sites with little influen ce from significant Pb sources. Lack of sourceoriented PM 10 data is a significant data gap in und erstanding 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 si ze fractions collected by different monitoring networks. Additionally, means and medians are presented for differe nt site classifications. The top chart uses a scale that fits all shown categories, up to the maximum 95 th percentile. The PbTSP monitor averages for the “source-oriented” subset (and other subset s that include those monitors; e.g., “TSP - all si tes”) dwarf the other categories. The bottom chart replots the data on
a smaller concentration scale for enhanced resolution. Using the na tional averages (left bars), the
Pb-TSP non-source-oriented annual means are about 2.6 times larger than the Pb-PM 10 “all sites” averages (recall that no Pb-PM 10 sites were classified as sour ce-oriented); using the national medians (right bars), the ratio was closer to 1.8. Restated as a PM 10 /TSP ratio it is 0.55, (i.e., 55 percent of TSP Pb is in the PM 10 fraction); the collocated sites analysis discussed above has a
median PM 10 /TSP ratio of 0.85) The Pb-PM 10 “all sites” averages are about 1.4 (using means) to 1.6 (using medians) times the Pb-PM 2.5 CSN urban averages. The Pb-PM 2.5 CSN urban averages
are about 3.3 times the Pb-PM 2.5 IMPROVE averages.2-670.00000.00100.00200.00300.00400.00500.00600.00700.00800.00900.01000.01100.01200.01300.01400.01500.01600.01700.01800.01900.0200TSP - allsitesTSP sourceorientedsitesTSP notsourceorientedsitesTSPprevioussourceorientedsitesTSP urbansitesTSP urbansites inCBSA's≥1MpopulationTSP urbansites inCBSA's <1MpopulationPM10 - allsitesPM10 urbansitesPM10 urbansites inCBSA's≥1MpopulationPM10 urbansites inCBSA's <1MpopulationPM2.5 CSN- all sitesPM2.5 CSNsourceorientedsitesPM2.5 CSNnot sourceorientedsitesPM2.5 CSNurban sitesPM2.5 CSNurban sites inCBSA's≥1MpopulationPM2.5 CSNurban sites inCBSA's <1MpopulationPM2.5IMPROVE(Nationalparks,mostly rural)meanmedian0.00000.02000.04000.06000.08000.10000.12000.14000.16000.18000.20000.22000.24000.26000.28000.3000TSP - allsitesTSP sourceorientedsitesTSP notsourceorientedsitesTSPprevioussourceorientedsitesTSP urbansitesTSP urbansites inCBSA's≥1MpopulationTSP urbansites inCBSA's <1MpopulationPM10 - allsitesPM10 urbansitesPM10 urbansites inCBSA's≥1MpopulationPM10 urbansites inCBSA's <1MpopulationPM2.5 CSN- all sitesPM2.5 CSNsourceorientedsitesPM2.5 CSNnot sourceorientedsitesPM2.5 CSNurban sitesPM2.5 CSNurban sitesin CBSA's≥1MpopulationPM2.5 CSNurban sitesin CBSA's <1MpopulationPM2.5 CSNIMPROVE(Nationalparks, rural)meanmedianFigure 2-37.National mean and median monitor level Pb annualmeans for different size cut PM networks, 2003-2005.2-68 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 0.612-69 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/m 3 in PM 2.5 and 15.04 ng/m 3 in TSP samples, with the averag e total mass concentration of 9.5 μg/m 3 and 19.1 μg/m 3 for PM 2.5 and TSP, respectively (Goforth and Christoforou, 2006); thus, Pb constituted a similar very small proportion of particles in each size fraction. Anothe r 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 proportiona lly greater in the fine and
ultrafine fractions of PM 10 . 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 PM 10 . In Boston, MA, Pb concentrations of 326 ng/m 3 and 75.6 ng/m 3 were reported from PM 2.5 and PM 10-2.5 (Thurston and Spengler, 1985). Overall, these findings suggest that for locations primarily im pacted by combustion sources, Pb concentrations appear to be higher in the fine fraction of particles. Howe ver, at locations impacted by noncombustion sources (e.g., agriculture), Pb cont ained in the larger particles can be of significantly higher concen trations than those fo r 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 networ ks - the Pb NAAQS compliance network, the PM 2.5 STN, the PM 2.5 IMPROVE network, and the PM 10 network – provide Pb
measurements for 3 different sizes of PM, and the PM 2.5 size is measured separately in urban and
remote locations.
Airborne concentrations of Pb-TSP in the Un ited 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 ar e 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 numb er 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/m 3 NAAQS level. However, when current concentrations are compared to altern ative NAAQS thresholds (e.g., 0.05 and 0.20 μg/m 3 ), the number of locations that exceed those levels, an d their associated populations, are much higher. For example, over 25 percent of the mon itored Pb-TSP population exceeded the 0.05 μg/m 3 max quarterly level in 2003-2005 and 5 pe rcent exceeded the 0.20 level.2-70 Some monthly variability is common for ambien t Pb concentrations. The current form of the standard (quarterly average) attempts to account for seasonal variability. As suggested during the last review, a shorte r averaging period (monthly) would better capture short-term increases in Pb concentrations (USEPA 1990) . Although there have onl y been 3 sites that violated the 1.5 μg/m 3 max quarterly average NAAQS dur ing 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

PM 10 -sized Pb are fairly well corr elated, however, almost all of the study sites were presumably
not source-oriented. Additional source-oriented Pb-PM 10 monitoring, collocated with Pb-TSP,
would benefit additional evaluations . If further analyses also corroborate a fairly strong TSPPM 10 Pb relationship for source-oriented sites, Pb-PM 10 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 nati onal 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 annua l average Pb air concen trations across the country. A national scale assessment is not yet available based on the 2002 NE I. A number of limitations are associated
with the 1996 and 1999 ambient concentration es timates (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 estimate s, they may prove informative with regard to relative patterns of concentrations across th e country, and are pres ented in that light.

2.4.1.1 Methods

To develop national-scale estimates of annua l average ambient Pb concentrations, EPA used the Assessment System for Population Exposure Nationwide (ASPEN) model. ASPEN
uses a Gaussian model formulation and climat ological 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 pollu tants in the atmosphere after being released (i.e., reactive decay), settling of pollutants out of the atmos phere (i.e., deposition), and transformation of one2-71 pollutant into another (i.e., secondary forma tion). ASPEN concentra tion estimates do not account for day-of-week or seasonal varia tions in emissions (USEPA, 2001a). For each source, the model calculates ground-leve l concentrations as a function of radial
distance and direction from the source at a set of r eceptors laid out in a ra dial grid pattern. For each grid receptor, concen trations are calculated for each of a standard set of stability class/wind speed/wind direction combinations. These concentr ations are averaged to gether 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 fr equency 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 pa tterns that might be im portant 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 fo r 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 sour ces are summed to give cumulative ambient concentrations in each census tract. By account ing for all identified so urce categories (including background concentrations, which are added to th e ASPEN-calculated con centrations), the sum of the concentration increments yields an esti mate of the overall Pb concentration within each census tract. For many pollutants modeled, to tal concentrations include a “background” component which includes concen trations due to natu ral sources, sources not in the emissions inventory, and long-range transp ort (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 estimat es, spatial allocation estimates, dispersion modeling) may be systemati cally underestimating ambient concentrations. This may be particularly true for Pb as metals tend to deposit rapidl y 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/n ata1999/99compare.html . The modeling system underestimation may also be due in part to a l ack of accounting for resuspension of previously emitted and deposited particles (these resuspe nded particles may be observed by the monitors, but they are not accounted for in the emissions inventory, and thus would not contribute to the2-72 model estimate). For more details on th e limitations of the 1999 NATA national scale
assessment, see http://www.epa.gov/ttn/atw/nata1999/limitations.html

. Because higher Pb concentrations are associ ated with localized sources, which are not well-characterized by this modeling approach, nati onal scale assessments such as this can only provide answers to questions about emissions, am bient air concentrations, exposures and risks across broad geographic areas (such as counties, states and the count ry) 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 a nd risks for specific individuals, or identifying exposures and risks in small geographic re gions 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 ge ographical 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 concentratio ns associated with loca lized sources with high
emissions. These results also s upport the general conclu sion that more detailed source and site specific analyses are needed when addressing Pb impacts.

2.4.1.3 Summary

The NATA national scale assessment estima tes 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 estimat ed 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-c losed major Pb smelter. Li mitations of the assessment, however, contribute to uncerta inty and potential underestima tion 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 mode l designed to estimate the formation and fate of oxidant precursors, primary and secondary particulat e 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 a nd boundary conditions. The CMAQ meteorological input files were derived from simulations of th e Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model (Grell et al., 1994). This model, commonly referred2-73 to as MM5, is a limited-area, nonhy drostatic, terrain-following system that solves for the full set of physical and thermodynamic equations which govern atmospheric motions. The lateral boundary and initial species conc entrations 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 si mulations for this review, a preliminary national scale air quality mode ling analysis was performed using version 4.6 of the CMAQ modeling system that simulates urban and region al air quality. Aerosol phase HAPs track toxic components within particulate ma tter and are treated as chemica lly inert but undergo the same microphysical processes and deposition rates dete rmined 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 D ecember 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 mode l 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 meteorol ogical data for that year produced by the MM5 model that were processed through th e Meteorology-Chemical Interface Processor (MCIP), version 3.1. The Sparse Matrix Oper ator Kernal Emissions (SMOKE) version 2.0 (http://cf.unc.edu/cep/e mpd/products/smoke/index.cfm) was used to produce model-ready emissions inputs for the CMAQ simulation ba sed 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) versi on 3.13 model from SMOKE. The BEIS3.13 model computes gridded, hourly, model-species emissi ons for combination with the anthropogenic emissions to put into CMAQ. Emissions are ca lculated for the U.S., Mexico, and Canada and accounts for CO, VOC, and NO X emissions from vegetation and soils. The meteorology data on which the biogenic emissions depend are the sa me as the meteorology data input to the CMAQ
model. CMAQ outputs include Pb-PM 2.5 , Pb-PM 10 and atmospheric depos ition. To evaluate predictions, we obtained obser vations of metals in PM 2.5 from the US EPA’s Air Quality System database (AQS). Observations are on a nationa l scale and have an aver aging period equal to 24 hours. Sampling frequencies range from severa l days to a week (see Section 2.3 for more discussion on Pb monitoring). Model performan ce varies by season but in general, predicted
concentrations of lead underestim ate observed concentrations (neg ative biases) and have a weak ability to match the time dependency of observati ons (low correlation coefficients). Lead predictions match observations within 50% at all locations. Comparis on between predictions
and observations is difficult when the observation e quals zero and the prediction is above or near2-74 the detection limit. In this case, the comparison gives an incorrec tly high value of model biases
because the actual observation lies below the detection limit by an unc ertain amount especially at rural locations such as desert, forests and agricultural areas.
There are several reasons for the differenc es between modeled and monitored values including emissions and meteorolog ical factors. One reason is that sources of emissions from
Canada and Mexico were not included in the m odeling. 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 so il by construction activities. Th ese 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 i nventory and thus are not input in to the model. Issues related to aerosol suspension of Pb by mechanical action are equally important for both size classes, PbPM 2.5 and Pb-PM 10
. Model resolution at 36 km 2 grids affects emission processes because grid cells use rates that are composites of many sources. This me thod removes how individual sources affect concentrations based on their time dependent emi ssions 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 residentia l areas in urban locations. Ob served concentrations reflect unpredictable activity in adj acent automotive traffic, construction and businesses.
Besides the errors in the me teorological and emissions i nputs, the CMAQ model does not
include an aerosol mode that re presents ultrafine particles (diameters < 50 nm). The aerosol mode is emitted by combustion a nd industrial sources such as di esel engines, boilers, metal foundries and plating or produced by gas to particle conversion near emission sources. Thus CMAQ can underpredict at urban and some subu rban locations where sources of ultrafine particles are more numerous because coagulation is too slow to grow ultrafine particles into fine particles. Issues rela ted to ultrafine lead emissions pertain to the PM 2.5 fraction since ultrafines would not be expected to grow into particles larger than PM 2.5 . PM 10 emissions are usually dominated by particles formed by mechanical ac tion and not from combus tion processes. These comparisons are expected to improve in the future from better Pb emissions inventories (suspension, biomass burning, and anthropogeni c fuel composition) and better CMAQ science (smaller grid resolution, better boundary conditions, and better algorithms).

2.5 POLICY-RELEVANT B ACKGROUND IN AIR

Some amount of Pb in the air derives fr om background sources, such as volcanoes, sea salt, and windborne soil particle s from areas free of anthropoge nic activity (CD, Section 2.2.1).2-75 The impact of these sources on current air concentra tions is expected to be quite low (relative to current concentrations) and ha s been estimated to fall within the range from 0.00002 μg/m 3 and 0.00007 μg/m 3 based on mass balance calculations (C D, Section 3.1 and USEPA 1986, Section 7.2.1.1.3). The midpoint in this range, 0.00005 μg/m 3 , 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 limitatio ns 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 assessmen t described in Chapter 4, the value of 0.00005 μg/m 3 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 po licy-relevant background in air is lik ely insignificant in comparison.

2.6 ATMOSPHERIC DEPOSITION

As described in Section 2.1.2, deposition is th e path by which Pb particles are removed from the atmosphere and transferred to othe r 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 appr oaches described in the litera ture for estimating atmospheric deposition, or transfer of Pb from the atmosphere to soil or water bodies. These include measurements of Pb in rainfall (wet depositi on) and on collection surf aces during dry periods (dry deposition); dry deposition has also been estimated via measuremen ts of airborne Pb particles coupled with estimates of deposition ve locity (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 atmo spheric deposition monitoring programs, the
information in this section is drawn from a vari ety 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 th e U.S. during the 1980s to early 1990s, likely reflecting the reduction in atmospheric levels duri ng that time period (CD, Section 2.3.2). As an example, Pirrone and others (1995) estimated an order of magnitude redu ction in dry deposition from 1982 to 1991 in Detroit, Michigan (CD, Section 2.3.2). Measurements of Pb in rainfall in2-76 Lewes, Delaware (a small town at mouth of Delaware Bay) have fallen from approximately 3

μ g/L in the early 1980s to less than 1 μ g/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 Estimate s since the Last Review

Contemporary rates of total Pb loadings to terrestrial ec osystems are estimated at approximately 1 to 2 mg/m 2 year (CD, p. AX7-36). In associ ation with the Great Lakes Water Quality Agreement between the United States an d Canada, a deposition monitoring network was established to estimate regional atmospheric inpu ts to the Great Lakes (Voldner and Eisenreich, 1989). Based on measurements from that networ k, 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/m 2 -year (CD, pp. 2-57 and 2-60; Sweet et al., 1998). For Lakes Superior and Michig an, dry deposition estimates we re greater than those for wet deposition by a factor of 1.5 to 2, while dry depos ition 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 estima ted 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

μ g/m 2 -year (CD, Section 2.3.2).

2.7 OUTDOOR DUST AND SOIL

Lead in outdoor dust and soil may be derive d from a range of s ources including current and historical air emissions sources, as well as miscellaneous nonair so urces (e.g., land disposal of wastes and subsequent weathering). Both media may play a substantial role in human and ecological exposures. With regard to human ex posures, 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 we ll 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 fa cility in Canada (CD, pp. 3-23 to 3-24).2-77 Additionally, Caravanos and othe rs (2006b) have described Pb in dust (particulate matter) deposited on surfaces in New York City. Lead le vels have been found to be higher in dust on or near roadways, or in older urban areas as comp ared 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 ingesti on 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 mechan ical processes including pedestrian traffic, agricultural operations, and construction.

2.7.2 Soil

A reservoir of 0.5 to 4 g/m 2 gasoline additive-derived Pb is estimated to exist in U.S. soils (CD, p. AX7-36), with most contained in th e 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 upta ke 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, fore st 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 biol ogically active parts of the watershed, unless later
disturbed or redistributed (CD, p. AX7-36). In areas of expos ed soil, however, there is potential for interaction with airborne Pb (a s 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 re sponding to reduced Pb de position rates associated with reduced atmospheric emissions of Pb, incl uding those associated wi th the phase-out of leaded gasoline (see Section 2.3.2.3). Situati ons near point sources and those involving historically deposited Pb near roadways are less well charac terized. 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 th at influence Pb mobility contributes to differences in current and projected temporal tr ends 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).2-78 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) projecte d 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 Friedla nd (2003) traced atmospherically de posited 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 penetr ation of atmospherically delivered Pb in soils is currently limited to the upper 20 cm and that the heterogeneous dist ribution of Pb in soils would
seem to indicate that the releas e of Pb to groundwater will be dispersed thereby reducing the likelihood of a large pulse to groundwater. Th is study and those of Wang and Benoit (1997), Johnson et al. (1995), and Zhang (2003) conclude that forest surface soil s do not act as sinks under current deposition rates for Pb and that a gradual migration in to mineral soils is occurring, making the possibility of a large pulse to ground water 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 associat ed stream waters (CD, p. AX7-91).
Studies in urban areas of southern Califo rnia, 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 illustrate s 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 l ong-term values for resuspension rate fall in the range of 10 -11 to
10 -7 per second, based on wind sp eeds, with the range of 10 -11 to 10 -10 proposed as a range appropriate to California’s sout h 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 Calif ornia over the next few hundred years (CD, pp. 2-65 to 2-67 and 3-18 to 3-20).2-79 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 establis hed point sources are not well characterized.
Information described in the CD for areas surr ounding smelters after implementation of pollution controls, although showing declines in Pb concen trations in outdoor dustf all, street dust and indoor dustfall, has not indicated a noticeable dec line in soil Pb concentrations (CD, pp. 3-23 to 3-24). Further, Pb concentrations in “clean” so il placed in areas influenced by current sources have been reported to exhibit in creasing temporal trends (USEPA, 2006c). Concentrations of Pb in the very top layer of material (within the upper 1 inch of so il, analyzed using portable x-ray fluorescence) at locations less than a mile from a primary Pb smelter exhibited statistically significant increasing concentrati on 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 th e 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 much2-80 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 appr oximately 100-250 meters , dropping to 200 mg/kg and below by distances of approxi mately 3-5 km (CD, Table 3-4). Soil Pb concentrations of 500-1100 mg/kg have been reported near U.S. mine s 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 wa stewater effluent from munici palities and industry, stormwater runoff, erosion, and accidental discharges (CD, p. AX7-142). Most Pb occurring in aquatic systems is associated with particles, with th e 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 stre ams to 27:1 in urban streams (CD, p. AX7-118).
Water columns have been described as “transie nt reservoirs” for pollutants (CD, p. 2-75). Once deposited to sediments, whether Pb is av ailable 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), resu spension to surface waters is unlikely. In more shallow systems, and additionally those influenc ed by land sources (e.g., stormwater runoff as well as point sources), resuspensi on may play a significant role in water column concentrations. For example, studies in San Francisco Bay, the so uthern arm of which as an average depth of 2 m, have indicated that Pb particles may be re mobilized from surface sediments into the water column (CD, AX7-141).

2.8.1 Temporal Trends

As discussed in the CD, many studies have i nvestigated 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 pha sing out of leaded gasoline. Using sediment cores, temporal changes in Pb deposition and associated sediment Pb
concentration have been documented. Sediment cores from the Okefenok ee Swamp indicate that Pb concentrations were approximately 0.5 mg/kg prior to industrial development, reached a maximum of approximately 31 mg/kg from a bout 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 c oncentrations (roughly from 1970-2001) in sediment
cores from 35 reservoirs and lakes in urban and reference settings found that number of lakes2-81 exhibiting decreasing trends in Pb concentration outnumbered in creasing 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 cha nge of 246%. The largest decreases were found in lakes located in dense urban watersheds, al though 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 regi onal 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 gasolin e 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 sa mples 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.2-82 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 ye ars, 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 analys is of these data, the mean dissolved Pb
concentration in ambient surface waters of the U.S. is estimated to be 0.66 μ g/L (range 0.04 to 30

μ g/L), as compared to a mean of 0.52 μ g/L (range 0.04 to 8.4 μ g/L) for the “natural” locations. The term “ambient” was used by NAWQA to descri be the combined contri bution of natural and anthropogenic sources, and a separate set of sa mples was identified for natural locations (e.g., “forest”, “rangeland”, and “reference” sites). Th e mean concentration of Pb in “ambient” bulk sediment (<63 microns, grain size) is 120 μ g/g dry weight (range 0.5 to 12,000 μ g/g), as compared to a mean of 109 μ g/g dry weight (range 0.5 to 12,000 μ g/g) for “natural” locations. Geographic distribution of Pb c oncentrations in surface waters and sediments in this data set are presented in Figures 2-40 and 2-41 (C D, 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 measur ed 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 m easured 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 va lues for lotic waters (CD, AX7-138).2-83 Figure 2-40. Spatial distribution of dissolved lead in surface water (N = 3445). [CD, Figure AX7-2.2.7.]2-84 Figure 2-41. Spatial distribution of total lead in bulk sediment <63 μm (N = 1466). [CD, Figure AX7-2.2.9]2-85 REFERENCES

American Society for Testing and Ma terials (ASTM International). (2006) Standard Specification for Aviation Gasoline’s. ASTM D 910 – 06. Available at www.astm.org

Binkowski, F. S., and Roselle, S. J., 2003. Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component, 1, Model description. J. Geophys. Res., 108(D6), 4183-4200, doi:10.1029/2001JD001409.

Byun, D.W., and Ching, J.K.S. (Eds.), 1999. Science Al gorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. EPA-600/R-99/030, US Environmental Protection Agency, Research Triangle Park, NC, http://www.epa.gov/a smdnerl/models3/doc/science/science.html.

Byun, D.W., and Schere, K. L., 2006. Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Mo deling System. Applied Mechanics Reviews, 59(2): 51-77.

Caravanos, J.; Weiss, A.L.; Jaeger, R.J. (2006a) An exterior and interior leaded dust deposition survey in New York City: results of a 2-year study. Environ. Res. 100: 159-164.

Caravanos, J.; Weiss, A.L.; Blaise, M. J.; Jaeger, R.J. (2006b) A survey of spatially distributed exterior dust lead loadings in New York City. Environ. Res. 100: 165-172.

Chevron Products Company (2000) Aviation Fuels Technical Review IFTR-3. http://www.chevron.com/products /prodserv/fuels/bulletin/avia tionfuel/pdfs/aviation_fuels.pdf

ChevronTexaco. (2005) Aviation Fu els Technical Review. FTR-3. http://www.chevronglobalaviation.co m/docs/aviation_tech_review.pdf.

DOE Energy Information Agency. (2006) Fuel production volume data obtained from http://tonto.eia.doe.gov/dnav/pet/hist/ mgaupus1A.htm accessed November 2006

. EC/R Incorporated. (2006) Secondary Lead Smelter In dustry – Source Characteri zation for Residual Risk Assessment. Prepared for USEPA Office of Air and Radiation, Office of Air Quality Planning and Standards, Research Triangle Park, NC. November.

Eldred, R. A.; Cahill, T. A. (1994) Tren ds in elemental concentrations of fine particles at remote sites in the United States of America. Atmos. Environ. 28: 1009-1019.

Federal Aviation Administration (F AA). Terminal Area Forecast (T AF) system can be found at http://aspm.faa.gov/main/taf.asp

Goforth, M. R.; Christoforou, C. S. (2006) Particle size di stribution and atmospheric metals measurements in a rural area in the South Eastern USA. Sci. Total Environ. 356: 217-227.

Grell, G.; Dudhia, J.; Stauffer, D. A Description of the Fifth-Generation Penn St ate/NCAR Mesoscale Model (MM5), NCAR/TN-398 STR; National Center for Atmospheric Research: Boulder CO, 1994; p 138.

Habibi K. (1973) Characterization of Particulate Matter in Vehicle Exhaust. Environ Sci & Technol 7(3):223-234.

Harrison, R. M.; Tilling, R.; Callen Ro mero, M. S.; Harrad, S.; Jarvis, K. (2003) A study of trace metals and polycyclic aromatic hydrocarbons in the roadside environment. Atmos. Environ. 37: 2391-2402.

Jackson, B.P., P.V. Winger, P.J. Lasi er (2004) Atmospheric lead deposition to Okefenokee Swamp, Georgia, USA. Environ Poll. 130: 445-451. Abu-Allaban, M.; Gillies, J. A.; Gertler, A. W.; Clayton, R.; Proffitt, D.2-86 (2003) Tailpipe, resuspended road dust, and brake-wear emission factors from on-road vehicles. Atmos. Environ. 37: 5283-5293.

Johnson, C. E.; Siccama, T. G.; Dris coll, C. T.; Likens, G. E.; Moeller, R. E. (1995) Changes in lead biogeochemistry in response to decreasing atmospheric inputs. Ecol. Appl. 5: 813-822.

Kaste, J.; Friedland, A.; Stürup, S. (2003) Using stable and ra dioactive isotopes to trace atmospherically deposited Pb in montane forest soils. Environ. Sci. Technol. 37: 3560-3567.

Lough, G. C.; Schauer, J. J.; Park, J.-S.; Shafer, M. M.; De minter, J. T.; Weinstein, J. P. (2005) Emissions of metals associated with motor vehicle roadways. Environ. Sci. Technol. 39: 826-836.

Mahler, B. J.; Van Metre, P. C.; Callender, E. (2006) Trends in metals in urban and reference lake sediments across the United States, 1970 to 2001. Environ. Toxicol. Chem. 25: 1698-1709.

Malm, W. C.; Sisler, J. F. (2000) Spatial patterns of ma jor aerosol species and selected heavy metals in the United States. Fuel Process. Technol. 65: 473-501.

Miller, E.K. and Friedland, A.J. (1994) Lead migration in forest soils: Response to changing atmospheric inputs.

Moon, N.K., and D.W. Byun. A Simple User’s Guide for ‘geos Zcmaq’ Code: Linking CMAQ with GEOS-CHEM, Version 1.0, Interim Report from Institiute for Mu ltidimensional Air Quality Studies (IMAQS), Univ. of Houston, TX, Aug. 2004, http://www.math.uh.edu/~dwbyun/Meetings/icapl/

Pirrone, N., G.J. Keeler, P.O. Warner. (1995) Trends of ambient concentrations and deposition fluxes of particulate trace metals in Detroit from 1982 to 1992. Sci Total Environ 162: 43-61.

Schauer JJ, Lough GC, Shafer MM, Christensen WF, Arndt MF, DeMinter JT, Park J-S. (2006) Characterization of metals emitted from motor vehicles. Health Effects Institute Report Number 113.

Schell, W. R.; Barnes, R. S. (1986) En vironmental isotope and anthropogenic tr acers of recent lake sedimentation. In: Fritz, P.; Fontes, J. C., eds. Handbook of enviro nmental isotope geochemistry; V. 2, the terrestrial environment, B. New York, NY: Elsevier Science Publishers; pp. 169-206.

Schroeder, W. H.; Dobson, M.; Kane, D. M.; Johnson, N. D. (1987) Toxic tr ace elements associated with airborne particulate matter: a review. JAPCA 37: 1267-1285.

Scudlark, J.; Church, T.; Conko, K.; O ndov, J.; Han, M. (1994) The wet depo sition of trace elements on Delmarva and their utility as emission source indicators. Annapo lis, MD: Maryland Department of Natural Resources, Chesapeake Bay Research and Monitoring Program; report no. CBRM AD 94 3. Available from: NTIS, Springfield, VA; PB94 178373. Available: http ://esm.versar.com/pprp/bibliography/CBRM AD 94 3/CBRM AD 94 3.pdf [24 August, 2006].

Scudlark, J.R.; Rice, K.C.; Conko, K. M.; Bricker, O.P.; Church, T.M. (2005) Transmission of atmospherically derived trace elements through an undeveloped, fore sted Maryland watershed. Water Air Soil Poll 163:53- 79.

Sweet, C. W.; Weiss, A.; Vermette, S. J. (1998) Atmospheric deposition of trace metals at three sites near the Great Lakes. Water Air Soil Pollut. 103: 423 439.

Singh, M.; Jaques, P. A.; Sioutas, C. (2002) Size distribution and diurnal characteristics of particle-bound metals in source and receptor sites of the Los Angeles Basin. Atmos. Environ. 36: 1675-1689.

Thurston, G. D.; Spengler, J. D. (198 5) A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston. Atmos. Environ. 19: 9-25.2-87 U.S. Environmental Protection Agency. (1986) Air Qu ality Criteria for Lead. Washington, DC, EPA/600/8- 83/028AF (NTIS PB87142386). Available online at: http://www.epa.gov/ttn/naaqs/standards/pb/s_pb_pr_cd.html

U.S. Environmental Protection Agency. (1990) 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 and http://www.epa.gov/ttn/naaqs/standards/pb/data/rnaaqsl_asti.pdf

U.S. Environmental Protectio n Agency. (1998) Study of Hazardous Air Pollutant Emi ssions from Electric Utility Steam Generating Units – Final Report to Congress. O ffice of Air Quality Planning and Standards. EPA 453/R-98-004a. February.

U.S. Environmental Protection Agency. (2001a) National-Scale Air Toxics Assessment for 1996. EPA-453/R-01- 003. Research Triangle Park, NC.

U.S. Environmental Protection Agency. (2001b) Quality A ssurance Project Plan for a S ite Characterization at the Herculaneum Lead Smelter. Herculaneum, Missouri, CERCLIS ID No.: MOD 006266373. Prepared for USEPA, Region VII, Superfund Division by USEPA Region VII Superfund Technical Assessment and Response Team 2. September 10.

U.S. Environmental Protection Agency. (2002c) 1996 Nationa l Scale Air Toxics Assessme nt. Office of Air Quality Planning and Standards. http://www.epa.gov/ttn/atw/nata/

U.S. Environmental Protection Agency. (2004c) Air QualityCriteria for Particulate Matter. Volume I. EPA 600/P99/002aF-bF, Washington, DC. Pages 1-4.

U.S. Environmental Protection Agency. (2006a) Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area Sources. AP 42, Fifth Edition. Office of Air Quality Planning and Standards. Current version available: http://www.epa.gov/ttn/chief/ap42/index.html

U.S. Environmental Protection Agency. (2006b) 1999 Nationa l Scale Air Toxics Assessme nt. Office of Air Quality Planning and Standards. http://www.epa.gov/ttn/atw/nata1999/

U.S. Environmental Protection Agency. (2006c) Lead soil trend analysis through May, 2006. Evaluation by individual quadrant. Herculaneum lead smelter site, Herculaneum, Missouri. Prepared by TetraTech for U.S. EPA, Region 7. Available on the web, at: http://www.epa.gov/region7/cleanup/superfu nd/herculaneum_pbtren d_thru_may2006.pdf

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.

U.S. Environmental Protection Agency. (2007b) Airport-specific emissions of lead from combustion of leaded aviation gasoline. http://www.epa.g ov/ttn/chief/net/2002inventory.html

Voldner, E.C. and Eisenreich, S.J. (1989) A Plan for Assessing Atmospheric Deposition to the Great Lakes, Water Quality Board , International Joint Commission, Windsor, Ontario.

Wang, E. X.; Benoit, G. (1997) Fate and transport of contaminant lead in spodosols: a simple box model analysis. Water Air Soil Pollut. 95: 381-397.

Yantosca, B., 2004. GEOS-CHEMv7-01002 User’s Guide, Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, October 15, 2004.2-88 Yohn, S., Long, D., Fett, J., Patino, L. (2004) Regional versus local influences on lead and cadmium loading to the Great Lakes region. Appl. Geochem. 19: 1157-1175.

Zhang, Y.-H. (2003) 100 years of Pb deposition and transpor t in soils in Champaign, Illinois, U.S.A. Water Air Soil Pollut. 146: 197-210.3-1 

### 3 POLICY-RELEVANT ASSESSMENT OF HEALTH EFFECTS EVIDENCE

3.1 INTRODUCTION

This chapter assesses key polic y-relevant information on th e known and potential health effects associated with exposure to ambient lead (Pb). The presentation here summarizes the qualitative assessment of health evidence containe d 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 pa rticular attention to those endpoints for which there is quantitative health evid ence available in this review th at 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 se veral key aspects of the health evidence for Pb. First, because exposure to atmospheric Pb pa rticles occurs not only via direct inhalation of airborne particles, but also vi a ingestion of deposited particle s (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 fo r young children (CD, p. 3- 27 to 3-28). Second, the exposure index or dose metric most common ly used and associated with health effects information is an internal biomarker (i.e., bl ood Pb). Additionally, th e 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 scal e of the environmental processes influencing Pb deposition and fate. Lastly, the na ture of the evidence for the hea lth effects of greatest interest for this review is epidemiological data strong ly supported by toxicologi cal data that provide biological plausibility and insi ghts on mechanisms of action. At the time of the last review, Pb was rec ognized 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

μ g/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 μ g/dL, to below 5 μ g/dL and possibly lower (CD, Section 8.4). In recognition of the multi-pat hway aspects of Pb, and use of an internal exposure metric in health risk assessment, Section 3.2 describe s our understanding of the internal disposition or distribution of Pb, and the use of blood Pb as an internal exposure or dose metric. Section 3.33-2 discusses the nature of Pb-induced health effects, giving emphasi s to those with the strongest evidence, particularly those associated with th e range of current expos ure levels. Potential impacts of Pb exposures on public health, includ ing recognition of potentially susceptible or vulnerable subpopulations, are di scussed in Section 3.4. Fina lly, 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, disc ussed 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 di stribution of Pb is an integral aspect of the relationship between exposure and effect. Th is section summarizes the current state of knowledge of Pb disposition pertaining to both i nhalation 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 smalle r particles, which are predominantly deposited in the pulmonary regi on, may, depending on solubility, be absorbed into the general circulation or tr ansported via phagocytic cells to the gastrointestinal tract (CD, pp. 4-3). Lead associated with la rger particles, that are predom inantly deposited in the head and conducting airways (e.g., nasal pha ryngeal and tracheobronchial re gions of respiratory tract), may be transported by mucociliary transport in to 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 tr act either directional th rough the ingestion route or indirectly following inhalation. The absorption efficiency of Pb from the gast rointestinal (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-solubl e form of Pb. Relative bioavailability of Pb
from contaminated soils from different industria l sites (e.g., near Pb smelters, mines, etc), as assessed in such models, have been found to diffe r 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 contaminat ed 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., matur ity of the GI tract), and nutritional status (e.g.,3-3 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 high er than those reported for adults (CD, pp. 4-5 to 4-6). Se veral studies have reported that the presence of food in the GI tract reduces the absorption of wa ter-soluble Pb (CD, p. 4-6). A contributing factor to this phenomenon is the presence of calcium, iron, an d 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 ab sorption 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 distri buted throughout the body (e.g., within days) with the bone serving as a large, long-term storage compartment, and so ft tissues (e.g., kidney, liv er, 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 evid ence indicates that Pb freely cro sses 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 represen ts approximately 70% of a child’s body burden of Pb, and this accumulation continues through adulthood, when more than 90% of the total Pb body burden is stored in the bone (CD, S ection 4.2.2). Accordingly, levels of Pb in bone are indicative of a person’s long- term, cumulative exposure to Pb. In contrast, blood Pb levels are usually indicative of recen t exposures. Depending on exposure dynamics, however, blood Pb may – through its interaction with bone - be indicative of past exposure or of cumulative body burden (CD, Section 4.3.1.5). Throughout life, Pb in the body is excha nged between blood and bone, and between blood and soft tissues (CD, Section 4.3.2), with va riation in these exchan ges reflecting “duration and intensity of the exposure, age and various physiological variables” (CD, p. 4-1). For example, resorption of bone (e.g., in pregna nt or nursing women, a nd associated with osteoporosis in postmenopausal women or, to a lesser magnitude, in older men) results in a
mobilization of Pb from bone in to circulation (CD, Sections 4.3.2.4 and 4.3.2.5). Past exposures that contribute Pb to the bone, consequently, may influence current levels of Pb in blood. Where past exposures were elevated in comparison to recent exposures, this influence may complicate interpretations with regard to recent exposure (CD, Sections 4.3.1.4 to 4.3.1.6). That is, higher blood Pb concentrations may be indicative of higher cumulative exposures or of a recent elevation in exposure (C D, pp. 4-34 and 4-133).
In several recent studies invest igating 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 associ ated with exposures to Pb in3-4 surface dust. Further, as stated in the CD “t hese 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 descri bed 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, conceptu ally viewed as the fast (half- life of ~28 days) and slow (halflife may be decades in adults) turnover pools, respectively (C D, 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 th e 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 a nd Figure 4-5). Additionally, response of the blood to reduction of a relatively brief Pb exposure appear s 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 (C D, 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-respon se relationships (CD, Chapter 5). The prevalence of the use of blood Pb as an expo sure index or biomarker is related to both the ease of blood sample collection (CD, p. 4-19; Se ction 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 agen cies, have for many years used blood Pb level as a metric for identifying children at risk of adverse health effects and for sp ecifying 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 μ g/dL as a level warranting individual intervention (CDC, 1991). In 1985, the CDC recognized a level of 25 μ g/dL for individual child intervention, and in 1991, they recognized a level of 15 μ g/dL for individual intervention and a
level of 10 μ g/dL for implementing community-wide pr evention 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 adve rse health effects in children with blood Pb levels below 103-5 μ g/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 Su rvey (NHANES). This survey has documented
the dramatic decline in mean bl ood Pb levels in the U.S. populati on that has occurred since the 1970s and that coincides with regu lations regarding leaded fuels, leaded paint, and Pb-containing plumbing materials that have re duced Pb exposure among the general population (CD, Sections 4.3.1.3 and 8.3.3; Schwemberger et al., 2005). Alt hough 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 soci oeconomic 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 fo r this review are summarized in subsequent subsections of this chapter. Bone measurements, as a result of the ge nerally slower Pb turnover in bone, are recognized as providing a better measure of cumu lative Pb exposure (CD, Section 8.3.2). The bone pool of Pb in children, however, is thought to be much more labile than that in adults due to the more rapid turnover of bone mineral as a resu lt of growth (CD, p. 4-27). As a result, changes
in blood Pb concentration in children more cl osely parallel changes in total body burden (CD, pp. 4-20 and 4-27). This is in contrast to ad ults, whose bone has accumulated decades of Pb exposures (with past exposures of ten greater than current ones), and for whom the bone may be a significant source long af ter exposure has ended (CD, Section 4.3.2.5). Given the association with exposure, particular ly recent exposure, and the relative ease of collection, blood Pb levels are exte nsively used as an index or biomarker of exposure by national and international health agencies (CD, Sec tion 4.3.1.5). Although recent methods are making
bone Pb measurements easier to collect (CD, Section 4.3.2.2) and consequently, their use more widespread, epidemiological and toxicological studies of Pb h ealth effects and dose-response relationships tend to be dominated by blood Pb as the exposure metric (CD, Sections 4.3.1.3, 8.3.2 and Chapter 5). Accordingly, blood Pb level is the index of exposure or exposure metric in this risk assessment. The use of concentration-response f unctions that rely on blood Pb (e.g., rather than

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 μ g/dL, the lack of a demonstrated threshold for adverse effects, and concerns for deflecting resources from children with higher blood Pb levels (CDC, 2005).3-6 ambient Pb concentration) as the exposure metric reduces uncertainty in the causality aspects of Pb risk estimates. The relationship between specific sources and pathways of exposure and blood Pb level is needed, however, in order to iden tify the specific risk co ntributions associated with those sources and pathways of greatest interest to this asse ssment (i.e., those related to Pb emitted into the air). For example, the blood Pb-response relationships developed in epidemiological studies of Pb-e xposed populations do not distingui sh among different sources or pathways of Pb exposure (e.g., inhalation, ingesti on of indoor dust, ingestion of dust containing leaded paint). In the exposure assessment for th is review, models that estimate blood Pb levels associated with Pb exposure (e.g., CD, Section 4.4) are used to inform estimates of contributions to blood Pb arising from ambient ai r related Pb as compared to c ontributions 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 carcinogeni city, primarily from animal studies, with limited human evidence of suggestive asso ciations (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 expos ure. These are neurological, hematological and immune effects for children, and neurological, hematological, cardiovascu lar and renal effects for adults (See Tables 3-1 and 3-2), with neurol ogical effects in children and cardiovascular effects in adults appearing to be of greatest public health concern (CD, p. 8-60). The toxicological and epidemiological information available since the time of the last review
“includes assessment of new evidence substantiati ng risks of deleterious e ffects on certain health

2 Lead has been classified as a probable human ca rcinogen by the International Agency for Research on Cancer, based mainly on sufficient anim al evidence, and as reasonably antici pated to be a human carcinogen by 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 ).3-7 endpoints being induced by distinct ly lower than previously dem onstrated Pb exposures indexed by blood Pb levels extending well below 10 μ g/dL in children and/or 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 indica te that some studies
have observed these effects at th e 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 describe d briefly in sections below, while detailed discussion of the evidence is presented in the CD.3-8Table 3-1.Summary of Lowest Observed Effect Levels for Key Lead-Induced Health Effects in Children (reproduced fromCD, Table 8-5).Lowest Observed EffectBlood Lead Level Neurological Effects Hematological Effects Immune Effects30 μg/dL Increased urinaryδaminolevulinic acid15 μg/dLBehavioral disturbances(e.g., inattention, delinquency)Altered electrophysiologicalresponsesErythrocyte protoporphyrin(EP) elevation10 μg/dLEffects on neuromotor functionCNS cognitive effects(e.g., IQ deficits)Inhibition ofδ-aminolevulinicacid dehydratase (ALAD)│Pyrimidine-5′-nuclotidase(Py5N) activity inhibitionEffects on humoral (↑serum IgE)and cell-mediated (↓T-cellabundance) immunity5 μg/dL(???)(???)0 μg/dLNote: Arrows depict cases where weight of overall evidence strongly substantiates likely occurrence of type of effect in association with blood-Pbconcentrations in range of 5-10 μg/dL, or possibly lower, as implied by (???). Although no evident threshold has yet been clearly established for thoseeffects, 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).3-9Table 3-2.Summary of Lowest Observed Effect Levels for Key Lead-Induced Health Effects in Adults (reproduced from CD,Table 8-6).Lowest Observed EffectBlood Lead LevelNeurological Effects Hematological Effects Cardiovascular Effects Renal Effects30 μg/dL Peripheral sensory nerveimpairmentErythrocyteprotoporphyrin (EP)elevation in malesImpaired Renal TubularFunction20 μg/dLCognitive impairment15 μg/dL Postural swayErythrocyteprotoporphyrin (EP)elevation in femalesIncreased urinaryδ-aminolevulinic acid10 μg/dL Inhibition ofδ-aminolevulinic aciddehydratase (ALAD)Elevated blood pressure5 μg/dL(???)Elevated serum creatine(↓creatine clearance)0 μg/dLNote: Arrows depict cases where weight of overall evidence strongly substantiates likely occurrence of type of effect in association with blood-Pbconcentrations in range of 5-10 μg/dL, or possibly lower, asimplied by (???). Although no evident threshold has yet been clearly established for thoseeffects, the existence of such effects at still lower blood-Pb levels cannot be ruled outbased on available data.Source: Adapted/updated from Table 1-16 of U.S. Environmental Protection Agency (1986a).3-10 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 investig ated and reported associations of effects on the neurodevelopment of children with these more recent blood Pb levels (CD, Chapter 6). Functional manifestations of Pb neurotoxicity during childhood include sensory, motor, cognitive and behavioral impacts. Nu merous epidemiological studies have reported neurocognitive, neurobehavioral, sensory, and motor function eff ects in children at blood Pb
levels below 10 μ g/dL (CD, Section 6.2). Further, “exte nsive experimental laboratory animal evidence has been generated that (a) substantiates well the plausibility of the epidemiologic findings observed in human children and adults and (b) expands our understanding of likely mechanisms underlying the neurotoxic eff ects” (CD, p. 8-25; Section 5.3). Cognitive effects associated with Pb e xposures that have been observed in epidemiological studies have included decrements in intelligence test results, such as the widely used IQ score, and in academic achievement as assessed by various standard ized tests as well as by class ranking and graduation rates (CD, Sect ion 6.2.16 and pp 8-29 to 8-30). As noted in the CD with regard to the latter, “Associations between Pb exposure and academic achievement
observed in the above-noted studie s were significant even after ad justing for IQ, suggesting that Pb-sensitive neuropsychological processing a nd learning factors not reflected by global intelligence indices might contribute to reduced performance on academic tasks” (CD, pp 8-29 to 8-30). Other cognitive effects observed in studies of children have include d effects on attention, executive functions, language, memory, learni ng and visuospatial processing (CD, Sections 5.3.5, 6.2.5 and 8.4.2.1), with attention and executive function effects associated with Pb exposures indexed by blood Pb levels below 10 μ g/dL (CD, Section 6.2.5 and pp. 8-30 to 8-31).
The evidence for the role of Pb in this suite of effects includes experimental animal findings (discussed in CD, Section 8.4.2.1; p. 8-31), which provide strong biological plausibility of Pb effects on learning abilit y, memory and attention (CD, Sec tion 5.3.5), as well as associated mechanistic findings. Further, Pb-induced defi cits observed in animal and epidemiological studies, for the most part, have been found to be persistent in the absence of markedly reduced environmental exposures (CD, Sections 5.3.5, 6.2.11, and 8.5.2). It is additionally important to note that there may be long-term consequences of such deficits over a lifetime. Poor academic skills and achievement can have "enduring and important effects on objective parameters of3-11 success” later in life, as well as increased risk of antisocial and delinquent behavior (CD, Section 6.2.16). Other neurological effects asso ciated with Pb exposures in dexed by blood Pb levels near or below 10 μg/dL include behavioral effects, su ch as delinquent behavior (CD, Sections 6.2.6 and 8.4.2.2), sensory effects, such as those rela ted to hearing and vision (CD, Sections 6.2.7, 7.4.2.3 and 8.4.2.3), and deficits in neur omotor function (CD, p. 8-36). Neurocognitive impact, specifically decrement in IQ in young children, is a focus of the quantitative risk assessment due to the strength of ev idence for association with blood Pb levels
below 10 μ g/dL, and the strength of the dose-response information at these exposure levels. As discussed in the CD (Section 8.4.2) a nd by Rice (1996), while there is no direct animal test parallel to human IQ tests, “in anim als a wide variety of tests that assess attention, learning, and memory suggest that Pb exposure {of animals} re sults in a global deficit in functioning, just as it is indicated by decrements in IQ scores in children” (CD, p. 8-27). The animal and epidemiological evidence for this en dpoint are consistent and complementary (CD, p. 8-44). As stated in the CD (p. 8-44): Findings from numerous experimental studies of rats and of nonhuman primates, as discussed in Chapter 5, parallel the obse rved human neurocognitive deficits and the processes responsible for them. Learning and other higher order cognitive processes show the greatest similarities in Pb-indu ced deficits between humans and experimental animals. Deficits in cognition are due to the combined and overlapping effects of Pbinduced perseveration, inability to inhib it responding, inability to adapt to changing behavioral requirements, aversion to dela ys, and distractibilit y. Higher level neurocognitive functions are affected in both animals and humans at very low exposure levels (< 10 μ g/dL), more so than simple cognitive functions. Further, “epidemiologic studies of Pb and ch ild development have demonstrated inverse
associations between blood Pb concentrati ons and children’s IQ and other outcomes at successively lower Pb exposure levels” over the pa st 30 years (CD, p. 6-64). This is supported by multiple studies performed over the past 15 y ears (see CD, Section 6.2.13), with particularly compelling evidence for decrements in IQ at blood Pb levels below 10 μ g/dL provided by a recent international pooled analys is of seven prospective studies (Lanphear et al., 2005; CD, Section 6.2.13). For example, this pooled analys is estimated a decline of 6.2 points (with a 95% confidence interval bounded by 3.8 and 8.6) in full scale IQ occurring between approximately 1
and 10 μ g/dL blood Pb level, measured concurrent with the IQ test (CD, p. 6-76). This analysis (Lanphear et al., 2005) is relied upon in the qua ntitative risk assessment for this endpoint.3-12 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 expose d in the workplace are at risk for various neurological effects including pe ripheral sensory nerve impair ment, visuomotor and memory impairment, and postural sway abnormalities, with a blood Pb concentration >14 μ g/dL being a possible threshold (CD, p. 6-87). Past occupa tional exposure also increases the risk of developing amyotrophic lateral sclerosis and mo tor neuron disease (CD, Section 6.3.5 and p. 6- 87). Essential tremor is also associated with Pb exposures, particularly for those with genetic susceptibility (CD, Sections 6.3.5 and 6.3.6 and p. 6-86). In elderly populations, significant associati ons have been reported between bone Pb levels and impaired cognitive performance or dysfunction (CD, Section 6.3.3 and 6.3.3.1), but not with blood Pb levels, perhap s indicating a role of cumulative and/or past Pb exposures (CD, p. 6-83). During demineralization of bone in the el derly, Pb may be releas ed into the blood, thus augmenting blood Pb associated with current ambient exposures (CD, Section 4.3.2.4). An increased susceptibility among the elderly to Pb effects on cognitive function is supported by animal evidence (Section 5.3.7). Wi th lifetime exposure, senescent animals have exhibited 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 indi cates a potential for cogn itive function effects in the elderly to be related to physio logical effects (regula tion of protein thought to play a role in Alzheimer’s disease) of Pb exposures in early childhood (CD, p. 5-67; Basha et al., 2005). Thus, early life exposure to Pb may c ontribute to neurocognitive eff ects later in lif e due to the redistribution of Pb body burden from bone to brain and by enhanced susceptibility caused by age-related degenerative changes in various organs, including brain (CD, p. 8-40).

3.3.3 Cardiovascular System

Epidemiologic and experimental toxicol ogy studies provide strong support for the relationship between Pb exposure and increase d adverse cardiovascular outcomes, including increased blood pressure, increased incidence of hypertension, and cardiova scular morbidity and mortality (CD, Sections 5.5, 6.5 and 8.4.3). The cardi ovascular effect most frequently examined in epidemiological studies is increased systol ic blood pressure in adults, which has been repeatedly associated with Pb exposur e (CD, Sections 8.4.3, 8.6.3, 6.5.2.3, and 6.5.7). The
association has been observed with Pb levels in bone and also, in some cohorts, with Pb in blood (including blood Pb levels below 10 μ g/dL). A recent meta-analysis by Nawrot and others (2005), that included a range of blood Pb levels from 2.3 to 63.8 μ g/dL, reported an association of increased systolic blood pre ssure and decreased diastolic pr essure with increased blood Pb3-13 level, including levels below 10 μ g/dL. The magnitude of cha nge observed has considerable significance at the population leve l (CD, p. 8-45, Section 8.6.3). The epidemiological evidence is supported by evidence in numerous animal st udies of arterial hypertension with low Pb exposures, an effect that persists in animals long after cessation of exposure (CD, Sections 5.5 and 8.4.3). Multiple studies reporting positive associations of blood pressure and hypertension with bone Pb levels highlight the important role of cumulative past Pb exposure in development of cardiovascular health effects (Sections 6.5.2.3 a nd 6.5.7). A study of young adults who lived as children in an area of high Pb exposures also indicates the potential role of childhood exposure. In this study, higher bone Pb leve ls were associated with high er systolic and diastolic blood pressure (CD, p. 6-138), while current blood Pb levels (mean of 2.2 μ g/dL) were not (CD, p. 6- 124). Systolic blood pressure exer ts a strong influence on more serious cardiovascular events by its role in hypertension and its adverse ca rdiovascular sequelae (CD, p. 8-83). Several analyses of National Health and Nutrition Examination Survey (NHANES) cohorts, including some recently released, have collectively suggested a “significant effect of Pb on 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 cardi ovascular mortality, consistent with projected likely increases in serious cardiov ascular events (stroke, heart a ttack) resulting from Pb-induced increases in blood pressu re (CD, Section 8.6.3).

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, S ection 5.7). A set of sc reening tests involving markers of nephrotoxic effects have been establ ished for screening individuals exposed to Pb occupationally or environmentally (CD, Secti on 5.7.1). In the epidemiological literature,
associations between blood Pb and indicators of renal function impairment (e.g., measures of glomerular integrity, such as creatinine levels in urine) have been found at blood Pb levels extending below 10 μ g/dL, to as low as ~2 to 4 μ g/dL (CD, Sections 6.4.4.1.5 and 8.4.5).
Associations are also observed with cumulative Pb dose, assesse d via bone Pb, and longitudinal renal function decline (CD, p. 6-94), indicating the potential role of earlier exposures. The findings for non-occupationa l populations since the la st review provide “strong
evidence that renal effects occur at much lowe r blood Pb levels than previously recognized”3-14 (CD, p. 6-113). These findings of lower Pb renal effects thresholds in environmental compared to occupational research may be a result of potentially larg er proportions of susceptible individuals in the general popula tion as compared to occupationa l cohorts (CD, p. 6-107). The
data available are not sufficient to determine wh ether these effects are related more to current blood-Pb levels, higher levels from pa st exposure, or both (CD, p. 8-49). The findings regarding Pb exposures and rena l effects are of particular concern with regard to certain susceptible subpopulations. At levels of exposure in the general U.S. population overall, Pb combined with other risk factors, such as diabetes, hypertension, or chronic renal insufficiency from causes unrelated to Pb, can result in clinical ly relevant effects. Notably, the size of such suscepti ble populations is increasing in th e United States due to obesity (CD, p. 6-113). That is, Pb is r ecognized as acting cumulatively w ith other renal risk factors to cause early onset of renal insufficiency and/or a steeper rate of re nal function decline in individuals already at risk fo r renal disease (CD, p. 6-107).

3.3.5 Heme Synthesis

It has long been recognized that Pb exposure is associat ed with disruption of heme synthesis in both children and adults. The evidence regarding effects on heme synthesis 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 target s of low-level Pb toxicity in children during the time of the last NAAQS review (USEPA, 1990), and was the primary focus for the initial setting of the Pb NAAQS in 1978 (USEPA, 1978).
Mechanisms associated with Pb interference with heme synthesis include inhi bition of the enzymes ALAD and ferrochelatase (Tab le 3-1; CD Sections 5.2.1, 6.9.1, 6.9.2; USEPA 1986). Inhibition of ALAD has been associated w ith increased blood Pb concentrations at and
somewhat below 10 μ g/dL, in children and adults (Tables 3-1 and 3-2; CD, Table 6-7). Blood Pb concentrations at and above approximately 15 μ g/dL, in children, and 15-30 μ g/dL, in adults,
are associated with elevation of erythrocyte proto porphyrin (EP), and notable reductions in hemoglobin synthesis (Tables 3-1 and 3-1; CD, p. 8-47; USEPA, 1986). In the setting of the Pb NAAQS in 1978, the Agency concluded that “the st ate of elevated EP must be regarded as potentially adverse to the health of young child ren” (USEPA, 1978). Blood Pb concentrations at and above 40 ug/dL are associated with frank anem ia, a clinical sign of severe Pb poisoning (CD, p. 8-47). The evidence regarding Pb disruption of heme synthesis and associated mechanisms is presented in detail in past CDs (USEPA 1986, 1977), with more recent findings, including the role of genetic polymorphisms, discussed in the current CD (Sections 8.4.4, 5.2.1, 6.9.1 and 6.9.2).3-15 3.3.6 Immune System

Since the time of the last review, ther e has been substantial research on the immunotoxicity of Pb. As summarized in th e CD, “studies across humans and a variety of animal models are in general agreement con cerning both the nature of the immunotoxicity induced by Pb as well as the exposure conditions that are required to produce immunomodulation” (CD, p. 5-244, Section 5.9 ). Lead is distinguished from other immunotoxicants, however, by the fact that the most sensitive biom arkers of its immunotoxicity are associated with specific func tional capacities that influence ri sk of disease, as opposed to being associated with changes in immune cell numbers or pat hological changes of lymphatic system organs (CD, Section 5.9.1). The main immune system targets of Pb are macrophages and T lymphocytes, leading to a potential fo r increased tissue inflammation, reduced cellmediated immunity, and increased risk of au toimmunity (See CD, Figure 5-18, Section 5.9.11). Additionally, Pb exposures in bot h animal and human studies ar e associated with increased production of IgE, an immunoglobulin involved in allergic respons es and asthma (CD, Section 5.9.3.2). These effects 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 μ g/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 ag ed 1-5, as estimated by the U.S. Centers for Disease Control, declined from 2.7 μ g/dL (95% CI: 2.5-3.0) in th e 1991-1994 survey period to
1.7 μ g/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 pe riod (CD, Section 4.3.1.3). These observation however, should not be interpreted to mean that bl ood Pb levels declined in all communities, or uniformly by this amount. As not ed 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 su ch 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 a nd potential public health impact s associated with exposure to
ambient Pb.

3 These levels are in contrast to the geometric mean blood Pb level of 14.9 μ g/dL reported for U.S. children (aged 6 months to 5 years) in 1976-1980 (CD, Section 4.3.1.3).3-16 3.4.1 At-risk Subpopulations

In considering at-risk subpopulations, we c onsidered evidence regarding those with increased susceptibility (i.e., phys iological factors contributing to a greater response for the same exposure), and those with increase d exposure (including that resul ting from behavior leading to increased contact with contaminated media). A be havioral factor of great impact on Pb exposure
is the incidence of hand-to-mout h activity that is prevalent in very young children (CD, Section 4.4.3). Physiological factors incl ude both conditions contributing to a subgroup’s increased risk of effects at a given blood Pb le vel, and those that contribute to blood Pb levels higher than those otherwise associated with a given Pb exposur e (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 residi ng in old housing with Pb-containing paint or near sources of ambient Pb, as well as socioeconomic f actors, 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 contribut ing to increased risk of Pb effects at a given blood Pb level are recognized in the CD (e .g., CD, Section 8.5.3): age, health status, and genetic composition (or genotype). With regard to age, the susceptibility of young children to the neurodevelopmental effects of Pb is well recognized (e.g., CD, Sections 5.3, 6.2, 8.4, 8.5, 8.6.2), although the specific ages of vulnerability have not been established (CD, pp 6-60 to 6- 64). Early childhood may also be a time of increa sed susceptibility for Pb immunotoxicity (CD, Sections 5.9.10, 6.8.3 and 8.4.6), and childhood exposures have been associated with increased
risk of cardiovascular and neurode generative effects in adulthood (C D, p. 8-74). Health status is another physiological factor in that subpopulations with pre-exis ting health conditions may be more susceptible (as compared to the general po pulation) for particular Pb-associated effects, with this being most clear fo r renal and cardiovascular outcomes. For example, African Americans as a group, have a higher frequency of hypertension than the general population or other ethnic groups (NCHS, 2005), and as a result may face a greater risk of adverse health impact from Pb-associated cardiovascular effects. A third physiological f actor relates to genetic polymorphisms. That is, subpopulations defi ned by particular genetic polymorphisms (e.g., presence of the δ -aminolevulinic acid dehydratase-2 [ALAD- 2] allele) have al so been recognized as sensitive to Pb toxicity, which may be due to increased susceptibility to the same internal dose
and/or to increased internal dose associated with same exposure (CD, p. 8-71, Sections 6.3.5, 6.4.7.3 and 6.3.6). While early childhood is recognized as a time of increased su sceptibility, a difficulty in identifying a discrete period of susceptibility fr om epidemiological studies has been that the period of peak exposure, reflected in peak bl ood Pb levels, is around 18- 27 months when hand-3-17 to-mouth activity is at its maximum (CD, p. 6-60). The earlier Pb literature described the first 3 years of life as a criti cal window of vulnerability to the neurodevelopmental impacts of Pb (CD, p. 6-60). Recent epidemiologic stud ies, 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 (C D, Section 8.4.2.7). The animal evidence supports our understanding of specific periods of developm ent 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 th e immune system to Pb across and within different periods of life stages indicat es that Pb exposures of duration as short as weeks to months may contribute to some effects. For example, the anim al evidence suggests that the gestation period is the most sensitive life stag e 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 sus ceptibility by contributi ng to increased blood Pb levels (i.e., increased internal dose levels) over those otherwise associated with a given Pb exposure (CD, Section 8.5.3). These include nutritional status, which plays a role in Pb absorption from the GI tract (CD, Section 5.10.2. 5); polymorphism for the vitamin D receptor, which studies suggest may contribute to increased Pb absorption from the GI tract (CD, Section 8.4.2.7); presence of the ALAD-2 allele, which studi es suggest contribute to increased blood Pb levels (Section 8.5.3); and bone de mineralization, such as occu rs during pregnancy, lactation, and aging, which appears to influence Pb release from bone into the blood (CD, Section 4.3.2). In summary, there are a vari ety of ways in which Pb exposed populations might be
characterized and stratified for cons ideration of public health impact s. 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 e xposure and susceptibili ty, and young children were selected as the priority population for the ri sk assessment in consideration of the health effects evidence regarding endpoints of greatest public health concern and in recogni tion of effects on the developing nervous system as a sentinel endpoint for public health impacts of Pb (see Section 3.3). We recognize, however, ot her population subgroups as describe d above may also be at risk
of Pb-related health effect s of public health concern.3-18 3.4.2 Potential Public Health Impact

There are several potential publ ic 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 impact s 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 scor es in a population, “a downward shift in the mean IQ value is not associated only with a substan tial increase in the percentage of individuals achieving very low scores, but also with substan tial decreases in percenta ges achieving very high scores” (CD, p. 8-81). For example, for a populat ion 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 rec ognize distinctions between p opulation and individual risk, the CD notes that a “point esti mate indicating a modest mean ch ange on a health index at the
individual level can have subs tantial implications at the popula tion level” (CD, p. 8-77). For example, “the import of a decline for an indivi dual’s well-being is lik ely 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 con cern for an individual’s wellbeing, the same increase in the population mean might be associated with substantial increases in the percentages of individuals w ith 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 Secti on 3.4.1, there are several population groups that may be susceptible or vulnera ble 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, a nd chronic renal insufficiency. Although comprehensive estimates of the size of these gr oups residing in proximity to policy-relevant
sources of ambient Pb have not been deve loped, total estimates of these population subpopulations within the U.S. are substantial (Table 3-3).3-19 Table 3-3. Population subgroups with characterist ics that may contribute to increased susceptibility or vulnerabilit y to Pb health effects.

Children a Living in poverty Adults b w. hypertension c Adults b w. Diabetes Adults b w. chronic kidney disease

Estimated number in U.S. population d 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 millim eters of mercury (mm Hg) or higher, using blood pressure lowering medications, or having been told at l east 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 surf ace soil concentrations of Pb indicates
elevated concentrations near stat ionary 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 monito rs near sources as compared with those more remote (Section 2.3.2.4). We are handicapped, howeve r, in our ability to characterize the size of at-risk populations in areas influenced by po licy-relevant sources of ambient Pb by the significant limitations of our monitoring and em issions 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 th e 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 s ources 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 uncertain ties 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 concentr ations 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 are3-20 informative with regard to considering the prev alence of airborne Pb emissions (and associated airborne Pb concentrations) and exposure of human populations. Air quality analyses of the limited mon itoring network indicates the numbers of monitoring sites exceeding alternat e NAAQS levels, with considera tion of different statistical forms (Section 2.3.2.5), and these analyses are su mmarized with regard to population size in
counties home to those monitori ng sites (Appendix 5.A). Informa tion 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 leve ls of sources expected to result in air Pb concentrations of interest, how ever, 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 ha ve 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 em issions 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.3-21 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 pot ential for historically deposited Pb near roadways to contribute to increased risks of Pb exposure and associated ri sk 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 child ren, living within close proximi ty 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 eviden ce and the evaluation a nd interpretation of that evidence in the CD, summarized briefly a bove, the following conclusions have been drawn:

• Lead exposures occur both by inhalati on and by ingestion. Ingestion of Pbcontaminated dust has a strong influen ce on blood Pb levels in children.

• Children, in general and especially low SE S children, are at increased risk for Pb exposure and Pb-induced adverse health effect s. This is due to several factors, including enhanced exposure to Pb via i ngestion of soil Pb and/or dust Pb due to childhood hand-to-mouth activity and poor nutritional status.

• Once inhaled or ingested, Pb is distri buted by the blood, with long-term storage accumulation in the bone. Bone Pb levels provide a strong measure of cumulative exposure which has been asso ciated 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”, “Hig hway”, “Major Road” or “Ramp”, as defined by the U.S. Census Feature Class Codes) (ICF, 2005).3-22 although difficulty of sample collecti on 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 co mplicating factor, i.e., fo r effects during childhood.

• Epidemiological studies have observed signi ficant 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 rele vant for the U.S. population, with children having blood Pb levels of 5-10 μ g/dL or, perhaps somewhat lower, being at notable risk.

• Pb exposure is associated with a variety of neurological effects in children, notably intellectual attainment, a ttention, and school performance. Both qualitative and quantitative evidence, with further support from animal research, indicates a robust effect of Pb exposure on neuroc ognitive ability at blood Pb leve ls levels in the range of 5 to 10 μ g/dL, and some analyses appear to show Pb effects on intellectual attainment in young children with blood Pb levels ranging from 2 to 8 μ g/dL

• Deficits in cognitive skills may have long-te rm 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-respons e relationship at lower blood Pb levels.

• For children, the evidence is also robust fo r Pb-induced disruption of heme synthesis at blood Pb levels of approximately 15 μ g/dL and higher. At blood Pb levels on the order of 10 μ g/dL, and slightly lower, associations have been found with effects to the immune system, resulting in altered macr ophage function, increased IgE levels and associated increased risk for autoimmunity and asthma.

• In adults, epidemiological studies have cons istently demonstrated associations between Pb exposure and increased risk of adve rse 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 μ g/dL. Animal evidence provides confirmation of Pb effects on cardiovascular functions. For th ese Pb effects, particularly susceptible subpopulations include those with a higher baseline blood pressure. For example, African Americans, as a group, have greater incidence of el evated 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 le vels and also with blood Pb below 10 μ g/dL, with the potential adverse impact of such effects being enhan ced for susceptible3-23 subpopulations including thos e with diabetes, hypert ension, and chronic renal insufficiency.

• Other Pb associated effects in adul ts occurring at or just above 10 μ g/dL include hematological (e.g., impact on heme synthesis pathway) and neurolog ical effects, with animal evidence providing support of Pb effects on these systems and evidence regarding mechanism of action.3-24 REFERENCES
Al-Saleh, I.; Nester, M.; DeVol, E.; Shinwari, N.; Munchari, L.; Al-Shahria, S. (2001) Relationships between blood lead concentrations, intelligence, and academic achieve ment of Saudi Arabian schoolgirls. Int. J. Hyg. Environ. Health 204: 165-174.

Centers for Disease Control (1991) Pr eventing lead poisoning in young children: a statement by the Centres for Disease Control. Atlanta, GA: U.S. Department of health and Human Services, Public Health Service; October 1. http://wonder.cdc.gov/wonder/prevguid/p0000029/p0000029.asp

Centers for Disease Control and Preventio n (2003) National Diabetes Fact Sheet. http://www.cdc.gov/diabetes/pubs/factsheet.htm

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

Coresh, J.; Astor, B.C.; Greene, T.; Eknoyan, G.; Levey, A.S. (2005) Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis 41(1): 1-12.

DeNavas-Walt, C.; Proctor, B.D.; Lee, C.H. (2006) Income , Poverty, and Health Insurance Coverage in the United States: 2005. U.S. Census Bureau, Current Population Reports, P60-231. U.S. Government Printing Office, Washington, DC.

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.

ICF, 2005. Estimating near roadway populations and areas for HAPEM6. Memorandum to Chad Bailey, Office of Transportation and Air Quality, U.S. EPA. D ecember 28. Docket EPA-HQ-OAR-2005-0036.

Lanphear, B. P.; Roghmann, K. J. (1997) Pathways of lead exposure in urban children. Environ. Res. 74: 67-73.

Lanphear, B. P.; Burgoon, D. A.; Rust, S. W.; Eberly, S. ; Galke, W. (1998) Environmental exposures to lead and urban children's blood lead levels. Environ. Res. 76: 120-130.

Lanphear, B. P.; Dietrich, K. N.;Auinger, P.; Cox, C. (2000) Cognitive deficits associated with blood lead concentrations <10 μ g/dL in US children and adolescents. Public Health Reports. 115: 521-529.

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 enviro nmental lead exposure and children's intellectual function: an international pooled analysis. Environ. Health Perspect. 113: 894-899.

Leggett, R. W. (1985) A model of the retention, translocat ion and excretion of systemic Pu. Health Phys. 49: 1115- 1137.

Leggett, R. W. (1992a) A retentio n-excretion model for americium in humans. Health Phys. 62: 288-310.

Leggett, R. W. (1992b) A generic age-specific biokinetic model for calcium-like elements. Radiat. Prot. Dosim.41: 183-198.3-25 Leggett, R. W. (1993) An age-specific kinetic model of l ead metabolism in humans. Envi ron. Health Perspect. 101: 598-616.

Muldoon, S. B.; Cauley, J. A.; Kuller, L. H. ; Morrow, L.; Needleman, H. L. ; Sc ott, J.; Hooper, F. J. (1996) Effects of blood lead levels on cognitive function of older women. Neuroepidemiology 15: 62 72.

National Center for Health Statistics. (2005) Health, United States, 2005. With Chartbook on Trends in the Health of Americans. Hyattsville, Maryland.

Payton, M.; Riggs, K. M.; Spiro, A., III; Weiss, S. T.; Hu , H. (1998) Relations of bone and blood lead to cognitive function: the VA Normative Aging Study. Neurotoxicol. Teratol. 20: 19 27.

Rhodes, D.; Spiro, A., III; Aro, A.; Hu, H. (2003) Relationship of bone and blood lead levels to psychiatric symptoms: The Normative Aging Study. J. Occup. Environ. Med. 45: 1144 1151.

Rice, D.C. (1996) Behavioral effects of lead: commonalitie s between experimental and epidemiologic data. Environ Health Persp 104 (Suppl 2): 337-351.

Rothenberg, S.J.; Rothenberg, J.C. (2005) Testing the dos e-response specification in epidemiology: public health aand policy consequences for lead. Environ. Health Perspect. 113: 1190-1195.

Schwemberger, MS, JE Mosby, MJ Doa, DE Jacobs, PJ Ashley, DJ Brody, MJ Brown, RL Jones, D Homa. May 27, 2005 Mortality and Morbidity Weekly Report 54(20):513-516.

U.S. Census Bureau. 2006. American Housing Survey for the United States: 2005. Current Housing Reports, Series H150/05. U.S. Government Printing Office, Washington DC.

U.S. Environmental Protection Agency. (1978) National Pr imary and Secondary Ambient Air Quality Standards for Lead. Federal Register 43(194): 46246-46263. Oct 5, 1978.

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.

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

. U.S. Environmental Protection Agency. (1994a) Guidance manual for the integrated exposure uptake biokinetic model for lead in children. Washington, DC: Office of Emergency and Remedial Response; report no. EPA/540/R-93/081. Available from: NTIS, Springfield, VA; PB93-963510.

U.S. Environmental Protection Agency. (1994b) Technical support document: parameters and equations used in integrated exposure uptake biokinetic model for lead in children (v 0.99d). Washington, DC: Office of Solid Waste and Emergency Response; report no. EPA/540/R-94/040. Available from: NTIS, Springfield, VA; PB94-963505.

U.S. Environmental Protection Agency. (2003) Framework for Cumulative Risk Assessment. Risk Assessment Forum, Washington, DC, EPA/630/P-02/001F. May

U.S. Environmental Protection Agency. (2006) Air Qu ality Criteria for Lead. Washington, DC, EPA/600/R5/144aF. Available online at: www.epa.gov/ncea/3-26 Weisskopf, M. G.; Wright, R. O.; Schwar tz, J.; Spiro, A., III; Sparrow, D.; Ar o, A.; Hu, H. (2004a) Cumulative lead exposure and prospective change in cognition among elderly men. The VA Normative Aging Study. Am. J. Epidemiol. 160: 1184 1193.

White, P. D.; Van Leeuwan, P.; Davis, B. D.; Maddaloni, M.; Hogan, K. A.; Ma rcus, A. H.; Elias, R. W. (1998) The conceptual structure of the integrated exposure uptake biokinetic model for lead in children. Environ. Health Perspect. 106(suppl. 6): 1513-1530.

Wright, R. O.; Hu, H.; Silverman, E. K.; Tsaih, S. W.; Sc hwartz, J.; Bellinger, D.; Pala zuelos, E.; Weiss, S. T.; Hernandez Avila, M. (2003) Apolipoprotein E genotype predicts 24 month bayley scales infant development score. Pediatr. Res. 54: 819 825.4-1 

### 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 th is chapter, the term "risk
assessment" will be used to refer to both th e 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 asse ssment 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 th e subject of a review by the Clean Air Scientific Advisory Committ ee (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 pub lic 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 perfor med 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 ar e 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 constraine d by significant limitations with re gard to data and tools needed for the assessment. Further, the multimedia and persistent nature of Pb and the role of multiple exposure pathways contributes si gnificant 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 char acterize 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 th ose emitting Pb into the ambient air (including resuspension of previously emitted Pb), and ambi ent air related exposures include inhalation of ambient air Pb as well as ingestion of Pb de posited out of the air (e.g., onto outdoor soil/dust or indoor dust). As illustrated in Fi gure 2-1 people are also exposed to Pb that originates from nonair sources, including leaded paint or drinki ng water distribution systems. For purposes of4-2 this assessment, however, the Pb from these nonai r 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 fr om air pathways to these exposure pathways could not be explicitly modele d, such that these exposures ar e treated as policy-relevant background. 2 In the Risk Assessment Report (USEPA, 2007b ), we have made every effort to completely describe the assessment design, da ta and methodology, to identify and describe limitations and simplifying assumptions, and to ch aracterize our understandi ng of the associated uncertainty in the exposure and risk estimates. Du e 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 associ ated results are briefly summarized in Section
4.2.7. We direct the reader to the Risk Assessm ent Report for a more complete presentation. The remainder of this chapter is organi zed as follows. Section 4.1.1 provides an overview of the human health risk assessment comp leted 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 th e 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 asse ssment (Section 4.4).

4.1.1 Overview of Risk Assessme nt 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 anal ysis, however, focused exclusively on Pb smelters including two s econdary and one primary smelter and did not consider exposures in a more general urban co ntext. Additionally, the analysis focused on children (birth through 7 years of age) and middl e-aged men. The staff evaluated impacts of alternate NAAQS on numbers of children and men w ith 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 analys is and the assessment co mpleted in 1990 involves the risk metric employed. Rather than estim ating the percentage of study populations with exposures above blood Pb levels of interest as was done in th e last review (i.e., 10, 12 and 15

1 This categorization of policy-relevant sources and ba ckground exposures is not intended to convey any particular policy decision at this stage regarding the Pb 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).4-3 μ g/dL), the current analysis estimates changes in h ealth 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 e xposure and policy-re levant exposures.

4.1.2 CASAC Advice on Pilot and Initial Ri sk Analyses in this Review

The staff consulted with the CASAC on the dr aft 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 pr iority on modeling the child IQ metric than the adult endpoint s (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 an alysis, and (c) concurring with use of the IEUBK biokinetic blood Pb model. Staff subsequently developed the pi lot phase assessment, intended to test the risk assessment methodology being developed for the full-sc ale assessment. The pilot is described in the first draft Staff Paper and accompanying te chnical report (USEPA, 2006b; ICF 2006), which was discussed by the CASAC Pb panel on Fe bruary 6-7 (Henderson, 2007a). Results from the pilot assessment, togeth er with comments received from CASAC and the public, informed decisions on the design of th e 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, incl uding 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) func tions to provide greater covera ge for potential impacts at lower exposure levels. The draft full-scale assessment was presente d in the July 2007 dr aft risk assessment report (USEPA, 2007a) that was released for public comment and provided to CASAC for review. In their review of the July draft ri sk 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, al ong with comments from CASAC and the public
resulted in additional modifications and enhancem ents 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 Assessm ent 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.4-4 4.2 DESIGN OF EXPOSURE AND RISK ASSESSMENTS

This section provides an overview of key elemen ts in the design of the risk assessment. In the discussion below, we highlight key aspect s of the assessment design, inputs, and methods, and we also highlight key uncertainties and limitati ons 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 a ssessment 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 (Sectio ns 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. Lastl y, 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 mo dels, and uncertainties occurs in the Risk Assessment Repor t (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 S ection 3.3, the health endpoint on which we focused in the quantitative health risk assessment for this review is deve lopmental neurotoxicity in children, with IQ decrement as the risk me tric. 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 neurobe havioral effects (speci fically neurocognitive deficits), including IQ decrements, appear to occur at lower blood leve ls than previously believed (i.e., at levels <10 μ g/dL). For example, the overall we ight 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 μ g/dL, and some analyses indicate Pb effects on intellectual attainment of young children ranging from 2 to 8 μ g/dL (CD, Sections 6.2, 8.4.2 and 8.4.2.6). That is, while blood Pb levels in U.S. children ages one to five years have decreased notably since the late 1970s, newer studies ha ve investigated and reported
associations of effects on the neurodevelopment of children with these more recent blood Pb levels (CD, Chapter 6).
The evidence for neurotoxic effects in children is a robust combination of epidemiological and toxicological eviden ce (CD, Sections 5.3, 6.2 and 8.5). The epidemiological evidence is strongly supported by animal studies that substantiate the biological plausibility of the associations , and provides an understanding of mechanisms of action for the effects (CD, Section 8.4.2). The selection of children’s IQ for the quantitative risk assessment4-5 reflects consideration of the ev idence presented in the CD as well as advice received from CASAC (Henderson, 2006, 2007a). The epidemiological studies that have i nvestigated blood Pb effects on IQ (see CD, Section 6.2.3) have considered a variety of specific blood Pb metrics, including: 1) blood
concentration “concurrent” with the response assessment (e.g., at the time of IQ testing), 2) average blood concentration over the “lifetime” of the child at the time of response assessment (e.g., average of measurements taken over child’s fi rst 6 or 7 years), 3) peak blood concentration during a particular age range, and 4) early childhood blood conc entration (e.g., the mean of measurements between 6 and 24 months age). All four specific blood Pb metrics have been correlated with IQ (see CD, p. 6- 62; Lanphear et al., 2005). In th e international pooled analysis by Lanphear and others (2005), however, the conc urrent and lifetime averaged measurements were considered “stronger predictors of lead-associated inte llectual deficits than was maximal measured (peak) or early chil dhood blood lead concentrations,” with the concurrent blood Pb level exhibiting the strongest relationship (CD, p. 6-29) . It is not clear in this case, or for similar findings in other studies, whether the cognitive deficits observed were due to Pb exposure that occurred during early childhood or were a functi on of concurrent exposure. Nevertheless, concurrent blood Pb levels likely reflected bo th ongoing exposure and preexisting body burden (CD, p. 6-32). Using concurrent blood Pb level as the exposur e metric and IQ as the response from the pooled dataset of seven international studi es, Lanphear and others (2005) employed mathematical models of various forms, includi ng linear, cubic spline, log-linear, and piece-wise linear, in their investigation of the blood Pb concentration-response relationship (CD, p. 6-29; Lanphear et al., 2005). They obser ved that the shape of the con centration-response relationship is nonlinear and the log-linear model provides a better fit over the full range of blood Pb measurements than a linear one (CD, p. 6-29 a nd pp. 6-67 to 6-70; Lanphear et al., 2005). In addition, they found that no indi vidual study among the seven was responsible for the estimated nonlinear relationship between Pb a nd deficits in IQ (CD p. 6-30). Others have also analyzed the same dataset and similarly concluded that, acro ss the range of the dataset’s blood Pb levels, a log-linear relationship was a sign ificantly better f it than the linear rela tionship (p=0.009) with
little evidence of resi dual confounding from included mode l variables (CD, Section 6.2.13; Rothenberg and Rothenberg, 2005). A nonlinear blood Pb concentration-response re lationship is also suggested by several
other studies that have observed that each μ g/dL increase in blood Pb ma y have a greater effect on IQ at blood Pb levels below 10 μ g/dL than at higher levels (CD, pp. 8-63 to 8-64). While this may at first seem at odds with certain fundament al toxicological concepts, a number of examples of non- or supralinear dose-response relationships exist in toxicology (CD, pp. 6-76 and 8-83 to4-6 8-89). 3 With regard to the effects of Pb on ne urodevelopmental outcome such as IQ, the CD suggests that initial neurodevelopmental effects at lower Pb levels may be disrupting very different biological mechanisms (e.g., early deve lopmental processes in the central nervous system) than more severe effect s of high exposures that result in symptomatic Pb poisoning and frank mental retardation (CD, p. 6-76). In comparing across the individual studies and the pooled analysis, it is observed that at higher bloo d Pb levels, the slopes derived for log-linear and linear models are almost identical, and for studies with lower blood Pb levels, the slopes appear to be steeper than those observed in studies involving higher blood Pb levels (CD, p. 8-78, Figure 8-7).
Given the evidence summarized here and descri bed in detail in the CD (Chapters 6 and 8), and in consideration of CASAC recomm endations (Henderson, 2006, 2007a, 2007b), the risk
assessment for this review relies on the functions presented by Lanphear and others (2005) that relate absolute IQ as a function of concurrent blood Pb or of th e log of concurrent blood Pb, and lifetime average blood Pb, respectively. As disc ussed above, the slope of the concentrationresponse relationship described by these functions is greater at the lower blood Pb levels (e.g.,
less than 10 μ g/dL). The impact of the nonlinear slope is illustrated by the estimates of IQ decrements associated with increases in blood IQ for different ranges of blood Pb level reported for the log-linear model (Lanphear et al., 2005). These estimates were IQ decrements of 3.9 (with 95% confidence interval, CI, of 2.4-5.3), 1.9 (95% CI, 1.2-2.6) a nd 1.1 (95% CI, 0.7-1.5),
for increases in concurrent blood Pb from 2.4 to 10 μ g/dL, 10 to 20 μ g/dL, and 20 to 30 μ g/dL, respectively (Lanphear et al., 2005). For an increase in concurrent blood Pb levels from <1 to 10 ug/dL, the log-linear model estimates a decline of 6.2 points in full scale IQ which is comparable to the 7.4 point decrement in IQ for an increase in lifetime mean blood Pb levels up to 10 ug/dL observed in the Rochester study (CD, pp 6-30 to 6-31).
Several studies have examined the relationshi p of IQ decrement with blood Pb, quantified by a variety of metrics, at lower blood Pb levels. On a change in IQ per μ g/dL basis, estimates of IQ decrement associated with blood Pb levels (concurrent, 24-month, peak, lifetime average or lifetime cumulative) below 10 μ g/dL range from -0.4 to -1.8 (CD, Table 8.7). At the upper end of this range are the slopes derived for the subsets of children in the Rochester and Boston cohorts for which peak bl ood Pb levels were <10 μ g/dL; these slopes are -1.8 (for concurrent blood Pb influence on IQ) and -1.6 (for 24-month bl ood Pb influence on IQ), respectively. The
numbers of children in these low blood Pb subs ets of the Rochester and Boston cohorts are 101 and 48, respectively. A similar st ratification of the pooled datase t by Lanphear and others (2005)

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).4-7 yielded a slope for the linear function of IQ change associated with concurrent blood Pb of -0.8 for the subset of the children in the pooled da ta set for which maximal or peak blood Pb levels
were below 10 μ g/dL. Of the 1333 children in the full pool ed dataset, there were 244 in this
subset. When the full dataset was restricted to a still smaller subset of 103 children for which peak blood Pb levels were below 7.5 μ g/dL the slope of concurrent blood Pb and IQ was -2.94 (Lanphear et al., 2005). The analysis of this latter subset supporte d the authors’ conclusions that “for a given increase in blood l ead, the lead-associated intellectual decrement for children with a
maximal blood lead level <7.5 μ g/dL was significantly greater than that observed for those with
a maximal blood lead level > 7.5 μ g/dL (p-0.015)” and that “envi ronmental lead exposure in
children who have maximal blood lead levels < 7.5 μ g/dL is associated with intellectual deficits”. This subset was composed primar ily of children from the Rochester cohort (69 children), with smaller numbers of children from 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 s ubset 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 descri bed above, and greater than thos e slopes from other studies for
blood Pb < 10 μ g/dL summarized in the CD (e.g., CD, Ta ble 8-7), providing some uncertainty with regard to the precise magnitude of slope for the full range of blood Pb below 7.5 μ g/dL. As discussed in the CD, threshold blood Pb le vels for these effects cannot be discerned from the currently available epidemiological studies, and the evidence in the animal Pb neurotoxicity literature does not define a threshol d for any of the toxic mechanisms of Pb (CD, Sections 5.3.7 and 6.2). In applying relationships observed with the poo led analysis (Lanphear et al., 2005) to the risk assessment, which includes blood Pb leve ls below the range repr esented by the pooled analysis, several alternative blood Pb concentr ation-response models were considered in recognition of a reduced confiden ce in our ability to character ize the quantita tive blood Pb concentration-response relationship at the lowest blood Pb levels repres ented in the recent epidemiological studies. The functions considered and employed in the initial risk analyses for this review include the following.

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

• Log-linear function with cutpoint, for bot h concurrent and lifetime average blood metrics, also applies the non linear relationship at blood Pb concentrations above the lower bound of blood Pb concentrations in th e pooled analysis dataset for that blood4-8 metric, but then applies zero ri sk to all lower blood Pb concentrations estimated in the risk assessment.

• Two-piece linear function, for both concurrent and lifetime average blood metrics, applies a two-piece linear model derived fr om the log-linear func tion to all blood Pb concentrations estimated in the risk assessment. In the additional risk analyses performed subs equent to the August 2007 CASAC public meeting (Section 4.1.2) using the core m odeling approach, the first two f unctions listed above and the following two functions were employed (see Section 5.3.1 of the Risk Assessment Report for
details on the forms of these functions as applied in this risk assessment).

• Population stratified dua l linear function for concurrent blood Pb, derived from the pooled dataset stratified at peak blood Pb of 10 μ g/dL and

• Population stratified dua l linear function for concurrent blood Pb, derived from the pooled dataset stratified at 7.5 μ g/dL peak blood Pb. In interpreting risk estimates derived using th e various functions, cons ideration should be given to the uncertainties with re gard to the precision of the coeffi cients used for each analysis. The coefficients for the log-linear model from Lanphear et al. (2005) had undergone a careful development process, including sensitivity analyses, using all available data from 1,333 children. The shape of the exposure-response relationship was first assessed through tests of linearity, then by evaluating the restricted cubi c spline model. After determ ining that the log-linear model provided a good fit to the data, covariates to adjust for potential c onfounding were included in the log-linear model with careful consideration of the stability of the parameter estimates. After the multiple regression models were develope d, regression diagnostics were employed to
ascertain whether the lead coefficients were aff ected by collinearity or influential observations. To further investigate the stability of the mode l, a random-effects model (with sites random) was applied to evaluate the results and also the eff ect of omitting one of the seven cohorts on the lead coefficient. In the various sensitivity analys es 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 c oncentrations as they approach zer o; therefore, in the first two functions the log-linear m odel is applied down to a cutpoint, se lected 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 μ g/dL and 10 μ g/dL, peak blood Pb, have not undergone such careful development. These analyses were primarily done to compare the lead-associated
decrement at lower blood lead concentrations an d higher blood lead concentrations. For these analyses, the study population was st ratified at the specified cutpoi nt and separate linear models4-9 were fitted to the data above and below the cutpoint. The fit of the model or sensitivity analyses were not conducted (or reported) on these coefficients. While thes e analyses are quite suitable for the purpose of investigating whether the sl ope at lower concentra tion levels are greater compared to higher concentration levels, use of su ch coefficients in a risk analysis to assess public health impact may be inappropriate. Further, only 103 children had maximal blood lead
levels less than 7.5 μ g/dL and 244 children had maximal blood lead levels less than 10 μ g/dL. While these children may better represent current blood lead levels, not fitting a single model using all available data may lead to bias. Slob et al. (2005) noted that th e usual argument for not considering data from the high dose range is th at different biological mechanisms may play a role at higher doses compared to lower doses. Ho wever, this does not mean a single curve across the entire exposure range cannot describe the rela tionship. The fitted curve merely assumes that the underlying dose-response follows a smooth curv e over the whole dose range. If biological mechanisms change when going from lower to higher doses, this change will result in a gradually changing slope of the dose-response. The major strength of th e Lanphear et al. (2005) study was the large sample size and the pooled analys is of data from seven different cohorts. In
the case of the 7.5 μ g/dL cutpoint, less than 10% of the ava ilable data is used in the analysis, with more than half of the data coming from one cohort (Rochester) and the six other cohorts contributing zero to 13 children to the analysis. Su ch an analysis dissipates the strength of the Lanphear et al. study. In consideration of the preceding discussion, we place greater confidence in the log-linear model form compared to the dual-linear stra tified models for our purposes in this risk assessment. Further, in considering risk estim ates derived from the four core functions (loglinear function with low-exposure linearization, log-linear function with cut point, dual linear function, stratified at 7.5 μg/dL peak blood Pb, and dual linear function, stratified at 10 μg/dL peak blood Pb), we have assigned greatest confid ence to risk estimates derived using the loglinear function with low-exposure li nearization since this function (a) is a nonlinear function that describes greater response per unit blood Pb at lower blood Pb levels consistent with multiple
studies identified in the discus sion above, (b) is based on fitting a function to the entire pooled dataset (and hence uses all of th e data in describing response ac ross the range of exposures), (c) is supported by sensitivity analyses showing the model coefficients to be robust, and (d) provides an approach for predicting IQ loss at the lo west exposures simulated in the assessment (consistent with the lack of evidence for a th reshold). Note, however , that risk estimates generated using the other three concentration-response functions are also presented to provide perspective on the impact of uncerta inty in this key modeling step.4-10 4.2.2 Case Studies

For this risk assessment, we have empl oyed 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 s cenarios 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 A ngeles) 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-sp ecific analysis which uses several simplifying assumptions regarding ambi ent 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 la rge 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 Secti on 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 result s which preclude the us e of this case study as illustrative of the larger set of areas in fluenced by similarly sized Pb sources. We note that risk estimates for this case study (p resented 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 analys is 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 studie s. The alternative NAAQS scenarios include 0.5, 0.2, 0.05, and 0.02 μg/m 3 (as maximum monthly averages) and 0.2 μg/m 3 (as a maximum quarterly average). The current NAAQS scenario for the urban case studies involved a "rolling4-11 up" of ambient air Pb levels from current c onditions. While EPA sta ff recognizes that it is extremely unlikely that Pb concentrations in ur ban areas (see Section 2.3.2.4) would rise to meet the current NAAQS and there are li mitations 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, includi ng a description of the derivation of Pb concentrations for air and other media are pr esented in Sections 2.3 (and subsections) and
Section 5.1.1 of the Risk A ssessment 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 ingesti on and indoor dust ingestion. We have further categorized the policy-relevant path ways into one of two categories, “recent air” or “past air”. Conceptually, the recent air catego ry includes those pathways involvi ng Pb that is or has recently been in the outdoor ambient air, including inhalation and ingesti on of indoor dust Pb derived from recent ambient air (i.e., air Pb that has pe netrated into the residence recently and loaded indoor dust). Past air includes exposure contributi ons from ingestion of out door 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 assessmen t, 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 ai r 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 associat ed with reducing ambi ent air Pb levels through alternative NAAQS (and increases in ex posure if the current NAAQS was reached in certain case studies), our mode ling has only affected the exposu re pathways we categorize as recent air (inhalation and ingestion of that portio n 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 re duction in ambient air Pb levels and a subsequent decrease in exposure through incidental soil ingestion and the contribution of outdoor so il to indoor dust). This approach is likely to underestimate reducti ons in ambient air related exposure and risk. Consequently, incremental reductions in expos ure and risk estimated for alternative NAAQS considered in the full-scale analysis, which reflect simulated reductions in the recent air4-12 category, are likely to be underpredictions of th e 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-releva nt exposure pathways (and subse quent parsing of recent air and past air) resulting from a nu mber of technical limitations. Key among these is that, while conceptually, Pb paint contribu tions to indoor dust Pb would be considered background and included in modeling background ex posures, 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 exposu re 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-re levant exposure pathways is bou nded 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 step s, 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) establishmen t 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 asso ciated 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 da ta (including site-specific and/or national datasets) and/or fate and transport modeli ng. Indoor dust Pb levels were predicted using a combination of (a) regression-based models that relate i ndoor dust to ambient air Pb and/or outdoor soil Pb, and (b) mechan istic 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 ra tes). For the point source case studies, regression-based models obtained from th e literature or developed based on sitespecific data were used, and a hybrid empi rical-mechanistic model was developed and used for the urban case studies. The model fo r urban case studies was developed as the4-13 available regression-based models had b een 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 con centrations estimated using the Integrated Exposure and Uptake Biokinetic (IEUBK) m odel. Concurrent or lifetime average blood Pb is estimated from the IEUBK output s as described in Section 3.2.1.1 of the Risk Assessment Report. For the point s ource and location-specific urban case studies, a probabilistic exposure model is used to ge nerate 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 char acterize interindividual va riability in blood Pb (e.g., reflecting differences in behavior and biokinetics rela ted to Pb). For the general urban case study, as demogra phic data for a specific location are not considered, the GSD is applied directly to th e 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 us ing the concentratio n-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 pres ented in two ways: (a) population-weighted distributions of IQ loss from which speci fic population percentiles are identified, and (b) for the location-specific urban case studi es, population risk in cidence distributions providing estimates of the num ber of children with specifi c amounts of IQ loss in a particular case study. There are a range of uncertainties associated with the development and application of these c oncentration-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 spec ific mix of modeling and empirical data used to characterize Pb levels in exposure media (e.g., ou tdoor soil and indoor dust) . Key elements of the approaches used in each of the case study ca tegories 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 thre e 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 st udies, two types of exposur e zones were modeled: (a) zones associated with sour ce oriented monitors and (b) zones associated with non4-14 source oriented monitors. Source oriented exposure zones extend one mile out from each source-oriented monitor, and ambient ai r 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 monito rs represent air Pb levels more common across the study area. Because the Los Angeles case study does not have any source oriented monitors, childr en in the study area were simply assigned to the nearest monitor. All three case st udies were modeled using the same soil Pb levels as that used in the general urba n case study (i.e., these were not locationspecific). The remainder of the modeling us ed 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 bloc k 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 se veral 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 assu med 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 scen arios 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 95 th 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 ar ea surrounding the fac ility 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 to ward "lower risk" exposure and risk distributions. These case studies used e ither U.S. Census 2000 block-level data (secondary Pb smelter case study) or a co mbination 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 sp ecific mix of empiri cal data and modeling used for the two case studies to characte rize 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 a ssessment, staff implemented multiple modeling approaches for each case study scenario in an effort to characterize the potential impact on exposure and risk estimates of un certainty associated w ith the limitations in the tools, data and
methods available for this risk assessment a nd with key analytical steps in the modeling approach (e.g., predictio n of indoor dust Pb levels given ch anges in outdoor ambient air Pb, prediction of IQ loss given specific Pb blood leve ls). These multiple modeling approaches are
described in Section 2.4.6.2 of the Risk Assessm ent Report. In consideration of comments4-15 provided by CASAC (Henderson, 2007b) on these an alyses regarding which modeling approach they felt had greater scientific support, we identi fied a smaller set of modeling combinations as the core approach for the subsequent new anal yses (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 spec ifically developed for urban residential applications,

• the IEUBK blood Pb model,

• the concurrent blood Pb metric,

• a geometric standard deviation (GSD) for c oncurrent blood Pb of 2.1 to characterize interindividual variability in blood Pb levels, and

• four different functions rela ting concurrent blood Pb to IQ loss, including two loglinear models (one with low-exposure linea rization and one with a cutpoint) and two dual-linear models with stratification, one st ratified 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 th is 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-res ponse models to provide coverage for uncertainty related to this key m odeling step, for reasons described in Section 4.2.1 above, we have greater conf idence in the log-linear with low-exposure linearization concentration-response function (LLL) and the results genera ted using that function are emphasized below in summarizing risk estimates. Exposure and risk estimates generated for th e initial full set of modeling approaches applied to the general urban cas e study and the two point source case studies are presented in detail in Chapters 3 and 4 of the Risk Asse ssment 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 appr oaches to address potential uncertainty in the overall analysis and illustrate the potential impact of that uncertain ty 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 Re port) and performed sensitivity analyses to characterize the potential impact of uncertainty in key analys is steps on exposure and risk estimates (see Sections 4.3.2, 5.3.3.4 of th e Risk Assessment Report).4-16 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 th ese assumptions, and re lated limitations and uncertainties of the assessment design and re sults in the Risk Assessment Report (USEPA, 2007b). Key assumptions, limitations and uncertaintie s 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 st aff to be particularly important to the interpretation of the exposure and risk estimates. .

Limitations in Assessment and Case Stud y Designs and Associated Uncertainty

• Temporal aspects : As described in Section 2.4.1 of the Risk Assessment Report, exposure for the simulated child population begi ns at birth and continues for 7 years, with Pb concentrations in all exposure me dia remaining constant throughout the period, and children residing in the same e xposure zone throughout the period. In characterizing exposure media concentrations , annual averages are derived and held constant through the seven year period. E xposure factors and physiological parameters vary with age of the cohort through the seven year exposure period, several exposure factors and physiological parame ters are varied on an annua l 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 c ontributes 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 Assessm ent Report, Section 2.2.1). It is by definition a general case study and not based on a speci fic 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 vari ation 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 valu es. Within the single exposure zone a theoretical population of unspeci fied size is assumed to be uniformly distributed. Thus this case study is a simplified representa tion of urban areas intended to inform our assessment of the impact of changes in am bient Pb concentrations on risk, but which carries with it attendant uncer tainties in our interpretati on 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 re gard to their relevance to specific urban residential exposures carry substantial uncer tainty and presumably an upward bias in risk, particularly for large areas, acr oss which air concen trations may vary substantially.4-17 • Point source case studies : Dispersion modeling was used to characterize ambient air Pb levels in the point source case studies. This approach simulates spatial gradients related to dispersion and deposition of Pb from emitting sources. In the case of the point sources modeled, sources were limited to those associated with the smelter operations, and did not include other sources such as resuspension of roadside Pb not immediately related to facility operations, and other stationary sources of Pb within or near the study area. This means that, with distance from the facility, there is likely underestimation of ambient ai r-related Pb exposure because with increased distance from the facility there would be increasing in fluence of other source s relative to that of the facility. We believe this limitation to have more significant impact on risk estimates associated with the full study than on those for the subareas, and to perhaps have a more significant impact on risk estimates associated with the smaller secondary Pb smelter (see below).

• Secondary Pb smelter case study : Air Pb concentration estimates derived from the air dispersion modeling completed for the second ary Pb smelter case study are subject to appreciably greater uncertainty than that for those for the primary Pb smelter case study due to a number of factors, includi ng: (a) a more limited and less detailed accounting of emissions and emi ssions sources associated with the facility (particularly fugitives), (b) a lack of prior air quality modeling analyses and performance analyses, and (c) a substantially smaller number of Pb -TSP monitors in the area that could be used to evaluate and provide confidence in model performance 4 . Further, as mentioned in the previous bullet, no air sources of Pb other than those associated with the facility were accounted for in the modeling. Give n the relatively smaller magnitude of 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 severa l aspects would be needed to broadly characterize risks for such a categor y of Pb exposure scenarios.

4 The information supporting the air dispersion modelin g for the primary Pb smelter case study (see Section 3.5.1.1 of the Risk Assessment Report) provides substantia lly greater confidence in es timates for that case study.4-18 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 limite d. The number of monitors available to represent air concentrations in these case studies ranged from six for Cleveland to 11 for Chicago. Accordingly, our estimates of the magnitude of and spatial variation of air Pb concentrations are subject to uncertain ty associated with the limited data. In applying the available data to each of these case studies, exposure zones, one corresponding to each monitor, were 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 descri bed in Section 5.1.3 of the Risk Assessment Report. Although this approach provides a sp atial 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 nei ghbor 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 re flected in the approach used here. This introduces significant uncertainty into the ch aracterization of risk for the urban case studies.

• Current NAAQS air quality scenarios : For the location-specific urban case studies, proportional roll-up procedures we re 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 recogni zes 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 th at it is assumed that the current spatial distribution of air concentra tions (as characterized by the current data) is maintained and increased Pb emissions contribute to in creased Pb concentrations, the highest of which just meets the current standard. Ther e 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 ope ration 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, attainme nt of the current NAAQS was simulated using air quality m odeling, 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 a nd 5.2.2.1 of the Risk Assessment Report). We recognize that there is significant uncertainty in si mulating conditions associated with the implementation of emissions reduc tion 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 target ed only at the specif ic area exceeding4-19 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, th ere 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 e qual to the values for the current conditions scenarios. That is, we are not simulating an impact of changes in air Pb concentrations on soil concentrations, or the associated impact on dust concentrations, blood Pb and risk estimates. In areas where air concen trations have been greater in the past, however, implementation of a reduced NAAQS might be expect ed to yield reduced soil Pb levels over the long term. As descri bed in Section 2.3.3 of the Risk Assessment Report, however, there is pot entially significant uncertainty associated with this specification, particularly with regard to im plications 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 studi es (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 cons ider house-to-house variability in f actors 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 th e 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 Sec tion 5.3.3.4 of the Risk Assessment Report). These last two sources of uncertainty re duce our confidence in estimates of apportionment of dust Pb between "recent air" and "other". In r ecognition of this limitation, we have, in evaluating exposure and risk re duction trends related to re ducing ambient air Pb levels, focused on changes in total blood Pb rather th an 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-s pecific regression model): There is uncertainty associated with the site-specific regression model appl ied in the remediation zone (see Section 3.1.4.2 of the Risk Assessment Report), and re latively 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 of4-20 a simple regression that pr edicts 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 th at influence dust Pb concentr ations and their absence in the regression contributes uncertainty to th e resulting estimates. To the extent that these unaccounted for variables ar e spatially related to the sm elter facility Pb sources, our estimates could be biased, not with rega rd 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-acc ounted-for variables are roadside dust Pb and historically contributi ons 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 Ri sk Assessment Report for additional discussion.

Limitations in Estimation of Blood Pb Con centrations and Associated Uncertainty

• Characterizing interindividual variability using a GSD : There is uncertainty associated with the GSD specified fo r 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. Interi ndividual variability in blood Pb levels for any study population (as descri bed by the GSD) will reflect, to a certain extent, spatial variation in media concentr ations, including outdoor ambient air Pb levels and indoor dust Pb levels. For each case study, there is signi ficant 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 residentia l child populations from whic h a GSD can be derived (e.g., NHANES datasets for more heterogeneous populations and indivi dual study datasets for likely more homogeneous populations near specific industrial Pb sources). This uncertainty associated with the GSDs in troduces significant uncer tainty in exposure and risk estimates for the 95 th population percentile.

• Exposure pathway apportionment for higher pe rcentile 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 pe rcentiles of blood Pb and IQ loss. In reality, pathway apportionment may shift as you consider high er exposure percentiles. For example, paint and/or drinking water e xposures may increase in importance, with air-related contributions decr easing as an overall percenta ge of blood Pb levels and associated risk. Because of this uncerta inty 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.4-21 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 subjec t to significant uncert ainty 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 relati onship at these lower blood Pb levels. For example, the pooled international datase t 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 exampl e, 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 inspecifying 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 Re port). The range of absolute IQ loss seen for a given case study/air quality scen ario combination when modeled using the four concentration-response functions is typi cally 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 patter n 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 an alysis 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 95 th population percentile, respectively. Each table presents estimated blood Pb levels resulting from total Pb exposure across all pathwa ys (policy-relevant and background), as well as estimates of percent contribution from "recent air" and "recent plus past ai r" exposure categories. As noted in Section 4.2.4 (and Section 3.4 of th e Risk Assessment Report), given the various limitations of our modeling tools, the contributio n to blood Pb levels from air-related exposure pathways and current levels of Pb emitted to the air (including via resuspen sion) are likely to fall
between contributions attributed to “recent air” an d those attributed to “r ecent 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 Pa per 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 comprehe nsive and detailed presentation provided in the Risk Assessment Report (USEPA, 2007b). Listed below are key observations related to the4-22 exposure assessment based on estimates presented in Tables 4-1 and 4-2. This section is organized by ambient air quality scenario ca tegory beginning with observations regarding estimated exposures under current conditions, fo llowed by observations related to the current NAAQS, and concluding with observations re garding the alternative NAAQS scenarios. In presenting these observations, we reference both median and 95 th population percentile
estimates of concurrent blood Pb levels. It is im portant to note that 5 percent of the child study population at each case study would have exposur es above the high-end exposures presented here, although due to technical lim itations 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 regard ing the blood Pb estimates for the current
conditions scenarios.

• Current Pb concentrations for the three loca tion-specific urban case studies in terms of maximum quarterly average are 0.09, 0.14 and 0.36 μg/m 3 for the study areas in Los Angeles, Chicago and Cleveland, respectivel y. In terms of maxi mum monthly average the values are 0.17 μ g/m 3, 0.31 μ g/m 3 and 0.56 μ g/m 3 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 st udies, with the percent asso ciated with ambient air Pb estimated to fall between the estimate for recen t air (17%) and that for recent plus past air (71%) (see Table 4-1). Estimates for the 95 th percentile range from 5.9 to 6.1 μg/dL with the percent associated with ambient ai r Pb estimated to fall between 15% and 68% (see Table 4-2).

• Two current conditions scenarios were consid ered for the general urban case study: 0.14 μg/m 3 as a maximum quarterly average, th e mean for large urban areas, and 0.87 μg/m 3 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 per cent associated with ambient air Pb is estimated to fall betw een 32% and 76% of total Pb exposure. Estimated total blood Pb levels for the 95 th 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 regardi ng blood Pb estimates for the current NAAQS
scenario in which ambient air Pb levels are si mulated to just meet the current NAAQS level of 1.5 μg/m 3 , 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 percent4-23 associated with ambient air estimated to fall between the estimates for recent air (50- 63%) and those for recent plus past air (8 1-86%) (see Table 4-1). Estimates of total blood Pb exposures for the 95 th 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 contribu tion from ambient air Pb to total blood Pb for the median and 95 th percentile child scenarios sugge sts the key role played by the interindividual GSD in determining 95 th percentile exposures fo r the location-specific urban case studies (i.e., spatial gradients, which are less sign ificant in these urban case studies, are less important in pr oducing elevated levels of exposure). Both the median and 95 th 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 95 th 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 95 th percentile estimate of 10.6 μg /dL (see Tables 4-1 and 4-2, respectively). Contributions of ambient air Pb to total blood Pb ar e somewhat higher in this case study than they are for the locatio n-specific urban case st udies. This finding is expected since the location-specific ur ban case study assumes that the entire study area is at the current NAAQS level, while th e location-specific ur ban case studies will have portions of the study areas at that level and the remainder at levels notably lowerthan the current NAAQS. Estimates for the median child for both the mean and highend current conditions scenarios indicate a de crease 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 95 th percentile child, with the mean current conditions scenario differing by 4 μg/dL fr om the current NAAQS scenario and the high-end current conditions s cenario differing by 3.4 μg/dL.

• For the primary Pb smelter case study (full study area) the estima te of median total blood Pb is 1.5 μg/dL with 53% of this result ing from recent plus past air (see Table 4- 1). The 95 th 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 95 th 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 c ontributions from other sources besides the smelter (e.g., resuspension of road dust, othe r industrial sources) th at might contribute to exposures further from the facility. This underprediction bias is more important for areas further from the facility which in clude a large segment of the modeled study population. By contrast, the general urban case study and lo cation-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 corresponding4-24 estimates for the full study area. This reflects the fact that the analysis of the subarea focused on a subpopulation experiencing signif icantly greater ambient air Pb levels (due to their proximity to the facility). Th e median estimate of total blood Pb level is 4.6 μg/dL with 87% of that exposure resulti ng from recent plus past air Pb (see Table 4-1). The 95 th 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 ambien t 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 significan tly greater ambient air Pb contributions.

Alternative NAAQS This section presents observations regard ing the blood Pb estimates resulting from modeling of exposure assuming that each of the ca se studies just meets each of the alternative NAAQS levels.

• The current air Pb concentrations in the st udy areas of the three location-specific urban case studies fall near and w ithin the upper end of th e range of alternate NAAQS considered (i.e., 0.2 μg/m 3 maximum monthly average to 0.5 μg/m 3 maximum quarterly average). In modeling alternat e 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 altern ative NAAQS were considered for Cleveland and only the two lowest were considered fo r Los Angeles. The remaining case studies were evaluated for the full set of alternative NAAQS.

• For the two location-specific urban case studies at wh ich the higher alternative NAAQS (0.5 and 0.2 μg/m 3 , maximum monthly average and 0.2 μg/m 3 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 ra nge is similar to the estimates for current conditions for the three case stud ies, which is expected given the similarity of current conditions to these alternative NAAQS. Estimat es of median total blood Pb levels for the lowest alternative NAAQS (0.02 and 0.05 μg/m 3 maximum monthly average) show a slight reduction compared with current c onditions (i.e., equal to or less than a 0.2 μg/dL reduction - see Table 4-1). Estimates of 95 th percentile total blood Pb levels for the higher alternative NAAQS (0.5 and 0.2 μg/m 3 maximum monthly average and 0.2 μg/m 3 maximum quarterly average) range from 5.7 to 6.0 μg/dL (see Table 4-2). These estimates differ from those for current condi tions by about 0.4 μg/dL or less. Estimates of 95 th percentile total blood Pb levels under the lowest alternative NAAQS (0.02 and 0.05 μg/m 3 maximum monthly average) shows a signi ficantly larger re duction 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) s hows no discernable reduction in median total blood Pb levels across any of the alternative NAAQS (see Table 4-1), again reflecting the fact that charac terization of ambient air Pb le vels for this study area did not consider sources other than the facility and consequent ly, portions of the study area4-25 further from the facility have relatively lo w ambient air-related exposures. However, a moderate reduction in 95 th percentile total blood Pb levels is seen across the alternative NAAQS, with the highest alternative NAAQS (0.5 μg/m 3 maximum monthly average) estimated at 4.2 μg/dL and the lowest alternate NAAQS (0.02 μg/m 3 maximum monthly average) estimated at 3.8 μg/dL (see Table 4-2).

• The primary Pb smelter (1.5km subarea) pres ents a much more substantial trend in reduction of estimated median total bl ood 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/m 3 maximum monthly average), decreasing to 2.3 μg/dL for the intermediate alternative NAAQS (0.2 μg/m 3 maximum monthly av erage) and finally dropping to 1.6 μg/dL for the lowest alternative NAAQS (0.02 μg/m 3 maximum monthly average) (see Table 4-1). This trend is also seen for 95 th percentile estimates of total blood Pb levels, but it is even stronger, with the hi ghest alternative NAAQS estimated at 8.5 μg/dL, dropping to 6.1 μg/dL (for the 0.2 μg/m 3 maximum monthly average NAAQS) and reaching 4.2 μg/dL with the lowest alternative NAAQS (see Table 4-2).4-26 Table 4-1. Summary of blood Pb estimates for median total blood Pb.

Policy-relevant source contribution (percent) of total blood Pb Air Quality Scenario

(and case study) Recent
Air b Recent Air plus Past Air b Concurrent
blood Pb
concentration (total Pb exposure) a
Location-specific (Chicago) Current NAAQS (1.5 μg/m 3 , max quarterly) 63% 83% 3.0 Current conditions (0.14 μg/m 3 max quarterly; 0.31 μg/m 3 max monthly) 22% 67% 1.8 Alternative NAAQS (0.2 μg/m 3 , max monthly) 17% 67% 1.8 Alternative NAAQS (0.05 μg/m 3 , max monthly) 6% 69% 1.6 Alternative NAAQS (0.02 μg/m 3 , max monthly) 1% 63% 1.6 Location-specific (Cleveland) Current NAAQS (1.5 μg/m 3 , max quarterly) 57% 86% 2.1 Current conditions (0.36 μg/m 3 max quarterly; 0.56 μg/m 3 max monthly) 17% 67% 1.8 Alternative NAAQS (0.5 μg/m 3 , max monthly) 39% 72% 1.8 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 12% 65% 1.7 Alternative NAAQS (0.2 μg/m 3 , max monthly) 6% 65% 1.7 Alternative NAAQS (0.05 μg/m 3 , max monthly) 1% 63% 1.6 Alternative NAAQS (0.02 μg/m 3 , max monthly) 1% 63% 1.6 Location-specific (Los Angeles) Current NAAQS (1.5 μg/m 3 , max quarterly) 50% 81% 2.6 Current conditions (0.09 μg/m 3 max quarterly; 0.17 μg/m 3 max monthly) 18% 71% 1.7 Alternative NAAQS (0.05 μg/m 3 , max monthly) 13% 69% 1.6 Alternative NAAQS (0.02 μg/m 3 , max monthly) 6% 63% 1.6 General urban Current NAAQS (1.5 μg/m 3 , max quarterly) 61% 84% 3.1 Alternative NAAQS (0.5 μg/m 3 , max monthly) 41% 73% 2.2 Current conditions –high-end (0.87 μg/m 3 , max quarterly) 38% 76% 2.1 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 35% 75% 2.0 Current conditions - mean (0.14 μg/m 3 , max quarterly) 32% 74% 1.9 Alternative NAAQS (0.2 μg/m 3 , max monthly) 26% 74% 1.9 Alternative NAAQS (0.05 μg/m 3 , max monthly) 12% 65% 1.7 Alternative NAAQS (0.02 μg/m 3 , max monthly) 6% 69% 1.6 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 53% 1.5 Alternative NAAQS (0.5 μg/m 3 , max monthly) 79% 1.4 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 50% 1.4 Alternative NAAQS (0.2 μg/m 3 , max monthly) 36% 1.4 Alternative NAAQS (0.05 μg/m 3 , max monthly) 50% 1.4 Alternative NAAQS (0.02 μg/m 3 , max monthly) NA

64% 1.4 Primary Pb smelter - 1.5km study area Current NAAQS (1.5 μg/m 3 , max quarterly) 87% 4.6 Alternative NAAQS (0.5 μg/m 3 , max monthly) 81% 3.2 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 72% 2.5 Alternative NAAQS (0.2 μg/m 3 , max monthly) 78% 2.3 Alternative NAAQS (0.05 μg/m 3 , max monthly) 65% 1.7 Alternative NAAQS (0.02 μg/m 3 , max monthly) NA

69% 1.6 a - All values are rounded to one decimal place. b -The term “past air” includes c ontributions from the outdoor soil/dust contribut ion to indoor dust, hist orical air contributi on to indoor dust, and outdoor soil/dust pathways; “recent air” refers to co ntributions from inhalation of ambient air Pb or ingestio n of indoor dust Pb predicted to be associated with outdoor ambient ai r Pb levels, with outdoor ambient air also potentially includi ng resuspended, previously deposited Pb (see Sect ion 2.4.3 of the Risk Assessment Report).4-27 Table 4-2. Summary of blood Pb level estimates for 95 th percentile total blood Pb.

Policy-relevant contribution (percent) of total blood Pb

Air Quality Scenario

(and case study) Recent
Air b Recent plus Past
Air b Concurrent blood
Pb concentration (total Pb exposure) a

Location-specific (Chicago) Current NAAQS (1.5 μg/m 3 , max quarterly) 65% 87% 10.2 Current conditions (0.14 μg/m 3 max quarterly; 0.31 μg/m 3 max monthly) 18% 68% 6.0 Alternative NAAQS (0.2 μg/m 3 , max monthly) 25% 70% 6.0 Alternative NAAQS (0.05 μg/m 3 , max monthly) 5% 65% 5.5 Alternative NAAQS (0.02 μg/m 3 , max monthly) 4% 67% 5.4 Location-specific (Cleveland) Current NAAQS (1.5 μg/m 3 , max quarterly) 31% 73% 7.4 Current conditions (0.36 μg/m 3 max quarterly; 0.56 μg/m 3 max monthly) 15% 67% 6.1 Alternative NAAQS (0.5 μg/m 3 , max monthly) 25% 70% 6.0 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 12% 67% 5.8 Alternative NAAQS (0.2 μg/m 3 , max monthly) 7% 67% 5.7 Alternative NAAQS (0.05 μg/m 3 , max monthly) 2% 65% 5.4 Alternative NAAQS (0.02 μg/m 3 , max monthly) 2% 66% 5.3 Location-specific (Los Angeles) Current NAAQS (1.5 μg/m 3 , max quarterly) 52% 80% 8.9 Current conditions (0.09 μg/m 3 max quarterly; 0.17 μg/m 3 max monthly) 19% 68% 5.9 Alternative NAAQS (0.05 μg/m 3 , max monthly) 9% 67% 5.5 Alternative NAAQS (0.02 μg/m 3 , max monthly) 4% 65% 5.4 General urban Current NAAQS (1.5 μg/m 3 , max quarterly) 60% 83% 10.6 Alternative NAAQS (0.5 μg/m 3 , max monthly) 39% 76% 7.4 Current conditions –high-end (0.87 μg/m 3 , max quarterly) 38% 75% 7.2 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 34% 74% 6.8 Current conditions - mean (0.14 μg/m 3 , max quarterly) 29% 72% 6.5 Alternative NAAQS (0.2 μg/m 3 , max monthly) 27% 72% 6.4 Alternative NAAQS (0.05 μg/m 3 , max monthly) 12% 68% 5.7 Alternative NAAQS (0.02 μg/m 3 , max monthly) 7% 67% 5.5 Primary Pb smelter - full study area Current NAAQS (1.5 μg/m 3 , max quarterly) 61% 4.6 Alternative NAAQS (0.5 μg/m 3 , max monthly) 74% 4.2 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 60% 4.0 Alternative NAAQS (0.2 μg/m 3 , max monthly) 63% 4.0 Alternative NAAQS (0.05 μg/m 3 , max monthly) 50% 3.8 Alternative NAAQS (0.02 μg/m 3 , max monthly) NA

84% 3.8 Primary Pb smelter - 1.5km study area Current NAAQS (1.5 μg/m 3 , max quarterly) 83% 12.3 Alternative NAAQS (0.5 μg/m 3 , max monthly) 89% 8.5 Alternative NAAQS (0.2 μg/m 3 , max quarterly) 89% 6.6 Alternative NAAQS (0.2 μg/m 3 , max monthly) 80% 6.1 Alternative NAAQS (0.05 μg/m 3 , max monthly) 78% 4.5 Alternative NAAQS (0.02 μg/m 3 , max monthly) NA

71% 4.2 a - All values are rounded to one decimal place. b -The term “past air” includes c ontributions from the outdoor soil/dust contribut ion to indoor dust, hist orical air contributi on to indoor dust, and outdoor soil/dust pathways, while “recent air” refers to contributions from inhal ation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with out door 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).4-28 4.4 RISK ASSESSMENT

Risk results generated for the full-scale anal ysis are summarized in Tables 4-3 through 4-
8 at the end of this sectio n. 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 95 th percentile. To reflect the variation in estimates derived from the four different concentration-re sponse functions, the lowest and highest estimates are presented for each scenario along with estimates for the LLL model. These are labeled in these tabl es as (a) low C-R function (this is either the dual linear with stratification at 10 μg/dL, peak bl ood Pb, or the log-linear with cutpoint, depending on the blood Pb level); (b) the LLL C-R function(log-linear with lowexposure linearization); a nd (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 es timates of total Pb-related IQ loss for the population median, and Table 4-6 presents estimates for the 95 th percentile. Both of these tables present estimated total IQ loss (reflecting both policy-relevant pathways and background sources) as well as the esti mated range of IQ loss associated with policy-relevant exposures alone (bounded by es timates for "recent air" and for "recent plus past air").

• IQ loss incidence estimate s for the three location-sp ecific 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 sc enarios 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 Pape r have resulted in our providing here only a brief summary of the risk assessment results. These results, however, ne ed to be understood in the context of the broader and more comprehens ive and detailed presen tation provided in the Risk Assessment Report (USEPA, 2007b). Listed below are key observations related to the risk assessment, and as such, they draw on estimat es presented in Tables 4-3 through 4-8. These observations are organized by type of ambient ai r quality scenario beginning with observations regarding the current co nditions scenarios, followed by obs ervations regarding the current NAAQS scenarios, and concluding with observa tions regarding the se t of alternative NAAQS
evaluated.4-29 As described in Section 4.2.5 above, we incl uded four blood Pb concentration-response functions relating blood Pb and IQ loss in the co re 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 f unctions (the log-linear with low-exposure linearization, or LLL function). We note that, ri sk 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 f unction 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 bl ood Pb) yields the highest risk estimates. Because of the greater confidence placed in the LLL function an d because it generates risk estimates generally bounded by the other three functions, we emphasi ze risk estimates ge nerated using the LLL
function in this discussion. All risk estimates discussed in the observations below, unless otherwise noted, were generated using the LLL f unction (i.e., results presented in Tables 4-3 through 4-8 under the LLL heading). It is important to point out th at the range of absolute IQ lo ss 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 acro ss 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 95 th 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 qua lity scenarios. In presenting these risk obser vations, as with presentation of the exposure observations,
we reference both median and 95 th percentile estimates of total IQ loss. It is important to note that, similar to the exposure assessment, 5 pe rcent of the child study population at each case study would have risk levels above the 95 th 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-relate d IQ loss for current
conditions at each of the case studies. Unless othe rwise stated, all risk estimates discussed below were generated using the LLL model (designate d as "LLL" in the risk results tables).4-30 • As mentioned earlier in discussing the expos ure results, current conditions for the three location-specific urban case studies in terms of maximu m quarterly average air Pb concentrations are 0.09, 0.14 and 0.36 μg/m 3 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/m 3 (maximum monthly average for Los Angeles case study) to 0.56 μg/m 3 maximum monthly average for Cleveland case study. The estimate for the Chicago cas e study is between these at 0.31 μg/m 3 as a maximum monthly average.

• For the three location-specific urban case studie s, 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 95 th percentile to tal 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 cons idered for the general urban case study: one based on the mean value for ambient ai r Pb levels in large urban areas (0.14 μg/m 3 as a maximum quarterly average) and a high- end ambient air Pb level in large urban areas (0.87 μg/m 3 as a maximum quarterly average). Estimates of median total IQ lossfor these two scenarios were very similar at 4.5 and 4.7 points for the mean and highend current conditions, respectively. Estimated 95 th 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 scenar io 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 regardi ng estimates of Pb-related IQ loss for the current NAAQS scenario for each of the case studie s. Note, that discussion of the contribution of recent air and recent plus past air to total IQ loss has been re served for the current NAAQS air quality scenario, since this scen ario was modeled for all of the case studies. As noted above, all
risk estimates discussed below were generated using the LLL mode l (i.e., designated as "LLL" in the risk results tables), unless otherwise noted.

• Estimates of median total IQ loss for th e 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 95 th percentile total IQ loss ra nge from 8.1 to 9 points, with between 2.6 and 5.8 points coming from recen t air and 5.9 and 7.6 points coming from recent plus past air (see Table 4-6). Both the median and 95 th percentile risk estimates for current NAAQS are signi ficantly higher than those for current conditions. Specifically, median IQ loss is approximat ely 0.5 to 1 point higher under the current NAAQS, while 95 th 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 95 th percentile child than for the median, the4-31 size of risk increase is similar b ecause the blood Pb increase for the 95 th percentile is taking place in a flatter portion of the c oncentration-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 95 th percentile child. Risk estimates ge nerated using the other three IQ loss concentration-response functions provides a considerably wider ra nge 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 consider ed (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-specifi c 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 scenar io compared to the current conditions scenario. It is estimated th at an additional 1% of the m odeled child populations at the three urban locations would have total IQ loss greater than 1.0 point under the current NAAQS scenario. Specifically, the Chica go 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 a pproximately 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 approximate ly 370,000) with total Pb-related IQ loss greater than 1.0 point unde r the current NAAQS scenario (see Table 4-7).

• By contrast, it is estimated that an ad ditional 5% to 17% of the modeled child populations at the three loca tion-specific case study areas would move from having total IQ loss below 7 IQ point s to above 7 IQ points if cu rrent conditions increased to levels near the current NAAQS. Specifical ly, the Chicago study area would have some 70,000 more children with Pb-related IQ loss gr eater than 7 points, the Cleveland study area would have some 600 more and Lo s 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 acr oss the distribution for each population with some of the children moving between highe r 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 me dian is around 4 points - see Table 4-3). Therefore, a hypothetical increase in ambient air Pb le vels 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 num ber of children with IQ loss in the range of 1, since current conditions estimates for Pb-related IQ loss ar e already well above 1 point.

• Current NAAQS scenario risk estimates for the general urban case study are slightly higher than those for the locati on-specific urban case studies, with estimates for median total IQ loss of 5.8 points and 95 th percentile estimates of 9.1 points of loss (see Tables 4-3 and 4-4, respectively). The fraction of IQ loss associated w ith ambient air Pb4-32 exposure for the general urban case study is si milar to that estimated for the locationspecific urban case studies. Total Pb-related IQ loss estimates (at both the median and 95 th percentile) are about 1 point higher th an those for the two current conditions scenarios (see Tables 4-7 and 4-8). The simila rity of the change in risk for the median and 95 th population percentiles (despite greater differences in blood Pb level reductions), reflects the differe nt slopes of the concentra tion-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 95 th 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 smelte r case study had markedly elevated risk levels for the current NAAQS scenario, co mpared with the full study area. This reflects the fact that the analysis of the subarea focu sed 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 95 th 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 a ssociated 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 alterna tive NAAQS scenarios. As noted above, all risk estimates discussed below were generated using the LLL m odel (i.e., designated as "LLL" in the risk results tables), unless otherwise stated.

• In modeling the alternate NAAQS for the thr ee 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 alte rnative NAAQS were considered for Chicago, all of the altern ative NAAQS were considered for Cleveland and only the two lowest were considered fo r Los Angeles. The remaining case studies were evaluated for the full range of alternative NAAQS.

• For the three location-specific urban case stud ies, median total IQ loss for the higher alternative NAAQS (0.5 and 0.2 μg/m 3 as monthly maximum average and 0.2 μg/m 3 as a maximum quarterly average) is estimate d to range from 4.1 to 4.2 points (see Table 4-3). This range is very close to the cu rrent conditions estimates for these three case studies, as would be expected since thes e alternative NAAQS fall near the range of current conditions at these locations. Estimat es of median total IQ loss for the lower alternative NAAQS levels (0.02 and 0.05 μg/m 3 as a maximum monthly average) show a slight reduction in total IQ loss for the median child compared with current4-33 conditions (i.e., equal to or less than a 0.2 IQ point reduction). Estimates of 95 th percentile IQ loss for the higher alternative NAAQS (0.5 and 0.2 μg/m 3 as monthly maximum average and 0.2 μg/m 3 as a maximum quarterly average) range from 7.4 to 7.5 points (see Table 4-4). Under the lowe r alternative NAAQS levels (0.02 and 0.05 μg/m 3 maximum monthly average) 95 th percentile IQ loss estimat es also show a slight reduction in the degree of IQ loss, compar ed 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 95 th percentile child (between the intermediate and the lower alternative NAA QS levels) reflects the IQ loss concentr ation-response functions which have a flatter curve at the higher blood Pb levels associated with the 95 th percentile child, which translates into reduced magnitudes of change in IQ loss.

• Population risk modeling in the three lo cation-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 nu mber 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 approxima tely 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 childr en 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 pe rcentile 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 95 th population percentile with the highe st alternative NAAQS (0.5 μg/m 3 maximum monthly average) estimated at 6.6 points and the lowest alternative NAAQS level (0.02 μg/m 3 maximum monthly average) esti mated at 6.3 μg/dL (Table 4-4).

• The 1.5 km subarea of the primary Pb smelte r risk estimates indicate a much more substantial trend in total IQ loss reducti on 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/m 3 maximum monthly average), decreasing to 5.0 points with the intermediate alternative NAAQS (0.2 μg/m 3 maximum monthly average) and dropping to 4.0 points with the lowest alternative NAAQS level (0.02 μg/m 3 maximum monthly average - see Table 4-3) . This trend is also seen with the 95 th population percentile, with the magnitude of IQ loss reduction matching that seen for the median population per centile. Specifically, for th e highest alternative NAAQS the IQ loss is estimated at 8.5 point s, dropping to 7.6 points for the 0.2 μg/m 3 maximum monthly average, and reaching 6.5 points for the lowest alternative NAAQS (see Table 4-4).4-34 Table 4-3. Summary of risk estimates for medians of total-exposure risk distributions.

Points IQ loss (total Pb exposure) a

C ase Study and Air Quality Scenario Low C-R function estimate LLL b High C-R function estimate Location-specific (Chicago) Current NAAQS (1.5 μ g/m 3 , max quarterly) 2.4 5.6 8.8 Current conditions (0.14 μ g/m 3 max quarterly; 0.31 μ g/m 3 max monthly) 1.4 4.2 5.2 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 1.4 4.2 5.2 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 1.3 4.0 4.8 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 1.3 4.0 4.7 Location-specific (Cleveland) Current NAAQS (1.5 μ g/m 3 , max quarterly) 1.7 4.7 6.3 Current conditions (0.36 μ g/m 3 max quarterly; 0.56 μ g/m 3 max monthly) 1.4 4.2 5.2 Alternative NAAQS (0.5 μ g/m 3 , max monthly) 1.4 4.2 5.2 Alternative NAAQS (0.2 μ g/m 3 , max quarterly) 1.4 4.1 5.0 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 1.3 4.1 4.9 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 1.3 4.0 4.7 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 1.2 3.9 4.6 Location-specific (Los Angeles) Current NAAQS (1.5 μ g/m 3 , max quarterly) 2.1 5.3 7.7 Current conditions (0.09 μ g/m 3 max quarterly; 0.17 μ g/m 3 max monthly) 1.4 4.2 5.1 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 1.3 4.0 4.8 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 1.3 4.0 4.7 General Urban Current NAAQS (1.5 μ g/m 3 , max quarterly) 2.5 5.8 9.2 Alternative NAAQS (0.5 μ g/m 3 , max monthly) 1.7 4.8 6.4 Current conditions – high-end (0.87 μ g/m 3 max quarterly) 1.7 4.7 6.3 Alternative NAAQS (0.2 μ g/m 3 , max quarterly) 1.6 4.6 5.9 Current conditions - mean (0.14 μ g/m 3 max quarterly) 1.5 4.5 5.6 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 1.5 4.4 5.6 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 1.3 4.1 5.0 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 1.3 4.0 4.8 Primary Pb smelter - full study area Current NAAQS (1.5 μ g/m 3 , max quarterly) 1.2 3.8 4.4 Alternative NAAQS (0.5 μ g/m 3 , max monthly) 1.0 3.7 4.2 Alternative NAAQS (0.2 μ g/m 3 , max quarterly) 0.9 3.6 4.2 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 0.9 3.6 4.1 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 0.9 3.6 4.0 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 0.9 3.6 4.1 Primary Pb smelter - 1.5km subarea Current NAAQS (1.5 μ g/m 3 , max quarterly) 3.7 6.8 11.2 Alternative NAAQS (0.5 μ g/m 3 , max monthly) 2.6 5.8 9.4 Alternative NAAQS (0.2 μ g/m 3 , max quarterly) 2.0 5.2 7.4 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 1.9 5.0 6.9 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 1.4 4.2 5.1 Alternative NAAQS (0.02 μ g/m 3 , 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.4-35 Table 4-4. Summary of risk estimates for 95th percentile of total-exposure risk distributions.

Points IQ loss (total Pb exposure) a

C ase Study and Air Quality Scenario Low C-R function estimate LLL b High C-R function estimate Location-specific (Chicago) Current NAAQS (1.5 μ g/m 3 , max quarterly) 4.7 9.0 12.1 Current conditions (0.14 μ g/m 3 max quarterly; 0.31 μ g/m 3 max monthly) 4.1 7.5 11.4 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 4.1 7.5 11.4 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 4.1 7.3 11.3 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 4.1 7.3 11.3 Location-specific (Cleveland) Current NAAQS (1.5 μ g/m 3 , max quarterly) 4.3 8.1 11.6 Current conditions (0.36 μ g/m 3 max quarterly; 0.56 μ g/m 3 max monthly) 4.1 7.6 11.4 Alternative NAAQS (0.5 μ g/m 3 , max monthly) 4.1 7.5 11.4 Alternative NAAQS (0.2 μ g/m 3 , max quarterly) 4.1 7.4 11.3 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 4.1 7.4 11.3 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 4.0 7.2 11.3 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 4.0 7.2 11.3 Location-specific (Los Angeles) Current NAAQS (1.5 μ g/m 3 , max quarterly) 4.5 8.6 11.8 Current conditions (0.09 μ g/m 3 max quarterly; 0.17 μ g/m 3 max monthly) 4.1 7.5 11.4 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 4.1 7.3 11.3 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 4.0 7.2 11.3 General Urban Current NAAQS (1.5 μ g/m 3 , max quarterly) 4.7 9.1 12.1 Alternative NAAQS (0.5 μ g/m 3 , max monthly) 4.3 8.1 11.6 Current conditions – high-end (0.87 μ g/m 3 max quarterly) 4.3 8.0 11.6 Alternative NAAQS (0.2 μ g/m 3 , max quarterly) 4.2 7.9 11.5 Current conditions - mean (0.14 μ g/m 3 max quarterly) 4.2 7.7 11.5 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 4.2 7.7 11.4 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 4.1 7.4 11.3 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 4.1 7.3 11.3 Primary Pb smelter - full study area Current NAAQS (1.5 μ g/m 3 , max quarterly) 3.7 6.8 11.2 Alternative NAAQS (0.5 μ g/m 3 , max monthly) 3.4 6.6 11.1 Alternative NAAQS (0.2 μ g/m 3 , max quarterly) 3.2 6.5 11.1 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 3.2 6.4 11.1 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 3.1 6.3 11.0 Alternative NAAQS (0.02 μ g/m 3 , max monthly) 3.1 6.3 11.0 Primary Pb smelter - 1.5km subarea Current NAAQS (1.5 μ g/m 3 , max quarterly) 5.0 9.5 12.4 Alternative NAAQS (0.5 μ g/m 3 , max monthly) 4.5 8.5 11.8 Alternative NAAQS (0.2 μ g/m 3 , max quarterly) 4.2 7.8 11.5 Alternative NAAQS (0.2 μ g/m 3 , max monthly) 4.1 7.6 11.4 Alternative NAAQS (0.05 μ g/m 3 , max monthly) 3.6 6.8 11.1 Alternative NAAQS (0.02 μ g/m 3 , 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.4-36 Table 4-5. Median IQ loss estimates fo r the current NAAQS scenario.

IQ Loss from policy relevant exposures (recent air plus past air) a Low C-R function
estimates LLL C-R function
estimates High C-R function
estimates Total IQ loss (total Pb exposure) b

Case study 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 plu s 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 pathw ays, while “recent air” refers to contributions from inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoo r 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). Re sults 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 Assessm ent Report).

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

IQ Loss from policy relevant exposures (recent air plus past air) a Low C-R function
estimates LLL C-R function
estimates High C-R function
estimates Total IQ loss (total Pb exposure) b

Case study 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 plu s 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 pathw ays, while “recent air” refers to contributions from inhalation of ambient air Pb or ingestion of indoor dust Pb predicted to be associated with outdoo r 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). Res ults 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 Assessm ent Report).4-37Table 4-7.Incidence of children with >1 point Pb-related IQ loss.1dual linear - stratified at7.5μg/dL peak blood Pblog-linear withlinearizationdual linear - stratified at10μ/dL peak blood Pblog-linear with cutpointAir Quality Scenario(for location-specific urban case studies)Incidence of>1 point IQlossDelta(change inincidencecompared tocurrentconditions)Incidenceof>1 point,IQ lossDelta(change inincidencecompared tocurrentconditions)Incidenceof >1point, IQlossDelta(change inincidencecompared tocurrentconditions)Incidenceof>1 point,IQ lossDelta(change inincidencecompared tocurrentconditions)Chicago(total modeled child population: 396,511)Chicago Current Conditions (Mean)391,602389,754 271,031 236,257Current NAAQS (1.5 μg/m3Maximum Quarterly)395,797 4,195395,528 5,773 347,415 76,384 314,053 77,795Alternative NAAQS (0.2 μg/m3Maximum Monthly)391,158 -444389,461 -293 271,444 412 235,559 -698Alternative NAAQS (0.05 μg/m3Maximum Monthly)389,572 -2,030387,407 -2,347 253,775 -17,256 224,394 -11,864Alternative NAAQS (0.02 μg/m3Maximum Monthly)389,176 -2,427386,630 -3,125 249,865 -21,166 219,294 -16,963Cleveland(total modeled child population: 13,990)Cleveland Current Conditions (Mean)13,80913,745 9,526 8,515Current NAAQS (1.5 μg/m3Maximum Quarterly)13,893 8413,857 112 10,664 1,137 9,769 1,254Alternative NAAQS (0.2 μg/m3Maximum Quarterly)13,770 -3813,703 -42 9,221 -305 8,160 -354Alternative NAAQS (0.5 μg/m3Maximum Monthly)13,789 -2013,720 -25 9,497 -29 8,464 -51Alternative NAAQS (0.2 μg/m3Maximum Monthly)13,759 -5013,694 -51 9,083 -443 8,010 -505Alternative NAAQS (0.05 μg/m3Maximum Monthly)13,729 -8013,642 -103 8,785 -741 7,720 -795Alternative NAAQS (0.02 μg/m3Maximum Monthly)13,720 -8813,628 -117 8,736 -790 7,668 -846Los Angeles(total modeled child population: 372,252)Los Angeles Current Conditions (Mean)282,216280,711 191,675 170,474Current NAAQS (1.5 μg/m3Maximum, Quarterly)285,272 3,056284,945 4,234 240,988 49,313 226,608 56,134Alternative NAAQS (0.05 μg/m3Maximum Monthly)281,112 -1,104279,658 -1,053 183,395 -8,280 161,914 -8,560Alternative NAAQS (0.02 μg/m3Maximum Monthly)280,740 -1,476279,057 -1,654 180,745 -10,929 158,234 -12,24024-38Table 4-8.Incidence of children with >7 points Pb-related IQ loss.1dual linear - stratified at7.5 ug/dL peak blood Pblog-linear withlinearizationdual linear - stratified at10 ug/dL peak blood Pblog-linear with cutpointAir Quality Scenario(location-specific urban case studies)Incidence of> 7 pointsIQ lossDelta(change inincidencecompared tocurrentconditions)Incidenceof> 7 pointsIQ lossDelta(change inincidencecompared tocurrentconditions)Incidenceof> 7 pointsIQ lossDelta(change inincidencecompared tocurrentconditions)Incidenceof> 7 pointsIQ lossDelta(change inincidencecompared tocurrentconditions)Chicago(total modeled child population: 396,511)Chicago Current Conditions (Mean) 136,709 33,664 63 1,015Current NAAQS (1.5 μg/m3Maximum Quarterly) 244,401 107,692 100,159 66,495 555 492 5,226 4,211Alternative NAAQS (0.2 μg/m3Maximum Monthly) 136,067 -642 32,546 -1,118 48 -16 1,007 -8Alternative NAAQS (0.05 μg/m3Maximum Monthly) 120,706 -16,003 27,367 -6,297 16 -48 864 -151Alternative NAAQS (0.02 μg/m3Maximum Monthly) 117,819 -18,890 26,027 -7,637 8 -56 690 -325Cleveland(total modeled child population: 13,990)Cleveland Current Conditions (Mean) 4,834 1,212 3 46Current NAAQS (1.5 μg/m3Maximum Quarterly) 6,139 1,305 1,858 647 4 2 105 59Alternative NAAQS (0.2 μg/m3Maximum Quarterly) 4,525 -309 1,073 -139 1 -2 40 -6Alternative NAAQS (0.5 μg/m3Maximum Monthly) 4,806 -28 1,180 -31 1 -2 43 -3Alternative NAAQS (0.2 μg/m3Maximum Monthly) 4,424 -410 1,026 -186 1 -2 43 -3Alternative NAAQS (0.05 μg/m3Maximum Monthly) 4,106 -728 886 -326 0 -3 24 -22Alternative NAAQS (0.02 μg/m3Maximum Monthly) 4,051 -783 866 -345 0 -3 27 -18Los Angeles(total modeled child population: 372,252)Los Angeles Current Conditions (Mean) 94,684 22,665 23 732Current NAAQS (1.5 μg/m3Maximum, Quarterly) 158,171 63,487 57,834 35,168 183 160 3,771 3,038Alternative NAAQS (0.05 μg/m3Maximum, Monthly) 87,303 -7,382 19,781 -2884 11 -11 624 -109Alternative NAAQS (0.02 μg/m3Maximum, Monthly) 83,909 -10,775 17,939 -4726 17 -6 498 -2354-39 REFERENCES

ICF International. (2006) Lead Human Exposure and Hea lth Risk Assessments and Ec ological Risk Assessment for Selected Areas. Pilot Phase. Draft Technical Report. Prepared for the U.S. EP A's Office of Air Quality Planning and Standards, Research Tria ngle 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 Assess ment 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 Expo sure and Health Risk Assessments. September 27, 2007.

ICF International. (2006). Lead Human Exposure and Hea lth Risk Assessments and Ecol ogical Risk Assessment for Selected Areas. Pilot Phase. Draft Technical Report. Prepared for the U.S. EP A'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 enviro nmental 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 dos e-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 Tria ngle Park, NC: Office of Ai r 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 an d 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-06-002.4-40 U.S. Environmental Protection Agency. (2007a) Lead Huma n Exposure and Health Risk Assessments for Selected Case Studies (Draft Report) Volume I. Human Exposu re and Health Risk Asse ssments – Full-Scale. and Volume II. Appendices. Office of Air Quality Planni ng and Standards, Resear ch Triangle Park, NC. EPA452/D-07-001a. and EPA-452/D-07-001b.

U.S. Environmental Protection Agency. (2007b) Lead: Huma n Exposure and Health Risk Assessments for Selected Case Studies, Volume I. Human Exposure and Hea lth Risk Assessments – Full-Scale. And Volume II. Appendices. Office of Air Quality Planning and Stan dards, Research Triangle Park, NC. EPA-452/R-07- 014a and EPA-452/R-07-014b.5-1 

### 5 THE PRIMARY LEAD NAAQS

5.1 INTRODUCTION

This chapter presents staff conclusions a nd recommendations for the Administrator to consider in deciding whether the existing primary Pb standard shoul d be revised and, if so, what revision is appropriate. Our c onclusions and recommendations ar e 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 a nd interested parties who commented on an early draft of this docum ent and the related Risk Assessment Report. In recommending policy options for the Admini strator’s considerati on, we note that the final decision on retaining or revi sing the current primary Pb sta ndard is largely a public health policy judgment to be made by the Administrato r. 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 recogn ition that the available health effects evidence generally reflects a continuum consisting of ambien t 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 requi rements 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 standard s that, in the Administrator's judgment, are requisite to protect public health with an ad equate margin of safety. In so doing, the
Administrator seeks to establish standards that ar e 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 backgr ound information on the current standard in Section 5.2, including both the basis for derivation of the curr ent standard and considerations and conclusions from the Staff Paper pr epared in the last review. The general approach used in this current review to evaluate the adequacy of the cu rrent 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 Secti on 5.4, and conclusions and recommendations with regard to elements of alternative standards fo r consideration are discu ssed in Section 5.5. Key
uncertainties and research recommendations re lated to setting a primary lead standard are
identified in Section 5.6.5-2 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 μ g/m 3 , 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 protectio n to the public, especially children as the particularly sensitive population subgroup, agai nst Pb-induced adverse health effects (43 FR 46246). The basis for selecting each of the elem ents 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 gr oups within the general pop ulation can be exposed for protracted periods without an unacceptable ri sk to health” (43 FR 46252). Consistent with section 109 of the Clean Air Act, the Agency sel ected a level for the current standard that was
below the concentration that was at that time id entified as a threshold for adverse health effects (i.e., 40 μ g/dl blood Pb), so as to provide an adequate ma rgin of safety. As st ated in the notice of final rulemaking,
“This estimate was based on EPA’s judgment in four key areas: (1) Determining the ‘sensitive population’ as th at group within th e general population
which has the lowest threshold for adverse effects or greate st potential for exposure. EPA concludes that young children, aged 1 to 5, are the sensitive population. (2) Determining the safe level of total le ad 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 μ g Pb/dl and that population blood lead, measured as the geometric mean, must be 15 μ g Pb/dl in order to place 99.5 percent of children in the United States below 30 μ g Pb/dl. (3) Attributing the contribution to blood lead from nonair pollution sources. EPA
concludes that 12 μ g Pb/dl of population blood lead fo r 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 μ g Pb/dl [the maximum safe level]. Taking into account exposure from other sources (12 μ g Pb/dl), EPA has designed the standard to
limit air contribution after ach ieving the standard to 3 μ g 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 μ g Pb/m 3 .” (43 FR 46252) EPA’s judgments in these key areas, as well as marg in of safety considerations, are discussed in the following subsections.5-3 5.2.1.1.1 Sensitive Population

The assessment of the science that wa s presented in the 1977 CD (USEPA, 1977), indicated young children, aged 1 to 5, as th e population group at partic ular risk from Pb exposure. Children were recognized to have a great er physiological sensitivit y than adults to the effects of Pb and a greater expos ure. In identifying young childr en as the sensitive population, EPA also recognized the occurrence of subgroups w ith enhanced risk due to genetic factors, dietary deficiencies or residence in urban areas. Yet informati on 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 establis h that these subgroups are more at risk than young children. Accordingly, young childre n, aged 1 to 5, were identified as the group which has the
lowest threshold for adverse e ffects 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 iden tified 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 threshol d for inhibition of this enzyme in
children at 10 μ g Pb/dL. The 1977 CD also reported a threshold of 15-20 μ g/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 synt hesis 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 path way. At the low end of the range (15-20 μ g/dL),
the initial detection of EP asso ciated with blood Pb was not conc luded to be associated with a significant risk to health. The upper end of the range (40 μ g/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 rec ognized the existence of thresholds for additional adverse effects (e.g., nervous system deficits) occurring for some children at just slightly hi gher blood Pb levels (e.g., 50 μ g/dL). Additionally, EPA stated that the maximum safe blood level should not be higher th an the blood Pb level recognized by the CDC as “elevated” (and indicative of the need fo r intervention). In 1978, that level was 30 μ g/dL 1

.

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

μ g/dL 1991. More details on this level are provided in Section 3.2.5-4 Having identified the maximum sa fe blood level in individual children, EPA next made a public health policy judgment regarding the targ et mean blood level for the U.S. population of young children (43 FR 46252-46253). With this j udgment, 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 th e sensitive subpopulation, and the recognition that there are special high-risk gr oups of children within the ge neral 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 resi ding in urban areas and 5 million in center cities where Pb exposure was thought likely to be “h igh”. Concern about these high-risk groups influenced EPA’s determination of 99.5 percen t, deterring EPA from selecting a population percentage lower than 99.5 (43 FR 46253). EPA th en used standard statistical techniques to calculate the population mean bl ood Pb level that would place 99.5 percent of the population
below the maximum safe level. Based on the th en 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 te chniques, EPA determined that a thus described population in which 99.5 percent of the popul ation has blood Pb levels below 30 μ g/dL would have a geometric mean blood level of 15 μ g/dL. EPA described 15 μ g/dL as "the maximum safe blood lead level (geometric mean) for a population of young child ren" (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 sour ces 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 estimat e of a safe mean population blood Pb level. Without this subtraction, EPA r ecognized that the combined exposure to Pb from air and nonair
sources would result in a blood Pb concentra tion exceeding the safe level (43 FR 46253). In developing an estimate of this nonair co ntribution, EPA recognized the lack of detailed or widespread information about the relative co ntribution of various so urces to children’s blood Pb levels, such that an estimate could only be made by inference from other empirical or theoretical studies, often involvi ng adults. Additionally, EPA rec ognized the expectation that the
contribution to blood Pb levels from nonair so urces would vary widel y, 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 μ g/m 3 . The average of5-5 the reported blood Pb levels for children of va rious ages in these areas was on the order of 12

μ g/dL. Thus, 12 μ g/dL was identified as the nonair contribution, and subtracted from the population mean target level of 15 μ g/dL to yield a value of 3 μ g/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 consis tent with an air contribution of 3 μ g Pb/dL, EPA reviewed studies assessed in the 1977 CD that repor ted changes in blood Pb with different air Pb levels. These studies included a study of childre n 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 μ g/dL blood Pb concentration to 1 μ g/m 3 air Pb concentration), and is recognized to fall within the range of valu es reported in the 1977 CD. On the basis of this 2-to-1 relationship, EPA concl uded that the ambient air standard should be 1.5

μ g Pb/m 3 (43 FR 46254).

5.2.1.1.5 Margin of Safety

In consideration of the appr opriate margin of safety during the development of the current NAAQS, EPA identified the following f actors: (1) the 1977 CD reported multiple biological effects of Pb in pract ically all cell types, tissues an d organ systems, of which the significance for health had not yet been fully studied; (2) no benefi cial effects of Pb at then
current environmental levels were recognized; (3) data were incomple te as to the extent to which children are indirectly exposed to air Pb that has moved to othe r environmental media, such as water, soil and dirt, and food; (4) Pb is chemi cally 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 marg in 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 μ g Pb/dL) and in determining the per centage of children to be placed below this maximum level (about 99.5 percent). Additionally, in establishing other factors used in calculating the standard, EPA us ed margin of safety consider ations in the sense of making careful judgment based on availabl e 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).5-6 EPA further recognized that, because of th e variability between individuals in a population experiencing a given leve l of Pb exposure, it was cons idered 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 fa ctors 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 coul d 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 cons idered a monthly averaging period, but concluded that “a requirement for the averaging of air qua lity data over calendar qu arter will improve the validity of air quality data gath ered without a signif icant 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 av ailable at the time indicated that the distribution of air Pb levels wa s such that there was little pos sibility that there could be sustained periods greatly above the average value in situations where the quarterly standard was achieved.

• A recognition that the monitoring networ k 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 po llution sources where young children would typically not encounter them for the fu ll 24-hour period reported by the monitor.

• Medical evidence available at the time indi cated that blood Pb le vels 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 tota l 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 th is air level would be safe for indefinite exposure for young children (43 FR 46250).
The indicator is tota l 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 inges tion as an exposure pathway for Pb that had deposited from the air and of the potential for Pb deposited from th e air to become re-suspe nded 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 ingestio n of materials contamin ated by deposition of5-7 lead from the air,” and that, “in addition to the in direct route of ingestion and absorption from the gastrointestinal tract, non-respirable Pb in the environment may, at some point become respirable through weathering or mechanic al action” (43 FR 46251).

5.2.2 Policy Options Considered in the Last Review

During the 1980s, EPA initiated a review of the ai r quality criteria and NAAQS for Pb. CASAC and the public were fully involved in th is 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 stat ionary sources of Pb emissions. Although such
sources were not considered to have made a si gnificant 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 potentia l 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 a nd adults in numerous Pb point source community
studies. Exceedances of the cu rrent NAAQS were found at that time only in the vicinity of nonferrous smelters or other point sources of Pb. In summarizing and interpreting the hea lth evidence presented in the 1986 CD and associated documents, the 1990 Staff Paper descri bed the collective impact on children of the
effects at blood Pb levels above 15 μ g/dL as representing a clear pattern of adverse effects worthy of avoiding. This is in cont rast to EPA’s identification of 30 μ g/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 μ g/dL there was a convergence of evidence of Pb-induced
interference with a divers e set of physiological func tions and processes, particularly evident in several independent studies s howing impaired neurobehavioral function and development. Further, the available data did not indicate a clear threshold in th is 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 percenta ges of children with bl ood Pb levels above 10

μ g/dL and above 15 μ g/dL for several air quality scenar ios developed for a small set of stationary source exposure case st udies. These analyses omit ted the subset of young children,
whom it was considered could not be substantia lly affected by any changes in atmospheric Pb

2 In 1991, the CDC reduced their advisory level for children’s blood Pb from 25 μ g/dL to 10 μ g/dL.5-8 emissions under different standards, such as those with excessive pica 3 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 significan tly affected by the smelters w ould have blood Pb levels below
15 μ g/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 μ g/m 3 to 0.5 μ g/m 3

. In light of the health effects evidence availa ble 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 prim ary Pb NAAQS (USEPA, 1990b, pp. xii to xiv): 1) “The range of standards ... should be from 0.5 to 1.5 μ g/m 3 .” 2) “A monthly averaging period would better capture short-term increases in lead exposure and would more fully protect childr en’s health than th e 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 curren t 1-in-6 day sampling schedule would sufficiently refl ect air quality and trends.” 5) “Because exposure to atmospheric lead partic les 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 in dicator 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 Admi nistrator that EPA should actively pursue a public health goal of minimizing the lead content of blood to the extent possi ble, and that the Pb NAAQS is an important component of a multimed ia 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.5-9 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 specificall y, CASAC judged that a standard within the range of 1.0 to 1.5 μ g/m 3 would have “relatively li ttle, if any, margin of safety;” that greater cons ideration should be given to a standard set below 1.0 μ g/m 3 ; and, to provide perspective in se tting the standard, it would be appropri ate to consider the distribution of
blood Pb levels associated with meeting a monthly standard of 0.25 μ g/m 3 , a level below the range considered by staff (id.).
After consideration of the documents deve loped 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-ai r 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 fo cus 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 facilitie s. 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 c onducting research and designing educational
outreach activities and materi als (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.5-10 5.3 APPROACH FOR CURRENT REVIEW

To evaluate whether it is appropriate to consider reta ining the current primary Pb standard, or whether considerati on of revisions is appropriate, we adopted an approach in this review that builds upon the general approach used in the initial se tting 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 rulemaki ng decisions were based on an integration of
information on health effects associ ated with exposure to ambient Pb ; expert judgment on the adversity of such effects on i ndividuals; and policy judgments as to when the standard is requisite to protect public health with an adequa te margin of safety, which were informed by air quality and related analyses, quantitative exposu re 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 st rategy to reduce Pb e xposures through various regulatory and awareness programs that focuse d on important non-air sources judged to pose more extensive public health risk s, reflecting in part the drama tic 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 add ition to the dramatic reduction of Pb in gasoline, an additional circumstance that has changed since the standa rd 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 te chnology-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 re view 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 crite ria pollutant and assesse s whether revocation of the standard is an appropriate opti on for the Administrator to consider. In developing conclusions and identifying pol icy options for the Pb standard in this review, staff has taken into acc ount both evidence-based and qu antitative exposure- and riskbased considerations. A series of general questions frame our a pproach to reaching conclusions and identifying options for consideration by the Ad ministrator in deciding whether to retain or revise the current primary Pb standard. Our re view of the adequacy of the current standard (section 5.4) addresses questions such as the following:

• To what extent does newly available info rmation reinforce or call into question evidence of associations with effects identified in the last review?5-11 • To what extent has evidence of new eff ects and/or sensitive populations become available since the last review?

• To what extent have important uncertainties identified in th e last review been reduced and have new uncertainties emerged?

• To what extent does newly available informa tion 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 ca ll 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 sugge sts that revision of the current standard may be appropriate to consider, we al so address whether the currentl y 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, especia lly likely causal associ ations, extend to air quality levels that are as low as or lower than had previously been observed, and what are the important uncertainties a ssociated 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 perspectiv e; and what are the important uncertainties associated with the estimated risks? To the extent that there is support for considera tion of a revised standar d, we then consider the specific elements of the standard (section 5.5) to identify ranges of standards (in terms of an indicator, averaging time, leve l, and form) that we conclude would be appropriate for the Administrator to consider in making public he alth policy judgments, based on the currently available information, as to the degree of protecti on that is requisite to pr otect 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 c onsidering different averaging times?

• What ranges of levels and forms of alternat ive standards are supported by the evidence, and what are the uncertainties and limitations in that evidence?

• To what extent do specific levels and fo rms of alternative standards reduce the estimated exposures of concern and risks attributable to P b, and what are the uncertainties associated with the es timated exposure and risk reductions?5-12 5.4 ADEQUACY OF THE CURRENT STANDARD

In considering the adequacy of the current st andard, staff first cons idered whether it is appropriate to maintain a NAAQS fo r Pb or to retain Pb on the lis t 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. si nce the standard was set, that primarily reflects the dramatic reduction of Pb in gasoline, whic h 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 majo r source categories emitting Pb compounds, and to establish risk-based standards, as appr opriate, for those categories of sources. In considering this issue, staff notes that CASAC specifically examin ed several scientific issues and related public health (and public welfare) issues that the CASAC Lead Review Panel 4 judged to be essential in determining whether delisting Pb or revoking the Pb NAAQS would be appropriate options for the Administ rator to consider. In its lette r 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 fr amed by the following series of questions:
(1) Does new scientific information accumu lated since EPA’s promulgation of the current primary Lead NAAQS of 1.5 μg/m 3 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 sa fety inherent in those PbB levels? (3) Have the activities that produced emissions and atmospheric redist ribution 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 re gulation 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 define d seven-member CASAC and additional subject-matter experts needed to provide an appropriate breadth of expertise for this review of the Pb NAAQS.5-13 conclude that “the existing state of science is consistent with con tinuing 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 Admini strator of September 27, 2007, based on its review of the second draft Risk Assessment Report (Henderson, 2007b; A ttachment B), CASAC st rongly reiterated its opposition to any considered delisting of P b, 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 indus try 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 agains t revoking the Pb NAAQS or delisti ng 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 currentl y available information does not support either delisting Pb as a crit eria pollutant or revoki ng, 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. si nce the standard was set, Pb continues to be
emitted into the ambient air from numerous and dive rse mobile and stationary sources. Further, currently available studies provide evidence of a dverse health effects associated with blood lead levels and environmental exposures well below those previously identified, and we note that
there is now no discernable thres hold 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 leve ls 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 availabl e information is sufficient to infer that ambient air Pb contributes to air pollution that may reas onably 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 re tain 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 em issions 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 po llutant program, that program is focused on stationary sources and is not di rected toward other types of s ources, 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.5-14 Having reached the general conclusion that it is appropriate to main tain a NAAQS for Pb, we discuss below the available evidence (secti on 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 acc ount the views expresse d by CASAC and public commenters (section 5.4.3) in reaching staff conclu sions on the adequacy of the current standard (section 5.4.4).

5.4.1 Evidence-based Considerations

In considering the broad array of health eff ects evidence assessed in the CD with respect to the adequacy of the current standard, staff has focused on those hea lth endpoints associated with the Pb exposure and blood levels most pe rtinent 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 suscep tibility of young children to the effects of Pb is well recognize d, in addition to more recent re cognition of effects of chronic exposure to low level Pb with advancing age (C D, Sections 5.3.7 and pp. 8-73 to 8-75). As summarized in Chapter 3 and discussed in deta il in the CD, the pren atal 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., s ee Section 3.4.1), as at the time the standard was set, young children continue to be recogn ized as the key sensitive population for Pb

exposures. With regard to the exposure levels at whic h 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 lowe st blood Pb levels
were those associated with production of anemia. EPA rec ognized as clearly adverse the impairment of heme synthesis and other Pb-rela ted 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.5-15 This change in the evidence since the time th e 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 μ g/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 eviden ce of adverse health effects in
children with blood Pb levels below 10 μ g/dL and the data demonstrating that no “safe” threshold for blood Pb had been identified (CDC, 2005). As discussed extensively in the CD and summ arized in Chapter 3, the current evidence demonstrates the occurrence of a variety of a dverse health effects, including those on the developing nervous system, associated with bloo d Pb levels extending well below 10 μg/dL to 5 μg/dL and possibly lower. For example, we note in particular the intern ational pooled analysis by Lanphear and others (2005), studi es 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), th e study of African-American inne r-city children from Detroit (Chiodo et al., 2004), and the cross-sectional analys is of a nationally representative sample from the NHANES, in which the mean blood Pb leve l 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 eff ects 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 e ndpoint identified in the 1978 rulema king, i.e., impairment of heme synthesis and other effects whic h result in childhood anemia. As when the standard was set in 1978, we r ecognize that there remain today contributions
to blood Pb levels from nonair sources. Estim ating 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 eval uate the multiple pathways of Pb exposure do not usually distinguish between outdoor soil /dust Pb resulting from hist orical emissions and outdoor soil/dust Pb resulting from recent emissions. Furt her, while indoor dust Pb has been identified as being a predominant contributor to children’s bl ood 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 ti me, 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).5-16 different pathways (air-related and other) co ntributing to indoor dust Pb. The exposure assessment performed for this review has empl oyed available data and methods to develop estimates intended to inform a characterization of these pathways (see Chapter 4).
Consistent with reductions in air Pb concentrations 6 which contribute to blood Pb, nonair contributions have also been reduced. For exampl e, Pb contributions to diet have been reported to have declined significantly since 1978, perhap s 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 indicati ng a dietary intake ranging from 2 to 10 μg Pb/day (CD, Section 3.4 and p. 8-14). Reductions in elevated blood Pb le vels in urban areas indicate that other nonair contributions to blood Pb (e.g., drinking water dist ribution 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 expo sure routes including both inhalati on and ingestion (CD, Sections 3.1.3.2, 4.2 and 4.4; Hilts et al., 2003). In 1978, the ev idence indicated a quantitative relationship between ambient air Pb and blood Pb – i.e., the ra tio describing the increase in blood Pb per unit of air Pb – that ranged from 1:1 to 1:2 (USEPA , 1977). In setting the sta ndard, the Agency relied on a ratio of 1:2, i.e., 2 μg/dL blood Pb per 1 μg/m 3 air Pb (43 FR 46252). The evidence now and in the past on this relationship is limite d by the circumstances in which the data are collected. We do not have specif ic 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 popula tions in differing Pb exposure circumstances, which inform us on this point. Many of the curr ently available reviews ofestimates for air-toblood ratios, which include air contributions from both inhalation a nd 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 ra tio 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.5-17 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 comp leted (Henderson, 2007a). While there is uncertainty in the absolute value of the air-to-b lood 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 th e blood Pb level associated with adverse health effects, the evidence fo r a greater ratio between blood Pb and air Pb, and the lack of
evidence for a threshold for the sensitive health endpoint, we conclude th at the current evidence calls into question the adequacy of the current standard. In part icular, 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 ma ximum 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 appe ars to be higher at today’s lower blood Pb levels than the estimates at the time the st andard was set, with current
estimates on the order of 1:3 to 1:5 and perhaps up to 1:10. Theref ore, considered in light of the framework employed in setting the standard in 1978, the more recen tly 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-base d considerations, staff has al so considered exposures and health risks estimated to occur upon just meeti ng 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 perspectiv e, taking into account key uncer tainties associated with the estimated exposures and risks. As discussed in the previous section (S ection 5.4.1), young childre n are the sensitive population of primary focus in this review. Accordingly, as desc ribed in Chapter 4, the exposure and risk assessment estimates Pb exposure for child ren (less than 7 years of age), and associated risk of neurocognitive effects in terms of IQ decrements. In add ition 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 ev idence of other health eff ects occurring at similar or higher exposures for young childre n, and that other health eviden ce 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 es timates derived using th e log-linear with low-5-18 exposure linearization (LLL) conc entration-response f unction, also noting th e range associated
with the other three functions. In interpreting the quantitative risk estimates for IQ decrement, we are aware of the significant implications of potentia l 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 mode st change in the mean for a health index at the individual level can have substantial impli cations at the pop ulation 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 in dividual 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 some what uniform manifestation of Pb-related decrements across the range of IQ scores in a population, “a downward sh ift in the mean IQ value is not associated only with a substantial in crease in the percentage of individuals achieving very low scores, but also with substantial decr eases 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 highl y 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 95 th . In so doing, we emphasize that in setting the standard in 1978, the Agency accorded risk management significance to the 99.5 th 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 shoul d be set so as to protect 99.5% of the population” (Henderson, 200 7a, p. 6). In considering es timates from the quantitative
assessment that will inform conclusions consiste nt with this objective, however, we and CASAC also recognize uncertain ties in our risk estimates at th e edges of the distribution and consequently report the 95 th percentile as our estimate of th e high end of the risk distribution (Henderson, 2007b, p. 3). In so doing, however, we note that there ar e 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 99.5 th 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 thecombined 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 cont ributions to blood Pb associated w ith Pb that was in the air in5-19 the past but not in the air recently. In the e xposure 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” cate gory were ingestion of outdoor soil/dust Pb and ingestion of the componen t of indoor dust Pb not a ssigned 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 asso ciated with our modeling tools that affected our estimates for these two categories. As a re sult, blood Pb levels and associated risks of greatest interest in this review – those associated with exposur e 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 w ith very few exceptions. Further, the national composite average maximum quarterly mean based on 198 active monitoring sites during 2003- 2005 is 0.17 μg/m 3 , an order of magnitude below the curr ent 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 n ear many of the larger sources and leads us to conclude that we are likely undere stimating the extent of occurrenc es 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 st udies in areas where air concentrations fall significantl y below the current standard. For the general urban case study, which is a simplified representation of urban area s (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 concentr ation-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 c ontributions to total IQ loss (LLL estimate) at the population median in these two scenarios indicate that IQ loss a ssociated with policy-relevant Pb falls somewhere between 1.3 and 3.6 points (Ris k Assessment Report, Table 5-9 entries for recent air and recent air plus past air). At the 95 th percentile for total IQ loss (LLL estimate), IQ loss associated with policy-relevant Pb is es timated 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-response5-20 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 95 th percentile estimates for total Pbrelated IQ loss across the three location-specific urban case studi es 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 95 th percentile for the three location-specific urban case studies, IQ loss associat ed 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 expos ure 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 curren t 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 standa rd would increase to ju st 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 th e standard by way of, for example, expansion of existing sources (e.g., facilities operating as seco ndary smelters exercising previously used capabilities as primary smelters) or by the c ongregation 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 st udy and in three locati on-specific urban case studies. In this scenario, we note substantial uncertainty in simulating how the profile of Pb concentrations might change in the hypothetical cas e where concentrations in crease 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 co nditions 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 qua rterly 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.5-21 Chicago and Los Angeles study areas, for which cu rrent conditions are estimated to be at or below one tenth of the current NAAQS. Our estimates of IQ loss (for child with me dian total IQ loss estimate) associated with recent air plus past air Pb at exposures al lowed by just meeting the current NAAQS in the primary Pb smelter case study (Table 4-5) diffe r 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 e xposure pathways for the full st udy area range from 0.6 point to 2.3 points (for the range of concentr ation-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-respons e function are 1.9 points IQ loss for the full study area and 6.0 points for the subarea. The 95 th 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 th e 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 th e high-end concentration-response function) (Table 4-5). The corresponding estimates based on the LLL concentr ation-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 co mparable estimates of IQ loss for children at the
95 th percentile range from 2.6 to 7.6 points for the LLL concentration-re sponse 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 poi nts using the LLL C-R function and a similar magnitude of difference for the 95 th percentile (Tables 4-3 and 4-4) . The corresponding estimate for the general urban case study is 1.1 to 1.3 point s 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 curr ent 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 wi th IQ loss greater than one point and with IQ loss greater than seven points in comparing cu rrent conditions to those associated with the current NAAQS indicate the potential for signifi cant numbers of children to be negatively5-22 affected if air Pb concentrations increased to le vels just meeting the current standard. Estimates
of the additional number of child ren with IQ loss greater th an 1 point (based on the LLL concentration-response function) in these thr ee 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 a dditional number of child ren with IQ loss gr eater than 7 points for the current NAAQS as compared to the curre nt conditions scenario range from 600 to 35,000 (Table 4-8). These latter values for the change in incidence of children wi th greater than 7 points Pb-related IQ loss represent 5 to 17 percent of the child ren (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 clearl y of a magnitude that might be reasonably be judged to be highly significant fr om a public health perspective, there are other, unquantified adverse neurocognitive effects that may occu r 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 desc ribed in detail in the CD, that were not quantified in the risk assessment, include: other neurological effects (sen sory, motor, cognitive and behavioral), immune system effects (incl uding 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 a ddition to the evidenceand risk/exposure-based informa tion discussed above, we have al so considered the advice and recommendations of CASAC, based on their review of the CD and the ea rlier 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 8 . 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 av ailable in the docket for this rulemaking, as will be transcripts of the public meetings held in conjunction wi th 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.5-23 level. No comments were receiv ed expressing the view that the current standard is adequate. One comment was received arguing not that the standard was inadequa te but rather that conditions justified that it should be revoked.
In both the 1990 review and this review of the sta ndard set in 1978, CASAC has recommended consideration of more health pr otective 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/m 3 , specifically recommended analyses of a standard set at 0.25 μg/m 3 , and also recommended a monthly averaging time (CASAC, 1990). In two letters to the Administrator during the current review, CASAC has consis tently recommended that the primary NAAQS should be “substantially lowered” from the current level of 1.5 μg/m 3 to a level of “0.2 μg/m 3 or less” (Henderson, 2007a, b). CASAC drew support for this recommendation from the current evidence, described in the CD, of health effects occurring at dr amatically 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, Attachme nt A), CASAC stated (Henderson, 2007a, p. 3): Despite the dramatic decrease in envi ronmental 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), de linquency, and criminal behavior (6–10). Among U.S. children, eight to fifteen years ol d, those in the highest quintile (> 2 μ g/dl) of lead exposure were four times more likel y to have doctor-diagnosed ADHD (11). Moreover, there is no evidence of a thres hold 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 μ g/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 μ g/dl, which is considerably lower than those considered acceptable for adults (19). Fina lly, although less definitive, there is also evidence that lead exposure during pregnancy is a risk factor fo r spontaneous abortion or miscarriage at PbB [blood Pb] concentrations < 10 μ g/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 epidemiol ogical, experimental and mechanistic studies”, and stated that “Consequently, it is the CASAC L ead Review Panel’s considered judgment that5-24 the NAAQS for Lead must be decreased to fully-p rotect 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 cr iteria pollutants in light of th e changed circumstances since the
current standard was set in 1978. We conclude th at 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 reache d by CASAC. Accordingly, we recommend that
Pb not be delisted as a cr iteria pollutant and that the Pb NAAQS not be revoked. In considering the adequacy of the current st andard, 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 a dverse 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, suppor ts the conclusion that airborne Pb exposure pathways (by inhalation and inge stion) contribute to blood Pb le vels 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 wa s set and may be several times higher. In areas projected to just meet the current st andard, the quantitative es timates of risk (for IQ decrement) associated with policy-relevant Pb indicate risk of a magnitude that may reasonably be judged to be hi ghly 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 cu rrent standard, the staff recognizes significant limitations with our current monitoring network th at indicate we are li kely underestimating the
extent of occurrences of relatively higher Pb concen trations (Section 2.3.2.1). In summary, staff draws conclu sions with regard to the ade quacy of the current standard from both the evidence and from the exposure and ri sk assessments, in light of related limitations
and uncertainties. We conclude that the overa ll body of evidence clearly calls into question the adequacy of the current standard and provides strong support for c onsideration of an alternative Pb standard that would provide an appreciable improvement in health protection for sensitive groups, including most notably young children, agains t an array of effects, most importantly including effects on the developing nervous system. We also conc lude that risks projected to remain upon meeting the current standard, base d 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 c onsideration should be given to5-25 revising the level of the standard so as to provide increased public health protection. Accordingly, we recommend that the Administra tor consider revision of the primary NAAQS for
Pb. Staff conclusions and recomm endations for the indicator, aver aging time, form and level for an alternative, more protective primary standard for Pb are disc ussed in the following sections.

5.5 ELEMENTS OF THE STANDARD

The four elements of the standard – indicat or, averaging time, form and level serve to
define the standard and must be considered co llectively in evaluating the health and welfare protection afforded by the standards. In the pr evious section, staff conc luded that the current standard is not adequate and shoul d be revised. In considering a revision to the current standard in the subsequent sections, we consider each of th e four elements of the st andard as to how they might be revised to provide a primary standard fo r 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-PM 10 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 i ngestion 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 so me point, become respir able through weathering
or mechanical action. EPA concluded that tota l airborne Pb, both resp irable and nonrespirable fractions should be addressed by the air standard. In the 1990 Staff Paper, staff reconsidered th is issue in light of information regarding limitations of the high-volume sampler used fo r 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 vi a direct inhalation, but via ingestion of deposited particles as well, especially among young children, the hi-vol provides a more complete measure of the tota l 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 recomm ended consideration of a change in the
indicator to utilize low-volume PM 10 sampling (Henderson, 2007a, b). In so doing, CASAC recognized the "importance of coarse dust contributi ons to total Pb ingesti on", that a scaling of the NAAQS level would be needed to accommodat e the loss of very large coarse-mode Pb5-26 particles, and that concurrent Pb-PM 10 and Pb-TSP sampling would be needed to inform development of scaling factors (Henderson, 200 7b). The September 2007 CASAC letter states that the CASAC Lead Panel “strongly encourag es the Agency to consider revising the Pb reference method to allow sample collection by PM 10 , rather than TSP samplers, accompanied by analysis with low-cost multi-elemental techniqu es 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 th e ingestion route and acknowledging that TSP sampling is likely to capture additional ve ry coarse particles which are excluded by PM 10 samplers, the Panel raised some concerns. The c oncerns 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 Pa nel indicated that these concerns may need to be addressed in implementing additional monitori ng sites and an increased frequency of sample collection that might be required with the substantial re duction in the level of the standard and the shorter averaging time that they recommend (Henderson, 2007b). In considering the appropriate indicator, sta ff concurs with previous Agency conclusions
that the health evidence indicates that Pb in a ll particle size fractions, not just respirable Pb,
contributes to Pb in blood and to associated hea lth effects. Additionally, the currently available information, from a very small set of collocated Pb-TSP and Pb-PM 10 monitoring sites, does not support the derivation of a scaling f actor which might be used to deri ve a level for the standard in
terms of Pb-PM 10 as the indicator. The staff recognizes, however, th at 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 wi th 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 th at there is an inherent tension between the perspective that Pb-TSP has high spatial variabil ity and the expectation that a national scaling
factor between the two indicators is possible. There are several options that staff suggest s be considered that might improve the
available database and facilitate consideration of such a move inthe 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 PM 10 that might be acceptably applied on a site-by-site basis where an appropriate relationship between Pb-TSP and Pb-PM 10 can be developed based on sitespecific data. Alternatively, us e of such a FEM might be approved, in combination with more limited Pb-TSP monitoring, in areas where the Pb-T SP data indicate ambient Pb levels are well below the NAAQS level. These examples are intended purely for purpos es of illustrating the5-27 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-PM 10 exhibits less spatial variability and that a scaling factor can be developed from Pb-PM 10 data to level for the standard in terms of Pb-TSP, staff recommends the
Administrator consider moving to a Pb-PM 10 indicator in the future. One of the issues to consider when moving to a Pb-PM 10 indicator is whether regula ting concentrations of Pb-PM 10 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-PM 10 /Pb-TSP ratios are sensitive to distance from emissions sources (due to faster depos ition of larger particles). As such, the use of a Pb-PM 10 indicator may have a significant influence on the degree of Pb controls needed from emission sources. In conclusion, the staff recommends retain ing Pb-TSP as the indicator for the primary standard, coincident with activit ies intended to encourage collecti on and development of datasets that will improve our understanding of nationa l and site-specific relationships between PM 10 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 de monstrate a relationship between Pb-TSP and PbPM 10
.

5.5.2 Averaging Time and Form In considering alternative Pb standards th at would provide incr eased public health protection, staff has taken into account both ev idence-based and exposure- and risk-based
considerations. We have also considered analyses of mon itoring 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/m 3 , 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 averag ing 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 curr ently available health effects evidence indicates a variety of neurological effects, as well as immune sy stem 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 be5-28 without adverse effect. Accordingly, to the exte nt 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 inhala tion and ingestion, with Pb taken into the body absorbed through the lungs and through the gastrointestinal tract. Studies on Pb uptake, elimination and distribut ion 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 distribu ted from plasma to red blood cells and throughout the body.

• Lead accumulates in the body and is only slow ly 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 to xicologically active fr action, respond quickly to increased Pb exposure, such that an ab rupt 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 th e time for blood Pb levels to stabilize after a reduction in exposure, for the dominant pha se for blood Pb responses to changes in exposure is on the order of 20 to 30 days fo r adults (CD, p. 4-25). Blood elimination half-lives are influenced by contributions from bone. Gi ven 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 th e temporal relationship between gasoline consumption data and blood lead data gene rally support the inference of a prompt response of children’s blood Pb levels to ch anges 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; Sc hwartz and Pitcher, 1989).

• The evidence with regard to sensitive neurolog ical effects is limited in what it indicates regarding the specific duration of exposure a ssociated 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 suppor ts our understanding of periods of development with increased vulne rability to specific types of effect (CD, Section 5.3), and indicates a potential importance of exposur es 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 importanc e 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, and5-29 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 dus t 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 dus tfall 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/ft 2 (52 ug/m 2 ) 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 relativel y 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, a nd 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 ther e was little possibility that there could be sustained periods greatly above the av erage value in situati ons where the quarterly standard was achieved. This may have been relate d to the pattern of lead emissions at the time the standard was set, which differed from the pa ttern today in that, due to emissions from cars and trucks at that time, emissions were more sp atially distributed. The air quality analysis in Chapter 2 for 2003-2005 indicates th e presence of areas in the U. S. currently where temporal variability does create differences between aver age quarterly levels and levels sustained for shorter than quarterly periods. For example, four percent of the monitori ng 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 wh en considering an average for any individual month (see Section 2.3.2.5). The same analysis indicates that this numb er is as high as ten percent for some alternate
lower levels. In further considering the a ppropriate form of the sta ndard 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 a nd 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 thre e year period, the5-30 monthly statistics derived are maximum monthly mean, s econd maximum monthly mean, average of three overall highest monthly means and average of three annual maximum monthly means; these statistical forms were also consider ed in the 1990 Staff paper. Additionally, the
maximum and 2 nd 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 fo rms 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 differen ce 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 analy zed yield an across-site average that is similar although slightly higher than the qu arterly average (e.g., Figure 2-8). In the 1990 Staff Paper, analyses of computer simulated values and of Pb measurements collected at point source orie nted sites included consideratio n of six different forms for a monthly average (USEPA, 1990b). These analys es 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 maximu m monthly average yiel ded the highest design
value 9 , and, among an intermediate design value set of three alternatives, th e 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 fo rm of the current standard being the maximum quarterly mean in any one year). These analyses i ndicate that with regard to either single-year or 3-year statistics for th e 2003-2005 dataset, a 2 nd 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 exceed ances than a maximum monthly mean. Other forms included in a subset of the analyses invo lved 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 othe r pollutants, from the current single-year period, staff took into account th e following. In a three-year approach, a monitor would be

9 The design value is the estimated ai r concentration at a specific location in terms of the standard (i.e., with regard to averaging time, form and indicator).5-31 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, th e 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 year s have passed without such an exceedance. This three-year approach would provide more stability in the ai r quality management process, and would help ensure that areas initially found to be viola ting the NAAQS have eff ectively controlled the contributing lead emissions before being redesign ated 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 meteorolog ical variability and the monitoring schedule. A three-year approach for Pb would be consistent with the pe riod used for the current NAAQS for PM 10 , PM 2.5 , and ozone. EPA established three-year periods for those NAAQS to provide more stabili ty to the air quality management process, consistent with CASA C recommendation made during the respective NAAQS review processes. In their review of the 1990 Staff Paper dur ing the last review, the CASAC Pb panel concurred with the staff recommendation to expr ess the lead NAAQS as a monthly standard not to be exceeded more than once in three years. Similarly, the current CA SAC, 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 th an 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 h ealth in light of the response of blood lead concentrations that occur at sub-qua rterly time scales” (Henderson, 2007a). With regard to form of the standard, the cu rrent 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 maximu m quarterly value”, while also indicating that “the most protective form would be the highest monthly average in a ye ar” (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 shor t exposures can lead to increases in blood Pb lead levels, 2) the time peri od 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 al so recognizes the limited evidence specific to the consideration5-32 of the particular duratio n of sustained airborne Pb levels ha ving the potential to contribute to the
adverse health effects identified as most relevant to this review. After considering the current evidence and anal yses 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 concl udes that it is appropriate to al so consider changing the duration of the time period evaluated in considering a ttainment, to a three-y ear period as is common practice for NAAQS for othe r 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 arra y of Pb-related adverse health e ffects than that afforded by the
current standard builds on our conclusion that the overall body of eviden ce indicates that the current standard is inadequate to protect public health and shou ld be appreciably lower (Section 5.4).

5.5.3.1 Evidence-based Considerations As an initial matter, staff r ecognizes obstacles that precl ude 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 he alth 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 sy stem), no threshold levels can be discerned from the evidence. As was recognized at the tim e 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 neurocognitiv e 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, su ch as the framework applied in the establishment of the standard. As discussed above (Secti ons 3.3 and 5.4.1), the body of evidence is much expanded from that available when the current sta ndard 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 th e key sensitive population for Pb exposures. Our recognition5-33 of young children as the sensitiv e population is based on the ev idence 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 curre nt evidence demonstrates the occurrence of adverse health effects, in cluding those on the developing ne rvous 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 developi ng 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 anem ia. 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 μ g/dL to
less than 5 μ g/dL, with some studies indicating Pb effects on intellectua l attainment of young children at blood Pb levels ranging from 2 to 8 μ g/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, w ith 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 sour ces to blood Pb. However, the available evidence and some preliminary analysis led CASAC to recommend consideration of 1.0 to 1.4

μ g/dL or lower as an estimate of the nona ir component of blood Pb (Henderson, 2007a, Appendix D). The value of 1.4 μ g/dL was the geometric mean blood Pb level derived from a simulation of current nonair exposures usi ng the IEUBK model (He nderson, 2007a, pp. F-60 to F-61). Regarding the relationship between air and bl ood, while the evidence demonstrates that
airborne Pb influences blood Pb concentra tions through a combination of inhalation and ingestion exposure pathways, estimates of the quan titative relationship (i .e., air-to-blood ratio) available in the evidence vary (USEPA, 1986a; Brunekreef, 1984) a nd there is uncertainty as to the values that pertain to current exposures. Studies summarized in the 1986 CD typically yield estimates in the range of 1:3 to 1:5, with some as high as 1:10 or higher (with regard to the air-

10 It should be noted that deposition of airborne Pb is a major source of Pb in food (as is house dust, which may also be attributable to deposition of ambient air l ead) (CD p. 3-54). Thus, although the risk assessment characterizes dietary Pb as "backgro und," reductions in ambient air Pb have the potential to reduce exposures through dietary Pb as well.5-34 influenced increase in blood Pb) (USEPA, 1986a; Br unekreef, 1984). Findings in a more recent study identified in the 2006 CD of blood Pb response to reduced air concentrati ons 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, re placement of the 1978 blood Pb target of 15 μg/dL for the child population geometric mean with a level of 2 μg/dL refl ecting, 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 ev idence (e.g., CD, p. E-9) 11 , and subsequent subtraction of 1 to 1.4 μg/dL, re presenting 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/m 3 . We note, however, that we cannot today identify a bl ood Pb level considered safe from all adverse health effects. Thus, putting the current evid ence 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 he re 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 th e 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 le vels for the standard that could provide an appropriate degree of public he alth protection, in addition to considering the health effects evidence (see preceding section), st aff has also considered the qua ntitative estimates of exposure and health risks attributable to policy-relevant Pb upon meeting specific alternative levels of
alternative Pb standards and the unc ertainties in the estimated expos ures and risks. As discussed above (Section 5.4.2), staff has based this evalua tion on the exposure and ri sk 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 w ith 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 desc ribed as a “safe level” for an individual child, while current epidemiological evidence using a large national databa se 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).5-35 different levels of IQ loss in three of the si x case studies (Chapter 4) . In so doing, we are mindful of the important uncertainties and limitati ons 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 concentrat ions at and below the lower end of those represented in the epidemiological studies char acterized in the CD; these uncertainties are
described in Section 4.2.1. As discussed in Section 4.2.7, we recognize im portant limitations in the design of, and data and methods employed in, the exposure and ri sk analyses, and the associated uncertainties with regard to the results. For example, the available monito ring 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 (S ection 2.3.2.1), which provides the potential for underestimation of current conditio ns. Additionally, we are uncertain about the proximity of existing monitors to other Pb s ources 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 c oncentrations to which th e target population is currently exposed. Additionally, we did not have sufficient informa tion and tools to evaluate all relevant sensitive groups (e.g., adults with chro nic kidney disease) or all Pb-related adverse health effects (e.g., neurological e ffects 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 qua ntitative 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 co ntribution of policy-relevant Pb to total Pb exposure and Pbrelated health risk. For example, given various li mitations 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 be tween the estimates for “recent air” and those for “recent” plus “past air”, and we recognize limitati ons 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 consid er the estimates of IQ loss associated with policy-relevant Pb at air Pb concentrations n ear those currently occurr ing in urban areas as5-36 illustrated by conditions in the three cities chos en for the location-specific urban case studies, e.g., 0.09 to 0.36 μg/m 3 as a maximum quarterly average or 0.17 to 0.56 μg/m 3 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 fo cus on risk estimates derived using the LLL concentration-response function. As described in Section 5.4.2, th e ambient air Pb related IQ loss (based on LLL function) associated with the median IQ loss for curren t conditions in the three location-specific case studies (see Table 5-9 of the Risk Assessment Repor t) is estimated to fall between the estimates for recent air (0.6-0.7 points) and those for recent pl us 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 cond itions scenarios in the general urban case study are still more significant with median estimat es 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 95 th percentile (for the current conditions scen ario in all of the urban case studies) extends higher than those fo r the median, ranging from 1.2-3.1 (recent air) to 5.2-6.0 points (recent plus past). As mentioned previously, a cu rrent conditions scenario was not assessed for the primary Pb smelter case study, in which current air Pb co ncentrations across the study area are generally near or greater than the current NAAQS. Howe ver, this case study il lustrates the potential impact of alternative NAAQS levels in comp arison 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 ch anges only to the recent air category of policy-relevant pathways and, conse quently, likely provide an underestimate of IQ loss associated with all policy-re levant 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/m 3 levels (in terms of a maximum monthly average), respectively (Table 4-3). From this it can be seen that in a situation wher e risks are driven by air concentrations that just meet the standard across the area asse ssed (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/m 3 , and additionally between t hose levels and the lower
alternative standard levels asse ssed. The difference between the al ternative standard levels of5-37 0.5 and 0.2 μg/m 3 is seen to a smaller degree in th e 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 ai r concentrations. Rather, the vast majority of the population in that case st udy resides in areas of notably lower levels. As recognized above, differences in total IQ lo ss for different air quality scenarios reflect changes in the risk assessment simulations only to the recent air catego ry 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 95 th percentile of population to tal IQ loss are greater than one poi nt 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 μ g/m 3 maximum quarterly average (0.17 μ g/m 3 maximum monthly average). The es timates for recent air (for the LLL concentration-response functio n) associated with the 95 th percentile total IQ loss in the two current conditions scenarios of the genera l urban case study are 2.2 and 3.1 points.
Next we consider the extent to which alternat ive standard levels below current conditions
are estimated to reduce blood Pb le vels and associated health ri sk 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 lo west alternative NAAQS (0.02 μg/m 3 ) 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 95 th percentile of the population,
the estimated reductions are simila r in terms of percentage, but ar e higher in absolute values (1.7 and 1.0 μg/dL). For the three lo cation-specific urban case studie s, median blood Pb estimates
associated with the lowest alternative standard are reduced from those associated with current conditions by approximately 10% in the Chica go and Cleveland study areas and 6% in the Los Angeles study area; similar percent reductions are estimated at the 95 th percentile total blood Pb. For the localized subarea of the primary Pb sm elter case study, a 65% reduction in both median
and 95 th percentile blood Pb (3 and 8.1 μg/dL, re spectively) is estimated for the lowest alternative NAAQS as compar ed 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, esti mated reductions in median Pb-related IQ loss

12 This can be compared to reductions in blood Pb, fo r the primary Pb smelter cas e study subarea estimated to be associated with a change in the leve l from the current standard to the 0.2 μg/m 3 level (either averaging time) which are approximately 45-50% for both the median and 95 th percentile values.5-38 associated with reduced exposures at th e lowest alternative NAAQS level (0.02 μg/m 3 ) were 0.5 and 0.7 points (LLL function) for the two current conditions scen arios. Reductions at the 95 th percentile were of a similar magnitude. Am ong the three location-sp ecific case study areas, estimated reductions in median Pb-related IQ lo ss 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 95 th percentile, based on the LLL function, is 0.3-0.4 points. Reduced exposures
associated with the lowest alte rnative 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 95 th percentile (based on LLL function). In considering estimated reductions in Pb-asso ciated IQ loss discussed above, we observe
that estimates for the 95 th 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 cas e studies. This is because of the nonlinear relationship between IQ decrement and blood Pb leve l 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 ai r Pb concentrations from current conditions to meet the lower alternative NAAQS (0.02 and 0.05 μg/m 3 , maximum monthly mean) is
estimated to reduce the number of children havi ng Pb-related IQ loss gr eater than one point by one half to one percent in each of the thre e 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/m 3 , maximum monthly mean. Further, just meeting the lowest alternative standard in these three study areas is estimated to reduce the numbe r of children having an IQ loss greater than seven points by one to two percent. This corr esponds to a range of approximately 350 (for the Cleveland study area) up to 8,000 (for the Chic ago study area) fewer ch ildren with total Pbrelated IQ loss gr eater than 7.0. In summary, in staff’s view (as noted a bove), 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 lo ss associated with poli cy-relevant Pb are of a magnitude that appears to fall near or within th is range for air quality scenarios involving levels at or above 0.09 μg/m 3 (maximum quarterly mean, or 0.17 maximum monthly mean). Estimated
reductions in risk associated w ith reducing air Pb concentrations from current conditions (in the urban case studies) to the tw o lower alternative levels evaluated (0.02 and 0.05 μg/m 3 ) appear to5-39 range from a few tenths to just below one IQ poi nt (for the LLL concentration-response function) (and up to 1.5 IQ points for the highest concentrat ion-response function). In considering changes in risk across the population associ ated with the two lower altern ative levels (as compared to current conditions), we estimate re ductions in the number of ch ildren with total Pb-related IQ loss greater than 1 or greater than 7 on the orde r of hundreds to thousands of children in the three location-specific ur ban case studies. In considering the exposure and risk informati on with regard to a level for the standard,
staff notes that at the time the standard was se t, 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-respons e functions used in the risk assessment and in CASAC recommendations on these functions with rega rd to a lack of a threshold. We therefore
have considered a different approach in this review. In considering these risk estimates , we note our conc lusion and CASAC’s recommendation regarding the hi gh public health significance of a population loss of 1 to 2 IQ points, our recognition in Secti ons 5.4.1 and 5.4.2 of other unquantif ied health effects, and the significant implications of potentia l shifts in the distribution of IQ for the exposed population
summarized in Section 5.4.2. Based on these f actors and the range of estimates summarized above for IQ loss associated with policy-relevant Pb for the curre nt conditions scenarios of the location-specific case studies, st aff concludes that reducing th e NAAQS to a level of 0.1 to 0.2 μg/m 3 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/m 3 , staff has considered risk as well as blood Pb reduction that might be achieved. Notable re ductions in blood Pb are estimated for the lower alternative standards as compared to the curren t 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 regula tory tools the Agency brings to the national task of elim inating blood Pb poisoning in the U.S. 13 In considering the public health significan ce of IQ loss, we have started with our conclusion that a population loss of 1-2 IQ poi nts 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, th ere 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 H ealth Risks to Children (Executive Order 13045, as amended) has identified childhood blood Pb poisoning as a priority public health issue in the United States.5-40 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 es timated in our assessment for the range of alternative levels that we considered, we focuse d 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 pub lic health perspective and we also recognize that nonair contributions to tota l Pb-related IQ loss are estimated to reach and exceed that amount, with air Pb contributions gene rally 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 polic y-relevant Pb-related IQ loss of less than one point IQ would lead to cons ideration 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 recogni zes, 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 cu rrent conditions (See Section 4.2.7). Thus, to the extent that incremental exposure reducti ons achieved through lowering the NAAQS are
concluded to contribute to notable incrementa l reductions in children’s blood Pb and to
associated reductions in health effects, staff suggests that cons ideration of NAAQS levels below 0.1 μg/m 3 (e.g., the lower levels included in the risk assessment of 0.02 and 0.05 μg/m 3 ) 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-rele vant sources to no more than 1-2 points IQ loss at the population-level, we note that standa rd levels in the range of 0.1 to 0.2 μg/m 3 may achieve that goal. We also note that ev en with lower levels of the sta ndard evaluated, while the range of policy-relevant IQ loss estimates is lower, the up per end of the range sti ll extends up to and in some cases above 1 point IQ loss. We note, how ever, appreciably greater uncertainty associated
with these estimates that increases with increasi ng difference of the alte rnative standards from current conditions (See Section 4.2.7). Alternatively, if the policy goal was to be de fined 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 leve l, this would suggest greater c onsideration for st andards in the lower part of the range evaluated (0.02-0.05 μg/m 3 ). 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 health5-41 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 ea rlier 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 standa rd recommended an appreciable reduction in the level, e.g., setting it at 0.2 μg/m 3 or less. In their advice to the Agency during this review CASAC has recognized the importance
of both the health effects eviden ce and the exposure and risk info rmation in selecting the level for the standard (Henderson, 2007a,b). In two sepa rate 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/m 3 or less”, reflecting th eir 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 ca rrying 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 curr ent NAAQS in 1978, while a nother 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/m 3

. The CASAC Pb Panel also provided advice regarding how the Agency should consider IQ loss estimates derived from th e 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 recomme nded 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 ANPR 14

.

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, co nsistent with the new NAAQS process. A public meeting of the CASAC Pb Panel is now being planned for mid-December.5-42 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 clea rly 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 consid eration of a range of standard levels that are appreciably below the le vel 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) le ads the staff to conclude that it is reasonable to consider a range for the le vel of the standard, for which the upper part is represented by 0.1 to 0.2 μ g/m 3 . We note that such levels (i n terms of monthly or quarterly averaging time) are currently seen in many urba n 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 a ssociated 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 evid ence 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 asso ciated with the effects of greatest public health concern, and the eviden ce 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 reco mmendations from CASAC. Having reached this evidence-based conclusion on the upper part of the range of levels appropriate for consideration, staff then consid ered 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 es timated to be associated with policy-relevant Pb in air quality scenarios involving levels at or above 0.17 μ g/m 3 Pb with a maximum monthly averaging time and form (o r levels at and above 0.09 μ g/m 3 with a maximum quarterly averaging time and form). Thus, the exposure and risk information supports and extends the evidencebased conclusion by indicating that important re ductions in blood Pb and in Pb-associated IQ loss may be gained or maintained by reducing th e level of the standard (in conjunction with a monthly averaging time) by at least an or der of magnitude to approximately 0.15 μg/m 3 or lower. Staff then considered the exposure and risk as sessment with regard to the lower part of the range of alternative levels fo r the standard. In l ooking at the lower standa rd levels evaluated5-43 in the quantitative risk assessment, we recogni ze 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 perspectiv e 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 a ssociated 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/m 3

. In conclusion, staff judges that a level for the standard set in the upper part of our recommended range (0.1-0.2 μg/m 3 , particularly with a monthly averaging time) is well supported by the evidence and also supported by estimates of risk as sociated 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 estim ates from the risk assessment. To provide some perspective on the implications of alternative prim ary 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 th e 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 limita tions 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, show n 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 R ecommendations on the Primary Pb NAAQS

Staff recommendations for the Administrator’ s consideration in making decisions on the primary standard for Pb, together with s upporting conclusions from section 5.4 and 5.5, are briefly summarized below. In making these re commendations, 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 th at 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.5-44 (1) Staff concludes that the currently ava ilable 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 cr iteria 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 pr ovides strong support for consideration of a Pb standard that would pr ovide an appreciable increas e in health protection for sensitive groups, including most notably young children, agai nst an array of effects, most importantly including effects on th e 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 hi ghly significant from a public health perspective, which reinforces our conclusion that considerati on 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 a ppropriate to continue to us e 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 obt aining data concerning relationships between Pb-TSP and PM 10 that might facilitate consideration of this issue in the next review.
(4) Staff concludes that it is a ppropriate to consider changi ng the standard to a monthly averaging time or to retaini ng the quarterly averaging time. In considering a form for a monthly averaging time, staff conclude s that a form of maximum or second maximum would be appropriate to consider . Further, staff concludes that it is appropriate to also consider changing th e 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 cu rrent single-year period).
(5) Staff concludes that it is appr opriate 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 a ppropriate degree of public health protection and would likely result in important improvements in protecting the health of sens itive groups. We recommend that consideration be given to a range of standard levels from approximately 0.1-0.2 μg/m 3 (particularly in conjunction with a monthly averaging time) down to the lower levels included in the5-45 exposure and risk assessment, 0.02 to 0.05 μg/m 3 . 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 UNCERTA INTIES 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 gath ering are outlined below. In some cases, research in these areas can go beyond aiding standard setting to aiding in the deve lopment 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 c onducted for this review is the concentrationresponse function for the relationship between blo od Pb levels in children and neurological effects, specifically IQ decrem ent. The functions applied in the assessment are derived from a recent analysis of pooled datasets from a nu mber of studies (La nphear 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 relati onship at the lower blood Pb levels, particularly below 5 μ g/dL, where the pooled analysis wa s quite limited with regard to number of observations. Additional epidemiological re search involving substantial populations with blood Pb levels in this lowe r 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-ris k 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 re gard to exposure pathway contributions, particular distinctions pertinent to policy-relevant expos ures and background
sources;

• Prediction of blood Pb levels for subg roups other than young children, including adults with consideration for the fu ll period of exposure from childhood into adulthood; and

• Model performance evaluation, with empha sis on applications pertaining to blood Pb response to ambient air-related pathways and responses to changes in exposures for those pathways.5-46 • 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 in door dust Pb. Research in this topic area generally, as well as in specific e nvironments, 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 ambien t 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 contri bute uncertainty to our characterization of ambient air Pb levels and associ ated exposures. Research in th e area of characterizing spatial variation in air Pb concentra tions 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-d ensity residential populations?).

• An important aspect to this re view 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 impr ove our understanding of these areas would contribute to reducing associat ed 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.5-47 REFERENCES
Bellinger, D.C. (2004) What is an adverse effect? A possibl e resolution of clinical and epidemiological perspectives on neurobehavioral toxicity. Environ. Res. 95: 394-405.

Bellinger, D. C. and Needleman, H. L. (2003) Intellectual impairment and blood lead levels [letter]. N. Engl. J. Med. 349: 500.

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

Canfield, R. L.; Henderson, C. R., Jr.; Cory-Slechta, D. A.; Cox, C.; Jusko, T. A.; Lanphear, B. P. (2003a) Intellectual impairment in children with blood lead concentrations below 10 μ g per deciliter. N. Engl. J. Med. 348: 1517-1526.

Canfield, R. L., Kreher, D. A., Cornwe ll, C., and Henderson, C. R., Jr. (2003b) Low-level lead exposure, executive functioning, and learning in early childhood. Child Neuropsychol. 9: 35-53.

Chiodo, L. M., Jacobson, S. W., Jacobson, J. L. (2004) Neurodevelopmental effects of postnatal lead exposure at very low levels. Neurotoxicol. Teratol. 26: 359-371.

Clean Air Scientific Advisory Committee (1990) Report of the Clean Air Scientific Advisory Committed (CASAC),Review of the OAQPS Lead Staff Paper and the ECAO Air Quality Criteria Document Supplement. EPASAB-CASAC-90-002. Washington, DC. January.

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

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

Lanphear, B. P.; Dietrich, K. N.;Auinger, P.; Cox, C. (2000) Cognitive deficits associated with blood lead concentrations <10 μ g/dL in US children and adolescents. Public Health Reports. 115: 521-529.

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 enviro nmental lead exposure and children's intellectual function: an international pooled analysis. Environ. Health Perspect. 113: 894-899.

Needleman, H. L., Leviton, A., Bellinger, D. (1982) Lead-a ssociated intellectual deficit [letter]. N. Engl. J. Med. 306: 367.

Rabinowitz, M. and Needleman, H.L. (1983) Petrol Lead sales and umbilical cord blood lead levels in Boston, Massachusetts [Letter]. Lancet 1(8314/5:63.

Schwartz, J., and Pitcher, H. (1989) The relationship betw een gasoline lead and blood lead in the United States. J Official Statistics 5(4):421-431.5-48 Téllez-Rojo, M. M., Bellinger, D. C., Arroyo-Quiroz, C. , Lamadrid-Figueroa, H., Mercado-García, A., SchnaasArrieta, L., Wright, R. O., Hernández-Avila, M., Hu, H. (2006) Longitudinal associations between blood lead concentrations < 10 μ g/dL and neurobehavioral development in environmentally-exposed children in Mexico City. Pediatrics 118: e323-e330.

U.S. Environmental Protection Agency. (1977) Air quality criteria for lead. Office of Research and Development. Washington, D.C. 20460. EP A-450/8-77-017. December.

U.S. Environmental Protection Agency. (1986a) Air quality criteria for lead. Research Tr iangle 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, Sp ringfield, VA; PB87-142378. Available on the web: http://cfpub2.epa.gov/ncea/cfm/recordisplay.cfm?deid=32647

U.S. Environmental Protection Agency. (1986b) Lead effe cts on cardiovascular functi on, early development, and stature: an addendum to U.S. EPA Air Quality Criteri a for Lead (1986). In: Air quality criteria for lead, v. 1. Research Triangle Park, NC: Office of Health an d Environmental Assessment, Environmental Criteria and Assessment Office; pp. A1-A67; EPA report no. EPA-600/8-83/028cF. 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/s_pb_pr_td.html

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. Available on the web: http://cfpub2.ep a.gov/ncea/cfm/recordis play.cfm?deid=45189

. 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).

Weiss, B. (1988) Neurobehavioral toxicity as a basis for risk assessment. Trends Pharmacol. Sci. 9: 59-62.

Weiss, B. (1990) Risk assessment: the insidious nature of neurotoxicity and the aging brain. Neurotoxicology 11: 305-314.

World Health Organization. (2000) Air Quality Guidelines for Europe. Chapter 6.7 Lead. WHO Regional Publications, European Series, No. 91. Copenhagen, Denmark.6-1 

### 6 ASSESSMENT OF THE SECONDARY STANDARD

6.1 INTRODUCTION
This chapter presents information in support of the review of th e secondary NAAQS for lead (Pb). The presentation of welfare effects information summarizes policy-relevant aspects of
the assessment of welfare effects evidence c ontained in the CD. Staff conclusions and recommendations on the secondary standard are based on the assessment and integrative synthesis of the welfare effects evidence presen ted in the CD, staff analyses and evaluations presented in Chapter 2 and in this chapte r, and the comments a nd advice of CASAC and interested parties who commented on an early draf t of this document and on the pilot phase Risk Assessment Report (USEPA, 2006; ICF, 2006). Welfare effects addressed by the seconda ry NAAQS include, but are not limited to, effects on soils, water, crops, vegetation, manmad e materials, animals, wildlife, weather, visibility and climate, damage to and deteriorat ion of property, and hazard s to transportation, as well as effects on economic values and on personal comfort and well-being. The presentation in this chapter recognizes se veral key aspects of the welfare eviden ce for Pb. Lead is persistent in the environment and accumulates in soils, aquatic systems (including sediments), and some biological tissues of plants, animals and ot her organisms, thereby providing long-term, multipathway exposures to organisms and ecosyst ems. 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-a cid batteries, and some pesticides which have significantly contributed to widesp read increases in Pb concentr ations 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 sect ion 6.2. Next in Section 6.3, we summarize the screening level ecological risk assessment conduc ted in support of the review the details of which are presented in the pilo t phase Risk Assessment Report. 1 In Section 6.4, we assess the secondary standard, draw conclusions and pres ent recommendations for the Administrator to consider in deciding whether th e existing secondary Pb standard should be revised and if so, what revision is appropriate.

1 As recognized in the December 2 006 draft Staff Paper, a full-scale ecological risk assessment has not been performed for this review.6-2 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 concentratio n responses for other criteria pollutants are much more fully understood than fo r Pb. In the case of Pb, it is difficult to generalize effects due to the nature of the data and the gene ral 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 categorie s of organisms and summarizing the limited
available information for broade r ecosystem effects of Pb.

6.2.1 Effects in Terrestrial Ecosystems
Ecosystems near smelters, mines and other i ndustrial sources of Pb have demonstrated a wide variety of adverse effects including decr eases in species divers ity, loss of vegetation, changes to community compositi on, decreased growth of vegeta tion, and increased number of invasive species. Apportioning these effects be tween Pb and other stressors is complicated because these point sources also emit a wide variety of other heavy metals as well as SO 2 which may cause toxic effects. There are no field st udies 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 contaminat ion occurs within 20 to 50 km of the emission source (CD, AX7.1.4.2). By far, the majority of Pb found in terrestria l 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 tr ansported across large distances. The evidence indicates that many sites receiv ing Pb predominantly through su ch 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 transpor t of Pb have experienced significant effects on ecosystem structure or functi on (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 e ffect 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 base d on forest type, climate, and litter cycling. Once in the soil, the migration and distribution of Pb is cont rolled by a multitude of factors6-3 including pH, precipitation, litter composition, and ot her 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 incr eased at lower pH. Howe ver, the reduction of pH may in turn decrease the solubility of disso lved organic material ( DOM). Given the close association between Pb mobility and comp lexation with DOM, a reduced pH does not necessarily lead to increased move ment 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 move ment of Pb into surface waters compared to those areas with neutral soils (i .e., pH of approximately 7.0). Th is appears to support the theory
that the movement of Pb in soil s is limited by the solubilization a nd transport of DOM. In sandy soils without abundant DOM, modera te 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 harv esting and organic matter remaining in soils is exposed to higher temperatur es and moisture which tend to increase rates of decomposition. Despite these effects, studies ha ve shown very little to no mobilization of Pb from soils to surface waters following clear cutt ing. 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 surf ace 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 re sult 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 fo r animals are inhalation and ingestion. Inhalation exposures, which would be limited to areas immediately su rrounding point sources, are not thought to be common and little informati on is available about inhalation in wildlife. Ingestion constitutes the main pathway of expos ure for most organisms whether by incidental ingestion or prey contamination. For higher or ganisms which may ingest either contaminated plants or soils/sediments, the form and species of Pb ingested influences uptake and toxicity as6-4 does the presence of other heavy metals. The relativ e toxicity of metal mixt ures and their effects on Pb toxicity is complex and varies greatly by species and metal. For plants, direct deposition onto surfaces and up take 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 me tals, there are many factors that may affect the mobility of Pb, including eleva tion 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 ch lorides and acetates are more bioavailable than Pb oxides. These factors directly relate to the ability of Pb comple xes to enter pore water in soils and sediments and thereby en ter root tissues.

6.2.1.2 Effects of Lead on Energy Flow and Biogeocycling

Lead in soils and leaf litter can have a si gnificant adverse effect on energy flow in terrestrial ecosystems through reducing the rate of litter decompos ition 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 relationshi ps between the roots of some types of plants and fungi and/or bacteria or by tying up nutrien ts 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 a nd residence times in soils (the time for Pb to move out of the biologically active layers of so il) can varying greatly, fo r 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 Supe rfund Program to identify the potential for adverse effect from various pollutants in so ils to ecosystems, a multi-stakeholder group, consisting of federal, state, private sector, a nd academic participants de veloped Ecological Soil Screening Levels (Eco-SSLs) for various po llutants 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 cons ervative in order to provide confidence that contaminants that could present an unacceptable risk are not screened out early6-5 in the evaluation process (intended to be a specific site under consideration of the Superfund Program). At or below these le vels, adverse effects are consid ered 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 ecol ogical receptors that commonly come into contact with soil or ingest biota that live in or on soil. ” They were derived sepa rately 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 availabl e literature, and 2) application of the TRV with information on soil intake, foraging habits, di et, 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 wa s developed (e.g., the reference dose for the most sensitive of the adverse ecological effect s on birds) for all species in each category. However, default assumptions regarding inci dental soil ingestion, foraging techniques, contaminant intake by prey, and overall diet com position generally resulted in different Eco-SSL values, expressed as soil concentrations, for th e 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 in vertebrates are 120 mg/ kg, 11 mg/kg, 56 mg/kg and 1700 mg/kg, respectively. Section 2. 7 discusses current concentrati ons of Pb in soils. Values range from 40 to 100 mg Pb/kg soil in remote fo rests where historic deposition of Pb from gasoline would be presumed to be the major s ource 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 e ffects from Pb. Soil screening values, including Eco-SSLs, were used in this way in the ecol ogical screening analyses conducted for this

assessment.

6.2.1.4 Effects on Plants

As discussed in Section 7.3.1 of the CD, atmo spheric deposition of Pb onto vegetation is the primary route of exposure to plants from atmo spheric Pb. Lead enters plant tissues primarily through direct transport, whether by surface depositi on or through the soil. There is some uptake6-6 through root cell walls via pore wate r but little Pb is tr anslocated to other parts of the plant by this mechanism. Most Pb that does enter pl ant tissues is deposited in the roots. Toxicity to plants occurs over a broad range of soil Pb concentrati ons (tens to thousands of mg/kg) due in part, to the in teraction between various soil pro cesses and the bioavailability of Pb to plants (CD, Section 7.1.4). Laboratory studies have shown gr eat 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 con centration responses for plants in the field: 1) the large number of confounding fact ors that need to be controlle d 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 conc entrations 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 laborat ory. 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 AX 7.1.4). Root cell elongation, another indicator of growth, was inhibited in ryegrass at <2.5 mg/kg Pb chloride a nd absence of root growth was observed at 5 mg/kg. Elevated t oxicity was also found for red spruce and ryegrass when exposed to Pb under low pH conditions (CD, Section AX 7.1.3.1). There is a wide breadth of studies discussed in the CD for various plants in the labor atory which indicate that Pb in concentrations found in soils near point sources could reduce pl ant growth. Despite this information, there are very few reports of phytotoxicity from Pb expos ure under field conditions. Indeed two studies
cited in Section AX7.1.3.2 of the CD found no indicat ion of toxicity in plants exposed to high soil concentrations of Pb and other heavy me tals near mining sites despite relatively high
concentrations of Pb in th e vegetation (4000 μg/g in Leit a et al., 1989). Overall, the phytotoxicity of Pb is considered relatively low because little Pb en ters plants from soil and what Pb does enter into plant tissue is deposited in ro ots 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 inges tion 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 organi sms (CD, AX7.1.3.1). Physiological effects from Pb exposure in birds and mammals include increased lipid peroxidation (fat breakdown) and e ffects on blood component production (CD, Section6-7 AX7.1.2.5). Lipid peroxidation and fatty acid change s have been linked to changes in immune system response and bone formation. Other adve rse effects may include changes in juvenile growth rates; delay of reproductive maturity; be havioral effects, such as decreased predator avoidance or lack of balance and coordination; and mortality. This cascade of effects has the potential to influence populati ons by reducing the number of or ganisms 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 su ccess, 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 range s of effects levels may be expected. Even in studies focused on reproductive eff ects in the same species, effects from doses ranging from <1 to >100 mg Pb/kg bw/day have been observed (C D, AX7.1.3.5). This varia tion is also true for other effects (e.g. behavioral a nd physiological effects) which ha ve 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 ha s been done since the 1986 CD on toxicity from Pb to birds not exposed to sediment (waterfowl). Soil Pb concentrations and pot ential toxicity to birds ha s 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 we ight 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 poi nt sources (CD, Sections 3.2 and AX7.1.2.3). Toxic effects to mammals from Pb exposure ha ve also been observe d over a wide range of doses in laboratory studies with little informa tion available for field populations or exposures.
Recent studies indicate that effect s on wildlife survival would likely occur at higher doses than the 2 to 8 mg/kg-day reported in the 1986 CD . Several studies ha ve 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 st udies 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 reproductiv e and growth effects (USEPA, 2005b). Soil concentrations ex ceeding 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 cu rrent Pb emission sources (CD, Section 3.2 and AX7.1.2.3).6-8 Several behavioral and physiologi cal processes seem to alter th e toxicity of Pb in birds and mammals. Nutritionally deficient diets, espe cially those low in calcium, lead to increased uptake of Pb from the diet. Studies have al so 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 deco mposition rates either by direct toxicity to specific groups of deco mposers, by deactivating enzymes excreted by decomposers to break down organic material or by binding with orga nic matter and making it resistant to the action of decomposers. Direct adverse effects to invertebrates, such as earthworms and nematodes, include decreased surv ival, growth and reproduction. Toxicity has
been observed in soil invertebrates and micr oorganisms at concentrations of hundreds to thousands of mg Pb/kg soil with significant variation due to so il 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 influe nced by point sources (CD, Sections 3.2 and AX7.1.2.3). Several physiological mechanisms for reduc ing 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 cont aminated substrates and reduced feeding has
also been observed in invertebrates.

6.2.1.7 Summary

Lead exists in the environment in various fo rms 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 ecosyste ms 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 ar e considered harmful to laboratory organisms.
Adverse effects associated with Pb include neur ological, physiological and behavioral effects6-9 which may influence ecosystem structure and fu nctioning. 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 pote ntial 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 f actors govern the fate and bioava ilability of Pb in aquatic systems. A significant portion of Pb remains bound to suspended particul ate matter in the water column and eventually settles into the substrate. Species, pH, salinity, te mperature, turbulence and other factors govern the bioavailability of Pb in surface waters (CD, Section 7.2.2).

6.2.2.1 Tools for Identifying Ecotoxi city 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 adopti ng water quality standard s. AWQC values are
available for freshwater and ma rine 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 te sting on aquatic organisms, including fish, invertebrates and algae and are considered to be values belo w which no adverse effect is anticipated (USEPA, 1993). Therefore these values are useful in identif ying locations for which there is the potential for advers e effect from Pb. Section 4.4 de scribes how these criteria were
used in the risk characterization for the eco logical 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. Ther e 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 toxi city tests. These me thods do not account for bioavailability or metal mixtures but are compat ible with data available from current water quality databases.6-10 6.2.2.2 Effects in Marine/Estuarine Ecosystems
This section gives a brief overview of the in formation 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 salin ity increases the amount of Cl - and CO 3 2- complexation and
reduces concentration of free Pb 2+ thereby producing compounds w ith 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 o ceans 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 eco systems include runoff from contaminated watersheds, direct atmospheric deposition and turnover of contamin ated sediment in areas of high turbulence. Lead is primarily found in the op en 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 bioavailabilit y. 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 e ffects in aquatic vertebrates.
Demonstrated effects in invertebrates include alteration of reproduc tion 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 sedi ment concentrations reached 1343 mg/kg dry weight. Inhibition of embryo development in commercial shellfish ha s been documented at
water concentrations of 50μg/L (CD, AX 7.2.4.3). The toxicity of Pb in the marine or estuar ine environment is highly dependent on salinity. A study of mysid shrimp reported a lethal conc entration for 50% of the test organisms (LC 50 ) of 1140 μg/L at a salinity of 5% and an LC 50 of 4274 μg/L at 25 % salini ty. There is also some evidence of gender sensitivity in th at male copepods were more sens itive 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 al so shown that deposit feeders were most affected by elevated
substrate concentrations.6-11 6.2.2.3 Effects in Freshwater Ecosystems
This section gives a brief overview of informati on available for Pb in freshwater systems. Most Pb in freshwater systems is in the inorgani c form. Speciation is important in bioavailability and is dependent upon factors such as pH, temper ature and water hardness. In freshwater, Pb typically forms strong inor ganic complexes with OH - and CO 3 2- and weak complexes with Cl -

. Organic Pb compounds in freshwater, which may in crease bioavailability, arise from both natural and anthropogenic sources. Concentrations of va rious 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 comple xes 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 me mbranes. 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 th e 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 variet y of effects including reduced species diversity, abundance and richness; decreased primary produc tivity, and alteration of nutrient cycling. Apportioning these effects between Pb and othe r 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 availabl e for single contaminants in controlled studies to permit6-12 evaluation of specific impacts on higher levels of organization (beyond the individual organism). Potential effects at the population level or higher are, of necessity, extrap olated from individual level studies. Available populat ion, 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 th e environment are with effects on individual organisms. However, several recent studies have attribut ed the presence of Pb to reduced primary productivity, increased respira tion, 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 produc tivity and increase respiration in an algal community. Laboratory microc osm 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 pr esence 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 effe cts in aquatic ecosystems comes from either laboratory studies which focused on only a few aspects of the natu ral system thereby neglecting
some of the factors known to in fluence bioavailability of Pb, or from complex natural systems with many stressors and various sources of anth ropogenic Pb, particularly direct mining waste inputs (CD, AX7.2.5.2). Thus, the effects of at mospheric Pb on aquatic ecological condition
remain to be defined. There is a paucity of data in the general lit erature 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 a bundance, diversity, redu ced primary productivity, and alteration of community stru cture (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 mediat e or exacerbate Pb effects.
Lead concentrations in sedi ment vary with depth and ar e attributable to increased anthropogenic inputs over the last few decades. Se veral studies have been undertaken to identify regional sources of Pb in eastern North Amer ica and the Great Lakes and have found positive correlations between Pb isotope ratios in th e Great Lakes and known aerosol emissions from
current and historic indust rial sources in Canada and the U.S. These studies seem to indicate that
current emissions are contribu ting 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).6-13 6.2.2.3.3 Effects on Algae and Aquatic Plants As primary producers in aquatic systems, alg ae and aquatic plants are vital to ecosystem function and provide the founda tion upon which the food web depends. Therefore impacts to these organisms can create a chain of effects th at 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 (Pb 2+ ) and as such is adsorbed onto cell walls and accumulates
in the cell wall or surface of th e plasma membrane of aquatic plants and algae (CD, AX7.2.3.1). Bioconcentration of Pb, the accumulation of Pb in side an organism, may be quite high for both algae and aquatic plants and have made them eff ective in the remediation of contaminated areas. In aquatic plants as in terrestr ial plants, Pb tends to be seque stered (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 plan ts the sequestered Pb tends to be metabolically
unavailable until a certain concentration is r eached 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 bioa vailability of Pb to these or ganisms (CD, AX7.2.3.1). Clinical signs of Pb toxicity in algae include defo rmation and disintegrati on of cells, shortened exponential growth phase, and inhibition of pigmen t 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 concentra tions of 1,000 μg/L Pb nitrate exposure
and an effects concentration for 50% of the test population (EC 50 ) 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 re quired concentrations in excess of 82,000 μg/L to elicit significant growth inhibiti on. In aquatic plants, toxicity studies have focused on the effects of Pb on plant growth, chlo rophyll concentration and protein content. An
EC 50 of 1,100 μg/L was reported for growth inhibition for Azolla pinnata , an aquatic fern, when exposed to Pb nitrate for 4 da ys. Studies with duckweed, Lemna gibba , have reported an EC 50 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 fo und in the vicinity of direct discharges from point sources but which woul d not be expected from ambient deposition. There are two main mechanisms by which alg ae 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 phyt ochelatins, polypeptides which chelate heavy metal ions and make them biologically unavailab le to the organism, ma y be synthesized in response to exposure to hea vy metals (CD, AX7.1.2.4).6-14 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 al ter 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 sedi ments. Studies which relate the effects of dietary exposure and toxic effects in aquatic sy stems are rare; however, it may be assumed that both dietary and waterborne expos ures are important to overall Pb toxicity (CD, AX7.2.4.3). Exposure to Pb can result in reduction of gr owth rates and reproductive rates as well as cause increased mortality. As discussed in Section 4.3.2.1 of this doc ument, 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 CaCO 3 /L) compared to hard water (124 mg CaCO 3 /L). The influence of pH on Pb toxic ity varies between i nvertebrate species. Studies have reported increasing mortality with decreasing pH in so me bivalves, cladoceran s, amphipods, gastropods and mayflies while some crustaceans and gastro pods have shown no relationship between pH and mortality under identical conditions. For the amphipod H. azteca , the lowest observed effect concentration (LOEC) for surviv al in hard water at pH 8.27 wa s 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). Overal l, adverse effects fo r the most sensitive invertebrates studied, amphipods a nd waterfleas, occurred at con centrations 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 whil e 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 th e 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 inve rtebrates detoxify Pb. Lead may be concentrated in some invertebrates by formation of granules which may be eventually excreted, sequestered within the exoskelet on and glandular cells, or bound to membranes in gills and other
tissues. Avoidance behaviors have b een documented for the aquatic snail, Physella columbiana
, but few studies were found that reported avoidance behaviors in invertebrates.6-15 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 sucker s and small sunfish, which feed directly from the sediment, but not in smallmouth bass, which feed at a higher trophic le vel. Bioconcentration
does occur in freshwater fish and bioconcentratio n factors (BCFs) for br ook trout and bluegill of 42 and 45, respectively, have been reported (CD, AX 7.2.3.1). Studies have also shown that fish accumulate Pb more rapidly in low pH environmen ts 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 a dverse blood chemistry effects in some fish. Symptoms of Pb toxicity in fish include th e production of excess mucous, spinal deformity, anemia, darkening of the dorsal region, degenera tion of the caudal fin, destruction of spinal neurons, enzyme inhibition, grow th inhibition, renal patholog y, reproductive effects, and mortality (CD, AX7.2.4.3). As in other organism s, 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 concentra tions at which no adverse effects were seen, have been reported (CD, AX7.2.4.3) for rainbow a nd brook trout in soft water as 4.1 to 7.6 μg/L Pb and 58 to 118μg/L Pb respectively. A LC 50 of 810 μg/L was found using fathead minnows at a pH of 6-6.5 while at the same water hardness the LC 50 was >5,400 μg/L at a pH range of 7 –
8.5. Other studies have shown alterations in bloo d chemistry in fish from chronic and acute exposures ranging from 100 to 10,000 μg/L Pb (C D, 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 physiologi cal and behavioral mechanisms by which fish reduce exposure and absorption of Pb. While the avoida nce response to Pb in fish has not been well studied, it is known for other metals and is t hought 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 sedime nt is the primary route of exposure for waterfowl. Studies by Beyer et al. (2000) in the Coeur d’Al ene watershed near mining and smelting activity have shown a ra nge of effects for waterfowl ba sed on sediment concentrations and corresponding blood Pb levels. This study suggested that a NOAEL blood concentration of6-16 0.20 mg/kg wet weight Pb corresponded to a sediment concentration of 24 mg/kg Pb. Subclinical poisoning (LOEL) occurred in swan s 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 LC 50 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 he alth and diet of the an imals, the correlation
between blood Pb levels and effects should be applicable irre spective of location-specific
variables. Given current concentr ations of Pb in sediment, it is likely that some adverse effects are occurring in waterfowl exposed to point sour ces of Pb, whether throu gh 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 sedime nt. Current levels of Pb in water and sediment also vary widely depending on the source of Pb. Conditions exist in wh ich 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 poten tial 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 fu ture. 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 id entify areas in which there is the potential for adverse effects to receptors based on current concentrations of Pb in water and sediment.6-17 6.3 SCREENING LEVEL RISK ASSESSMENT
A screening level ecological risk assessment wa s 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 Ecologic al Risk Assessment for Selected Areas, Pilot
Phase (ICF, 2006). Funding constraints have precl uded performance of a full-scale ecological
risk assessment. The discussion here is focuse d on the screening level assessment performed in the pilot phase (ICF, 2006) and takes into consideration CASA C 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 st udies 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 vul nerable location was identifie d 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 ambi ent 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 se rve to identify those locations at which there is the greatest
likelihood for adverse effect. Si milarly, the national-scale scr eening assessment evaluated the potential for ecological impacts associated with the atmospheric deposition of Pb at surface water and sediment monitoring loca tions across the United States. Figure 6-1 illustrates the use of information and models in ea ch phase of the analysis. Table 6-1 specifies the information types a nd 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 appro ach for each case study differed based on the nature of the case study (e.g., type of sour ce, land use) and the site-specifi c measurements available. In
cases where the available measurements were not sufficient to characte rize the study area (e.g., due to insufficient spatial coverage), these da ta were used for performance evaluation of modeling tools.6-18 Figure 6-1. Overview of Ecological Screening Assessment.6-19 Table 6-1. Models and Measurements Used for Ec ological 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

Models n/a AERMOD a n/a n/a Deposition

to soil Measurements n/a n/a n/a n/a

Models n/a MPE b n/a n/a

Soil conc. 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

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

Surface water conc. 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)

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

conc. 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

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 CaCO 3 ) c n/a n/a U.S. EPA Pb freshwater AWQC for aquatic life derived based on site-specific measured water hardness (conc. of CaCO 3 ) c Screening ecotoxicity benchmarks

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 R egulatory Model (AERMOD) (USEPA, 2004) b Multiple Pathways of Exposure (MPE) (USEPA, 1998) c These screening values are ba sed on measured ecotoxicity data6-20 6.3.2 Measures of Exposure and Effect
The measures of exposure for these analyses ar e total Pb concentratio ns in soil, dissolved Pb concentrations in fresh surface waters (wat er column), and total Pb concentrations in freshwater sediments. These exposure concentra tions were estimated for the three case studies and the national-scale screening analyses as described below:

• For the primary Pb smelter case study, measur ed 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 ba sed on EPA’s MPE methodology (USEPA, 1998)
and data available from similar locations.

• For the near roadway nonurban case study, measur ed soil concentration data collected from two interstate sampling locations, one with fairly high-density development (Corpus Christi, Texas) and another w ith medium-density development (Atlee, Virginia), were used to develop estimates of Pb in soils for each location.

• For the national-scale surface wa ter and sediment screening analyses, measurements of
dissolved Pb concentrations in surface wate r 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 e xposure assessment focused on
these locations.
The Hazard Quotient (HQ) approach was used to compare estimated Pb media concentrations with ecotoxicity screening valu es for soils, surface waters, and sediments around the primary Pb smelter, for soils only around th e 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 concentrati on) / (ecotoxicity screening value)6-21 For each case study, HQ values were calculate d 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 ecotoxici ty 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 me dium are unlikely to pose signific ant risks to ecological receptors whereas HQ values greater than 1.0 indicate that the expected exposure ex ceeds 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 concentratio ns in freshwaters and sediments throughout the U.S. and three case study locations were select ed: 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 scen arios, respectively, while the near roadway non-urban location repres ents 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 usi ng 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 na tional ambient water quality criteria (AWQC), both chronic and acute, for the protection of freshw ater aquatic life. In this assessment, we
identified locations at which Pb concentra tions 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 su rface water screen is based entirely on the geographic coverage of available measurements of dissolved Pb in the se lected database, it was important that the most appropriate dataset be used. It was conclude d that of the commonly available databases, including NWIS, STORET and NAWQA, the NAWQA data set is most6-22 appropriate for a nationwide aquati c 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 se t, 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 ther efore do not represent cu rrent conditions. The NAWQA data set, on the other hand, provides representative (t hough not complete) coverage of the United States. The portion of the database containing samples throu gh 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 elemen ts 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 aquati c risk screen than the
other two data sets and was ther efore used for this screen.

6.3.3.1.2 Lead in Sediments Possible risks to sediment dwelling organisms we re 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 cr iteria. 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 follo wing criteria coul d 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 subj ect 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, elev ation, etc.) of Pb conc entration in various
environmental media.6-23 Based on these criteria, we selected the H ubbard Brook Experimental Forest (HBEF) in the White Mountains of New Hampshire for th e ecologically vulnerabl e case study. The HBEF was established by the USDA Forest Service in 1955 as a major cent er for hydrologic research in New England. The USDA proposed to use the sm all 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 factor s. The first grant was aw arded 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 fr om 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, privat e institutions, government agencies, foundations and corporations
have resulted in one of the most extensive a nd 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 ecologi cal risks for this cas e 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 on e of the largest primary Pb smelters in the world, is the only remaining ope rating Pb smelter in the United St ates, and is also the longest operating smelter in the world, sustaining ne arly 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 comp rise an active Superfund site and are subject to ongoi ng evaluation under the Superfund pr ogram 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 fr om those used by the Superfund program. The primary Pb smelter property is bordered on the east by the Miss issippi River, on the southeast by Joachim Creek, on the west and nor th-northwest by residential areas, and on the south-southwest by a slag pile. A large part of the slag pile is locate d in the floodplain wetlands
of Joachim Creek and the Mississippi River. Ecological features near the f acility include the Mississippi Ri ver, streams, emergent and scrub-shrub wetlands, and successional and matu re bottomland hardwood forest tracts (ELM, 2005). Bottomland hardwood forests and agricultural fields are present to the west, south, and6-24 east of the characterization area between the smelter’s slag storage area and Joachim Creek. The most mature bottomland hardwood forest is adja cent 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, mi gratory birds such as the redtailed hawk, belted kingfisher, a nd great blue herons utilize the habitat near the facility. The federally threatened bald eagle has been spotted on fac ility property, which is known to be within
the habitat for the bird. The facili ty is also within the habitat of the Indiana bat, which is on the federal and state endangered specie s lists. In addition, the state and federally endangered pallid sturgeon has been identified in the Mississippi Ri ver adjacent to and downs tream 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 th e facility. The surrounding area includes emergent and scrub shrub wetlands, forests, freshwater creeks, ponds , rivers, croplands, pastureland, and developed
urban areas. The Pea River wate rshed drains into the Gulf of Mexico. The watershed is underlain by coastal plain sediments, includ ing sand, clay, and limestone; and the topography can be characterized as gentle to moderate rolling hills (CPYRWMA 2006). Diversity of terrestrial and aquatic animal sp ecies is relatively high. The C hoctawhatee and Pea River basins, in which the secondary Pb smelter is located, contain 43 species of ma rine, estuarine, and freshwater fish species (Cook and Kopaska-Me rkel, 1996). Anadromous fish species (i.e., saltwater fish that must spawn in freshwater) fo und in the Pea River basi n include the following: the threatened Gulf sturgeon, Alabama shad, stri ped 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 va riety of birds, mammals, invert ebrates, and vascular plants.
Other terrestrial fauna found in the region include migrator y 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). Acco rding to NatureServe and the U.S. Fish and Wildlife Service (USFWS), no species in Pike Count y are on the federal e ndangered species list (Outdoor Alabama, 2003). A few species, however , are recognized as candi dates for the federal
list.6-25 6.3.3.5 Near Roadwa y Nonurban Case Stud y The Houston, Texas near roadway urban case st udy for the human hea lth risk assessment used surrogate soil Pb concentration data meas ured at a sampling location in downtown Corpus Christi, Texas (Turer and Maynard, 2003). Fo r the ecological screening assessment, nonurban case study locations that provide soil concentrati on data were sought with the expectation that ecological receptors would be more likely to occu r 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 alongsid e 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 ne ar oil refineries in Corpus Ch risti, Texas (Turer and Maynard, 2003) and (2) Interstate 95 in Atlee, Virginia, wh ich 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 pe rcent low intensity re sidential, and 25 per cent high intensity residential (Vogelmann et al., 2001). The remain ing 5 percent of the su rrounding 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 th e Atlee, Virginia study locations showed 26 percent developed: 2 percent lo w-intensity residential and comme rcial and 24 percent industrial and transportation. The remaining 74 percent incl uded 25 percent deciduous forest, 14 percent woody wetlands, and 12 percent pasture (Vogelmann et al., 2001). Smalle r proportions of mixed forest, evergreen forest, row crops, and tran sitional (barren) areas were also found.

6.3.4 Screening Values
The following is a brief summary of specific ec ological screening valu es 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 us ing 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 ex amined, and as appropriate, augmented or6-26 replaced with current species-specific informa tion. For example, the assumptions employed for deriving the Eco-SSLs for avia n 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) w ith the TRVs for Pb (USEPA, 2005b) and consideration of the inputs on di et 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 va lues (USEPA, 2005a) while the sc reening 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 pilo t 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 si te-specific water hardness data . AWQC values for chronic exposures are called the criterion continuous co ncentration (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 consid ered 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 deve loped by MacDonald et al (2000) which focus
on total Pb concentrations in sediment. Thes e criteria for Pb include a threshold effect concentration (TEC) and a proba ble effect concentration (PEC ) of 35.8 mg/kg dry weight and6-27 128 mg/kg dry weight, respectivel y. The methodology for these se diment 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 concentratio ns 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 ar eas around the case study s ites with ecotoxicity benchmark values for each of three media: soil, surface water, and sediment. HQs less than or equal to one suggest that ecol ogical 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 CaCO 3 ) is 1.2 μg/L, and higher for wa ters 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 asse ssment 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 lo cations were retained for analysis. Next, the location-specific CCC and CMC values were de termined 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 initia l screening value of 1.2 μg/L was too high to identify all locations for
which dissolved Pb concentrations exceeded th e CCC for the protection of aquatic life. Many
waters in the United States are softer than anticipated (i.e., measured CaCO 3 concentrations down to 1 mg/L).
A second screen was therefore conducted in which dissolved Pb measurements greater than the quantitation limit (QL) bu t 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 CaCO 3 were used to calculat e a site-specific CCC as described above. To attempt to isolate those locations where air derived Pb is the major source6-28 of Pb to water, land-use information was obt ained 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 othe r 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 ar e lacking to apportion Pb between air and nonair sources at mining sites. Theref ore, 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 mi xed, 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 uncerta inty 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 0.616-29 When the 15 sampling locations in Table 6-3 ar e compared to NEI data, only three appear to be near facilities emitting rela tively 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 locati ons appear to be with in 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 facilitie s 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 faci lities; 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 su rface water data identified 15 locations with water column levels of dissolved Pb that were gr eater than hardness adjusted chronic criteria for the protection of aquatic life (with one locati on 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 con centrations is unclear.

6.3.5.2 National-scale Sediment Screen

Sediment characterization for the 15 sites id entified 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 valu es 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 possibl e to find co-located sediment and surface water samples. It was expected, therefore, that so me of the 15 sites of interest would not have sediment samples available from the same lo cation. 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 screen ing values, which as described in Section 6.3.4.3, are the TEC and PEC for sediment dwelling orga nisms from the consensus-based approach to sediment quality criteria (MacDonald et al., 2000).6-30 Table 6-4. Concentrations of Total Pb in Sediment s at Locations Near or Matching the 15 Sites at which Dissolved Pb Concentrations Exceeded the AWQC, Excluding Mining Sites.

Pb Emissions (tons/year) (b) Sediment Hazard Quotients Basin ID State Land Use Match Total [Pb] (mg/kg dry sediment) SW HQ a :

max [Pb]/CCC Fac < 20 km Fac < 50 km No. Upstream NPDES permits for

metals [Pb]/ TEC [Pb]/P EC 45 RIOG NM Mixed Yes 23 1.03 0.068 0.095 0 0.64 0.18
2 CONN CT Mixed Near 68 1.13 6.1 7.0 0 1.9 0.53
46 NROK WA Urban Near 47.3 1.14 0.39 0.43 0 1.3 0.37
46 NROK ID Forest Yes 24.9 1.32 0.0 0.0 1 0.70 0.19 47 GRSL UT Rangeland Yes 2900 1.45 0.0 0.36 1 81 23
31 OZRK MO Forest Yes 2300 1.89 0.0 0.34 ND 64 18
58 OAHU HA Mixed Yes 59 1.98 4.9 4.9 ND 1.6 0.46

1 NECB RI Mixed Yes 240 2.51 4.1 11.7 6 6.7 1.9
2 CONN CT Mixed Near 68 2.53 0.081 11.3 0 1.9 0.53
46 NROK ID Mixed Yes 1620 2.69 0.34 0.43 4 45 13
31 OZRK AR Mixed Yes 28 3.46 0.0 0.01 0 0.78 0.22

31 COOK AK Reference Yes 239 14.91 0.0 0.0 0 6.7 1.9

a Data collected for corresponding surface water locations Abbreviations: [Pb] = total Pb concentration in sediments (mg/kg dr y sediment). CCC = Criterio n Continuous Concentration (or chronic AWQC). TEC = threshold effect concentr ation, 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 concentrati ons in the water column exceeded the CCC (i.e., chronic AWQC) for the protection of aquatic or ganisms 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 em issions were less than 0.07 tons per year at all three locations). Five of the PEC-based HQs exceeded 1.0, indica ting 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 downs tream from several NPDES-permitted discharges of metals to surface waters (10t h entry). Two other locations were found in Utah and Montana and it is possible that these con centrations reflect historical se diment contamination from mining operations.6-31 Of the locations for which air emissions of Pb appear to be more likely to be contributing to ongoing Pb contamination of surface water an d sediments (i.e. locations in Connecticut, Hawaii, and Rhode Island, respectively), only on e, 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 contam ination of sediment through deposition of current air emissions to surface waters, sediment at these three loca tions 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 lead ed gasoline. The Quienebaug River in Connecticut (a near match between the Jewett City a nd 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 se diment dwelling organisms may be considered “probable”. However, the contribution of air emissions to these con centrations is unknown.

6.3.5.3 Primary Pb Smelter Case Study

A Characterization Area Invest igation (CAI) was performed at the primary Pb smelter facility by ELM Consulting in 2005. The investiga tion area included the sme lter, 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 th e smelter, 6 to 7 km south of the facility. The area was evaluated for the potential for ecologica l 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 asse ssment, 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 se diment samples were taken from backwater and low flow areas along Joachim Creek both upstrea m and downstream of th e 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 clus ters 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 (incl uding the “reference areas”) had HQs that exceeded6-32 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 clus ters all had HQs less than 1.0 as results were
all below the detection li mit of 3.0μg/L. However, three sedi ment 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 per ceived 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 0.026-33 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 effect s to terrestrial organisms (plants, birds and mammals) and to sediment dwelling organism s. While the contri bution to these Pb concentrations from air as compared to nonair so urces is not quantified, air emissions from this facility are substa ntial (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 S ection 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 so il concentrations for the secondary Pb smelter were compared to empirical data obtai ned 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 seco nd characterization of soil concentrations. Specifically, measurements from a surrogate seconda ry 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). The6-34 averages for 1-, 5-, or 10-km interval distances from the secondary Pb sm elter facility and the associated soil HQs calculated for each in terval 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 c oncentrations for locations greate r that 1 km from the facility were associated with HQs less than 1.0 for th is dataset. The three-times-higher-scaled soil concentration dataset, developed based on soil data from similar lo cations, resulted in avian HQs greater than 1.0 for all di stance intervals evaluated, including the farthest interval modeled, 10 to 20 km from the facility. The scal ed 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 concentrati on in soils associated with the secondary Pb smelter case study were associated with HQs a bove 1 for plants, birds and mammals, indicating potential for adverse effect to those receptor groups.6-35 6.3.5.5 Near Roadway Nonurban Case Study
Table 6-9 presents the HQ calculated for the Co rpus Christi, Texas, near roadside soil concentration data. HQs for birds were greater th an 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), bi rds 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 eco logical risk assessment briefly de scribed above is described in detail in the pilot phase Risk Assessment Report and appendices (IC F, 2006). The results for the ecological screening assessment for the three case studies and the nation al-scale screen for surface water and for sediment indicate a potenti al for adverse effect from ambient Pb to multiple ecological receptor gr oups in terrestrial and aquati c locations. The screening assessment did not provide clear categorization of contributions from air and nonair sources although air Pb emissions are estimated to be su bstantial in some loca tions 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 characteri ze 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 sme lter case study, the near roadway non-urban case study and associated with the national-scale surface water and sediment screens that are6-36 presented in the pilot phase Risk Assessment Report and appendices (ICF, 2006). Note that some limitations for the ecotoxicity screening va lues 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 spec ific 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 uncerta inty as to whether other sources might have actually contributed to the Pb exposure estimates.

• A limitation to using the selected ecotoxicity sc reening values is that they might not be sufficient to identify risks to some thr eatened or endangered species or unusually sensitive aquatic ecosystems (e.g., CD, p. AX7-110).

• The methods and database from which the surface water screeni ng values (i.e., the AWQC for Pb) were derived is somewhat da ted and new data and approaches may now be available to estimated the aquatic toxi city 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-speci fic characteristics that might affect the bioavailability of Pb in sediments in the deri vation of the sediment quality criteria used for this ecological risk screen (CD, S ections 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 assimilati on efficiency parameters, it was based on a
conservative estimate of the relative bioava ilability 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 concentra tion protective of bird s 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 th e assimilation efficiency of Pb acetate added to pelleted diets is much less than 50 percent (Appendix L, ICF, 2006).6-37 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 othe r contaminants) attributable to the primary Pb
smelter without a need to attri bute the source of Pb in ecologi cally sensitive areas (ELM, 2003; ELM, 2005). For purposes of the Pb NAAQS revi ew, it is important to distinguish areas impacted primarily from current or historic ai r deposition of Pb from areas impacted primarily from nonair sources (e.g. erosi on 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. Therefor e, 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 fl ooding 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 rath er 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, scal ed modeled data is also reported in this assessment. A full discussion of the modeling steps and the fa te 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 ecol ogical 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 wate r ecosystem impacts of Pb from near roadway runoff of Pb contaminated soils. This may underestimate risks to aquatic receptors via this exposure pathway.6-38 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 comm unity 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 NAW QA 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 dissolv ed Pb that was used until the fall of 2000 (i.e., 1 μg/L).

• Fewer than 15 percent of samples analy zed for dissolved Pb between 1994 and 2004 were assessed with the lower quantitation li mit 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 re presented in the NAWQA database. In addition, where the land use around a sampli ng 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 informati on 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 so me 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 givenlocation, no attempt was made to match sampling dates and times to develop timespecific CCC values.6-39 • The water hardness measured at some locati ons was less than the lowest value of 20 mg/L of CaCO3 used to develop the equa tion to calculate a CCC. The CCC equation is not necessarily valid at va lues less than 20 mg/L CaCO3.

• It is not known how quickly dissolved Pb concentrati ons 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 locat ions from the NAWQA database at which
dissolved concentrations of Pb in surface wate rs 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 conclusi vely link any of the locations with probable
adverse effects of Pb in sediments on benthi c 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 c onducted 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 si tes 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 su rface 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 in terest, some of which we re 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 de rivation of the sediment quality criteria used for
this risk screen.6-40 6.4 THE SECONDARY LEAD NAAQS

6.4.1 Introduction
This section and subsections present sta ff conclusions and recommendations for the Administrator to consider in deciding whether the existing secondary Pb standard should be revised and, if so, what revisi on is appropriate. The staff c onclusions and recommendations presented here are based on the assessment and in tegrative synthesis of in formation 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 CASA C who commented on an early draft of this document and the related Risk Assessment Report. In recommending policy options for the Admini strator’s considerati on, we note that the final decision on retaining or revi sing the current secondary Pb sta ndard is largely a public policy judgment to be made by the Administrator. Th e Administrator’s final decision should draw upon scientific information and analyses about welf are effects, exposure and risks, as well as judgments about the appropriate response to the ra nge of uncertainties that are inherent in the scientific evidence and analyses. In evaluating whether it is appropr iate to consider retaining the current secondary Pb standard, or whether consideration of revisions is appr opriate, we intend to focus on the extent to which a broader body of sc ientific evidence is now available that would
inform such decisions. Section 109 of the Clean Air Act requires th e Administrator to establish a secondary standard that, in the judgment of the Administrator, are requis ite to protect the public welfare from any known or anticipated adverse effects as sociated with the presence of the pollutant in the ambient air. In so doing, th e Administrator seeks to establish standards th at are neither more nor less stringent than necessary for this purpos e. The Act does not require that secondary
standards be set to eliminate all risk of adverse we lfare effects, but rather at a level requisite to protect public welfare from those effects that are judged by the Ad ministrator to be adverse. We discuss the background for the current standard in Section 6.4.2, and the general approach used in considering th e adequacy of the current standa rd and alternatives in Section 6.4.3. Further, staff conclusions and recommenda tions for the Administrator to consider in deciding whether the existing sec ondary Pb standard should be re vised and, if so, what revised standard is appropriate is disc ussed in Section 6.4.4, and consider ation 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 μ g Pb/m 3

, as a maximum arithmetic mean averaged over a calendar quarter), the basis for which is6-41 summarized in section 5.2.1. At the time the stan dard was set, the Agency concluded that the primary air quality standard w ould adequately protect agains t known and anticipated adverse effects on public welfare, as the Agency stated that it did not have evidence that a more restrictive secondary stan dard was justified. In the rationa le for this conclusion, the Agency
stated that the available eviden ce cited in the 1977 CD indicated that “animals do not appear to be more susceptible to adverse effects from l ead than man, nor do adverse effects in animals occur at lower levels of exposure than comp arable effects in humans” (43 FR 46256). The Agency recognized that Pb may be deposited on th e 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 fromsoil, 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 evid ence indicating that ambient levels of Pb result in significant damage to manmade materials and Pb effects on visibi lity and climate are minimal. The secondary standard was subsequently co nsidered during the 1980s in development of the 1986 CD (USEPA, 1986) and the 1990 Staff Pa per (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 an imals used to investigate human lead toxicity risks.” Further, in a departure from the conclusions when the standard was set, staff highlighted concerns over potentia l 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 persis tence of lead in the environment, such accumulations are expected to continue as l ong 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 em issions to protect against potential ecosystem effect s. 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 le ad in gasoline ... may have mitigated or delayed the potential risk for lead-induced eco system effects occurri ng 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 histor ical accumulation of lead, and where the natural soil sinks for lead ma y 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 pendi ng development of a stronger database that more accurately quantifies ecological effects of different lead concentrations, consideration6-42 should be given to retaining a secondary standard at or below the level of the then-current secondary standards of 1.5 μg/m 3

.

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 ri sk-based approaches. A series of general questions frame our approach to reaching conclusi ons about the adequacy of the current standard and identifying options for consideration by th e Administrator as to whether consideration should be given to retaining or revising the cu rrent secondary Pb sta ndard. Examples of questions that we address in our review include the following:

• To what extent has evidence of new effect s 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 inform ation 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 impor tant uncertainties associated with that
evidence?
Our approach to considering these questions recognizes that the ava ilable welfare effects evidence generally reflects laboratory-based ev idence of toxicological effects on specific organisms exposed to concentrations of Pb at which scientists genera lly agree that adverse effects are likely to occur. It is widely recognized, however, th at environmental exposures are likely to be at lower concentr ations 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 curre nt standard, staff has considered the environmental effects evidence presented in deta il in the CD and summarized above in Section 6.2, the screening-level risk assessment summar ized above in Section 6.3, and CASAC advice
and recommendations contained in their letter s (Appendices A and B) and voiced during their public meetings on the first draft staff paper (F ebruary 6-7, 2007) and on the second draft lead human exposure and health risk assessment (August 28-29, 2007).6-43 6.4.4.1 Evidence–based Considerations
In considering the welfare effects evidence wi th respect to the ade quacy of the current standard, we consider not only the array of evidence newly asse ssed in the CD but also that
assessed in the 1986 CD and summarized in the 1 990 Staff Paper. As discussed extensively in the latter two documents, there was a significantl y improved characterization of environmental effects of Pb in the ten years after the Pb NAAQS was set. A nd, in the subsequent nearly 20 years, many additional studies on Pb effects in th e 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 releva nt 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 te rrestrial ecosystems. These studies show that the binding of Pb to organic materi als 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 so me ecosystems than others and may remain in the active zone of the soil, where expos ure 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 so il 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 le vels 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 e ffects 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 i ndicate that in some estuarine6-44 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 w ith regard to Pb toxi city, and findings of lower effect levels for previously unte sted species (CD, p. AX7-176 and Section AX7.2.4.3).

• Currently available studies have also show n effects on community structure, function and primary productivity, although some conf ounders 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 revi ew of the Pb NAAQS. Current information on Pb critical loads is lacking for many processes and inte ractions 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 desp ite wide variations in Pb concentrations in soils throughout the country, Pb concentratio ns are in excess of concentrations expected from geologic or other non-anthropogenic forces. In particular, the deposition of gasoline-derived Pb into forest soil s has produced a legacy of slow moving Pb that remains bound to organic materials despite the rem oval 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, concen trations of Pb in soil may exceed by orders of magnitude the concen trations which are considered harmful to laboratory organisms (CD, Sections 3.2 and AX7.1.2.3). We recognize, however, that major difficulties ar ise in attempting to quantify the role of
current ambient Pb in the environment. For exam ple, 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 advers ity in natural systems is very sparse due in no small part to the difficulty in determining the effects of conf ounding factors such as multiple metals or factors influencing bioavailability in field studies. Nonetheless, th e 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 environm ental Pb exposures of potential concern.
Conditions exist in which adve rse effects to aquatic organisms and thereby ecosystems may be anticipated given experimental result s. While the evidence does not indicate that
dissolved Pb in surface water constitutes a thr eat to those ecosystems that are not directly influenced by point sources, the evidence regarding Pb in sediment is less clear (CD, Sections6-45 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 a quatic communities. The long resi dence time of Pb in sediment and its ability to be resuspended by turbulence ma ke Pb likely to be a factor for the foreseeable future. Accordingly, staff conc ludes 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 consider ations discussed ab ove, staff has also considered the findings of the sc reening level ecologica l 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. Re sults 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. Si milarly, the national-scale scr eening assessment evaluated the potential for ecological impacts associated with th e atmospheric deposition of Pb released into
ambient air at surface water and sediment m onitoring locations across the United States. However, as noted above, funding constraints ha ve 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 un certainties associated with eac h. In general, their use here recognizes their strength in iden tifying media concentratio ns with the potentia l for adverse effect and their relative nonspecificit y 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 ecosystem s 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 a ssociated with the primary Pb smelter case study exceeded screening values for those medi a indicating potential for adverse effect to6-46 terrestrial organisms (plants, bi rds and mammals) and to sedime nt dwelling organisms. While
the contribution to these Pb con centrations from air as compared to nonair sources has not been quantified, air emissions from this facility ar e substantial (see Appendi x D, USEPA 2007; ICF 2006). Additionally, estimates of Pb concentration in soils asso ciated 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 recep tor 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 establishm ent of the current standard. The national-scale screens, which are not fo cused 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 severa l aquatic systems, nationally, exceed ed screening values In the
case of the national-scale screen of surface wate r 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 dwel ling organisms may be cons idered “probable”. A complicating factor in interpreting the findings for the nati onal-scale screening assessments is the lack of clear apportionment of Pb contributions from air as compared to nonair sources, such as industrial and municipa l discharges. While th e 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 de cades ago (presumably re flecting the banning of Pb-additives from gasoline used by cars and truc ks). 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 so me systems for much longer than in others (Section 2.8; CD, pp.AX7-141 to AZX7-145). The case study locations included in the scre ening assessment, with the exception of the primary Pb smelter site, are currently meeting th e current Pb standard, yet Pb occurs in some locations, particularly in soil a nd aquatic sediment, at concentrat ions above the screening levels, indicative of a potential for harm to some terrestri al and sediment dwelling organisms. While the role of airborne Pb in determining these Pb c oncentrations is unclear, the historical evidence
indicates that airborne Pb can cr eate such concentrations in sedi ments. Further, whether such concentrations may be related to emissions prior to establishment of the current standard is also6-47 unclear. The staff suggests that such concentrati ons 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 re commendations 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 cu rrent NAAQS, the CASAC letter said: The unanimous judgment of the Lead Panel is that lead should not be delisted as a criteria pollutant, as define d by the Clean Air Act, for which primary (public health based) and secondary (public welfare based) NA AQS are established, and that both the primary and secondary NAAQS shou ld be substantially lowered.

Specifically with regard to the secondary NAAQS, the CASAC Pb Panel stated that the December 2006 draft documents presented “compe lling scientific evidence that current atmospheric lead concentrations and deposition – combined with a large reservoir of historically deposited lead in soils, sediments and surface wa ters – continue to cause adverse environmental effects in aquatic and/or terres trial 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 concentra tions are below the level of th e current primary and secondary lead standards” and “Thus, from an environmen tal perspective, there ar e 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 la rge 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 envi ronment. 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 th e ecological effects of airborne lead pollution and to consider developing altern ative secondary standard s 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 recommen dations on the Agency’s consideration of the secondary standard in this review in th eir letter of Septembe r 2007 (Henderson, 2007b). In6-48 that letter, they restated thei r recommendation from the March le tter 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 th e secondary standard in influencing the long-term
environmental burden of Pb and a need for enviro nmental monitoring to assess the success of the
standard in this role: The large environmental burden of historical ly-deposited lead is currently decreasing. Accordingly, the goal should be to set the s econdary Lead NAAQS such that there is no reversal of the current downward trend in le ad concentrations in the environment. The limited funds available for monitoring en vironmental 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 pers istence of Pb, continued emissions of Pb by many and varied sources (Section 2.2), and the harmfu l environmental effects associated with Pb (Section 6.2; CD, Chapter 7 and Appendix AX7), st aff 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 info rmation now available, the secondary standard should be revised to provide requisite protec tion from Pb-related a dverse effects on public welfare, staff has considered evidence describe d 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 th at exist today reflect atmospheric Pb concentrations and associated deposition, in combin ation with a large rese rvoir of historically deposited Pb in environmental media. As di scussed above in Secti on 6.4.4.1, the information presented in detail in the CD and summarized in Section 6.2 indi cates that there is evidence, largely qualitative, that suggests the potential for adverse environmental impacts under the
current standard. Given the limited data on Pb e ffects in ecosystems, it is necessary to look at evidence of Pb effects on organisms and extrapolat e 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 occurri ng, particularly near point source s, 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 ha s been, although location-sp ecific dynamics of Pb in soil lead to differences among lo cations as to the timeframe for Pb to be retained in surface6-49 soils or sediments where it may be available to ecological receptors (s ee 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 sta ndard that suggest th e potential for adverse
environmental effects. For the reasons discussed above, staff concur s with CASAC with rega rd to consideration of Pb in the environment today and the adequ acy of the current stan dard. Accordingly, we suggest that there is a need for the Administrator to consider a re vision of the current standard to provide increased protection ag ainst reasonably anticipated a dverse 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 revi sion to the current standard, we
consider the four elements of the standar d, the information available, and advice and recommendations from CASAC, regarding how th e elements might be revised to provide a secondary standard for Pb that protects against adverse environmental effect. With regard to the pollutant indicator fo r 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 recep tors 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, a nd the media in which it occurs, information is insufficient to identify an indicator other th an Pb-TSP that would pr ovide 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 rec ognizes that the evidence demonstrates that Pb is a cumulative pollutant with environmental effe cts 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 regardi ng critical loads is cu rrently insufficient for
such use in this review. We conclude that ther e is not a basis at this time for recommending any revision to the current averaging time.6-50 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 whic h Pb-related adverse ecosystem effects would not occur. In addition, the cumulativ e and persistent nature of Pb requires protection over long periods of time even from accumulations resulti ng from small amounts of deposition. Staff also
notes the influence of airborne Pb on Pb in a quatic 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, quantita tive 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 N AAQS review cycle, the EPA needs to initiate new measurement activities in rural areas —i ncluding 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 ambi ent air, soils, deposition, surface waters, sediments and biota, along with other inform ation 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 th e Agency “identify the necessary funds to support needed continuing research on the ecological effects of airborne lead pollution and to consider developing alternative secondary standard s such as critical loads for lead, which may be different from primary standards in indi cator, averaging time, level or form.” In conclusion, staff concludes that in th e 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 pr otection against adverse envir onmental effect. Accordingly,
staff recommends that the Administrator consider a revised secondary sta ndard set identical to the indicator, averaging time, form, and level of a revised primary standard.6-51 REFERENCES

Alabama Natural Heritage Inventory. (2001) Alabama Inventory List: The Rare, Threatened and Endangered Species, Animals and Natural Communities of Alabama. Montgomery, AL: Privately printed by the Alabama Natural Heritage Program.

Beyer, W. N.; Audet, D. J.; Heinz, G. H.; Hoffman, D. J.; Day, D. (2000) Relation of waterfowl poisoning to sediment lead concentrations in the Coeur d'Alene River basin. Ecotoxicology 9: 207-218.

Borgmann, U.; Couillard, Y.; Doyle, P.; Dixon, D. G. (2 005) Toxicity of sixty-three metals and metalloids to Hyalella azteca at two levels of water hard ness. Environ. Toxicol. Chem. 24: 641-652.

Boyle, E.A., Bergquist, B.A. , Kayser, R.A. and Mahowald , N. (2005) Iron, manganese , and lead at Hawaii Ocean Time-series station ALOHA: Tempor al variability and an intermediate water hydrothermal plume. Geochimica et Cosmochimica Acta, Vol. 69, No. 4, pp. 933–952.

Choctawhatchee Pea and Yellow Rivers Watershed Management Authority. (2006) About the Watersheds: The Rivers.

Cook, M.R. and Kopaska-Merkel, D.C. (1996) Hydrolog ic Characterization of the WaterResources of the Choctawhatchee-Pea Rivers Watershed - Phase I. Tus caloosa, AL: Geological Su rvey of Alabama. 137p.

ELM Consulting LLC. (2003) Sampling Analysis Plan: Charact erization Area Investigation: The Doe Run Company Lead Smelter, Herculaneum, Mi ssouri (Version 1). Prepared for the Doe Run Company. March.

ELM Consulting LLC. (2005) Characterization Area Investigation Report. July.

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 Expo sure and Health Risk Assessments. September 27, 2007.

ICF International. (2006) Lead Human Exposure and Hea lth Risk Assessments and Ec ological Risk Assessment for Selected Areas. Pilot Phase. Draft Technical Report. Prepared for the U.S. EP A'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

MacDonald, D.D., Ingersoll, C.G., and Berger, T.A. (2000) Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosyste ms. Archives of Environmental Contamination and Toxicology. 39:20-31.

MacDonald, D.D., Ingersoll, C.G., Smorong, D.E., Lind skoog, R.A., Sloane, G., and Biernacki, T. (2003) Development and Evaluation of Numerical Sediment Quality Assessment Guidelines for Florida Inland Waters. British Columbia: MacDonald Environmenta l Sciences, Lt. Columbia, MO: U.S. Geological Survey. Prepared for: Florida Department of Environmental Protection, Tallahassee, FL. January.

NatureServe. (2006) NatureServe Explorer: An Online Encyclopedia of Life [Web Application]. Version 5.0. Arlington, VA and Pike County, AL: NatureServe. Av ailable at http://www.natureserve.org/explorer.6-52 Outdoor Alabama. (2003) Listing of Al abama Species on the Federal List for Threatened and Endangered Species or Whose Status is a Concern. Available athttp://www.outdooralabama.com/watchablewildlife/regulations/endangered-species.cfm.

Speiran, G.K. (1998) Selected heavy meta ls and other constituents in soil and st ormwater runoff at the Interstate 95 Interchange near Atlee, Virginia, April 1993-Ma y 1997. U.S. Geological Survey. Water-Resources Investigations Report 98-4115, 39p.

Turer, D.G. and Maynard, J.B. (2003) Heavy metal contamination in highway soils. Comparison of Corpus Christi, Texas and Cincinnati, Ohio shows organic matter is key to mobility. Clean Technologies and Environmental Policy. 4(4):235-245.

U.S. Environmental Protection Agency (USEPA). ( 1984) Ambient Water Quality Criteria forLead - 1984. Washington, DC: Office of Water Regulations and Standards, Criteria and Standards Division. EPA 440/5- B4-027. Available at http://yosemite.epa.gov/water/ owrccatalog.nsf/065ca07e299b46 4685256ce50075c11a/05edf009d cde1d7d85256b060072307e!OpenDocument.

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-600/8-83/028cF. Available from: NTIS, Springfield, VA; PB87-142378. Available on the web: http://cfpub2.epa.gov/ncea/cfm/ recordisplay.c fm?deid=32647

U.S. Environmental Protection Agency. (1990) Review of the national ambient air quality standards for lead: assessment of scientific and technical information, OAQ PS 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.

U.S. Environmental Protection Agency. (1993) Office of Water Policy and Technical Guidance on Interpretation and Implementation of Aquatic Life Metals Criteria: A Memorandum by Martha G. Prothro. Washington, DC: U.S. Environmental Protection Agency, Office of Water. October. Available at http://www.epa.gov/waterscience/library /wqcriteria/metalsinterpret.pdf.

U.S. Environmental Protection Agency. (1998) Methodolog y for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions. Cincinnati, OH: Update to EPA/600/6-90/003, EPA/NCEA (EPA 600/R-98/137). Available at oaspub.epa.gov/eims/eimscomm.getfile?p_download_id=427339.

U.S. Environmental Protection Agency. (2005a) Guidance for Developing Ecological Soil Screening Levels. Washington, DC: Office of Solid Waste and Emergency Response.OSWER Directive 9285.7-55. November.

U.S. Environmental Protection Agency. (2005b) Ecological Soil Screening Levels forLead, Interim Final. Washington, DC: Office of Solid Waste and Emergency Response. OSWER Directive 9285.7-70. Available at http://www.epa.gov/eco tox/ecossl/pdf/eco-ssl_lead.pdf.

U.S. Environmental Protection Agency. (2005c) Procedures for the Derivation of Equilibrium Partitioning Sediment Benchmarks (EBSs) for the Protection of Benthic Organisms:Metal Mixtures (Cadmium, Copper, Lead, Nickel, Silver, and Zinc). Washin gton, DC: Office of Research and Development. EPA-600-R-02-011.

U.S. Environmental Protection Agency. (2006) Air Quality Criteria for Lead, Volume I and II of II. Research Triangle Park, NC: National Cent er for Environmental Assessment – RTP Office. EPA/600/R-5/144aF. October. Available at: http://cfpub.epa.gov /ncea/cfm/recordisplay.cfm?deid=1588236-53 U.S. Environmental Protection Agency. (2007) Lead Human Exposure and Health Risk Assessments for Selected Case Studies, Volume I. Human Exposure and Hea lth Risk Assessments – Full-Scale. And Volume II. Appendices. Office of Air Quality Planning and Stan dards, Research Triangle Park, NC. EPA-452/R-07- 014a, EPA-452/R-07-014b

U.S. Geological Survey. (2004) National Water Quality Assessment Program. Available at http://water.usgs.gov/nawqa/data.

Vogelmann, J.E., Howard, S.M., Yang, L., Larson, C.R., Wy lie, B.K., and Van Driel, J.N. (2001) Completion of the 1990’s National Land Cover Data Set for the conterminous United States. Photogrammetric Engineering and Remote Sensing. 67:650-662. Available at http://edc.usgs.gov/products/landcover/nlcd.html

. Yohn, S.; Long, D.; Fett, J.; Patino, L. (2004) Regional vers us local influences on lead and cadmium loading to the Great Lakes region. Appl. Geochem. 19: 1157-1175.

Young, T. F.; Sanzone, S., eds. (2002) A framework for assessing and reporting on ecological condition: an SABreport. Washington, DC: U.S. Environmental Protection Agency, Science Advisory Board; report no. EPASAB-EPEC-02-009. Available: h ttp://www.epa.gov/sab/ pdf/epec02009.pdf [9 December, 2003]

## 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 1 st Draft Lead Staff Paper and Draft Lead Exposure and Risk Assessments

Dear Administrator Johnson:

The Clean Air Scientific Advisory Comm ittee (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 1 st Draft Lead Air Quality Criteria Document (AQCD) in September 2006 (EPA -CASAC-06-010). On December 7, 2006, Mr. Marcus Peacock, the EPA Deputy Administrato r, issued a memorandum providing his final decisions on revisions to the process by whic h the National Ambient Air Quality Standards (NAAQS) are reviewed. In this memo, Deputy Admi nistrator Peacock directed that this revised NAAQS review process should begin with the cu rrent, 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_pr ocess_report_march2006_attachments.pdf ).

On February 6–7, 2007, the CASAC’s Lead Pa nel conducted a peer review of EPA’s Draft Review of the National Ambient Air Quality Standards for Lead: Policy Assessment of Scientific and Tec hnical Information (1 st Draft Lead Staff Paper, December 2006) and a related draft technical support document, Lead Human Exposure and Health Risk Assessments and Ecological Risk Assessment fo r Selected Areas: Pilot P hase, Draft Technical Report (Draft Lead Exposure and Risk Assessments, December 2006). In addition, on March 9, 2007, the Lead Panel held a public teleconferen ce 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 Appendi x 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 co mments are provided in Appendix F.2 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:

• 1 st Draft Lead Staff Paper had no staff-derived options for keeping or altering the current Lead NAAQS.

• The Draft Lead Exposure and Risk Assessmen ts document did not have a full discussion of the risk associated with different opti ons for keeping or alte ring 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 pro cess, 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 NAA QS 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 CASA C 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 administrativ e discussions with the chartered members of the CASAC to learn directly from these seven member s 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 sta ff during the Panel’s Marc h 9 teleconference that EPA has modified its timeline both for the gene ric NAAQS review process and the current Lead NAAQS review in particular, such that the Lead Panel will now review the 2 nd 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 sc ientific information found in the Agency’s Final Lead AQCD, which was also reviewed by the Lead Panel, in its review of EPA’s 1 st Draft Lead Staff Paper and the Draft Lead Exposure and Risk Assessments document. The Lead Panel’s recommendations and the associated sc ientific 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 PM 10 samplers rather than with total suspended particulate (TSP) samplers, and that th e averaging time be decreased from quarterly to monthly.

The reasons for these recomme ndations are given below.3 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 Comm ission restricted the allowable am ount 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-bas ed paints had been us ed (5). Although it is difficult to quantify the extent of decrease in bloo d 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 leve ls in the U.S. population (6).

Despite the dramatic decrease in environmental lead exposur e, lead toxicity remains a major public health problem. Environmental lead exposure in children has been associated with increased risks for reading problems, school failu re, Attention Deficit Hyperactivity Disorder (ADHD), delinquency, and criminal behavior (6–10). Among U.S. children, eight to fifteen years old, those in the highest quint ile (> 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 th e decrements in intellectual (cognitive) functions in children are proportionately greater at Pb B 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 ri sk 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 incr eased 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 miscarriag e 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 protecti on of public health and public welfare from exposure to lead in the environment. One of thes e 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 th e de-listing of lead, as presented as a policy option in the 1 st Draft Lead Staff Paper. In addressi ng this question, the L ead Panel examined several scientific issues and rela ted public health and public welfar e 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 accumulate d since EPA’s promulgation of the current primary Lead NAAQS of 1.5 μg/m 3 in 1978 suggest that science previously overstated the toxicity of lead ? Here, the Lead Panel’s answer clea rly is No. The data accumulated over the past three decades make it apparent that adverse health effects on both humans and4 other species appear at blood lead concentr ations and environmental exposures well below those previously thought to pose impor tant risks. Indee d, if anything, this improved scientific understand ing indicates that scien tific studies previously underestimated the toxicity of lead.

2. Have past regulatory and other controls on le ad 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 e xposures to lead have been decreased, leading to laudable decreases in PbB concentrations to an average approaching 2 μg/dl. However, there remains a significant se gment of the populati on with blood-lead concentrations above 5 μg/dl — and some ev en above 10 μg/dl — and scientific evidence supports the contention that these PbB concentr ations do not provide an adequate margin of safety. In fact, this ev idence suggests these blood lead concentrations below 5 μg/dl are associated with unacceptable adverse effects.

3. Have the activities that produced emissions and atmospheric redistribut ion of lead in the past changed to such an extent that society can have confid ence 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 ha ve 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 re processing facilities produce considerable environmental exposures once the concentra tions of lead in environmental media equilibrate. The Lead Panel conclude s that past success in decreasing PbB concentrations in human populati ons 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 le ad sufficiently low throughout the United States that future regulati on of lead exposures can be effectively accomplished by regulation of lead-based products and allow able amounts of lead in soil and/or water? Lead Panel concludes that th e 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 co mponent of a successful control strategy. Maintaining appropriate Lead NAAQS is consid ered by the Lead Panel to be an essential component of a national program to decrea se 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 analys is of residual risk, is appropriate for point sources. However, the most widesp read source of airborne lead throughout the5 nation is the historically-depos ited 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 existin g state of science is c onsistent with continuing to list ambient lead as a criteria air pollutant for which fully-protective NAAQS are required.

Additional Analyses to In form Decisions About a Prim ary (Health-Based) NAAQS for Lead
Despite the dramatic decreases in amounts of airborne lead exposures and humanpopulation blood-lead concentrations following the pha se-out of leaded gasoline, lead toxicity remains a major public health problem. As discu ssed above, there is incr easing evidence of leadinduced toxicity at the lowest contemporary expo sures to lead — resul ting in significant IQ deficits in children (11–14), and increased freque ncy of ADHD (11) and cardiovascular disease (16–19). Although less definitive, there is eviden ce that lead exposure is a risk factor for spontaneous abortion and renal disease (20–21).

Although relatively few counties in the United St ates 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 c ontrast with exposure to lead-b ased paint, is more widely dispersed. Thus, reducing exposure from ai r lead will broadly reduce population blood lead levels.

In 1978, EPA established a primary Lead NAAQS of 1.5 μg/m 3 to ensure that 99.5% of the public did not exceed a blood-lead concentrati on 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 adopt ed in 1973 that requiring the phas e-out of leaded gasoline, the 1978 Lead NAAQS was instrumental in helping to produce the dramatic decreases in air lead and blood-lead concentrations ove r 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 he alth hazard markers iden tified by a wealth of new epidemiological, experiment al and mechanistic studies.

Consequently, it is the CASAC Lead Review Panel’ s considered judgment that the NAAQS for Lead must be decreased to fully-pr otect both the health of children and adult populations.

The EPA pilot-phase human health risk a ssessment focused on three case study locations ( i.e. , primary lead smelter, secondary lead smelte r, and near-roadway urban). While the case study approach undertaken in the risk assessmen t is enlightening and provides a potentially6 useful framework for understanding lead exposure for some discrete populations within the U.S., there are some additional considerations and anal yses that the Lead Panel strongly feels could help inform a scientific ally-defensible NAAQS for lead. In part icular, 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 resu lting 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 possibili ties in Appendix D attached to th is 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 un certainty, preferably in a quantitative manner, in two key areas: (1) the rela tionship 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 eff ects. This type of review by the Agency will be necessary to highlight the streng ths and weaknesses of the ava ilable approaches and help to elucidate a primary Lead NAAQS that is scientif ically-defensible and ad equately-protective of public health.

As described in Appendix D, the Lead Pane l 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 sc ientific evidence, for setting a new level for the primary Lead NAAQS, as follows: Approach A relate s air lead levels to blood lead levels using the approach used in previous lead NAAQS cal culations; Approach B uses an epidemiologic approach to derive an adjusted slope factor relating air lead leve ls to blood levels taking into account all exposure pathways between air lead a nd 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 lo ss of 1-2 IQ points is highly si gnificant from a public health perspective (22). Therefore, the primary lead st andard 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 substan tial reduction in the primary Lead NAAQS
, to a level of about 0.2 μg/m 3 or less . CASAC recognizes that these prel iminary 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 appropriate ness of which all depend on certa in scientific assumptions and the risk management criteria that are chosen. Imposing more stringent cr iteria would result in a lower (that is, more stringent) range of prim ary Lead NAAQS levels, whereas less stringent7 criteria would result in th e calculation of a higher ( i.e. , less stringent) range of primary lead standards.

Possible Revision to Lead Indicator from TSP to Low-Volume PM 10 As revisions to the level, form, and averag ing time of the Lead NAAQS are considered, CASAC also recommends that EP A revise the indicator. Curren tly, 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 PM 10 standards were promulgated in 1987. TSP sample rs capture particles w ith an imprecise and variable upper particle cut si ze in the range of approximate ly 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 emi ssion (PIXE) or X-ray fluorescence (XRF). Consequently TSP sampling by imprecise samplers is primarily conducted onl y for lead analysis and these filters are rarely analyzed for other species.

If Lead NAAQS monitoring wa s based on (low-volume) PM 10 sampling on Teflon filters, the resulting data would be correlated with TSP lead, as suggested by limited data in the 1 st Draft Lead Staff Paper, but would have substantia lly improved sampling precision. The Lead Panel recognizes that either monitoring system woul d be subject to variab ility based on location, particularly near sources. Ot her advantages of low-volume PM 10 sampling include:

1. Focus on those biologic ally-relevant particles that, when inhaled, are deposited in the thoracic region;

2. Larger spatial-scale represen tativeness for populati on exposures to m onitored particles which remain airborne longer;

3. Could utilize more widespread PM 10 and “air toxics“ metals sampling networks, leading to collection of more data at lower costs;

4. Potential for inexpensive multi-elemental anal ysis by XRF or PIXE would provide useful supplemental metals information for health effects studies and source apportionment;

5. Potential for automated sequential PM 10 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 PM 10 mass; and, if collocated with PM 2.5 Federal Reference Methods (FRM), c ould provide needed information on PM 10–2.5 mass and speciation.

Reasons for retaining the current TSP lead in dicator include: preser vation 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 tr act and ultimately be ingested, or which may deposit on surfaces and be ingested via hand-to-m outh activity of children. Some such coarse particles might be missed by PM10 samplers. Pres umably 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 PM 10 and TSP lead sampling could help develop site-specific scaling factors at sites with highest concentrations wh ere long-term historical records are important.8 Given the advantages of using PM 10 , the CASAC Lead Review Panel recommends that the Agency revise the lead indicator to utilize low-volume PM 10 sampling, and also develop equivalent analytical met hods 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 wi th a change in the Lead NAAQS is possible use of a different averaging time. Currently, qua rterly averaging is used. However, studies suggest that blood lead concentra tions respond at shorter time s cales than would be captured completely by quarterly values. Here, the L ead Panel recommends that the Agency conduct monthly averaging instead of quarterly.

One consideration involved in using a shorte r 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 (assumi ng no samples are considered invalid). This may make the average susceptible to anomalous ly-high events. On the other hand, this may motivate more frequent sampling in those areas whos e air concentrations are near the level of the Lead NAAQS, which would increase the protecti on of public health and significantly decrease the impact of a single high lead exposure even t. One could also c onsider having the lead standards based on the second highest monthly aver age, a form that appears to correlate well with using the maximum quarterly value.

The CASAC Lead Review Panel recommends adop ting monthly averaging as being more protective of human health in light of the res ponse of blood lead concentrations that occur at sub-quarterly time scales, and further recommends t hat the most protective form would be the highest monthly average in a year . An area could choose to in crease sampling frequency to make the monthly average less susceptible to more extreme events. Such a change is consistent with either using TSP or PM 10 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 1 st Draft Lead Staff Paper a nd Chapter 7 of the “Pilot Phase“ Draft Lead Exposure and Risk Asse ssments technical support document present compelling scientific evidence that current atmo spheric lead concentrations and deposition — combined with a large reservoir of historically-deposited lead in soils, sediments and surface waters — continue to cause a dverse environmental effects in aquatic and/or terrestrial ecosystems, especially in the vici nity of large emission sources. These effects persist in some cases at locations where current airborne lead co ncentrations are below the levels of the current primary and secondary lead standards.

Thus, from an environmental pe rspective, there are convinci ng reasons to both retain lead as a regulated criteria air pollutant and to lower the level of the current secondary standard.9 Since concentrations of histor ically deposited lead in soil s throughout the U.S. (averaging 0.5 to 4 g/m 2 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 ecos ystems downwind. This potential for harm is not adequately recognized in the 1 st Draft Lead Staff Paper and the Draft Lead Exposure and Risk Assessments technical suppor t document, but is a concern th at warrants careful continued monitoring in the future.

In addition, while neither the 1 st Draft Lead Staff Paper nor the Draft Lead Risk/Exposure Assessments document provide a cl ear 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 st andard. The EPA is also encouraged to identify the necessary funds to support needed continuing research on the ecologica l effects of airborne lead pollution and to consider developing al ternative secondary standards such as critical loads for lead, which may be different from primary st andards in indicator, averaging time, level or form

. The CASAC continues to be pleased to provi de advice to you concer ning the scientific basis for the setting of the primary and seconda ry Lead NAAQS. In addition, the CASAC looks forward to continued dialog with Agency offi cials and staff aimed at improving EPA’s NAAQS review process in a manner that enhances the e fficiency 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 2 nd 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 Panel10 Appendix D – Population-Based Appr oaches to Risk Assessment An alyses for the Primary Lead NAAQS Appendix E – Issues Related to the Setting of the Secondary Lead NAAQS Appendix F – Review Comments from Indi vidual CASAC Lead Review Panel Members

References

1. Pirkle JL, Kaufmann RB, Brody DJ, Hickman T, Gunter EW, Paschal DC. Exposure of the U.S. population to lead, 1991–1994. Environ Health Perspect 1998;11:745–50.

2. Mahaffey KR, Annest JL, Roberts J, Murphy RS . National estimates of blood lead levels: United States, 1976–1980. Association with sele cted demographic and socioeconomic factors. New Engl J Med 1982;307:573–579.

3. Committee on Toxicology, Assembly of life Sciences, National Research Council. Recommendations for the prevention of lead poisoning in children. Nutrition Rev 1976;34:321–327.

4. Bolger PM, Carrington CD, Capar SG, Adams MA . Reductions in dietary lead exposure in the United States. Chem Spec Bioavail 1991;3:31–36.

5. Jacobs DR, Friedman W, Clickner RP, et al. Th e prevalence of lead-bas ed paint hazards in U.S. Housing. Env Health Perspect 2002;110:A599–A606.

6. Needleman HL, Gunnoe C, Leviton A, et al . Deficits in psychologic and classroom performance of children with el evated dentine lead levels. N Engl J Med 1979;300:689–95.

7. Needleman HL, Schell A, Bellinger D, Leviton A, Allred EN. The long-term effects of exposure to low doses of lead in childhood: An 11-year follow-up report. N Engl J Med 1990;322:83–88.

8. Denno D. Biology and Violence. New York: Cambridge University Press, 1990.

9. Needleman HL, Reiss JA, Tobin MJ, Biesecker GE, Greenhouse JB. Bone lead levels and delinquent behavior. JAMA . 1996;275:363–369.

10. Dietrich K, Ris M, Succop P, Berger O, Born shein R. Early exposure to lead and juvenile delinquency. Neurotox Teratol 2001;23:511–518.

11. Braun J, Kahn RS, Froehlich T, Auinger P, Lanphear BP. Exposures to environmental toxicants and attention deficit hypera ctivity disorder in U.S. children. Environ Health Perspect 2006;114:1904–1909.

12. Canfield RL, Henderson CR, Cory-Slechta DA, Cox C, Jusko TA, Lanphear BP. Intellectual impairment in children with blood lead con centrations below 10 micrograms per deciliter. N Engl J Med 2003;348:1517–1526.

13. Lanphear BP, Hornung R, Khoury J, et al. Lo w-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. Defi cits in cognitive function and achievement in Mexican first-graders with lo w blood lead concentrations. Environ Res. 2006;100:371–386.11 15. Tellez-Rojo MM, Bellinger DC, Arroyo-Quiroz C, et al. Long itudinal associations between blood lead concentrations lower than 10 μg /dl and neurobehavioral development in environmentally exposed ch ildren 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 hypertensi on in perimenopausal and postmenopausal

women. JAMA 289:1523–1532.

18. McDonald JA Potter NU. Lead’s legacy? Earl y 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
μ mol/L (10 μ g/dl) and mortality among U.S. Adults. Circulation 2006;114;1388–1394.

20. Lin JL, Lin-Tan DT, Hsu KH, Yu CC. 2003. En vironmental 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, Ri os C, Blanco J. Blood lead levels measured prospectively a nd 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.A-1 

### Appendix A – Roster of the Clean Air Scientific Advisory Committee

U.S. Environmental Protection Agency Science Advisory Board (SAB) Staff Office Clean Air Scientific Advi sory Committee (CASAC)

CHAIR Dr. Rogene Henderson , Scientist Emeritus, Lovelace Re spiratory Research Institute, Albuquerque, NM

MEMBERS Dr. Ellis Cowling , University Distinguished Professo r 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 Medi cine, National Jewish Medical and Research Center, Denver, CO

Dr. Douglas Crawford-Brown , Director, Carolina Environmental Program; Professor, Environmental Sciences and E ngineering; and Professor, P ublic Policy, Department of Environmental Sciences and Engineering, Univers ity of North Carolina at Chapel Hill, Chapel Hill, NC

Mr. Richard L. Poirot , Environmental Analyst, Air Pollutio n 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 Medici ne, 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. Butterf ield, 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)B-1 

### Appendix B – Roster of the CASAC Lead Review Panel

U.S. Environmental Protection Agency Science Advisory Board (SAB) Staff Office Clean Air Scientific Advi sory 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 Me dicine, Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies, Center for the Evaluation of Value and Risk, Tufts New Engla nd 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 Ru tgers University, Piscataway, NJ

Dr. Ellis Cowling *, University Distinguished Profe ssor 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 Medi cine, National Jewish Medical and Research Center, Denver, CO

Dr. Douglas Crawford-Brown *, Director, Carolina Environmental Program; Professor, Environmental Sciences and E ngineering; and Professor, P ublic Policy, Department of Environmental Sciences and Engineering, Univers ity of North Carolina at Chapel Hill, Chapel Hill, NC

Dr. Bruce Fowler , Assistant Director for Science, Di vision of Toxicology and Environmental Medicine, Office of the Director, Agency for Toxic Substances and Disease Registry, U.S. Centers for Disease Control and Pr evention (ATSDR/CDC), Chamblee, GA

Dr. Andrew Friedland , Professor and Chair, Environmen tal Studies Program, Dartmouth College, Hanover, NH

Dr. Robert Goyer [M.D.] , Emeritus Professor of Pathology, Facu lty of Medicine, University of Western Ontario (Canada), Chapel Hill, NC

Mr. Sean Hays , President, Summit Toxicology, Allenspark, COB-2 Dr. Bruce Lanphear [M.D.] , Sloan Professor of Children’s Environmental Health, and the Director of the Cincinnati Ch ildren’s Environmental Health Ce nter at Cincinnati Children’s Hospital Medical Center and the Univer sity 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, Sc hool of Marine Sciences, Virginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA

Mr. Richard L. Poirot *, Environmental Analyst, Air Pollut ion 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 At mospheric 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

AdministratorC-1 

### Appendix C – Agency Charge to the CASAC Lead Review Panel

Charge to the CASAC Pb Panel

Within each of the main sections of the firs t 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 qua lity characterizations and analyses clearly communicated, appropriately char acterized, 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 eviden ce 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 appropr iateness of staff’s discussion and conclusions in Chapter 3 on key issues related to quantit ative 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 di scussion of potential thresholds in concentration-response re lationships presented in Chapter 3?

Human Exposure and Health Risk An alysis, Pilot-Phase (Chapter 4):

1. To what extent are the assessment, interpretation, and presentation of the results of the pilot exposure analysis, including char acterization of Pb concentrati ons in media, the modeling of multi-pathway Pb exposure and application of bi okinetic blood Pb models, as presented in Chapter 4 technically sound, appropriately balanced, and clearly communicated?

2. Are the methods used to conduct the pilot e xposure analysis, including the modeling of population-level distributions of total blood Pb levels and the path way-apportionment of those blood Pb levels ( e.g. , air-inhalation, versus soil-ingestion versus dust-ingestion, versusC-2 background) technically sound? Does the Pane l have any suggestions for improvements in the methods used?

3. What are the Panel’s views on the staff in terpretation of the pe rformance evaluation completed for the pilot analysis (and descri bed in Chapter 4) with regard to the representativeness of indi vidual modeling steps comple ted 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-resp onse functions and blood Pb metrics ( i.e. , lifetime average, concurrent blood lead) used in the p ilot 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 concentra tion-response functions employed, technically sound? Does the Panel have any suggesti ons for improvements in the methods used?

6. To what extent does the sensitiv ity 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) histori cally-deposited Pb ( e.g. , near-roadway dust/soil lead from leaded gasoline); and (b) newly-emitted Pb. Does the Panel have specific recommendations regarding appr oaches 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 conclusi ons 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; an d, how might this be considered in the fullscale risk assessment give n current capabilities?C-3 Pb-related welfare effects and screening le vel ecological risk a ssessment (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 t echnically 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 estim ate current levels of Pb in soil, freshwater, and sedime nt 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, interpreta tion, 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, appropriate ly balanced, and clearly communicated?

7. Does the Panel feel that ade quate screening criteria (ecot oxicity screeni ng 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 associ ated with the exposure analysis clearly and appropriately characterized in Chapte r 6 and the risk as sessment report?D-1 

### Appendix D – Population-Based Approaches to Risk Assessment on Analyses for the Primary Lead NAAQS

The CASAC Lead Review Panel considered three separate, but re lated, population-based analytical approaches as examples to be consider ed 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 le vels using the approach established in previous lead NAAQS calculations;

• Approach B uses an epidemiologic approach to de rive an adjusted sl ope factor relating air lead levels to blood levels taking into account all pathways be tween air lead and blood lead; and

• Approach C relates air lead levels to blood lead leve ls and then to IQ loss in children.

These approaches consider existing info rmation 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/m 3 (m 3 /dl) used in 1978, 5.0 m 3 /dl used by the World Hea lth Organization (WHO) in 2000, 10.0 m 3 /dl noted in recent studies (see th e discussion in Approach B below), and assuming 20.0 m 3 /dl as a maximum; and

 the most sensitive toxicity endpoint ( i.e. , IQ loss in children).

In addition, the CASAC Lead Review Panel c onsiders that a popula tion loss of 1-2 IQ points is highly significant from a public hea lth 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 substan tial reduction in the primary Lead NAAQS
, to a level of about 0.2 μg/m 3 or less . CASAC recognizes that these prel iminary calculations are dependent upon the results of the Agency’s forthcoming uncerta inty analyses and: the values chosen for the percent of the population left at risk; acceptable bl ood levels and IQ loss; and slope factor — the appropriateness of which all depend on certain scie ntific assumptions and the risk management criteria that are chosen. Imposi ng more stringent criteria would result in a lower (that is, moreD-2 stringent) range of primary Lead NAAQS levels, wh ereas less stringent cr iteria would result in the calculation of a higher ( i.e. , less stringent) range of pr imary lead standards.


### Approach A
The first approach (A) relates air lead levels to blood lead (PbB) levels using a simplified and modified empirical-determinis tic approach that is essentially the same approach used in previous EPA NAAQS (1978) and World H ealth 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-t o-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 revi ewed by the Panel, per the Agency’s Final Lead AQCD indicates that the concentration of lead in blood shown to be harmful ha s 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 ar e 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 subt racted to give the air Pb-based contributions to PbB. Panel member Dr. Paul Mushak (Append ix F) calculated the non -air portion using the Integrated Exposure Uptake Biokinetic M odel 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 suggest ed NAAQS values of 0.22, 0.11, or 0.06 μg/m 3

. Use of a “not-to-be-exceeded” value of 2.5 μg/d l and the same slope factors produce air lead values half as high, i.e. , 0.11, 06 and 0.03 μg/m 3 , respectively. (Note that, for the 1.25 mean PbB scenario, the non-air PbB contribution is the dom inant source and modeling does not provide an exact value; see Dr. Paul Mushak’s detail ed 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 (NHANE S) 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 us e, and usage of 426 MT/day in 1976. Between 1976 and when the phase-out of lead from gasoline wa s completed, air lead concentrations in U.S.D-3 cities fell a little less than 1 μg/m 3 (24). These two facts imply a ratio of 9–10 μg/dl per μg/m 3 reduction in air lead, taking a ll pathways into account.

Under this scenario, a de crease of mean air lead concentrations of 0.1 μg/m 3 could be expected to produce a further d ecrease in average bl ood lead concentrations of 0.9–1.0 μg/dl. Assuming a slope of three IQ poi nts per μg/dl reduction in blood l ead, which is indicated by the pooled analysis of low concentration lead exposur e (13), this further decr ease 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 a bove 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/m 3 (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, a nd 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 be tween air lead levels associat ed with PbB, dependent on the slope factor used. (Approach A does not consid er IQ loss or any othe r health effect.)

These considerations are summarized in Tables 1 and 2, and are al so contained in the individual review comments from various L ead Panel members attached as Appendix F. Depending on the slope factor selected between 5 and 20 m 3 /dl, the estimate of blood lead concentrations from various air lead concentratio ns varies by a factor of four (Table 1). For example, using the linear estimate of IQ loss a ssociated 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 lo ss of 1-2 IQ points to be highly significant from a public health perspective, the Lead Panel ther efore considers this extent of loss in IQ as a “change in IQ not to be exceeded.” Depending upon the slope factor se lected, this results in a range of 0.025–0.200 μg/m 3 ( i.e. , about a 7.5- to 60-fold decrease from the current primary Lead NAAQS) as the estimated air lead concentr ation to consider under Approach A.

References

23. Schwartz J, Pitcher H. The relationship betw een 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 Envi ronmental 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-4 TABLE 1. Relationship of Blood Lead (PbB) to Air Lead (Pb-Air) by Differing Slope Factors

PbB ( μ g/dl) Pb-Air ( μ g/m 3)

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 (m 3 /dl) = PbB/Pb-Air; S.F. value varies with increasing impact of indirect Pb-Air pathway (Dust Pb + Soil Pb)D-5TABLE 2.Relationship of IQ Point Losses toIncreases in Pb-Air and Pb-Air-BasedBlood Lead (PbB) Values Above Zeroa,b,cS.F. = 5S.F. = 10S.F. = 20Pb-Air(μg/m3)PbBdIQ Losse,fPbBIQ LossPbBIQ Loss00000000.0100.05< 10.10< 10.20< 10.0250.13< 10.25< 10.501.50.0500.25< 10.501.51.003.00.1000.501.51.003.02.006.00.200 1.00 3.0 2.00 6.0 4.00 12.0a Pb-Air-related increases affecting IQpoint loss through calculated PbB values using 3 slope factors per Table 1b IQ vs. PbB dose-response relationships based on Lanphearet al., 2005 (13): sub-7.5μg/dl linear segment, average slope = 3.0,combining slopes of 2.9 and 3.1 for concurrent and lifetime average dosemetrics, respectivelyc Slope factors as defined in Table 1 and textd PbB as derived in Table 1e Rounding for values < 1 IQ pointf Population, not individual, IQloss/gain projections; U.S. CDC 2007 estimates 23,380,860 U.S. children 0-71 months of age.Source: U.S. Centers for Disease Control, 2007. CDC Surveillance Data. (Last updated 2/16/2007). URL:http://www.cdc.gov/nceh/surv/stats.htm[accessed 3/8/2007]E-1 

### Appendix E – Issues Related to the Se tting of the Secondary Lead NAAQS

Chapters 6 of the Agency’s 1 st Draft Lead Staff Paper a nd Chapter 7 of EPA’s Draft (Pilot-Phase) Lead Exposure and Risk Assessments document summarize a very large body of scientific knowledge about envi ronmental and ecological effects of atmospherically deposited lead on the biota, soils, sediments, and surf ace and ground waters of terrestrial and aquatic ecosystems in various parts of the U.S. and near by parts of the world. This significant body of scientific knowledge includes environmental eff ects of both historically -deposited lead and continuing air dispersal or reen trainment of lead compounds from primary and secondary lead smelters, along roadsides, and in ecologi cally-sensitive areas such the Hubbard Brook Experimental Forest as descri bed 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), thes e two chapters do not contain adequate discussion of the special character istics 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 environm ental effects of lead is presen ted in a way that is directly relevant to the issue of whethe r the EPA Administrator should retain, increase, or decrease the present primary and secondary National Ambien t Air Quality Standard (NAAQS) for lead. These identical standards were established in 19 78 and have been maintained ever since at a level of 1.5μg/m 3 as a quarterly average (maximum ar ithmetic mean averaged over a calendar quarter).

The Lead Panel believes that especially Ch apter 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 purpose s 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 or iginal 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 s econdary 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 pollutant s for NAAQS since 1970.

Despite the limitations mentioned in the la st 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 lea d, and further presented in the aforementioned chapters in the 1 st

Draft Lead Staff Paper and the Draft (Pilot- Phase) Lead Exposure and Risk Assessments documents, provide compelling scien tific justification for both:E-2 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-des igned 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 si milarly well designed (but contemporarily scientifically well-informed) primary and secondary NAAQS for lead — standards with levels and forms that may be different fr om, rather than identical to each other.

There are several features of the environmen tal 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 pest icides 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 ti ssues 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 mu ch 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 terres trial and aquatic ecosy stems of the Earth — some of which are undoubtedly even more sensitive to lead than human infants;

4. The very substantial decreases in current ai r 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 batte ry processing facilities in more recent
years. Thus, most current exposures of living organisms in natura l and managed terrestrial and aquatic ecosystems are caused primarily by re distribution of envir onmentally persistent airborne lead compounds deposited in soils , sediments, and surface waters during the latter earlier decades of the 20 th century.

5. The continuing airborne resuspension and dispersa l 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 ma tter and aerosols — especially along roadways.

These distinctive properties of lead suggest to some polic y makers that ecological and environmental effects of lead might be manage d 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 th e EPA are such that none of these alternativeE-3 methods would be anywhere near as effective in protecting p ublic health or welfare as maintaining for the foreseeable future as appropriately well-des igned (and contemporarily scientifically well-grounded) prim ary 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 consen sus scientific judgment that the present level (1.5μg/m 3 ) of the primary Lead NAAQS should be decr eased substantially and that appropriate adjustments probably also should be made in the indicator, averagi ng time, and statistical form of the primary NAAQS for lead.

The scientific evidence on ecological and e nvironmental effects of lead summarized in the Draft Lead Staff Paper and the Draft L ead Exposure and Risk Assessments documents indicate that any significant de crease in the present level of the primary Lead NAAQS will very likely have similarly significant beneficial effects on the magnit ude 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, Mexic o, the Great Lakes, and also in the open-water regions of the Atlantic Ocean.

Since concentrations of historically deposit ed lead in soils throughout the United States (averaging 0.5 to 4 grams/m 2 of land area) are changing onl y 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 th e biosphere into the foreseeable future. Fires, changes in land use, or climatic events such as regional dust storms c ould 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 ecologica l effects of lead in the environment, as described in these document, it is very disappoi nting to note that the EP A apparently lacks (or chooses not to expend) funds for any additional ec ological risk assessment work for this current (2006–2008) review of the NAAQS for lead. This di sappointment 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 th at 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 pro cesses 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 consider ed very carefully in th e future as air quality management tools for use in this country as well as in other countries around the world.

## 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 2 nd Draft Lead Human Exposure and Health Risk Assessments Document

Dear Administrator Johnson:

The Clean Air Scientific Advisory Comm ittee (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 Standa rds (OAQPS) to conduct a peer review of the Agency’s Lead Human Exposure and Health Risk Assessments for Selected Case Studies, Draft Report (2 nd Draft Lead Human Exposure and Health Risk Assessment s, July 2007). This letter provides the Lead Panel’s advice and recommendations to you concer ning the Agency’s exposure/risk assessment that supports the sett ing of a primary National Ambient Air Quality Standard (NAAQS) for Lead, the secondary Lead NAAQS, and implementa tion issues associated with EPA’s revised NAAQS review process. The CASA C roster is found in Appendix A of this report, and the Lead Panel roster is attached as A ppendix B. The charge questions provided to the Lead Panel by EPA staff are contained in Appendix C to this report, and Panelists’ i ndividual review comments are provided in Appendix D.



### Review of EPA’s 2 nd 

### Draft Human Lead Exposure and Health Risk Assessments

1. Overall Evaluation

Overall, the CASAC Lead Review Panel judges that the Agency’s 2 nd 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 per tinent data and analyses. In particular, in order to support the establishment of National Ambient Air Qu ality 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 locales2 with varying lead exposure levels. More gene rally, in the view of the Committee, the second draft exposure/risk assessments document was missi ng 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, th e Final Staff Paper for Lead — when these are released by EPA in early November 2007.

2. Reiteration of CASAC Support fo r 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 c oncomitant 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 1 st 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 aime d at deriving an estimated range of alternative levels for the primary Lead NAAQS. On the basis of the CASA C’s preliminary scientific analyses and risk management assumptions, EPA needs to substantially lower th e level of the primary NAAQS for Lead

, to 0.2 μg/m 3 or less

. In the unanimous opinion of the Lead Panel, EPA has not presented any rigorous analyses or other information in its 2 nd 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 re vising the Pb NAAQS should be 0.2 μg/m 3 on a monthly
average.

3. Need Population-Based Risk Assessments of Ur ban 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 assessm ent. In addition, the Pa nel noted that a risk assessment of this type would typi cally include two key components:
(1) A quantitative description of the relati onship 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 informa tion for several urban areas where there3 are multiple monitors. The Committee recognizes th at the nature of the national airborne lead database is limite d by both its size ( i.e. , a total of 189 PM-TSP [total suspended particulates] sites measuring ambient lead), as well as the se lective location of monitors to mostly sourceoriented sites. Some additional PM 10 measurements are available (see the section below on the use of PM 10 samplers to monitor lead).

In spite of these limitations , the CASAC strongly believes th at 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 re levant to a few atypical loca tions, do not meet the needs of supporting a Lead NAA QS. 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 2 nd Draft Lead Human Exposure and Health Risk Assessments. In order to estimate the magnitude of risk, the Ag ency should estimate expos ures and convert these exposures to estimates of blood leve ls and IQ loss for children living in specific urban areas. In addition, the Agency should make IQ change esti mates across the range of exposures to provide estimates for the change in median, as well as the 5 th - to the 95 th 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 e xposure. The Panel urges that PM 10 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 particul ate 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 shoul d 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 chos en cities are of the range of what is seen in a broad spectrum of U.S. urban areas.

4. Completeness of 2 nd Draft Lead Exposure and Risk Assessments
In a more general sense, the CASAC consid ers that EPA’s 2nd Draft Lead Exposure and Risk Assessments document was missing certain ke y information and analytical components and was therefore incomplete. Specifically, the Committee believes that a properly comprehensive exposure/risk assessment in support of reviewin g either primary or secondary NAAQS should be accompanied by documentation that includes a discus sion of the four, policy-relevant elements of selecting a specific NAAQS — th at is, indicator variable, averag ing time, statistical form, and ranges of alternative levels of the standard — al ong 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 deta ils on both the scope of the exposure assessment and the results from the modeling, to include com ponents that would descri be: selection of urban areas to be modeled; the time periods and the p opulations that were modeled; and the results from modeling the current lead stan dard and proposed alternative NAAQS.

Furthermore, with respect to the primary ( public-health based) Lead NAAQS, a complete public-health risk assessment should include docu mentation that provides appropriate details on4 both the scope of the health risk model and the a ssociated modeling results vis-à-vis: selection of health endpoint categories; sele ction of study areas; and air qual ity considerations for both the current NAAQS and any proposed alternative levels of the standard. Finally, the exposure/risk assessment should include a thorough di scussion of the key uncertainties.

Accordingly, the CASAC requests that EPA tail or 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-S tate Dose Metric for Both Dose-Response Functions and Use in the Full Risk Assessment
Agency staff selected the Integrated E xposure 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 IEUB K model as the biokinetic modeling method for blood lead estimates and its deta iled rationale for doing so. Th e rationale provi ded by EPA staff for its choice is, overall, scientifically-sound.

In addition, OAQPS selected conc urrent 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 selec tion by Agency staff and the scie ntific rationales for doing so, particularly its reliance on the findings of the international pooled analys is of many longitudinal studies of cognitive deficits at lower exposures (doses) reported in Lanphear et al. (2005).

6. Predicting IQ Changes Based on Co ncurrent 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 lowe r blood Pb levels, where most of the population will be located. Epidemiologic data indi cate that the slope of the line below 7.5 μ g/dL is approximately minus three (-3) IQ decrements per 1 μ g/dL blood lead and the vast majority of children in the U.S. have maximal baseline Pb blood levels below 7.5 μ g/dL (Lanphear et al. , EHP 2005; MMWR 2005). On a population level, the mean in crease in blood lead concentration from airborne lead would generally be up to, but not exceeding, a blood lead concentration of 7.5 μ g/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 demonstr ate 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/m 3 . However, the risk analysis scenarios pr esented 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 path-5 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/m 3 maximum monthly average, and onl y fall to 13% under an alternativ e primary Lead standard of 0.05 μg/m 3 maximum monthly average. Since there is no known thre shold 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 sa fety, and hence, the question becomes:
What percentage of the population of ch ildren 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 ov erall toxicity of Pb?

EPA staff should identify what levels of th e 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, ta rget 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 le vels around this level, including much lower levels, to provide guidance as to how alternative st andards would lead to changes in health. For example, if the analys es conducted by EPA staff suggest that a 0.1 μg/m 3
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/m 3 , both above and below, as well as much lower levels, e.g. , on the order of 0.05 and 0.01 μg/m 3 . Further, the Agency should provide additional analyses to adequately inform both the Administrator and CASAC as to how uncertainties impact the level of protectiven ess of the proposed alternative standards.

8. Use of PM 10 Samplers to Monitor for Airborne Lead

Another recommendation that the CASAC provided in its March 2007 letter was to consider use of PM 10 samplers to monitor lead. A substant ial 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 moni toring sites, as well as the frequency of sample collection. Improved sampling precis ion will also be needed as more locations fall closer to standards and to support future h ealth assessments as ambient l ead concentrations are further reduced. For these and other reasons outlin ed in our previous advisory letter, the Lead Panel strongly encourages the Agency to consider revising the Pb refe rence method to allow sample collection by PM 10 , 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-CA SAC-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 like ly to capture additional very coarse particles which are excluded by PM 10 samplers. However, the precision of TSP samplers is poor, the upper particle cut size varies widely as a func tion of wind speed and di rection, and the spatial non-homogeneity of very coarse particles cannot be efficiently captured by a national monitoring network. Generally, it can be expected that PM 10 Pb will represent a large fraction of, and be6 highly correlated with TSP Pb. Ambient Lead data from the (few) collocated TSP and PM 10 sites presented in the 1 st Draft Pb Staff Paper exhibited a high correlation (r = 0.96), with slopes (PM 10 Pb/TSP Pb) ranging from 0.85 to 1. A single quantita tive adjustment factor could be developed from a short period of collocated sampling at multiple sites; or a PM 10 Pb/TSP Pb “equivalency ratio” could be determined on a regional or site-specific basis.

9. Other, Non-IQ-Related Eff ects of Lead in Ambient Air
While the CASAC agrees with the Agency’s choi ce 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 ra mifications of lead exposure on other endpoints that have societal and individua l implications of great importan ce. Neurological developmental and functional effects in children exposed to Pb can lead to negative and disruptive behaviors well into teenage years. Moreove r, while the adult nervous system has long been recognized as a target of Pb toxicity, epidemiologic and experi mental toxicology data are emerging that support the relationship between Pb expos ure and increased adverse cardi ovascular outcomes, including increased blood pressure, increased incidence of hypertension, and cardiova scular morbidity and mortality at lower and lo wer 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” exposur e and risk assessment document is focused entirely on human exposures a nd health risks. Agency staff made it quite clear in the 1 st 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 cy cle for the NAAQS for Lead. T hus, 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 Ma rch 2007 letter; and that this be reflected in the welfareeffects sections of both the Final Lead Expos ure and Risk Assessments document and the Final Lead Staff Paper.

With respect to secondary Pb standards, th e 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, quantitativ e 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 meas urement 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 conc entrations in the ambi ent 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 assessi ng environmental lead exposures and risks in the next review cycle. Depending on the results of these Pb monito ring activities, the Agency may need to set the level of the secondary Lead NAAQS as a to-be-determined fraction of the level of7 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 th e 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 scient ific 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 al so continues to recommend that the Agency “ identify the necessary funds to support needed continuing research on the ecologica l effects of airborne lead pollution and to consider developing alterna tive secondary standards such as critical loads for lead, which may be different from primary st andards in indicator, averaging time, level or form .” (For additional details, see the individua l 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 Le ad NAAQS such that there is no reversal of the current downward tr end 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 Ag ency’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 proce ss and has since evolve d into the Agency’s new, revised process. Specifically, the Lead Panel in 2006 conducted a peer-review of the 1 st and 2 nd Drafts of EPA’s Lead Air Quality Criter ia Document (AQCD) and, in February 2007, reviewed OAQPS’ 1 st Draft Lead Staff Paper and the Draf t 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 re view process midway through the current Lead NAAQS review, this was to be th e 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 subj ect (March 27, 2007), the Panel expr essed its concern that, in the absence of being given an opportunity to review even a 2 nd Draft Lead Staff Paper, or to review a second draft or a final lead exposure/risk asse ssment 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 — n eeded to properly advise you concerning the setting of NAAQS for Lead that would be adequately-pro tective of both human health and welfare. In response to CASAC’s concerns, the Agency agre ed 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 A ugust 28–29 public advisory meeting on the 2 nd Draft Lead Human Exposure and Health Risk Assessments, OAQPS staff informed me as the CASAC Chair that a recent (A ugust 24, 2007) Federal court order was requiring the Agency to produce a Final Lead Staff Paper by the previ ously-agreed-upon date of November 1, 2007 —8 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 adm ittedly been both confusing and vexing, the Committee is looking forward to reviewing the Final Lead Exposure a nd Risk Assessments document, the Final Staff Paper for Lead, and the Lead PA in a m eeting 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 Draf t 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 implemen ted 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 po llutants. 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 rele vant 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/expos ure assessments associated with the NAAQS reviews for other crit eria pollutants.

In closing, the CASAC is pleased to advise you and OAQPS staff on the 2 nd Draft Lead Human Exposure and Health Risk Assessments document. As both EPA and the Committee continue to work through the details associ ated with implementation of the revised NAAQS review process, we would ask that the Agency en sure that the CASAC receive the full breadth of information and supporting analyses necessary to provide timely, expe rt 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)9 

### NOTICE

This report has been written as part of the activities of the U.S. Environmental Protection Agency’s (EPA) Clean Air Scie ntific Advisory Committee (CASAC), a Federal advisory committee administra tively-located under the EPA Science Advisory Board (SAB) Staff Office that is ch artered to provide ex tramural scientific information and advice to the Administrato r and other officials of the EPA. The CASAC is structured to provi de balanced, expert assessm ent of scientific matters related to issue and problems facing the Agen cy. This report has not been reviewed for approval by the Agency and, hence, the co ntents of this report do not necessarily represent the views and policie s of the EPA, nor of other agencies in the Executive Branch of the Federal government, nor doe s mention of trade names or commercial products constitute a recommendation for us e. CASAC reports are posted on the SAB Web site at: http://www.epa.gov/sab

.A-1 

### Appendix A – Roster of the Clean Air Scientific Advisory Committee

U.S. Environmental Protection Agency Science Advisory Board (SAB) Staff Office Clean Air Scientific Advi sory Committee (CASAC)

CHAIR Dr. Rogene Henderson , Scientist Emeritus, Lovelace Re spiratory Research Institute, Albuquerque, NM

MEMBERS Dr. Ellis Cowling , University Distinguished Professo r 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 Medi cine, National Jewish Medical and Research Center, Denver, CO

Dr. Douglas Crawford-Brown , Director, Carolina Environmental Program; Professor, Environmental Sciences and E ngineering; and Professor, P ublic Policy, Department of Environmental Sciences and Engineering, Univers ity of North Carolina at Chapel Hill, Chapel Hill, NC

Mr. Richard L. Poirot , Environmental Analyst, Air Pollutio n 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 Medici ne, 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. Butterf ield, 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)B-1 

### Appendix B – Roster of the CASAC Lead Review Panel

U.S. Environmental Protection Agency Science Advisory Board (SAB) Staff Office Clean Air Scientific Advi sory 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 Me dicine, Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies, Center for the Evaluation of Value and Risk, Tufts New Engla nd Medical Center, Boston, MA

Dr. Deborah Cory-Slechta , Professor of Environmental Me dicine, Department of Environmental Medicine, University of Rochester Sc hool of Medicine and Dentistry, Rochester, NY

Dr. Ellis Cowling *, University Distinguished Profe ssor 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 Medi cine, National Jewish Medical and Research Center, Denver, CO

Dr. Douglas Crawford-Brown *, Director, Carolina Environmental Program; Professor, Environmental Sciences and E ngineering; and Professor, P ublic Policy, Department of Environmental Sciences and Engineering, Univers ity of North Carolina at Chapel Hill, Chapel Hill, NC

Dr. Bruce Fowler , Assistant Director for Science, Di vision of Toxicology and Environmental Medicine, Office of the Director, Agency for Toxic Substances and Disease Registry, U.S. Centers for Disease Control and Pr evention (ATSDR/CDC), Chamblee, GA

Dr. Andrew Friedland , Professor and Chair, Environmen tal Studies Program, Dartmouth College, Hanover, NH

Dr. Robert Goyer [M.D.] , Emeritus Professor of Pathology, Facu lty of Medicine, University of Western Ontario (Canada), Chapel Hill, NC

Mr. Sean Hays , President, Summit Toxicology, Allenspark, COB-2 Dr. Bruce Lanphear [M.D.] , Sloan Professor of Children’s Environmental Health, and the Director of the Cincinnati Ch ildren’s Environmental Health Ce nter at Cincinnati Children’s Hospital Medical Center and the Univer sity 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, Sc hool of Marine Sciences, Virginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA

Mr. Richard L. Poirot *, Environmental Analyst, Air Pollut ion 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 At mospheric 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

## APPENDIX 2A:

LARGEST STATIONARY SOURCE CATEGORIES FOR Pb IN THE 2002 NEI2A-1 Appendix 2A. Largest Stationary Sou rce Categories for Pb in the 2002 NEI.

Boilers and Process Heaters Materials including coal, oil, natural gas (or, at ti mes, 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, t he quantity of fuel burned, and PM control devices applied.

Industrial, commercial and institutional boilers and proc ess heaters are used at a wide variety of facilities (e.g., refineries, chemical and manufacturing plants, et c), as well as in a “stand alone” mode to provide heat for large building complexes. Consequently, t here are thousands of thes e 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 emitti ng 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 highe st Pb emissions, oil-fired utility plants emit somewhat lower amounts, and gas-fired plants emit very low le vels 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 form s 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 al most 60% of U.S. produ ction (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 re leased at heights of 25-30 feet), depending on the throughput of the furnace, the type and operating char acteristics of the emissi on control system, and the Pb content in the metal charged to the furnace. Regulations promulgated in 2004 are projected to yield emissions reductions of a pproximately 25 tpy for this category (USEPA, 2004b). Hazardous Waste Incineration/ Combustion Facilities Hazardous waste combustors include hazardous wast e incinerators, as well as boilers and industrial furnaces that burn hazardous waste for energy or mate rial recovery (e.g., production of halogen acid from the combustion of chlorine-bearing materials). I ndustrial furnaces burning hazardous waste include cement kilns, lightweight aggregate kilns, and hydr ochloric acid production furnaces. Lead is a trace contaminant in the hazardous waste, fossil fuels, an d raw materials used in the combustors. In 2005, there were nearly 270 hazardous waste combustor sour ces 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 promul gated in 2005, EPA estimates that cumula tive 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 thr ee operating primary Pb smelters in the U.S: one in Montana and two in Miss ouri, emitting an estimated total of about 260 tpy Pb. In 2002, there were two in operation (estimated emis sions 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-2 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 ma terials, 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 stoc kpile 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 emi ssions (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 th is 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 othe rs, 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 ammuni tion production plant and a weapons testing facility and these two facilities account for over 75% of the category emissions. Mining This category includes various mini ng facilities that extract ore from the earth containing Pb, zinc, copper and/or other non-ferrous metals (such as gold and silv er), 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 co pper 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 gr eater 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 facilitie s 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 seve ral 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 pr omulgated 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 munici pal 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 wa ste 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 emi ssions 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 projecte d 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 gla ss, 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. Lead2A-3 may also be added to flat glass for use in microwav es 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 facilitie s are typically of the order of 35-60 feet high. As of 2005, about 22 tons of Pb is emi tted from glass manufacturing annually in the U.S. Glass plants are located in 35 States (RTI, 2006). U.S. EP A 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 ar c furnace (EAF), the primary raw material is scrap metal, which is melted and refined using electric en ergy. 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, wi th estimated total nationwide Pb and Pb compound emissions of approximately 80 tons, an d the average per facility is approx imately 0.75 tpy. Stack heights range from heights of 30 - 50 feet. The facilities ar e 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 cate gory, 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 oxi de. 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 co mpound 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 proc esses. Seven primary copper sm elters are currently operating in the U.S. Six of these seven smelters use conv entional smelter technology which includes batch converter furnaces for the conversion of matte grade co pper to blister copper, while the seventh uses a continuous flash furnace. Two of the three largest sm elters 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 em issions 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 vari ous waste materials such as tires). Lead, a trace contaminant both of the raw materials and some fuel mate rials (e.g., coal, tires), is emitted with particulate material from the kiln stacks, which range in hei ght 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 genera lly 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 tp y. In 1999, U.S. EPA promulgated a national emissions standard, including a limit for PM (a s a surrogate for metal HAP, including Pb), for this category (USEPA, 1999b).2A-4 REFERENCES
Calspan Corporation. (1977) Assessment of Industrial Hazardous Waste Practices in the Metal Smelting and Refining Industry. Volume III: Ferrous Smelting and Re fining. 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 In dustry – Source Characteri zation 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. Ch aracterization, 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 Gla ss Manufacturing Industry, Gl ass 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. Me mo 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 Protectio n Agency. (1998) Study of Hazardous Air Pollutant Emi ssions from Electric Utility Steam Generating Units – Final Report to Congress. O ffice 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/a tw/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) Pr ogram. [13 October, 2005] Available: http://www.epa.gov/opptintr/pbt/cheminfo.htm2A-5 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 Pl anning 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/a tw/mactfnlalph.html

U.S. Environmental Protection Agency. (2003a) Emissi on 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 He aters: 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 Agen cy. (2005) “Technical Support Do cument 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.

## APPENDIX 2B:

ADDITIONAL DETAILS OF AIR QUALITY ANALYSESTable 2B-1. Pb-TSP monitoring site information and 3-year statisticscomp.yearscomp.qtrscomp.monthsannualmeanmaxquarterlymeanmaxmonthlymean2nd maxmonthlymeanaverageof 3overallhighestmonthlymeansaverageof 3annualmaxmonthlymeans011090003 2 31.79056 -85.97917 AL PikeTroy, AL29,605 461 314.5 12 10 31 0.6875 1.9233 2.6600 2.4200 2.2867 1.6852011090006 1 31.79278 -85.98056 AL PikeTroy, AL29,605 461 314.5 12 10 31 0.3808 0.9100 1.6900 1.3400 1.3233 1.0901060250005 1 32.67611 -115.48333 CA ImperialEl Centro, CA142,361 16,385 1,290 1 0.02 11 34 0.0175 0.0248 0.0404 0.0380 0.0380 0.0330060371103 2 34.06659 -118.22688 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 29,329 1,633 1 0.33 12 36 0.0225 0.0627 0.1460 0.0280 0.0673 0.0663060371301 1 33.92899 -118.21071 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 47,423 5,066 1 0.03 12 34 0.0188 0.0313 0.0440 0.0360 0.0380 0.0353060371601 1 34.01407 -118.06056 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 13,333 1,066 1 0.02 9 27 0.0186 0.0300 0.0480 0.0340 0.0373 0.0343060374002 2 33.82376 -118.18921 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 20,131 1,232 1 0.03 12 36 0.0149 0.0400 0.0960 0.0440 0.0552 0.0427060374004 2 33.79236 -118.17533 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 61,497 6,697 1 0.02 10 28 0.0112 0.0938 0.1020 0.0840 0.0673 0.0447060375001 1 33.92288 -118.37026 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 19,148 1,680 1 0.01 5 14 0.0222 0.0667 0.1700 0.0220 0.0693 0.0910060375005 1 33.95080 -118.43043 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 33,968 1,358 1 0.01 7 17 0.0057 0.0118 0.0150 0.0120 0.0123 0.0135060651003 2 33.94603 -117.40063 CA RiversideRiverside-San Bernardino-Ontario,C3,254,821 16,320 1,278 1 0.03 12 36 0.0097 0.0114 0.0160 0.0140 0.0147 0.0147060658001 3 33.99958 -117.41601 CA Riverside Riverside-San Bernardino-Ontario,C3,254,821 16,247 1,678 1 0.03 12 35 0.0121 0.0179 0.0220 0.0220 0.0213 0.0213060711004 1 34.10374 -117.62914 CA San Bernardino Riverside-San Bernardino-Ontario,C3,254,821 18,777 1,578 1 0.03 12 35 0.0142 0.0343 0.0800 0.0200 0.0394 0.0387060719004 1 34.10688 -117.27411 CA San Bernardino Riverside-San Bernardino-Ontario,C3,254,821 14,861 1,755 1 0.03 12 36 0.0186 0.0773 0.1420 0.0680 0.0873 0.0580080010005 1 39.79601 -104.97754 CO Adams Denver-Aurora, CO 2,157,756 2,025 183 1.9 1 3 12 36 0.1697 0.5558 1.1037 0.4397 0.6195 0.5148080010006 1 39.82574 -104.93699 CO Adams Denver-Aurora, CO 2,157,756 3,313 256 1 0.0 3 12 31 0.0304 0.0957 0.2086 0.0726 0.1085 0.1085080310002 4 39.75119 -104.98762 CO Denver Denver-Aurora, CO 2,157,756 22,019 974 1 0.0 3 12 34 0.0315 0.1780 0.2955 0.2297 0.1906 0.1254080310015 1 39.70012 -104.98714 CO Denver Denver-Aurora, CO 2,157,756 14,438 809 1 0.0 1 7 20 0.0153 0.0212 0.0305 0.0196 0.0228 0.0244080410011 1 38.83139 -104.82778 CO El Paso Colorado Springs, CO 537,484 10,581 552 1 0.0 3 12 35 0.0156 0.0891 0.1387 0.1314 0.0955 0.0551080650001 1 39.24778 -106.29139 CO Lake Edwards, CO 49,471 5,903 361 1 0.0 2 11 28 0.0165 0.0224 0.0310 0.0310 0.0305 0.0294100010002 1 38.98472 -75.55556 DE Kent Dover, DE 126,697 352 22 0.0 1 4 12 0.0033 0.0040 0.0051 0.0041 0.0044 0.0051100031007 1 39.55111 -75.73083 DE New Castle Philadelphia-Camden-Wilmington,P5,687,147 2,041 2090.01 4 10 0.0039 0.0046 0.0058 0.0051 0.0054 0.0058100031008 1 39.57778 -75.61111 DE New Castle Philadelphia-Camden-Wilmington,P5,687,147 3,170 1600.01 4 9 0.0052 0.0063 0.0081 0.0058 0.0065 0.0081100032004 1 39.73944 -75.55806 DE New Castle Philadelphia-Camden-Wilmington,P5,687,147 34,053 2,649 1 0.01 4 11 0.0097 0.0115 0.0163 0.0161 0.0142 0.0163100051002 1 38.64444 -75.61306 DE Sussex Seaford, DE 156,638 5,450 390 1 0.0 1 4 12 0.0033 0.0042 0.0048 0.0042 0.0043 0.0048120571065 5 27.89222 -82.53861 FL Hillsborough Tampa-St. Petersburg-Clearwater, FL2,395,997 14,463 612 1 0.01 4 12 0.0049 0.0062 0.0094 0.0080 0.0082 0.0094120571066 1 27.96028 -82.38250 FL Hillsborough Tampa-St. Petersburg-Clearwater, FL2,395,997 5,793 465 1 1.3 13 12 35 0.5835 1.2600 1.7400 1.3800 1.4733 1.4733120571073 1 27.96583 -82.37944 FL Hillsborough Tampa-St. Petersburg-Clearwater, FL2,395,997 4,541 340 1 1.3 13 12 35 0.1934 0.2933 0.4800 0.4400 0.4467 0.4133120571075 5 28.05000 -82.37806 FL Hillsborough Tampa-St. Petersburg-Clearwater, FL2,395,997 10,691 490 1 0.01 4 12 0.0041 0.0054 0.0105 0.0072 0.0075 0.0105121030004 5 27.94639 -82.73194 FL PinellasTampa-St. Petersburg-Clearwater, FL2,395,997 13,048 557 1 0.01 4 12 0.0028 0.0041 0.0067 0.0039 0.0048 0.0067121030018 5 27.78556 -82.74000 FL PinellasTampa-St. Petersburg-Clearwater, FL2,395,997 11,289 571 1 0.02 8 24 0.0042 0.0071 0.0112 0.0103 0.0103 0.0107121033005 1 27.87583 -82.69639 FL PinellasTampa-St. Petersburg-Clearwater, FL2,395,997 2,151 58 1 0.03 12 36 0.0006 0.0067 0.0200 0.0000 0.0067 0.0067130890003 2 33.69833 -84.27333 GA DeKalbAtlanta-Sandy Springs-Marietta, GA4,247,981 7,888 663 1 0.03 12 36 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000132150011 1 32.43083 -84.93167 GA MuscogeeColumbus, GA-AL281,768 10,871 1,037 1 0.3 1 1 1 10 34 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000150032004 1 21.39667 -157.97167 HI HonoluluHonolulu, HI876,156 23,622 1,207 1 0.13 12 35 0.0014 0.0029 0.0072 0.0025 0.0040 0.0038170310001 1 41.67275 -87.73246 IL CookChicago-Naperville-Joliet, IL-IN-WI9,098,316 13,648 971 1 0.03 12 35 0.0143 0.0229 0.0360 0.0250 0.0270 0.0270170310022 2 41.68920 -87.53932 IL CookChicago-Naperville-Joliet, IL-IN-WI9,098,316 22,040 1,708 1 0.23 12 36 0.0270 0.0353 0.0440 0.0420 0.0427 0.0407170310026 1 41.87333 -87.64507 IL Cook Chicago-Naperville-Joliet, IL-IN-WI9,098,316 28,739 1,203 1 0.03 12 34 0.0405 0.0613 0.0900 0.0860 0.0820 0.0753170310052 1 41.96743 -87.74982 IL Cook Chicago-Naperville-Joliet, IL-IN-WI9,098,316 42,187 2,877 1 0.03 12 32 0.0214 0.0260 0.0400 0.0380 0.0360 0.0353170313103 1 41.96528 -87.87639 IL Cook Chicago-Naperville-Joliet, IL-IN-WI9,098,316 10,302 670 1 0.03 12 34 0.0149 0.0271 0.0440 0.0240 0.0307 0.0280170313301 1 41.78278 -87.80528 IL CookChicago-Naperville-Joliet, IL-IN-WI9,098,316 23,749 1,678 1 0.03 12 35 0.0308 0.0750 0.1950 0.1140 0.1263 0.1155170314201 1 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-WI9,098,316 6,070 303 1 0.02 8 24 0.0113 0.0133 0.0175 0.0160 0.0165 0.0168170316003 1 41.87194 -87.82611 IL CookChicago-Naperville-Joliet, IL-IN-WI9,098,316 14,862 1,071 1 0.03 12 32 0.0303 0.0387 0.0500 0.0480 0.0480 0.0480171170002 2 39.39804 -89.80975 IL Macoupin St. Louis, MO-IL 2,721,491 40 2 0.0 3 12 36 0.0103 0.0113 0.0140 0.0140 0.0133 0.0133171190010 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 0.3280 0.9100 0.2880 0.4620 0.4620171193007 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 0.0193 0.0320 0.0240 0.0267 0.0262171430037 1 40.69889 -89.58474 IL Peoria Peoria, IL 366,899 12,643 1,109 1 0.0 3 12 35 0.0137 0.0279 0.0320 0.0300 0.0300 0.0240171630010 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 0.0707 0.1050 0.0980 0.0990 0.0913180350008 1 40.15806 -85.42111 IN Delaware Muncie, IN 118,769 2,108 104 1 0.0 1 3 12 34 0.2944 0.4657 0.7371 0.5991 0.6011 0.5585180350009 2 40.15944 -85.41556 IN Delaware Muncie, IN 118,769 980 82 0.0 1 1 6 13 2.6732 4.0931 5.775 5.0220 4.2890 2.8611180890023 1 41.65278 -87.43944 IN Lake Chicago-Naperville-Joliet, IL-IN-WI9,098,316 5,959 603 1 6.5 13 12 34 0.0389 0.0691 0.0910 0.0783 0.0786 0.0714180892008 1 41.63944 -87.49361 IN LakeChicago-Naperville-Joliet, IL-IN-WI9,098,316 7,144 612 1 0.03 12 33 0.0219 0.0296 0.0590 0.0484 0.0496 0.0496180892011 2 41.59250 -87.47194 IN LakeChicago-Naperville-Joliet, IL-IN-WI9,098,316 9,815 729 1 0.03 12 34 0.0368 0.1352 0.3050 0.0778 0.1522 0.1397180930004 1 38.88944 -86.55194 IN LawrenceBedford, IN45,922 393 320.02 10 26 0.0270 0.0270 0.0270 0.0270 0.0270 0.0270site poc lat longsumpoint /nonptPb EITPYw/in 1milestate county_namecbsa_namecbsa_pop00sourceoriented?prev.sourceoriented?(see endnotes)3-year data capture(complete periods)3-year metricspopulationnear site(mile radius)underage 5pop.(mileradius)urban2B-1Table 2B-1. Pb-TSP monitoring site information and 3-year statisticscomp.yearscomp.qtrscomp.monthsannualmeanmaxquarterlymeanmaxmonthlymean2nd maxmonthlymeanaverageof 3overallhighestmonthlymeansaverageof 3annualmaxmonthlymeanssite poc lat longsumpoint /nonptPb EITPYw/in 1milestate county_namecbsa_namecbsa_pop00sourceoriented?prev.sourceoriented?(see endnotes)3-year data capture(complete periods)3-year metricspopulationnear site(mile radius)underage 5pop.(mileradius)urban180970063 1 39.76083 -86.29722 IN MarionIndianapolis-Carmel, IN1,525,104 12,176 875 1 1.7 13 12 36 0.0320 0.0770 0.1123 0.0802 0.0854 0.0843180970076 1 39.75889 -86.28972 IN MarionIndianapolis-Carmel, IN1,525,104 9,171 602 1 1.7 13 12 35 0.0142 0.0254 0.0360 0.0346 0.0317 0.0251180970078 1 39.81110 -86.11447 IN MarionIndianapolis-Carmel, IN1,525,104 14,196 1,175 1 0.02 11 33 0.0108 0.0251 0.0288 0.0240 0.0251 0.0184181010001 1 38.89028 -86.76083 IN Martin84 50.03 12 34 0.0272 0.0299 0.0358 0.0270 0.0299 0.0299181630006 2 37.97167 -87.56722 IN Vanderburgh Evansville, IN-KY342,815 13,666 817 1 0.03 12 33 0.0065 0.0126 0.0286 0.0170 0.0181 0.0150260490021 4 43.04722 -83.67028 MI GeneseeFlint, MI436,141 9,889 994 1 0.03 12 36 0.0100 0.0153 0.0209 0.0189 0.0188 0.0185261130001 1 44.31056 -84.89194 MI MissaukeeCadillac, MI44,962 58 30.03 12 33 0.0032 0.0056 0.0080 0.0046 0.0057 0.0054261630001 2 42.22861 -83.20833 MI WayneDetroit-Warren-Livonia, MI 4,452,557 14,329 798 1 0.03 12 35 0.0087 0.0107 0.0124 0.0115 0.0116 0.0112261630005 1 42.26722 -83.13222 MI WayneDetroit-Warren-Livonia, MI 4,452,557 11,314 923 1 0.22 11 34 0.0166 0.0259 0.0340 0.0315 0.0322 0.0308261630015 4 42.30278 -83.10667 MI WayneDetroit-Warren-Livonia, MI 4,452,557 17,729 1,771 1 0.03 12 36 0.0178 0.0252 0.0299 0.0278 0.0278 0.0275261630019 1 42.43083 -83.00028 MI WayneDetroit-Warren-Livonia, MI 4,452,557 28,362 2,628 1 0.03 12 34 0.0103 0.0138 0.0149 0.0141 0.0144 0.0143261630027 1 42.29222 -83.10694 MI WayneDetroit-Warren-Livonia, MI 4,452,557 6,024 516 1 1.1 11 5 14 0.0256 0.0267 0.0353 0.0340 0.0341 0.0296261630033 2 42.30667 -83.14889 MI WayneDetroit-Warren-Livonia, MI 4,452,557 17,402 1,843 1 0.53 12 34 0.0236 0.0410 0.0601 0.0406 0.0464 0.0451270370001 1 44.83333 -93.11500 MN DakotaMinneapolis-St. Paul-Bloomington, 2,968,806 5,074 404 1 3.2 12 8 24 0.0781 0.1153 0.2300 0.2100 0.2107 0.2042270370020 1 44.76535 -93.03248 MN DakotaMinneapolis-St. Paul-Bloomington, 2,968,806 162 70.03 12 32 0.0051 0.0100 0.0200 0.0120 0.0140 0.0133270370421 1 44.77720 -93.04097 MN DakotaMinneapolis-St. Paul-Bloomington, 2,968,806 478 240.01 9 27 0.0037 0.0069 0.0120 0.0100 0.0100 0.0100270370423 1 44.77500 -93.06278 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 886 83 0.0 3 12 34 0.0018 0.0050 0.0100 0.0060 0.0073 0.0067270370442 1 44.74036 -93.00556 MN Dakota Minneapolis-St. Paul-Bloomington, 2,968,806 168 11 0.3 2 10 28 0.0027 0.0062 0.0080 0.0060 0.0067 0.0067270530050 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 0.0093 0.0120 0.0117 0.0112 0.0112270530963 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 0.0071 0.0100 0.0080 0.0085 0.0085270530964 1 44.88855 -93.19538 MN Hennepin Minneapolis-St. Paul-Bloomington, 2,968,806 209 0 1 0.0 1 4 14 0.0045 0.0114 0.0180 0.0080 0.0112 0.0110270530965 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 0.0080 0.0140 0.0100 0.0107 0.0107270530966 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 0.0080 0.0120 0.0100 0.0107 0.0101270530967 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 0.0142 0.0225 0.0157 0.0161 0.0163270530968 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 0.0033 0.0080 0.0050 0.0060 0.0065270531007 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 0.0067 0.0080 0.0067 0.0069 0.0069271231003 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 0.0129 0.0350 0.0200 0.0243 0.0210271377001 1 47.52336 -92.53631 MN St. Louis Duluth, MN-WI 275,486 8,942 428 1 0.1 3 12 33 0.0047 0.0362 0.0900 0.0100 0.0360 0.0347271377555 1 46.73264 -92.16337 MN St. Louis Duluth, MN-WI 275,486 4,527 287 1 0.0 3 12 34 0.0014 0.0031 0.0050 0.0040 0.0043 0.0043290930016 1 37.62528 -91.12917 MO Iron 58 4 0.0 1 3 12 34 0.6918 1.3070 4.1933 1.4540 2.2878 2.2878290930021 1 37.65417 -91.13056 MO Iron 58 4 0.0 1 3 12 36 0.5460 0.7187 0.9960 0.9840 0.9773 0.9773290930023 1 37.50333 -90.69556 MO Iron 138 7 0.0 1 1 *# 1 6 18 0.2291 0.3433 0.6320 0.4275 0.4865 0.3281290930024 1 37.47972 -90.69028 MO Iron 32 2 0.0 1 1 *# 1 6 18 0.5898 0.6677 1.6026 0.9927 1.0864 0.8292290930025 1 37.51056 -90.69750 MO Iron 138 7 0.0 1 1 *# 1 5 14 0.2477 0.3263 0.6320 0.4189 0.4723 0.3480290930026 1 37.45917 -90.68639 MO Iron 32 2 0.0 1 1 *# 1 5 15 0.2266 0.2523 0.3555 0.3370 0.3418 0.2127290930027 1 37.48611 -90.69000 MO Iron 32 2 0.0 1 1 * 3 12 33 0.2678 0.8761 1.4414 0.9300 1.0305 0.6387290930029 1 37.47167 -90.68944 MO Iron 32 2 0.0 1 1 * 3 12 32 0.2824 0.7148 1.4740 1.1410 1.1597 0.5722290930030 1 37.46639 -90.69000 MO Iron 32 2 0.0 1 1 *# 1 6 18 0.1665 0.2017 0.3330 0.2797 0.2734 0.1742290990004 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 1.4750 2.0731 1.8962 1.8591 1.7524290990005 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 0.6779 1.0655 0.9278 0.9277 0.8018290990008 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 0.1857 0.3700 0.3100 0.3128 0.2661290990009 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 0.1664 0.1750 0.1560 0.1595 0.1583290990010 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 0.0813 0.1680 0.1040 0.1207 0.1153290990011 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 1.3047 2.2070 1.3510 1.5975 1.3399290990013 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 0.8683 3.5680 0.6420 1.6167 1.5650290990015 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 1.9277 3.2884 2.2993 2.6139 2.4954291892003 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 0.0500 0.0500 0.0500 0.0500 0.0333295100085 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 0.0216 0.0290 0.0255 0.0240 0.0290340231003 1 40.47222 -74.47139 NJ Middlesex New York-Northern New Jersey-Lon18,323,002 13,850 1,124 1 1.7 12 10 27 0.0403 0.1537 0.1878 0.1428 0.1496 0.1312360470122 1 40.71980 -73.94788 NY KingsNew York-Northern New Jersey-Lon18,323,002 92,660 5,785 1 0.12 9 22 0.0276 0.0333 0.0360 0.0350 0.0345 0.0345360632008 1 43.08216 -79.00099 NY NiagaraBuffalo-Niagra Falls, NY Metropolit1,170,111 6,795 386 1 0.01 4 12 0.0054 0.0060 0.0080 0.0080 0.0080 0.0080360713001 1 41.46107 -74.36343 NY OrangePoughkeepsie-Newburgh-Middletow621,517 1,481 991.8 12 9 26 0.0606 0.0820 0.1580 0.1100 0.1207 0.1073360713002 1 41.45887 -74.35392 NY OrangePoughkeepsie-Newburgh-Middletow621,517 1,257 861.8 12 9 26 0.1257 0.2417 0.4025 0.2400 0.2835 0.2248360713004 1 41.47633 -74.36827 NY OrangePoughkeepsie-Newburgh-Middletow621,517 6,816 434 1 0.02 9 26 0.0305 0.0386 0.0400 0.0400 0.0383 0.03512B-2Table 2B-1. Pb-TSP monitoring site information and 3-year statisticscomp.yearscomp.qtrscomp.monthsannualmeanmaxquarterlymeanmaxmonthlymean2nd maxmonthlymeanaverageof 3overallhighestmonthlymeansaverageof 3annualmaxmonthlymeanssite poc lat longsumpoint /nonptPb EITPYw/in 1milestate county_namecbsa_namecbsa_pop00sourceoriented?prev.sourceoriented?(see endnotes)3-year data capture(complete periods)3-year metricspopulationnear site(mile radius)underage 5pop.(mileradius)urban360850067 1 40.59733 -74.12619 NY RichmondNew York-Northern New Jersey-Lon18,323,002 21,834 1,3730.01 4 11 0.0059 0.0082 0.0140 0.0125 0.0122 0.0140390170015 2 39.48990 -84.36407 OH ButlerCincinnati-Middletown, OH-KY-IN 2,009,632 4,668 373 1 0.02 8 24 0.0107 0.0248 0.0650 0.0160 0.0320 0.0405390290019 1 40.63111 -80.54694 OH Columbiana East Liverpool-Salem, OH 112,075 5,385 322 1 0.0 3 12 36 0.0144 0.0253 0.0300 0.0300 0.0287 0.0247390290020 1 40.63972 -80.52389 OH Columbiana East Liverpool-Salem, OH 112,075 6,414 354 1 0.0 3 12 36 0.0158 0.0247 0.0310 0.0310 0.0307 0.0307390290022 1 40.63500 -80.54667 OH Columbiana East Liverpool-Salem, OH 112,075 3,318 202 1 0.0 3 12 36 0.0139 0.0367 0.0800 0.0300 0.0433 0.0427390350038 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 0.0300 0.0600 0.0360 0.0427 0.0423390350042 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 0.0280 0.0430 0.0390 0.0373 0.0373390350049 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 0.2367 0.4500 0.2600 0.3233 0.3100390350050 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 0.0550 0.1000 0.0940 0.0920 0.0880390350061 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 0.3600 0.5600 0.4700 0.3600 0.2090390350069 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 0.0233 0.0470 0.0370 0.0377 0.0343390490025 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 0.0197 0.0270 0.0210 0.0227 0.0203390510001 1 41.57528 -83.99639 OH Fulton Toledo, OH 659,188 1,503 110 1 0.3 1 2 11 36 0.1332 0.2667 0.6100 0.5300 0.5200 0.5067390910003 1 40.34306 -83.75500 OH Logan Bellefontaine, OH 46,005 1,536 108 1 0.1 3 12 36 0.0922 0.1467 0.2700 0.2000 0.2233 0.2233390910005 1 40.34278 -83.76028 OH Logan Bellefontaine, OH 46,005 1,546 126 1 0.1 1 3 12 36 0.1058 0.1467 0.2200 0.2100 0.2067 0.2067390910006 1 40.34111 -83.75778 OH Logan Bellefontaine, OH 46,005 1,217 87 1 0.1 1 3 12 36 0.1578 0.2667 0.3600 0.3600 0.3467 0.3467390910007 1 40.34472 -83.75444 OH Logan Bellefontaine, OH 46,005 2,156 185 1 0.1 3 12 36 0.1497 0.2200 0.2600 0.2500 0.2500 0.2333391670008 1 39.43361 -81.50250 OH Washington Parkersburg-Marietta, WV-OH 164,624 1,947 114 0.0 3 12 36 0.0054 0.0100 0.0130 0.0100 0.0110 0.0097391670009 1 39.37696 -81.53730 OH Washington Parkersburg-Marietta, WV-OH 164,624 314 21 0.0 1 5 14 0.0073 0.0495 0.0880 0.0140 0.0383 0.0510401159005 2 36.98580 -94.84920 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0412 0.0613 0.0927 0.0630 0.0677 0.0927401159006 1 36.98460 -94.82490 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0316 0.0378 0.0623 0.0420 0.0485 0.0623401159007 1 36.97190 -94.85180 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0505 0.1030 0.1257 0.1140 0.1033 0.1257401159008 1 36.97160 -94.82500 OK Ottawa Miami, OK 33,194 1,573 117 0.0 1 4 11 0.0312 0.0408 0.0708 0.0363 0.0474 0.0708410510246 7 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR-1,927,881 24,303 1,771 1 0.01 4 11 0.0081 0.0101 0.0110 0.0105 0.0106 0.0110420030002 1 40.50056 -80.07194 PA Allegheny Pittsburgh, PA 2,431,087 19,559 1,045 1 0.0 3 12 30 0.0096 0.0378 0.0503 0.0377 0.0387 0.0338420032001 1 40.39667 -79.86361 PA Allegheny Pittsburgh, PA 2,431,087 10,120 769 1 0.2 3 12 35 0.0396 0.0567 0.1140 0.0660 0.0811 0.0801420070505 1 40.68500 -80.32500 PA Beaver Pittsburgh, PA 2,431,087 6,497 218 1 0.0 2 11 31 0.0563 0.1531 0.2300 0.2280 0.2167 0.1848420110005 1 40.46630 -75.75890 PA Berks Reading, PA 373,638 692 44 4.8 1 2 11 33 0.0618 0.0940 0.1580 0.1560 0.1400 0.1380420110717 1 40.47667 -75.75917 PA Berks Reading, PA 373,638 575 39 1 4.8 1 2 11 30 0.1301 0.1800 0.2820 0.2740 0.2737 0.2513420111717 1 40.37722 -75.91444 PA Berks Reading, PA 373,638 7,376 390 1 2.1 1 3 12 33 0.2570 0.3967 0.8020 0.5180 0.6013 0.6013420210808 1 40.34806 -78.88278 PA Cambria Johnstown, PA 152,598 2,606 115 1 0.0 3 12 36 0.0383 0.0569 0.0920 0.0560 0.0647 0.0647420250105 1 40.80306 -75.60833 PA Carbon Allentown-Bethlehem-Easton, PAN740,395 8,477 513 1 0.02 11 33 0.0779 0.2493 0.3560 0.2980 0.2924 0.2093420450002 1 39.83556 -75.37250 PA Delaware Philadelphia-Camden-Wilmington,P5,687,147 10,156 859 1 0.03 12 35 0.0372 0.0400 0.0400 0.0400 0.0400 0.0393421010449 1 39.98250 -75.08306 PA Philadelphia Philadelphia-Camden-Wilmington,P5,687,147 8,653 413 1 0.0 1 1 3 12 31 0.0203 0.0350 0.0380 0.0360 0.0365 0.0344421290007 1 40.16667 -79.87500 PA Westmoreland Pittsburgh, PA 2,431,087 7,739 445 1 0.0 3 12 36 0.0352 0.0400 0.0400 0.0400 0.0400 0.0400450031001 1 33.43253 -81.89233 SC Aiken Augusta-Richmond County, GA-SC 499,684 437 24 0.0 1 4 12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000450130007 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 0.0022 0.0070 0.0034 0.0042 0.0035450190003 2 32.88394 -79.97754 SC Charleston Charleston-North Charleston, SC 549,033 4,401 275 1 0.0 3 12 34 0.0014 0.0041 0.0104 0.0078 0.0077 0.0072450190046 1 32.94275 -79.65718 SC Charleston Charleston-North Charleston, SC 549,033 63 4 0.0 3 12 33 0.0005 0.0032 0.0068 0.0035 0.0043 0.0035450190047 1 32.84461 -79.94804 SC Charleston Charleston-North Charleston, SC 549,033 7,000 294 1 0.0 1 4 12 0.0022 0.0037 0.0058 0.0052 0.0052 0.0058450410001 1 34.19794 -79.79885 SC Florence Florence, SC 193,155 3,426 224 1 0.0 1 4 13 0.0010 0.0026 0.0063 0.0023 0.0035 0.0041450410002 1 34.16764 -79.85040 SC Florence Florence, SC 193,155 1,795 106 1 0.0 2 8 24 0.0011 0.0034 0.0102 0.0054 0.0063 0.0052450430006 1 33.36378 -79.29426 SC Georgetown Georgetown, SC 55,797 5,247 427 1 0.3 1 2 11 32 0.0072 0.0166 0.0420 0.0200 0.0270 0.0252450430007 1 33.34973 -79.29821 SC Georgetown Georgetown, SC 55,797 1,579 119 0.3 3 12 35 0.0002 0.0017 0.0054 0.0016 0.0023 0.0023450430009 1 33.37399 -79.28570 SC Georgetown Georgetown, SC 55,797 2,447 185 1 0.3 3 12 35 0.0038 0.0081 0.0158 0.0148 0.0133 0.0120450430010 1 33.36960 -79.29840 SC Georgetown Georgetown, SC 55,797 6,173 511 1 0.3 3 12 33 0.0049 0.0169 0.0265 0.0132 0.0166 0.0153450450008 2 34.84045 -82.40291 SC Greenville Greenville, SC 559,940 7,967 381 1 0.0 3 12 34 0.0023 0.0071 0.0125 0.0066 0.0086 0.0088450452002 1 34.94165 -82.22961 SC Greenville Greenville, SC 559,940 7,266 494 1 0.0 3 12 32 0.0001 0.0006 0.0018 0.0018 0.0017 0.0006450470001 1 34.18111 -82.15224 SC Greenwood Greenwood, SC 66,271 7,853 667 1 0.0 3 12 32 0.0028 0.0063 0.0112 0.0106 0.0101 0.0082450470002 1 34.16520 -82.16048 SC Greenwood Greenwood, SC 66,271 1,490 116 0.0 3 12 31 0.0071 0.0163 0.0320 0.0272 0.0279 0.0213450510002 2 33.70460 -78.87745 SC Horry Myrtle Beach-Conway-North Myrtle196,629 4,510 227 1 0.03 12 35 0.0009 0.0020 0.0053 0.0040 0.0042 0.0042450630005 2 33.78560 -81.11978 SC Lexington Columbia, SC 647,158 736 66 0.0 1 4 12 0.0018 0.0033 0.0052 0.0050 0.0049 0.0052450631002 2 33.96900 -81.06533 SC Lexington Columbia, SC 647,158 8,086 551 1 0.0 3 12 32 0.0046 0.0179 0.0356 0.0125 0.0192 0.0188450790006 4 34.00740 -81.02329 SC Richland Columbia, SC 647,158 17,143 574 1 0.0 1 4 12 0.0030 0.0069 0.0090 0.0072 0.0071 0.00902B-3Table 2B-1. Pb-TSP monitoring site information and 3-year statisticscomp.yearscomp.qtrscomp.monthsannualmeanmaxquarterlymeanmaxmonthlymean2nd maxmonthlymeanaverageof 3overallhighestmonthlymeansaverageof 3annualmaxmonthlymeanssite poc lat longsumpoint /nonptPb EITPYw/in 1milestate county_namecbsa_namecbsa_pop00sourceoriented?prev.sourceoriented?(see endnotes)3-year data capture(complete periods)3-year metricspopulationnear site(mile radius)underage 5pop.(mileradius)urban450790007 2 34.09584 -80.96230 SC RichlandColumbia, SC647,158 4,405 233 1 0.03 12 36 0.0004 0.0014 0.0042 0.0030 0.0031 0.0027450790019 1 33.99330 -81.02414 SC Richland Columbia, SC 647,158 15,569 287 1 0.0 3 12 35 0.0048 0.0097 0.0144 0.0138 0.0137 0.0137450790021 1 33.81655 -80.78114 SC Richland Columbia, SC 647,158 123 10 0.0 3 12 35 0.0001 0.0012 0.0038 0.0000 0.0013 0.0013450830001 2 34.94774 -81.93255 SC Spartanburg Spartanburg, SC 253,791 7,505 552 1 0.0 3 12 34 0.0018 0.0035 0.0062 0.0060 0.0060 0.0057450850001 1 33.92423 -80.33774 SC Sumter Sumter, SC 104,646 4,990 407 1 0.0 3 12 35 0.0025 0.0064 0.0108 0.0104 0.0101 0.0101450910005 1 34.96303 -81.00085 SC York Charlotte-Gastonia-Concord, NC-SC1,330,448 3,453 221 1 0.02 11 27 0.0021 0.0042 0.0082 0.0058 0.0063 0.0052470930027 1 35.98306 -83.95222 TN KnoxKnoxville, TN616,079 8,586 826 1 5.8 11 9 26 0.0182 0.0233 0.0400 0.0400 0.0387 0.0200470931017 1 35.97500 -83.95444 TN KnoxKnoxville, TN616,079 7,817 763 1 5.8 11 9 26 0.0143 0.0193 0.0375 0.0240 0.0285 0.0192471570044 1 35.08750 -90.07250 TN ShelbyMemphis, TN-MS-AR1,205,204 6,730 548 1 0.0 1 1 1 6 17 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100471633001 1 36.52556 -82.27333 TN SullivanKingsport-Bristol-Bristol, TN-VA 298,484 942 650.4 13 12 35 0.1249 0.1959 0.2843 0.2360 0.2501 0.2381471633002 3 36.52472 -82.26806 TN SullivanKingsport-Bristol-Bristol, TN-VA 298,484 942 650.4 13 12 36 0.0614 0.1463 0.2920 0.1540 0.1880 0.1772471633003 1 36.52806 -82.26833 TN SullivanKingsport-Bristol-Bristol, TN-VA 298,484 942 650.4 13 12 35 0.0651 0.1259 0.2322 0.1260 0.1476 0.1476471870100 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 102.6 12 8 23 0.2527 0.9867 1.9120 0.8200 1.1579 1.0540471870102 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 102.6 12 8 23 0.2575 0.6953 0.9460 0.6000 0.7093 0.5390471871101 1 35.79944 -86.66500 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 102.6 12 8 24 0.0811 0.3027 0.7020 0.1820 0.3333 0.4090480610006 1 25.89251 -97.49382 TX CameronBrownsville-Harlingen, TX335,227 14,803 1,422 1 0.03 12 35 0.0053 0.0085 0.0090 0.0090 0.0089 0.0071480850003 1 33.14250 -96.82472 TX CollinDallas-Fort Worth-Arlington, TX 5,161,544 3,837 4153.2 13 12 35 0.2271 0.3453 0.7954 0.4436 0.5595 0.5203480850007 2 33.14722 -96.82556 TX CollinDallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 1 3.2 13 12 34 0.1186 0.2111 0.4760 0.3006 0.3408 0.3040480850009 1 33.14472 -96.82889 TX CollinDallas-Fort Worth-Arlington, TX 5,161,544 3,837 4153.2 13 12 33 0.4961 0.6982 0.9692 0.8914 0.8710 0.8710481130018 1 32.74556 -96.78250 TX DallasDallas-Fort Worth-Arlington, TX 5,161,544 6,451 491 1 0.03 12 34 0.0274 0.0804 0.2338 0.0880 0.1299 0.1286481130057 2 32.77890 -96.87306 TX DallasDallas-Fort Worth-Arlington, TX 5,161,544 4,591 578 1 0.03 12 35 0.0362 0.0611 0.1029 0.1016 0.0986 0.0947481130066 2 32.73972 -96.78278 TX DallasDallas-Fort Worth-Arlington, TX 5,161,544 8,270 622 1 0.0 1 1 1 7 20 0.0090 0.0209 0.0420 0.0280 0.0320 0.0340481410033 1 31.77694 -106.50167 TX El PasoEl Paso, TX679,622 13,680 1,005 1 0.01 6 17 0.0120 0.0585 0.0600 0.0540 0.0540 0.0420482011034 4 29.76799 -95.22058 TX HarrisHouston-Sugar Land-Baytown, TX 4,715,407 14,785 1,770 1 0.03 12 36 0.0081 0.0220 0.0478 0.0230 0.0283 0.0260484790016 1 27.51083 -99.51972 TX WebbLaredo, TX193,117 14,880 1,441 1 0.03 12 36 0.0121 0.0163 0.0230 0.0214 0.0217 0.0217490351001 1 40.70861 -112.09472 UT Salt LakeSalt Lake City, UT968,858 215 23 1 0.02 11 32 0.0421 0.0762 0.1188 0.1072 0.1106 0.1106721270003 1 18.44917 -66.05306 PR San JuanSan Juan-Caguas-Guaynabo, PR 2,509,007 319 5 13 12 36 0.0014 0.0100 0.0125 0.0120 0.0122 0.0082# Data for 2004-2005 did not meet completeness criteria..* 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 datafor 2004-2005 were considered for the "previous" source oriented characterization.2B-4Table 2B-2. Pb-TSP monitoring site information and 1-year statisticsmaxquarterlymean,2003maxmonthlymean,20032nd maxmonthlymean,2003maxquarterlymean,2004maxmonthlymean,20042nd maxmonthlymean,2004maxquarterlymean,2005maxmonthlymean,20052nd maxmonthlymean,2005011090003 2 31.79056 -85.97917 AL PikeTroy, AL29,605 461 314.5 11.9233 2.6600 2.4200 1.2267 1.7800 1.0000 0.3948 0.6156 0.3346011090006 1 31.79278 -85.98056 AL PikeTroy, AL29,605 461 314.5 10.9100 1.6900 0.8900 0.8433 1.3400 0.9400 0.1661 0.2402 0.1600060250005 1 32.67611 -115.48333 CA ImperialEl Centro, CA142,361 16,385 1,290 1 0.00.0248 0.0404 0.0357 0.0179 0.0205 0.0191 0.0229 0.0380 0.0278060371103 2 34.06659 -118.22688 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 29,329 1,633 1 0.30.0627 0.1460 0.0260 0.0253 0.0280 0.0280 0.0179 0.0250 0.0200060371301 1 33.92899 -118.21071 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 47,423 5,066 1 0.00.0300 0.0440 0.0360 0.0313 0.0320 0.0320 0.0233 0.0300 0.0280060371601 1 34.01407 -118.06056 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 13,333 1,066 1 0.00.0300 0.0480 0.0340 0.0215 0.0300 0.0300 0.0160 0.0250 0.0240060374002 2 33.82376 -118.18921 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 20,131 1,232 1 0.00.0400 0.0960 0.0440 0.0147 0.0180 0.0160 0.0125 0.0140 0.0140060374004 2 33.79236 -118.17533 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 61,497 6,697 1 0.00.0938 0.1020 0.0840 0.0146 0.0160 0.0160 0.0120 0.0160 0.0125060375001 1 33.92288 -118.37026 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 19,148 1,680 1 0.00.0667 0.1700 0.0220 0.0107 0.0120 0.0100060375005 1 33.95080 -118.43043 CA Los Angeles Los Angeles-Long Beach-Santa Ana12,365,627 33,968 1,358 1 0.00.0093 0.0120 0.0100 0.0118 0.0150 0.0100060651003 2 33.94603 -117.40063 CA RiversideRiverside-San Bernardino-Ontario,C3,254,821 16,320 1,278 1 0.00.0113 0.0160 0.0120 0.0114 0.0140 0.0125 0.0113 0.0140 0.0100060658001 3 33.99958 -117.41601 CA RiversideRiverside-San Bernardino-Ontario,C3,254,821 16,247 1,678 1 0.00.0179 0.0200 0.0200 0.0144 0.0220 0.0200 0.0169 0.0220 0.0180060711004 1 34.10374 -117.62914 CA San Bernardino Riverside-San Bernardino-Ontario,C3,254,821 18,777 1,578 1 0.00.0343 0.0800 0.0200 0.0150 0.0180 0.0160 0.0160 0.0180 0.0180060719004 1 34.10688 -117.27411 CA San Bernardino Riverside-San Bernardino-Ontario,C3,254,821 14,861 1,755 1 0.00.0773 0.1420 0.0680 0.0144 0.0160 0.0160 0.0133 0.0160 0.0150080010005 1 39.79601 -104.97754 CO AdamsDenver-Aurora, CO2,157,756 2,025 1831.9 10.1739 0.2509 0.2016 0.1384 0.1898 0.1887 0.5558 1.1037 0.4397080010006 1 39.82574 -104.93699 CO AdamsDenver-Aurora, CO2,157,756 3,313 256 1 0.00.0388 0.0443 0.0406 0.0404 0.0726 0.0346 0.0957 0.2086 0.0428080310002 4 39.75119 -104.98762 CO DenverDenver-Aurora, CO2,157,756 22,019 974 1 0.00.0290 0.0467 0.0284 0.0222 0.0339 0.0262 0.1780 0.2955 0.2297080310015 1 39.70012 -104.98714 CO DenverDenver-Aurora, CO2,157,756 14,438 809 1 0.00.0212 0.0305 0.0196 0.0151 0.0184 0.0183080410011 1 38.83139 -104.82778 CO El PasoColorado Springs, CO537,484 10,581 552 1 0.00.0117 0.0165 0.0120 0.0100 0.0100 0.0100 0.0891 0.1387 0.1314080650001 1 39.24778 -106.29139 CO LakeEdwards, CO49,471 5,903 361 1 0.00.0192 0.0277 0.0209 0.0224 0.0310 0.0310 0.0187 0.0296 0.0170100010002 1 38.98472 -75.55556 DE KentDover, DE126,697 352 220.00.0040 0.0051 0.0041100031007 1 39.55111 -75.73083 DE New Castle Philadelphia-Camden-Wilmington,P5,687,147 2,041 2090.00.0046 0.0058 0.0051100031008 1 39.57778 -75.61111 DE New Castle Philadelphia-Camden-Wilmington,P5,687,147 3,170 1600.00.0063 0.0081 0.0058100032004 1 39.73944 -75.55806 DE New Castle Philadelphia-Camden-Wilmington,P5,687,147 34,053 2,649 1 0.00.0115 0.0163 0.0161100051002 1 38.64444 -75.61306 DE SussexSeaford, DE156,638 5,450 390 1 0.00.0042 0.0048 0.0042120571065 5 27.89222 -82.53861 FL Hillsborough Tampa-St. Petersburg-Clearwater,F2,395,997 14,463 612 1 0.00.0062 0.0094 0.0080120571066 1 27.96028 -82.38250 FL Hillsborough Tampa-St. Petersburg-Clearwater,F2,395,997 5,793 465 1 1.3 10.7400 1.3800 0.7800 1.2600 1.7400 1.0400 1.1188 1.3000 1.2000120571073 1 27.96583 -82.37944 FL Hillsborough Tampa-St. Petersburg-Clearwater,F2,395,997 4,541 340 1 1.3 10.2533 0.4800 0.4400 0.2333 0.3400 0.2800 0.2933 0.4200 0.3200120571075 5 28.05000 -82.37806 FL Hillsborough Tampa-St. Petersburg-Clearwater,F2,395,997 10,691 490 1 0.00.0054 0.0105 0.0072121030004 5 27.94639 -82.73194 FL PinellasTampa-St. Petersburg-Clearwater,F2,395,997 13,048 557 1 0.00.0041 0.0067 0.0039121030018 5 27.78556 -82.74000 FL PinellasTampa-St. Petersburg-Clearwater,F2,395,997 11,289 571 1 0.00.0056 0.0103 0.0093 0.0071 0.0112 0.0051121033005 1 27.87583 -82.69639 FL PinellasTampa-St. Petersburg-Clearwater,F2,395,997 2,151 58 1 0.00.0067 0.0200 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000130890003 2 33.69833 -84.27333 GA DeKalbAtlanta-Sandy Springs-Marietta, GA4,247,981 7,888 663 1 0.00.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000132150011 1 32.43083 -84.93167 GA MuscogeeColumbus, GA-AL281,768 10,871 1,037 1 0.3 1 1 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000 0.1000150032004 1 21.39667 -157.97167 HI HonoluluHonolulu, HI876,156 23,622 1,207 1 0.10.0029 0.0072 0.0021 0.0015 0.0017 0.0015 0.0017 0.0025 0.0019170310001 1 41.67275 -87.73246 IL CookChicago-Naperville-Joliet, IL-INW9,098,316 13,648 971 1 0.00.0157 0.0250 0.0180 0.0229 0.0360 0.0200 0.0167 0.0200 0.0180170310022 2 41.68920 -87.53932 IL CookChicago-Naperville-Joliet, IL-INW9,098,316 22,040 1,708 1 0.20.0286 0.0360 0.0350 0.0314 0.0420 0.0420 0.0353 0.0440 0.0360170310026 1 41.87333 -87.64507 IL CookChicago-Naperville-Joliet, IL-INW9,098,316 28,739 1,203 1 0.00.0613 0.0860 0.0620 0.0557 0.0900 0.0700 0.0347 0.0500 0.0420170310052 1 41.96743 -87.74982 IL CookChicago-Naperville-Joliet, IL-INW9,098,316 42,187 2,877 1 0.00.0250 0.0280 0.0260 0.0257 0.0400 0.0300 0.0260 0.0380 0.0280170313103 1 41.96528 -87.87639 IL CookChicago-Naperville-Joliet, IL-INW9,098,316 10,302 670 1 0.00.0180 0.0240 0.0220 0.0140 0.0160 0.0160 0.0271 0.0440 0.0240170313301 1 41.78278 -87.80528 IL CookChicago-Naperville-Joliet, IL-INW9,098,316 23,749 1,678 1 0.00.0750 0.1950 0.0360 0.0520 0.1140 0.0700 0.0246 0.0375 0.0225170314201 1 42.14000 -87.79917 IL CookChicago-Naperville-Joliet, IL-INW9,098,316 6,070 303 1 0.00.0133 0.0175 0.0160 0.0120 0.0160 0.0140170316003 1 41.87194 -87.82611 IL CookChicago-Naperville-Joliet, IL-INW9,098,316 14,862 1,071 1 0.00.0373 0.0480 0.0400 0.0333 0.0460 0.0420 0.0387 0.0500 0.0360171170002 2 39.39804 -89.80975 IL MacoupinSt. Louis, MO-IL2,721,491 40 20.00.0113 0.0140 0.0100 0.0113 0.0140 0.0100 0.0107 0.0120 0.0120171190010 1 38.69417 -90.15361 IL MadisonSt. Louis, MO-IL2,721,491 8,014 529 1 1.3 10.3280 0.9100 0.0620 0.1515 0.2880 0.0900 0.1033 0.1880 0.0750171193007 2 38.86056 -90.10583 IL MadisonSt. Louis, MO-IL2,721,491 5,397 360 1 0.10.0173 0.0320 0.0240 0.0175 0.0240 0.0200 0.0193 0.0225 0.0200171430037 1 40.69889 -89.58474 IL PeoriaPeoria, IL366,899 12,643 1,109 1 0.00.0167 0.0220 0.0180 0.0129 0.0180 0.0100 0.0279 0.0320 0.0300171630010 2 38.61222 -90.16028 IL St. ClairSt. Louis, MO-IL2,721,491 3,512 430 1 0.30.0563 0.0940 0.0720 0.0529 0.0750 0.0520 0.0707 0.1050 0.0980180350008 1 40.15806 -85.42111 IN DelawareMuncie, IN118,769 2,108 104 1 0.0 10.2341 0.3394 0.3138 0.4657 0.7371 0.4653 0.4642 0.5991 0.4671180350009 2 40.15944 -85.41556 IN DelawareMuncie, IN118,769 980 820.0 10.8073 1.2183 0.967273 4.0931 5.7750 5.0220 1.3890 1.5900 1.3923180890023 1 41.65278 -87.43944 IN LakeChicago-Naperville-Joliet, IL-INW9,098,316 5,959 603 1 6.5 10.0435 0.0620 0.0510 0.0691 0.0910 0.0783 0.0462 0.0613 0.0578180892008 1 41.63944 -87.49361 IN LakeChicago-Naperville-Joliet, IL-INW9,098,316 7,144 612 1 0.00.0277 0.0413 0.0335 0.0289 0.0590 0.0318 0.0296 0.0484 0.0363180892011 2 41.59250 -87.47194 IN LakeChicago-Naperville-Joliet, IL-INW9,098,316 9,815 729 1 0.00.0453 0.0610 0.0420 0.0358 0.0532 0.0463 0.1352 0.3050 0.0778180930004 1 38.88944 -86.55194 IN LawrenceBedford, IN45,922 393 320.00.0270 0.0270 0.0270 0.0270 0.0270 0.0270 0.0270 0.0270 0.0270180970063 1 39.76083 -86.29722 IN MarionIndianapolis-Carmel, IN1,525,104 12,176 875 1 1.7 10.0508 0.0812 0.0584 0.0770 0.1123 0.0638 0.0329 0.0594 0.03801-year metricssourceoriented?prev.sourceoriented?(see endnotes)populationnear site(mile radius)underage 5pop.(mileradius)urbansumpoint /nonptPb EITPYw/in 1milestate county_namecbsa_namecbsa_pop00site poc lat long2B-5Table 2B-2. Pb-TSP monitoring site information and 1-year statisticsmaxquarterlymean,2003maxmonthlymean,20032nd maxmonthlymean,2003maxquarterlymean,2004maxmonthlymean,20042nd maxmonthlymean,2004maxquarterlymean,2005maxmonthlymean,20052nd maxmonthlymean,20051-year metricssourceoriented?prev.sourceoriented?(see endnotes)populationnear site(mile radius)underage 5pop.(mileradius)urbansumpoint /nonptPb EITPYw/in 1milestate county_namecbsa_namecbsa_pop00site poc lat long180970076 1 39.75889 -86.28972 IN MarionIndianapolis-Carmel, IN1,525,104 9,171 602 1 1.7 10.0143 0.0190 0.0178 0.0186 0.0204 0.0190 0.0254 0.0360 0.0346180970078 1 39.81110 -86.11447 IN MarionIndianapolis-Carmel, IN1,525,104 14,196 1,175 1 0.00.0057 0.0110 0.0075 0.0103 0.0154 0.0094 0.0251 0.0288 0.0240181010001 1 38.89028 -86.76083 IN Martin84 50.00.0299 0.0358 0.0270 0.0270 0.0270 0.0270 0.0270 0.0270 0.0270181630006 2 37.97167 -87.56722 IN Vanderburgh Evansville, IN-KY342,815 13,666 817 1 0.00.0051 0.0075 0.0060 0.0126 0.0286 0.0170 0.0083 0.0088 0.0088260490021 4 43.04722 -83.67028 MI GeneseeFlint, MI436,141 9,889 994 1 0.00.0153 0.0189 0.0166 0.0121 0.0156 0.0132 0.0117 0.0209 0.0155261130001 1 44.31056 -84.89194 MI Missaukee Cadillac, MI44,962 58 30.00.0040 0.0042 0.0039 0.0032 0.0040 0.0036 0.0056 0.0080 0.0046261630001 2 42.22861 -83.20833 MI WayneDetroit-Warren-Livonia, MI 4,452,557 14,329 798 1 0.00.0096 0.0101 0.0100 0.0101 0.0111 0.0105 0.0107 0.0124 0.0115261630005 1 42.26722 -83.13222 MI WayneDetroit-Warren-Livonia, MI 4,452,557 11,314 923 1 0.20.0247 0.0340 0.0208 0.0259 0.0315 0.0310 0.0191 0.0268 0.0228261630015 4 42.30278 -83.10667 MI WayneDetroit-Warren-Livonia, MI 4,452,557 17,729 1,771 1 0.00.0207 0.0247 0.0214 0.0252 0.0299 0.0256 0.0204 0.0278 0.0252261630019 1 42.43083 -83.00028 MI WayneDetroit-Warren-Livonia, MI 4,452,557 28,362 2,628 1 0.00.0136 0.0141 0.0141 0.0108 0.0149 0.0124 0.0138 0.0140 0.0125261630027 1 42.29222 -83.10694 MI WayneDetroit-Warren-Livonia, MI 4,452,557 6,024 516 1 1.1 10.0173 0.0240 0.0112 0.0267 0.0353 0.0340261630033 2 42.30667 -83.14889 MI WayneDetroit-Warren-Livonia, MI 4,452,557 17,402 1,843 1 0.50.0410 0.0601 0.0406 0.0262 0.0384 0.0381 0.0269 0.0368 0.0301270370001 1 44.83333 -93.11500 MN DakotaMinneapolis-St. Paul-Bloomington, 2,968,806 5,074 404 1 3.2 10.2100 0.0380 0.1153 0.2300 0.1920 0.0979 0.1725 0.0900270370020 1 44.76535 -93.03248 MN DakotaMinneapolis-St. Paul-Bloomington, 2,968,806 162 70.00.0086 0.0200 0.0100 0.0060 0.0080 0.0060 0.0100 0.0120 0.0100270370421 1 44.77720 -93.04097 MN DakotaMinneapolis-St. Paul-Bloomington, 2,968,806 478 240.00.0057 0.0100 0.0060 0.0069 0.0080 0.0067 0.0069 0.0120 0.0080270370423 1 44.77500 -93.06278 MN DakotaMinneapolis-St. Paul-Bloomington, 2,968,806 886 830.00.0050 0.0100 0.0060 0.0033 0.0060 0.0040 0.0029 0.0040 0.0040270370442 1 44.74036 -93.00556 MN DakotaMinneapolis-St. Paul-Bloomington, 2,968,806 168 110.30.0062 0.0080 0.0020 0.0027 0.0060 0.0050 0.0036 0.0060 0.0050270530050 1 45.00123 -93.26712 MN HennepinMinneapolis-St. Paul-Bloomington, 2,968,806 16,318 923 1 0.00.0079 0.0100 0.0080 0.0093 0.0120 0.0100 0.0060 0.0117 0.0080270530963 1 44.95540 -93.25827 MN HennepinMinneapolis-St. Paul-Bloomington, 2,968,806 46,218 3,929 1 0.20.0071 0.0080 0.0075 0.0064 0.0075 0.0060 0.0050 0.0100 0.0067270530964 1 44.88855 -93.19538 MN HennepinMinneapolis-St. Paul-Bloomington, 2,968,806 209 0 1 0.00.0114 0.0180 0.00800.0040 0.0020270530965 1 45.00448 -93.24005 MN HennepinMinneapolis-St. Paul-Bloomington, 2,968,806 19,106 1,095 1 0.40.0080 0.0100 0.0080 0.0047 0.0080 0.0067 0.0073 0.0140 0.0060270530966 1 44.98133 -93.26615 MN HennepinMinneapolis-St. Paul-Bloomington, 2,968,806 17,156 439 1 0.00.0080 0.0120 0.0060 0.0079 0.0100 0.0100 0.0053 0.0083 0.0080270530967 1 44.99646 -93.23488 MN HennepinMinneapolis-St. Paul-Bloomington, 2,968,806 14,621 580 1 0.4 10.0076 0.0100 0.0080 0.0142 0.0225 0.0157270530968 1 44.89301 -93.23323 MN HennepinMinneapolis-St. Paul-Bloomington, 2,968,806 11,243 789 1 0.00.0033 0.0080 0.0050 0.0031 0.0050 0.0050270531007 1 45.04182 -93.29873 MN HennepinMinneapolis-St. Paul-Bloomington, 2,968,806 14,889 1,118 1 0.00.0067 0.0080 0.0060 0.0043 0.0060 0.0050 0.0029 0.0067 0.0040271231003 1 44.96322 -93.19023 MN RamseyMinneapolis-St. Paul-Bloomington, 2,968,806 9,247 474 1 0.10.0100 0.0200 0.0100 0.0129 0.0350 0.0180 0.0067 0.0080 0.0080271377001 1 47.52336 -92.53631 MN St. LouisDuluth, MN-WI275,486 8,942 428 1 0.10.0362 0.0900 0.0080 0.0021 0.0040 0.0040 0.0100 0.0100 0.0040271377555 1 46.73264 -92.16337 MN St. LouisDuluth, MN-WI275,486 4,527 287 1 0.00.0020 0.0040 0.0020 0.0027 0.0040 0.0020 0.0040 0.0050 0.0040290930016 1 37.62528 -91.12917 MO Iron58 40.0 10.6593 1.2160 1.1720 0.7893 1.4540 1.0340 1.3070 4.1933 1.2120290930021 1 37.65417 -91.13056 MO Iron58 40.0 10.5850 0.9840 0.7820 0.7187 0.9960 0.9280 0.6627 0.9520 0.8660290930023 1 37.50333 -90.69556 MO Iron138 70.0 1 1 *# 0.3433 0.6320 0.4275 0.0131 0.0242 0.0136290930024 1 37.47972 -90.69028 MO Iron32 20.0 1 1 *# 0.6677 1.6026 0.9927 0.0333 0.0558 0.0337290930025 1 37.51056 -90.69750 MO Iron138 70.0 1 1 *# 0.3263 0.6320 0.4189 0.0642 0.0640 0.0600290930026 1 37.45917 -90.68639 MO Iron32 20.0 1 1 *# 0.2523 0.3555 0.3370 0.0621 0.0700 0.0660290930027 1 37.48611 -90.69000 MO Iron32 20.0 1 1 * 0.8761 1.4414 0.9300 0.1654 0.3080 0.2200 0.1257 0.1667 0.1480290930029 1 37.47167 -90.68944 MO Iron32 20.0 1 1 * 0.7148 1.4740 1.1410 0.0893 0.1025 0.0900 0.1027 0.1400 0.0980290930030 1 37.46639 -90.69000 MO Iron32 20.0 1 1 *# 0.2017 0.3330 0.2797 0.0089 0.0154 0.0125290990004 1 38.26330 -90.37850 MO JeffersonSt. Louis, MO-IL2,721,491 2,418 197 1 58.8 11.4750 2.0731 1.8962 1.1215 1.4317 1.1765290990005 3 38.26722 -90.37944 MO JeffersonSt. Louis, MO-IL2,721,491 2,418 197 1 58.8 10.5438 0.7900 0.6157 0.6779 1.0655 0.9278 0.3742 0.5499 0.4180290990008 1 38.26194 -90.39417 MO JeffersonSt. Louis, MO-IL2,721,491 2,418 19758.8 10.1500 0.2583 0.1350 0.1368 0.1700 0.1570 0.1857 0.3700 0.3100290990009 1 38.28444 -90.38194 MO JeffersonSt. Louis, MO-IL2,721,491 9,804 820 1 0.0 10.1664 0.1440 0.1100 0.1413 0.1750 0.1475 0.1064 0.1560 0.1125290990010 1 38.24110 -90.37680 MO JeffersonSt. Louis, MO-IL2,721,491 2,799 215 1 0.0 10.0680 0.1040 0.0900 0.0700 0.0740 0.0680 0.0813 0.1680 0.0660290990011 1 38.26820 -90.37380 MO JeffersonSt. Louis, MO-IL2,721,491 2,418 197 1 58.8 10.5321 1.0490 0.7327 1.3047 2.2070 1.3510 0.4200 0.7638 0.7153290990013 1 38.27361 -90.38000 MO JeffersonSt. Louis, MO-IL2,721,491 3,570 318 1 58.8 10.2717 0.4850 0.3355 0.8683 3.5680 0.6400 0.3379 0.6420 0.2725290990015 1 38.26167 -90.37972 MO JeffersonSt. Louis, MO-IL2,721,491 1,988 178 1 58.8 11.4906 2.2993 1.9442 1.4760 1.8987 1.8531 1.9277 3.2884 2.2541291892003 1 38.64972 -90.35056 MO St. LouisSt. Louis, MO-IL2,721,491 12,303 512 1 0.00.0000 0.0000 0.0000 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL2,721,491 9,140 783 1 0.00.0216 0.0290 0.0255340231003 1 40.47222 -74.47139 NJ MiddlesexNew York-Northern New Jersey-Lo 18,323,002 13,850 1,124 1 1.7 10.0419 0.0875 0.0360 0.1146 0.1878 0.1428 0.1537 0.1182 0.0874360470122 1 40.71980 -73.94788 NY KingsNew York-Northern New Jersey-Lo 18,323,002 92,660 5,785 1 0.10.0293 0.0350 0.0300 0.0333 0.0360 0.0325 0.0309 0.0325 0.0300360632008 1 43.08216 -79.00099 NY NiagaraBuffalo-Niagra Falls, NY Metropoli1,170,111 6,795 386 1 0.00.0060 0.0080 0.0080360713001 1 41.46107 -74.36343 NY OrangePoughkeepsie-Newburgh-Middletow621,517 1,481 991.8 10.0820 0.1580 0.0940 0.0746 0.1100 0.0917 0.0453 0.0540 0.0460360713002 1 41.45887 -74.35392 NY OrangePoughkeepsie-Newburgh-Middletow621,517 1,257 861.8 10.2417 0.2080 0.1700 0.2369 0.4025 0.2400 0.0520 0.0640 0.0500360713004 1 41.47633 -74.36827 NY OrangePoughkeepsie-Newburgh-Middletow621,517 6,816 434 1 0.00.0313 0.0333 0.0320 0.0307 0.0320 0.0320 0.0386 0.0400 0.0400360850067 1 40.59733 -74.12619 NY RichmondNew York-Northern New Jersey-Lo 18,323,002 21,834 1,3730.00.0082 0.0140 0.0125390170015 2 39.48990 -84.36407 OH ButlerCincinnati-Middletown, OH-KY-IN2,009,632 4,668 373 1 0.00.0129 0.0160 0.0150 0.0248 0.0650 0.01302B-6Table 2B-2. Pb-TSP monitoring site information and 1-year statisticsmaxquarterlymean,2003maxmonthlymean,20032nd maxmonthlymean,2003maxquarterlymean,2004maxmonthlymean,20042nd maxmonthlymean,2004maxquarterlymean,2005maxmonthlymean,20052nd maxmonthlymean,20051-year metricssourceoriented?prev.sourceoriented?(see endnotes)populationnear site(mile radius)underage 5pop.(mileradius)urbansumpoint /nonptPb EITPYw/in 1milestate county_namecbsa_namecbsa_pop00site poc lat long390290019 1 40.63111 -80.54694 OH Columbiana East Liverpool-Salem, OH112,075 5,385 322 1 0.00.0253 0.0300 0.0300 0.0142 0.0220 0.0140 0.0150 0.0220 0.0190390290020 1 40.63972 -80.52389 OH Columbiana East Liverpool-Salem, OH112,075 6,414 354 1 0.00.0247 0.0300 0.0240 0.0190 0.0310 0.0290 0.0191 0.0310 0.0190390290022 1 40.63500 -80.54667 OH Columbiana East Liverpool-Salem, OH112,075 3,318 202 1 0.00.0367 0.0800 0.0200 0.0180 0.0300 0.0150 0.0142 0.0180 0.0170390350038 1 41.47694 -81.68194 OH CuyahogaCleveland-Elyria-Mentor, OH 2,148,143 7,329 585 1 0.10.0300 0.0600 0.0300 0.0253 0.0360 0.0320 0.0223 0.0310 0.0300390350042 1 41.48222 -81.70889 OH CuyahogaCleveland-Elyria-Mentor, OH 2,148,143 18,776 1,575 1 0.00.0233 0.0300 0.0200 0.0230 0.0430 0.0230 0.0280 0.0390 0.0290390350049 1 41.44667 -81.65111 OH CuyahogaCleveland-Elyria-Mentor, OH 2,148,143 9,720 758 1 0.0 1 1 0.2367 0.4500 0.2500 0.1380 0.2200 0.1500 0.1503 0.2600 0.2600390350050 1 41.44250 -81.64917 OH CuyahogaCleveland-Elyria-Mentor, OH 2,148,143 8,771 695 1 0.0 1 1 0.0400 0.0700 0.0500 0.0543 0.1000 0.0500 0.0550 0.0940 0.0820390350061 2 41.47506 -81.67596 OH CuyahogaCleveland-Elyria-Mentor, OH 2,148,143 6,141 444 1 0.3 1 1 0.3600 0.5600 0.4700 0.0257 0.0440 0.0300 0.0183 0.0230 0.0180390350069 1 41.51918 -81.63794 OH CuyahogaCleveland-Elyria-Mentor, OH 2,148,143 23,566 1,961 1 0.10.0233 0.0290 0.02800.0470 0.0370 0.0210 0.0270 0.0200390490025 1 39.92806 -82.98111 OH FranklinColumbus, OH1,612,694 15,220 1,226 1 0.6 10.0167 0.0200 0.0200 0.0197 0.0270 0.0210 0.0085 0.0140 0.0130390510001 1 41.57528 -83.99639 OH FultonToledo, OH659,188 1,503 110 1 0.3 10.2667 0.5300 0.2500 0.2460 0.3800 0.2800 0.1867 0.6100 0.4200390910003 1 40.34306 -83.75500 OH LoganBellefontaine, OH46,005 1,536 108 1 0.10.1467 0.2700 0.2000 0.1337 0.2000 0.1900 0.1070 0.2000 0.1700390910005 1 40.34278 -83.76028 OH LoganBellefontaine, OH46,005 1,546 126 1 0.1 10.1300 0.1900 0.1100 0.1467 0.2100 0.1300 0.1467 0.2200 0.1900390910006 1 40.34111 -83.75778 OH LoganBellefontaine, OH46,005 1,217 87 1 0.1 10.1967 0.3200 0.2100 0.2667 0.3600 0.2700 0.2267 0.3600 0.2800390910007 1 40.34472 -83.75444 OH LoganBellefontaine, OH46,005 2,156 185 1 0.10.1500 0.2100 0.1500 0.2200 0.2600 0.2500 0.1700 0.2300 0.1900391670008 1 39.43361 -81.50250 OH Washington Parkersburg-Marietta, WV-OH 164,624 1,947 1140.00.0100 0.0100 0.0100 0.0072 0.0130 0.0100 0.0051 0.0062 0.0054391670009 1 39.37696 -81.53730 OH Washington Parkersburg-Marietta, WV-OH 164,624 314 210.00.0495 0.0880 0.0110 0.0106 0.0140 0.0130401159005 2 36.98580 -94.84920 OK OttawaMiami, OK33,194 1,573 1170.00.0613 0.0927 0.0630401159006 1 36.98460 -94.82490 OK OttawaMiami, OK33,194 1,573 1170.00.0378 0.0623 0.0420401159007 1 36.97190 -94.85180 OK OttawaMiami, OK33,194 1,573 1170.00.1030 0.1257 0.1140401159008 1 36.97160 -94.82500 OK OttawaMiami, OK33,194 1,573 1170.00.0408 0.0708 0.0363410510246 7 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR1,927,881 24,303 1,771 1 0.00.0101 0.0110 0.0105420030002 1 40.50056 -80.07194 PA AlleghenyPittsburgh, PA2,431,087 19,559 1,045 1 0.00.0255 0.0280 0.0260 0.0115 0.0230 0.0143 0.0378 0.0503 0.0377420032001 1 40.39667 -79.86361 PA AlleghenyPittsburgh, PA2,431,087 10,120 769 1 0.20.0567 0.1140 0.0660 0.0394 0.0630 0.0525 0.0546 0.0632 0.0629420070505 1 40.68500 -80.32500 PA BeaverPittsburgh, PA2,431,087 6,497 218 1 0.00.0913 0.1920 0.0483 0.0925 0.1325 0.1000 0.1531 0.2300 0.2280420110005 1 40.46630 -75.75890 PA BerksReading, PA373,638 692 444.8 10.0757 0.1000 0.0760 0.0940 0.1560 0.1060 0.0881 0.1580 0.0950420110717 1 40.47667 -75.75917 PA BerksReading, PA373,638 575 39 1 4.8 10.1238 0.1980 0.1580 0.1800 0.2820 0.2650 0.1736 0.2740 0.2320420111717 1 40.37722 -75.91444 PA BerksReading, PA373,638 7,376 390 1 2.1 10.3860 0.4840 0.4560 0.3967 0.5180 0.4580 0.3907 0.8020 0.2400420210808 1 40.34806 -78.88278 PA CambriaJohnstown, PA152,598 2,606 115 1 0.00.0364 0.0460 0.0320 0.0453 0.0560 0.0400 0.0569 0.0920 0.0417420250105 1 40.80306 -75.60833 PA CarbonAllentown-Bethlehem-Easton, PA-N740,395 8,477 513 1 0.00.0992 0.1300 0.1240 0.1150 0.1420 0.1225 0.2493 0.3560 0.2980420450002 1 39.83556 -75.37250 PA DelawarePhiladelphia-Camden-Wilmington,P5,687,147 10,156 859 1 0.00.0364 0.0380 0.0380 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400421010449 1 39.98250 -75.08306 PA Philadelphia Philadelphia-Camden-Wilmington,P5,687,147 8,653 413 1 0.0 1 1 0.0350 0.0380 0.0360 0.0269 0.0355 0.0312 0.0236 0.0298 0.0266421290007 1 40.16667 -79.87500 PA Westmoreland Pittsburgh, PA2,431,087 7,739 445 1 0.00.0369 0.0400 0.0400 0.0393 0.0400 0.0400 0.0400 0.0400 0.0400450031001 1 33.43253 -81.89233 SC AikenAugusta-Richmond County, GA-SC499,684 437 240.00.0000 0.0000 0.0000450130007 1 32.43654 -80.67785 SC BeaufortHilton Head Island-Beaufort, SC 141,615 4,928 330 1 0.00.0022 0.0070 0.0016 0.0020 0.0034 0.0023 0.0000 0.0000 0.0000450190003 2 32.88394 -79.97754 SC Charleston Charleston-North Charleston, SC 549,033 4,401 275 1 0.00.0041 0.0104 0.0048 0.0026 0.0078 0.0030 0.0013 0.0033 0.0000450190046 1 32.94275 -79.65718 SC Charleston Charleston-North Charleston, SC 549,033 63 40.00.0032 0.0068 0.0035 0.0007 0.0020 0.0020 0.0006 0.0018 0.0000450190047 1 32.84461 -79.94804 SC Charleston Charleston-North Charleston, SC 549,033 7,000 294 1 0.00.0037 0.0058 0.0052450410001 1 34.19794 -79.79885 SC FlorenceFlorence, SC193,155 3,426 224 1 0.00.0026 0.0063 0.00230.0020450410002 1 34.16764 -79.85040 SC FlorenceFlorence, SC193,155 1,795 106 1 0.00.00000.0021 0.0054 0.0032 0.0034 0.0102 0.0000450430006 1 33.36378 -79.29426 SC Georgetown Georgetown, SC55,797 5,247 427 1 0.3 10.0166 0.0420 0.0077 0.0123 0.0200 0.0190 0.0072 0.0135 0.0100450430007 1 33.34973 -79.29821 SC Georgetown Georgetown, SC55,797 1,579 1190.30.0017 0.0054 0.0000 0.0000 0.0000 0.0000 0.0005 0.0016 0.0000450430009 1 33.37399 -79.28570 SC Georgetown Georgetown, SC55,797 2,447 185 1 0.30.0081 0.0158 0.0094 0.0042 0.0055 0.0048 0.0069 0.0148 0.0072450430010 1 33.36960 -79.29840 SC Georgetown Georgetown, SC55,797 6,173 511 1 0.30.0102 0.0132 0.0102 0.0169 0.0265 0.0078 0.0033 0.0063 0.0050450450008 2 34.84045 -82.40291 SC GreenvilleGreenville, SC559,940 7,967 381 1 0.00.0071 0.0125 0.0066 0.0050 0.0120 0.0060 0.0049 0.0018 0.0018450452002 1 34.94165 -82.22961 SC GreenvilleGreenville, SC559,940 7,266 494 1 0.00.0000 0.0000 0.0000 0.0006 0.0018 0.0018 0.0000 0.0000 0.0000450470001 1 34.18111 -82.15224 SC Greenwood Greenwood, SC66,271 7,853 667 1 0.00.0053 0.0086 0.0082 0.0063 0.0112 0.0106 0.0021 0.0048 0.0036450470002 1 34.16520 -82.16048 SC Greenwood Greenwood, SC66,271 1,490 1160.00.0100 0.0202 0.0168 0.0163 0.0320 0.0272 0.0094 0.0116 0.0115450510002 2 33.70460 -78.87745 SC HorryMyrtle Beach-Conway-North Myrtle196,629 4,510 227 1 0.00.0018 0.0032 0.0020 0.0020 0.0040 0.0034 0.0020 0.0053 0.0016450630005 2 33.78560 -81.11978 SC LexingtonColumbia, SC647,158 736 660.00.0033 0.0052 0.0050450631002 2 33.96900 -81.06533 SC LexingtonColumbia, SC647,158 8,086 551 1 0.00.0179 0.0356 0.0090 0.0067 0.0125 0.0096 0.0036 0.0082 0.0036450790006 4 34.00740 -81.02329 SC RichlandColumbia, SC647,158 17,143 574 1 0.00.0069 0.0090 0.0072450790007 2 34.09584 -80.96230 SC RichlandColumbia, SC647,158 4,405 233 1 0.00.0006 0.0018 0.0000 0.0007 0.0020 0.0020 0.0014 0.0042 0.0030450790019 1 33.99330 -81.02414 SC RichlandColumbia, SC647,158 15,569 287 1 0.00.0097 0.0138 0.0104 0.0078 0.0144 0.0090 0.0096 0.0128 0.0122450790021 1 33.81655 -80.78114 SC RichlandColumbia, SC647,158 123 100.00.0012 0.0038 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00002B-7Table 2B-2. Pb-TSP monitoring site information and 1-year statisticsmaxquarterlymean,2003maxmonthlymean,20032nd maxmonthlymean,2003maxquarterlymean,2004maxmonthlymean,20042nd maxmonthlymean,2004maxquarterlymean,2005maxmonthlymean,20052nd maxmonthlymean,20051-year metricssourceoriented?prev.sourceoriented?(see endnotes)populationnear site(mile radius)underage 5pop.(mileradius)urbansumpoint /nonptPb EITPYw/in 1milestate county_namecbsa_namecbsa_pop00site poc lat long450830001 2 34.94774 -81.93255 SC Spartanburg Spartanburg, SC253,791 7,505 552 1 0.00.0035 0.0060 0.0058 0.0026 0.0062 0.0054 0.0021 0.0048 0.0022450850001 1 33.92423 -80.33774 SC SumterSumter, SC104,646 4,990 407 1 0.00.0064 0.0108 0.0066 0.0047 0.0104 0.0060 0.0044 0.0090 0.0034450910005 1 34.96303 -81.00085 SC YorkCharlotte-Gastonia-Concord, NC-SC1,330,448 3,453 221 1 0.00.0042 0.0082 0.0050 0.0021 0.0058 0.0032 0.0008 0.0017 0.0016470930027 1 35.98306 -83.95222 TN KnoxKnoxville, TN616,079 8,586 826 1 5.8 10.0100 0.0100 0.0100 0.0233 0.0400 0.0400 0.0100 0.0100 0.0100470931017 1 35.97500 -83.95444 TN KnoxKnoxville, TN616,079 7,817 763 1 5.8 10.0100 0.0100 0.0100 0.0193 0.0375 0.0240 0.0100 0.0100 0.0100471570044 1 35.08750 -90.07250 TN ShelbyMemphis, TN-MS-AR1,205,204 6,730 548 1 0.0 1 1 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100471633001 1 36.52556 -82.27333 TN SullivanKingsport-Bristol-Bristol, TN-VA 298,484 942 650.4 10.1515 0.1940 0.1718 0.1577 0.2843 0.1488 0.1959 0.2360 0.2300471633002 3 36.52472 -82.26806 TN SullivanKingsport-Bristol-Bristol, TN-VA 298,484 942 650.4 10.0719 0.0846 0.0730 0.1024 0.1550 0.0840 0.1463 0.2920 0.0933471633003 1 36.52806 -82.26833 TN SullivanKingsport-Bristol-Bristol, TN-VA 298,484 942 650.4 10.0679 0.0844 0.0810 0.1259 0.2322 0.0750 0.0739 0.1260 0.0830471870100 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 102.6 10.9867 1.9120 0.8200 0.1287 0.1960 0.1800471870102 2 35.80222 -86.66028 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 102.6 10.6953 0.9460 0.6000 0.0887 0.1320 0.1100471871101 1 35.79944 -86.66500 TN Williamson Nashville-Davidson--Murfreesboro, 1,311,789 165 102.6 10.0853 0.1160 0.1120 0.3027 0.7020 0.1820480610006 1 25.89251 -97.49382 TX CameronBrownsville-Harlingen, TX335,227 14,803 1,422 1 0.00.0085 0.0090 0.0090 0.0076 0.0080 0.0078 0.0040 0.0042 0.0040480850003 1 33.14250 -96.82472 TX CollinDallas-Fort Worth-Arlington, TX 5,161,544 3,837 4153.2 10.3006 0.4436 0.3518 0.2473 0.3220 0.2854 0.3453 0.7954 0.4396480850007 2 33.14722 -96.82556 TX CollinDallas-Fort Worth-Arlington, TX 5,161,544 3,837 415 1 3.2 10.1337 0.2458 0.2223 0.1241 0.1902 0.1728 0.2111 0.4760 0.3006480850009 1 33.14472 -96.82889 TX CollinDallas-Fort Worth-Arlington, TX 5,161,544 3,837 4153.2 10.6600 0.8914 0.6658 0.5926 0.7524 0.6670 0.6982 0.9692 0.7368481130018 1 32.74556 -96.78250 TX DallasDallas-Fort Worth-Arlington, TX 5,161,544 6,451 491 1 0.00.0318 0.0640 0.0250 0.0804 0.2338 0.0500 0.0467 0.0880 0.0680481130057 2 32.77890 -96.87306 TX DallasDallas-Fort Worth-Arlington, TX 5,161,544 4,591 578 1 0.00.0611 0.1016 0.0708 0.0447 0.1029 0.0913 0.0563 0.0796 0.0700481130066 2 32.73972 -96.78278 TX DallasDallas-Fort Worth-Arlington, TX 5,161,544 8,270 622 1 0.0 1 1 0.0178 0.0260 0.0217 0.0209 0.0420 0.0280481410033 1 31.77694 -106.50167 TX El PasoEl Paso, TX679,622 13,680 1,005 1 0.00.0585 0.0600 0.05400.0147 0.0240 0.0160482011034 4 29.76799 -95.22058 TX HarrisHouston-Sugar Land-Baytown, TX 4,715,407 14,785 1,770 1 0.00.0136 0.0230 0.0140 0.0220 0.0478 0.0104 0.0054 0.0073 0.0058484790016 1 27.51083 -99.51972 TX WebbLaredo, TX193,117 14,880 1,441 1 0.00.0142 0.0230 0.0147 0.0156 0.0206 0.0202 0.0163 0.0214 0.0196490351001 1 40.70861 -112.09472 UT Salt LakeSalt Lake City, UT968,858 215 23 1 0.00.0628 0.1188 0.0752 0.0718 0.1057 0.1016 0.0762 0.1072 0.1032721270003 1 18.44917 -66.05306 PR San JuanSan Juan-Caguas-Guaynabo, PR 2,509,007 319 5 10.0042 0.0125 0.0000 0.0000 0.0000 0.0000 0.0100 0.0120 0.0120* These sites were classified as "previous" source-oriented but because production (and related lead emissions) at the associated source was not terminated until December, 2003, onlydata for 2004-2005 were considered for the "previous" source oriented characterization.# Data for 2004-2005 did not meet completeness criteria..2B-8Table 2B-3. Pb-TSP monitoring site distribution statisticsAll sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual 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 0.0957 0.1332 0.2527 0.4778 2.6732max quarter mean189 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 0.1857 0.2667 0.4657 0.8761 4.0931max monthly mean189 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 0.2955 0.4760 0.9100 1.6900 5.77502nd 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 0.2360 0.3100 0.5300 0.9927 5.0220average of 3 overall highestmonthlymeans189 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 0.2501 0.3418 0.6013 1.1597 4.2890average of 3 annual max monthlymeans189 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 0.2093 0.3100 0.5390 1.0540 2.8611Source-oriented sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual 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 0.3327 0.4869 0.5866 0.9109 2.6732max quarter mean60 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 0.7167 0.8930 1.2823 1.6992 4.0931max monthly mean60 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 1.4577 1.7150 2.1401 3.4282 5.77502nd 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 0.9289 1.0669 1.3655 2.0977 5.0220Not source-oriented sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual 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 0.0272 0.0308 0.0372 0.0433 0.1497max quarter mean129 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 0.0495 0.0613 0.0773 0.1030 0.2493max monthly mean129 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 0.0880 0.1000 0.1387 0.2300 0.35602nd 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 0.0440 0.0630 0.0880 0.1140 0.2980Previous source-oriented sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual 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 0.1105 0.1105 0.1214 0.1214 0.1214max quarter mean9 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 0.2367 0.2367 0.3600 0.3600 0.3600max monthly mean9 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 0.4500 0.4500 0.5600 0.5600 0.56002nd 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 0.2600 0.2600 0.4700 0.4700 0.4700Urban sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual 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 0.0404 0.0780 0.1200 0.2601 1.4501max quarter mean140 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 0.0979 0.1534 0.2430 0.4312 1.9277max monthly mean140 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 0.1814 0.2469 0.4050 0.8560 3.56802nd 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 0.1056 0.2100 0.2930 0.5645 2.2993Urban sites, located in MSA's≥1 million populationn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual 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 0.0396 0.0563 0.1000 0.3711 1.4501max quarter mean91 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 0.0957 0.1537 0.2367 0.8683 1.9277max monthly mean91 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 0.1878 0.2338 0.4760 1.7400 3.56802nd 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 0.1016 0.2100 0.2880 0.9278 2.2993Urban sites, located in MSA's < 1 million populationn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual 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 0.0779 0.1000 0.1332 0.1578 0.2944max quarter mean49 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 0.1000 0.1467 0.2493 0.2667 0.4657max monthly mean49 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 0.1387 0.2600 0.3560 0.6100 0.80202nd 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 0.1314 0.2100 0.2980 0.5180 0.59912B-9Table 2B-4. Pb-TSP metric correlationsannualmean, 2003-2005max Qmean, 2003-2005max Mmean, 2003-20052nd max Mmean, 2003-2005avg. of 3overallhighest Mmeans,2003-2005avg. of 3annual maxM means,2003-2005annualmean, 2003-2005max Qmean, 2003-2005max Mmean, 2003-20052nd max Mmean, 2003-2005avg. of 3overallhighest Mmeans,2003-2005avg. of 3annual maxM means,2003-2005189 189 189 189 189 189140 140 140 140 1401400.0934 0.1738 0.3079 0.2066 0.2253 0.19420.0594 0.1100 0.1958 0.1295 0.1455 0.1350annual mean, 2003-2005 1.00 0.96 0.88 0.97 0.94 0.90annual mean, 2003-2005 1.00 0.95 0.83 0.97 0.94 0.94max Q mean, 2003-20051.00 0.94 0.99 0.98 0.94max Q mean, 2003-20051.00 0.93 0.99 0.99 0.98max M mean, 2003-20051.00 0.92 0.98 0.97max M mean, 2003-20051.00 0.88 0.97 0.962nd max M mean, 2003-20051.00 0.97 0.932nd max M mean, 2003-20051.00 0.97 0.97average of 3 overall highest Mmeans, 2003-20051.00 0.98average of 3 overall highest Mmeans, 2003-20051.00 1.00average of 3 annual max M means,2003-20051.00average of 3 annual max M means,2003-20051.00 1.00annualmean, 2003-2005max Qmean, 2003-2005max Mmean, 2003-20052nd max Mmean, 2003-2005avg. of 3overallhighest Mmeans,2003-2005avg. of 3annual maxM means,2003-2005annualmean, 2003-2005max Qmean, 2003-2005max Mmean, 2003-20052nd max Mmean, 2003-2005avg. of 3overallhighest Mmeans,2003-2005avg. of 3annual maxM means,2003-200560 60 60 60 60 6091 91 91 91 91910.2596 0.4781 0.8572 0.5738 0.6259 0.53330.0711 0.1343 0.2442 0.1530 0.1762 0.1634annual mean, 2003-2005 1.00 0.95 0.85 0.96 0.93 0.88annual mean, 2003-2005 1.00 0.95 0.82 0.97 0.93 0.94max Q mean, 2003-20051.00 0.93 0.99 0.98 0.92max Q mean, 2003-20051.00 0.93 0.99 0.99 0.99max M mean, 2003-20051.00 0.89 0.97 0.96max M mean, 2003-20051.00 0.87 0.96 0.962nd max M mean, 2003-20051.00 0.97 0.902nd max M mean, 2003-20051.00 0.97 0.96average of 3 overall highest Mmeans, 2003-20051.00 0.97average of 3 overall highest Mmeans, 2003-20051.00 1.00average of 3 annual max M means,2003-20051.00average of 3 annual max M means,2003-20051.00

Correlation coeeficientsnumber of sitesmean (μg/m3)

Correlation coeeficientsStatistic (Q = quarterly, M = monthly)Source-oriented sitesnumber of sitesmean (μg/m3)Statistic (Q = quarterly, M = monthly)mean (μg/m3)

Correlation coeeficientsStatistic (Q = quarterly, M = monthly)Urban sitesAll sitesStatistic (Q = quarterly, M = monthly)number of sitesUrban sites in CBSA's≥1M populationnumber of sitesmean (μg/m3)

Correlation coeeficients2B-10Table 2B-5. Pb-TSP metric ratio distribution statisticsTSP CategoryRatioSites min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxratio of max quarterly mean to annualmean1.0000 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 2.5474 2.7853 3.2023 3.9233 5.9868 12.0000ratio of max monthly mean to annualmean1.0000 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 4.9871 5.7675 6.5038 8.5462 11.8424 39.0000ratio of 2nd max monthly mean toannual mean0.0000 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 2.7650 3.0986 3.5200 3.9800 6.6439 12.1935ratio of max quarterly mean to annualmean1.0000 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 2.3486 2.4706 2.7642 3.2865 3.8592 7.5516ratio of max monthly mean to annualmean1.0000 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 4.5092 4.6536 5.3012 6.2826 10.2029 13.5518ratio of 2nd max monthly mean toannual mean1.0000 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 2.6395 2.7960 3.3263 3.5193 3.8639 9.8590ratio of max quarterly mean to annualmean1.0000 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 2.6899 2.9390 3.4555 4.1647 7.3577 12.0000ratio of max monthly mean to annualmean1.0000 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 5.5927 6.3223 7.6473 9.1396 12.1935 39.0000ratio of 2nd max monthly mean toannual mean0.0000 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 2.8245 3.2174 3.8274 4.5000 7.5267 12.1935ratio of max quarterly mean to annualmean1.0000 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 1.9492 2.3170 2.3170 7.5516 7.5516 7.5516ratio of max monthly mean to annualmean1.0000 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 3.7062 4.6478 4.6478 11.7469 11.7469 11.7469ratio of 2nd max monthly mean toannual mean1.0000 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 8.4131 9.8590 9.8590 14.2747 14.2747 14.2747ratio of max quarterly mean to annualmean1.0000 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 2.5890 2.7581 3.1770 3.9279 5.6878 12.0000ratio of max monthly mean to annualmean1.0000 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 5.1122 5.8364 6.6889 8.4122 11.7723 36.0000ratio of 2nd max monthly mean toannual mean0.0000 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 2.7859 3.0213 3.7220 4.1463 7.4078 12.1935ratio of max quarterly mean to annualmean1.0000 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 2.4317 2.7033 3.0063 3.8141 7.3577 12.0000ratio of max monthly mean to annualmean1.0000 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 4.6478 5.6357 6.4892 8.0000 9.3770 36.0000ratio of 2nd max monthly mean toannual mean0.0000 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 2.5969 2.7659 3.0986 3.6621 7.2888 9.8590ratio of max quarterly mean to annualmean1.0000 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 2.7241 3.1395 3.4555 3.9762 4.8718 7.6772ratio of max monthly mean to annualmean1.0000 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 5.5497 5.8680 7.3340 9.3326 12.1935 19.1113ratio of 2nd max monthly mean toannual mean1.0000 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 3.8199 3.9800 4.4010 5.5005 8.4269 12.193514091491896012913Urban sitesUrban sites in CBSAs >1M populationUrban sites in CBSAs <1M populationAll sitesSource-oriented sitesNon-source-oriented sitesPrevious source-orientedsites2B-11Table 2B-6. Pb-PM10 monitoring site informationqtrs years monthsannualmeanmaxquarterlymeanmaxmonthlymean080770017 1 39.06363 -108.56102 CO MesaGrand Junction, CO116,255 1 1 4 13 0.0049 0.0056 0.0085110010043 1 38.91889 -77.01250 DC District of Columbia Washington-Arlington-Alexandria, 4,796,183 1 2 7 20 0.0048 0.0085 0.0097120571065 5 27.89222 -82.53861 FL Hillsborough Tampa-St. Petersburg-Clearwater,F2,395,997 1 2 8 23 0.0062 0.0207 0.0469120573002 5 27.96565 -82.23040 FL Hillsborough Tampa-St. Petersburg-Clearwater,F2,395,9972 8 24 0.0035 0.0048 0.0075121030018 5 27.78556 -82.74000 FL PinellasTampa-St. Petersburg-Clearwater,F2,395,997 1 1 4 12 0.0022 0.0030 0.0047121030026 5 27.85004 -82.71459 FL PinellasTampa-St. Petersburg-Clearwater,F2,395,997 1 1 6 17 0.0023 0.0034 0.0045130890002 1 33.68801 -84.29033 GA DeKalbAtlanta-Sandy Springs-Marietta, GA4,247,981 1 3 12 34 0.0026 0.0046 0.0106170314201 6 42.14000 -87.79917 IL CookChicago-Naperville-Joliet, IL-IN-W9,098,316 1 1 4 12 0.0060 0.0076 0.0094211930003 1 37.28306 -83.22028 KY Perry3 12 34 0.0040 0.0066 0.0078250250042 6 42.32944 -71.08278 MA SuffolkBoston-Cambridge-Quincy, MA-NH4,391,344 1 1 6 22 0.0049 0.0085 0.0151261630033 1 42.30667 -83.14889 MI WayneDetroit-Warren-Livonia, MI 4,452,557 1 3 12 35 0.0212 0.0390 0.0667295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL2,721,491 1 2 10 30 0.0127 0.0170 0.0256360850106 1 40.57811 -74.18430 NY Richmond New York-Northern New Jersey-Lo18,323,0021 4 11 0.0071 0.0117 0.0150360850111 1 40.57997 -74.19872 NY Richmond New York-Northern New Jersey-Lo18,323,0021 4 11 0.0074 0.0123 0.0160360850131 1 40.58806 -74.16882 NY Richmond New York-Northern New Jersey-Lo18,323,0021 4 10 0.0069 0.0115 0.0120360850132 1 40.58061 -74.15158 NY Richmond New York-Northern New Jersey-Lo18,323,0021 4 11 0.0095 0.0223 0.0300410390060 7 44.02631 -123.08374 OR LaneEugene-Springfield, OR322,959 1 1 3 9 0.0023 0.0032 0.0040410510030 7 45.49742 -122.67467 OR Multnomah Portland-Vancouver-Beaverton, OR1,927,881 1 1 4 11 0.0056 0.0104 0.0123410510080 7 45.49667 -122.60222 OR Multnomah Portland-Vancouver-Beaverton, OR1,927,881 1 2 7 22 0.0055 0.0088 0.0144410510244 8 45.53500 -122.69889 OR Multnomah Portland-Vancouver-Beaverton, OR1,927,881 1 2 7 21 0.0065 0.0098 0.0190410510246 7 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR1,927,881 1 2 8 23 0.0097 0.0273 0.0608410610119 7 45.33897 -117.90480 OR UnionLa Grande, OR24,5302 7 20 0.0016 0.0027 0.0030410670111 7 45.47020 -122.81585 OR Washington Portland-Vancouver-Beaverton, OR1,927,881 1 1 3 11 0.0025 0.0032 0.0051440070022 1 41.80795 -71.41500 RI Providence Providence-New Bedford-Fall Rive1,582,997 1 3 12 36 0.0098 0.0547 0.1529440070029 1 41.81644 -71.43790 RI Providence Providence-New Bedford-Fall Rive1,582,997 1 1 4 13 0.0061 0.0092 0.0142450250001 2 34.61537 -80.19879 SC Chesterfield2 8 20 0.0029 0.0049 0.0071481390017 1 32.47361 -97.04250 TX EllisDallas-Fort Worth-Arlington, TX 5,161,5441 6 17 0.0151 0.0211 0.0370481410041 1 31.76054 -106.50045 TX El PasoEl Paso, TX679,622 1 1 4 12 0.0118 0.0167 0.0253482011035 1 29.73371 -95.25759 TX HarrisHouston-Sugar Land-Baytown, TX4,715,407 1 3 12 36 0.0077 0.0106 0.0116482011039 1 29.67005 -95.12849 TX HarrisHouston-Sugar Land-Baytown, TX4,715,407 1 3 12 31 0.0056 0.0113 0.0136490110004 1 40.90297 -111.88447 UT DavisOgden-Clearfield, UT442,656 1 2 10 29 0.0059 0.0081 0.0111530110030 7 45.64168 -122.68123 WA ClarkPortland-Vancouver-Beaverton, OR1,927,881 1 1 3 11 0.0032 0.0051 0.0061530330080 1 47.56833 -122.30806 WA KingSeattle-Tacoma-Bellevue, WA 3,043,878 1 3 11 32 0.0046 0.0085 0.0146530630016 1 47.66083 -117.35722 WA SpokaneSpokane, WA417,939 1 1 4 12 0.0059 0.0108 0.0211530630050 1 47.69545 -117.37030 WA SpokaneSpokane, WA417,9391 4 12 0.0049 0.0090 0.0168530630052 1 47.66512 -117.42909 WA SpokaneSpokane, WA417,9391 4 12 0.0037 0.0055 0.0088530630053 1 47.68220 -117.30480 WA SpokaneSpokane, WA417,9391 4 12 0.0051 0.0078 0.0134550270007 1 43.43500 -88.52778 WI DodgeBeaver Dam, WI85,8971 4 10 0.0054 0.0082 0.0153site poc lat longurbandata completeness(complete periods)3-year metricsstate county_namecbsa_namecbsa_pop002B-12Table 2B-7. Pb-PM10 monitoring site distribution statisticsAll sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean38 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 0.0077 0.0097 0.0118 0.0151 0.0212max quarter mean38 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 0.0167 0.0207 0.0223 0.0390 0.0547max monthly mean38 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 0.0253 0.0300 0.0469 0.0667 0.1529Urban sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean25 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 0.0087 0.0098 0.0118 0.0127 0.0212max quarter mean25 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 0.0169 0.0207 0.0273 0.0390 0.0547max monthly mean25 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 0.0254 0.0469 0.0608 0.0667 0.1529Urban sites, located in MSA's≥1 million populationn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean20 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 0.0087 0.0097 0.0113 0.0170 0.0212max quarter mean20 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 0.0189 0.0240 0.0332 0.0469 0.0547max monthly mean20 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 0.0363 0.0539 0.0637 0.1098 0.1529Urban sites, located in MSA's < 1 million populationn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean5 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 0.0089 0.0118 0.0118 0.0118 0.0118max quarter mean5 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 0.0137 0.0167 0.0167 0.0167 0.0167max monthly mean5 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 0.0232 0.0253 0.0253 0.0253 0.02532B-13Table 2B-8. Pb-PM2.5 monitoring site informationqtrs years monthsannualmeanmaxquarterlymeanmaxmonthlymean010050002 5 31.66414 -85.60623 AL BarbourEufaula, AL-GA31,6362 8 25 0.0026 0.0033 0.0053010730023 5 33.55306 -86.81500 AL JeffersonBirmingham-Hoover, AL1,052,238 1 3 12 36 0.0180 0.0296 0.0475010731009 5 33.45972 -87.30556 AL JeffersonBirmingham-Hoover, AL1,052,2383 12 34 0.0021 0.0032 0.0044010732003 5 33.49972 -86.92417 AL JeffersonBirmingham-Hoover, AL1,052,238 1 3 12 36 0.0450 0.0967 0.2091010890014 5 34.69083 -86.58306 AL MadisonHuntsville, AL342,376 1 3 12 34 0.0024 0.0040 0.0057010970003 5 30.76972 -88.08750 AL MobileMobile, AL399,843 1 3 12 36 0.0038 0.0060 0.0096011011002 5 32.40694 -86.25639 AL Montgomery Montgomery, AL346,528 1 3 12 34 0.0045 0.0083 0.0115011030011 5 34.51861 -86.97694 AL MorganDecatur, AL145,867 1 3 11 30 0.0029 0.0042 0.0060011130001 5 32.47639 -84.99917 AL RussellColumbus, GA-AL281,768 1 1 3 9 0.0030 0.0037 0.0063020200018 5 61.20667 -149.82083 AK Anchorage Municipa Anchorage, AK319,605 1 1 6 17 0.0043 0.0067 0.0101020900010 6 64.84111 -147.72000 AK Fairbanks North Star Fairbanks, AK82,840 1 1 3 8 0.0034 0.0053 0.0070040130019 5 33.48385 -112.14257 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 2 7 20 0.0030 0.0057 0.0100040134009 5 33.40642 -112.14434 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,8761 4 9 0.0062 0.0123 0.0228040137003 5 33.28936 -112.15732 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,8761 4 10 0.0027 0.0049 0.0067040137020 5 33.47333 -111.85418 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,8761 4 11 0.0026 0.0038 0.0058040138006 5 33.43671 -112.09141 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 1 5 13 0.0042 0.0067 0.0084040139997 7 33.50364 -112.09500 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 3 12 36 0.0027 0.0047 0.0069040139998 5 33.45513 -111.99610 AZ Maricopa Phoenix-Mesa-Scottsdale, AZ 3,251,876 1 1 6 18 0.0033 0.0047 0.0075040191028 5 32.29515 -110.98230 AZ PimaTucson, AZ843,746 1 3 12 35 0.0017 0.0022 0.0035050030005 5 33.13944 -91.95000 AR Ashley1 2 9 21 0.0027 0.0055 0.0082051190007 5 34.75611 -92.27583 AR PulaskiLittle Rock-North Little Rock, AR 610,518 1 3 12 33 0.0029 0.0042 0.0061051450001 5 35.24861 -91.71528 AR WhiteSearcy, AR67,165 1 2 9 22 0.0026 0.0046 0.0063060070002 5 39.75750 -121.84222 CA ButteChico, CA203,171 1 3 12 36 0.0026 0.0039 0.0054060190008 5 36.78139 -119.77222 CA FresnoFresno, CA799,407 1 3 12 36 0.0030 0.0050 0.0066060250005 5 32.67611 -115.48333 CA ImperialEl Centro, CA142,361 1 3 12 36 0.0119 0.0172 0.0342060290014 5 35.35611 -119.04028 CA Kern Bakersfield, CA 661,645 1 3 11 32 0.0026 0.0046 0.0061060371103 5 34.06659 -118.22688 CA Los Angeles Los Angeles-Long Beach-Santa An12,365,627 1 3 12 36 0.0053 0.0098 0.0228060631009 5 39.80833 -120.47167 CA Plumas 1 3 12 36 0.0025 0.0041 0.0054060658001 5 33.99958 -117.41601 CA Riverside Riverside-San Bernardino-Ontario, 3,254,821 1 3 12 36 0.0058 0.0088 0.0151060670006 5 38.61417 -121.36694 CA Sacramento Sacramento--Arden-Arcade--Rosev1,796,857 1 3 12 36 0.0022 0.0031 0.0047060670010 5 38.55833 -121.49194 CA Sacramento Sacramento--Arden-Arcade--Rosev1,796,857 1 3 12 36 0.0029 0.0037 0.0052060730003 5 32.79139 -116.94167 CA San Diego San Diego-Carlsbad-San Marcos,C2,813,833 1 3 12 36 0.0039 0.0059 0.0078060731002 5 33.12778 -117.07417 CA San Diego San Diego-Carlsbad-San Marcos,C2,813,833 1 3 12 36 0.0035 0.0050 0.0064060850005 5 37.34850 -121.89500 CA Santa Clara San Jose-Sunnyvale-Santa Clara,C1,735,819 1 3 12 36 0.0026 0.0063 0.0138060990005 5 37.64167 -120.99361 CA Stanislaus Modesto, CA 446,997 1 3 12 36 0.0033 0.0065 0.0090061072002 5 36.33222 -119.29028 CA Tulare Visalia-Porterville, CA 368,021 1 3 12 36 0.0034 0.0046 0.0060061112002 5 34.27750 -118.68472 CA Ventura Oxnard-Thousand Oaks-Ventura,C753,197 1 3 12 33 0.0020 0.0032 0.0042080010006 5 39.82574 -104.93699 CO Adams Denver-Aurora, CO 2,157,756 1 3 12 36 0.0077 0.0163 0.0185080410011 5 38.83139 -104.82778 CO El Paso Colorado Springs, CO 537,484 1 3 12 34 0.0019 0.0028 0.0048080670008 5 37.26861 -107.87500 CO La Plata Durango, CO 43,941 1 1 3 8 0.0014 0.0016 0.0024080770003 5 39.09083 -108.56389 CO Mesa Grand Junction, CO 116,255 1 1 3 8 0.0015 0.0021 0.0031080770017 5 39.06363 -108.56102 CO Mesa Grand Junction, CO 116,255 1 2 9 25 0.0023 0.0035 0.0056081230008 5 40.20917 -104.82306 CO Weld Greeley, CO 180,936 3 12 36 0.0020 0.0034 0.0054090090027 5 41.30111 -72.90278 CT New Haven New Haven-Milford, CT 824,008 1 2 8 20 0.0029 0.0043 0.0066100010003 5 39.15500 -75.51806 DE Kent Dover, DE 126,697 1 3 12 34 0.0024 0.0038 0.0051100032004 5 39.73944 -75.55806 DE New Castle Philadelphia-Camden-Wilmington,5,687,147 1 3 12 32 0.0042 0.0084 0.0114110010042 6 38.88083 -77.03250 DC District of Columbia Washington-Arlington-Alexandria,4,796,183 1 1 6 18 0.0037 0.0058 0.0075110010043 5 38.91889 -77.01250 DC District of Columbia Washington-Arlington-Alexandria,4,796,183 1 3 12 36 0.0035 0.0063 0.0093120330004 6 30.52500 -87.20417 FL Escambia Pensacola-Ferry Pass-Brent, FL 412,153 1 3 12 36 0.0019 0.0026 0.0042120571075 5 28.05000 -82.37806 FL Hillsborough Tampa-St. Petersburg-Clearwater,F2,395,997 1 1 4 12 0.0023 0.0034 0.0052120573002 5 27.96565 -82.23040 FL Hillsborough Tampa-St. Petersburg-Clearwater,F2,395,9972 8 24 0.0027 0.0042 0.0069120730012 5 30.43972 -84.34833 FL Leon Tallahassee, FL 320,304 1 3 12 36 0.0020 0.0034 0.0049120861016 5 25.79417 -80.20611 FL Miami-Dade Miami-Fort Lauderdale-Miami Bea5,007,564 1 3 12 36 0.0020 0.0068 0.0163121030026 5 27.85004 -82.71459 FL Pinellas Tampa-St. Petersburg-Clearwater,F2,395,997 1 1 5 16 0.0025 0.0039 0.0088130210007 5 32.77944 -83.64694 GA Bibb Macon, GA 222,368 3 12 34 0.0029 0.0069 0.0147site poc lat longurbandata completeness(complete periods)3-year metricsstate county_namecbsa_namecbsa_pop002B-14Table 2B-8. Pb-PM2.5 monitoring site informationqtrs years monthsannualmeanmaxquarterlymeanmaxmonthlymeansite poc lat longurbandata completeness(complete periods)3-year metricsstate county_name cbsa_name cbsa_pop00130510017 5 32.09278 -81.14417 GA ChathamSavannah, GA293,000 1 2 8 22 0.0017 0.0029 0.0041130590001 5 33.94583 -83.37222 GA Clarke Athens-Clarke County, GA 166,079 1 3 12 29 0.0021 0.0029 0.0041130690002 5 31.52430 -82.76510 GA Coffee Douglas, GA 45,022 3 12 30 0.0013 0.0022 0.0032130890002 5 33.68750 -84.29028 GA DeKalb Atlanta-Sandy Springs-Marietta,G4,247,981 1 3 12 36 0.0027 0.0042 0.0077131150005 5 34.26333 -85.27250 GA Floyd Rome, GA 90,565 3 12 33 0.0023 0.0030 0.0040132150011 5 32.43083 -84.93167 GA Muscogee Columbus, GA-AL 281,768 1 3 12 32 0.0036 0.0101 0.0086132450091 5 33.43333 -82.02194 GA Richmond Augusta-Richmond County, GA-SC499,684 1 3 12 32 0.0025 0.0038 0.0067132950002 5 34.96611 -85.29750 GA Walker Chattanooga, TN-GA 476,531 1 1 3 9 0.0033 0.0040 0.0051150032004 5 21.39667 -157.97167 HI Honolulu Honolulu, HI 876,156 1 3 12 34 0.0010 0.0021 0.0031160270004 5 43.56240 -116.56323 ID Canyon Boise City-Nampa, ID 464,840 1 3 12 36 0.0022 0.0046 0.0096170310057 5 41.91473 -87.72273 IL Cook Chicago-Naperville-Joliet, IL-IN-W9,098,316 1 3 12 35 0.0071 0.0115 0.0172170310076 5 41.75137 -87.71375 IL Cook Chicago-Naperville-Joliet, IL-IN-W9,098,316 1 3 12 36 0.0054 0.0063 0.0087170314201 5 42.14000 -87.79917 IL Cook Chicago-Naperville-Joliet, IL-IN-W9,098,316 1 3 12 36 0.0040 0.0054 0.0085170434002 5 41.77120 -88.15250 IL DuPage Chicago-Naperville-Joliet, IL-IN-W9,098,316 1 2 8 23 0.0047 0.0063 0.0072171150013 5 39.86694 -88.92556 IL Macon Decatur, IL 114,706 3 12 35 0.0067 0.0142 0.0228171192009 5 38.90278 -90.14306 IL Madison St. Louis, MO-IL 2,721,491 1 3 12 32 0.0090 0.0208 0.0413180030004 5 41.09472 -85.10194 IN Allen Fort Wayne, IN 390,156 1 2 7 20 0.0257 0.1674 0.3091180372001 5 38.39139 -86.92917 IN Dubois Jasper, IN 52,511 1 1 4 12 0.0042 0.0051 0.0063180390003 5 41.66778 -85.96944 IN Elkhart Elkhart-Goshen, IN 182,791 1 1 4 12 0.0044 0.0048 0.0056180650003 5 40.01167 -85.52361 IN Henry New Castle, IN 48,508 3 12 36 0.0037 0.0055 0.0074180890022 5 41.60667 -87.30472 IN Lake Chicago-Naperville-Joliet, IL-IN-W9,098,316 1 3 11 32 0.0097 0.0128 0.0204180892004 5 41.58528 -87.47444 IN Lake Chicago-Naperville-Joliet, IL-IN-W9,098,316 1 2 8 24 0.0090 0.0120 0.0244180970078 5 39.81110 -86.11447 IN Marion Indianapolis-Carmel, IN 1,525,104 1 3 12 36 0.0048 0.0071 0.0087181411008 5 41.69361 -86.23667 IN St. Joseph South Bend-Mishawaka, IN-MI 316,663 1 1 4 12 0.0042 0.0054 0.0072181630012 5 38.02167 -87.56944 IN Vanderburgh Evansville, IN-KY 342,815 1 3 12 34 0.0031 0.0057 0.0080191130037 5 42.00833 -91.67861 IA Linn Cedar Rapids, IA 237,230 1 3 12 35 0.0033 0.0044 0.0071191530030 5 41.60306 -93.64306 IA Polk Des Moines-West Des Moines, IA 481,394 1 3 12 33 0.0027 0.0037 0.0058191630015 5 41.53000 -90.58750 IA Scott Davenport-Moline-Rock Island, IA376,019 1 3 12 33 0.0065 0.0084 0.0118201730010 5 37.70111 -97.31389 KS Sedgwick Wichita, KS 571,166 1 2 9 18 0.0021 0.0032 0.0053202090021 5 39.11750 -94.63556 KS Wyandotte Kansas City, MO-KS 1,836,038 1 3 12 36 0.0048 0.0066 0.0100210190017 5 38.45917 -82.64056 KY Boyd Huntington-Ashland, WV-KY-OH 288,649 1 3 12 35 0.0043 0.0060 0.0096210590005 5 37.78083 -87.07556 KY Daviess Owensboro, KY 109,875 1 1 4 12 0.0037 0.0044 0.0061210590014 5 37.74111 -87.11806 KY Daviess Owensboro, KY 109,875 1 2 8 19 0.0023 0.0038 0.0036210670012 5 38.06500 -84.50000 KY Fayette Lexington-Fayette, KY 408,326 1 3 12 36 0.0038 0.0066 0.0101211110043 5 38.23222 -85.82528 KY Jefferson Louisville-Jefferson County, KY-IN1,161,975 1 3 12 36 0.0042 0.0070 0.0100211110048 5 38.24056 -85.73167 KY Jefferson Louisville-Jefferson County, KY-IN1,161,975 1 3 12 36 0.0048 0.0071 0.0133211170007 5 39.07250 -84.52500 KY Kenton Cincinnati-Middletown, OH-KY-IN2,009,632 1 3 12 36 0.0048 0.0106 0.0170211250004 5 37.08722 -84.06333 KY Laurel London, KY 52,715 1 3 12 36 0.0037 0.0048 0.0095211451004 5 37.06556 -88.63778 KY McCracken Paducah, KY-IL 98,765 1 3 12 36 0.0029 0.0043 0.0059211930003 5 37.28306 -83.22028 KY Perry 3 12 34 0.0041 0.0059 0.0079212270007 5 36.99333 -86.41833 KY Warren Bowling Green, KY 104,166 1 3 12 35 0.0033 0.0056 0.0098220150008 5 32.53417 -93.74972 LA Bossier Shreveport-Bossier City, LA 375,965 3 12 32 0.0046 0.0089 0.0147220330009 5 30.46111 -91.17694 LA East Baton Rouge Baton Rouge, LA 705,973 1 3 11 31 0.0051 0.0101 0.0198240030019 5 39.10111 -76.72944 MD Anne Arundel Baltimore-Towson, MD 2,552,994 1 2 7 17 0.0033 0.0061 0.0087240053001 5 39.31083 -76.47444 MD Baltimore Baltimore-Towson, MD 2,552,994 1 2 9 26 0.0054 0.0080 0.0101240330030 5 39.05528 -76.87833 MD Prince George's Washington-Arlington-Alexandria,4,796,183 1 1 4 12 0.0039 0.0069 0.0099250130008 5 42.19446 -72.55571 MA Hampden Springfield, MA 680,014 2 10 25 0.0025 0.0035 0.0045250250042 6 42.32944 -71.08278 MA Suffolk Boston-Cambridge-Quincy, MA-N4,391,344 1 3 12 35 0.0027 0.0039 0.0056260050003 5 42.76778 -86.14861 MI Allegan Allegan, MI 105,665 1 3 11 35 0.0035 0.0055 0.0079260330901 5 46.49361 -84.36417 MI Chippewa Sault Ste. Marie, MI 38,543 1 3 12 36 0.0023 0.0038 0.0046260770008 5 42.27806 -85.54194 MI Kalamazoo Kalamazoo-Portage, MI 314,866 1 3 11 33 0.0050 0.0068 0.0097260810020 5 42.98417 -85.67139 MI Kent Grand Rapids-Wyoming, MI 740,482 1 3 12 35 0.0048 0.0083 0.0104261130001 5 44.31056 -84.89194 MI Missaukee Cadillac, MI 44,962 3 12 33 0.0022 0.0057 0.0102261150005 5 41.76389 -83.47194 MI Monroe Monroe, MI 145,945 3 12 32 0.0042 0.0050 0.0074261610008 5 42.24056 -83.59972 MI Washtenaw Ann Arbor, MI 322,895 1 2 10 30 0.0038 0.0060 0.00872B-15Table 2B-8. Pb-PM2.5 monitoring site informationqtrs years monthsannualmeanmaxquarterlymeanmaxmonthlymeansite poc lat longurbandata completeness(complete periods)3-year metricsstate county_name cbsa_name cbsa_pop00261630001 5 42.22861 -83.20833 MI WayneDetroit-Warren-Livonia, MI 4,452,557 1 3 12 36 0.0042 0.0051 0.0063261630033 5 42.30667 -83.14889 MI Wayne Detroit-Warren-Livonia, MI 4,452,557 1 3 12 33 0.0118 0.0182 0.0329270530963 5 44.95540 -93.25827 MN Hennepin Minneapolis-St. Paul-Bloomington2,968,806 1 3 12 36 0.0031 0.0041 0.0072270953051 5 46.20703 -93.75941 MN Mille Lacs 3 11 31 0.0017 0.0023 0.0036271095008 5 43.99691 -92.45037 MN Olmsted Rochester, MN 163,618 1 3 12 35 0.0027 0.0043 0.0067271230871 5 44.96145 -93.03589 MN Ramsey Minneapolis-St. Paul-Bloomington2,968,806 1 2 9 27 0.0042 0.0073 0.0084280350004 5 31.32364 -89.28717 MS Forrest Hattiesburg, MS 123,812 1 3 12 35 0.0048 0.0128 0.0302280430001 5 33.83611 -89.79722 MS Grenada Grenada, MS 23,263 3 11 31 0.0017 0.0032 0.0056280470008 5 30.39014 -89.04972 MS Harrison Gulfport-Biloxi, MS 246,190 1 3 11 32 0.0023 0.0034 0.0062280490018 5 32.29681 -90.18831 MS Hinds Jackson, MS 497,197 1 3 12 31 0.0046 0.0071 0.0112280670002 5 31.68844 -89.13506 MS Jones Laurel, MS 83,107 1 3 12 35 0.0030 0.0073 0.0180290470005 5 39.30306 -94.37639 MO Clay Kansas City, MO-KS 1,836,038 3 12 36 0.0026 0.0040 0.0050290530001 5 38.79500 -92.91806 MO Cooper 3 12 33 0.0020 0.0028 0.0041290990012 5 38.43778 -90.36139 MO Jefferson St. Louis, MO-IL 2,721,491 1 3 12 36 0.0089 0.0126 0.0191291860005 5 37.89694 -90.42222 MO Ste Genevieve 3 12 34 0.0045 0.0084 0.0095292070001 5 36.97000 -90.14000 MO Stoddard 1 4 11 0.0034 0.0044 0.0068295100085 6 38.65630 -90.19810 MO St. Louis (City) St. Louis, MO-IL 2,721,491 1 3 12 36 0.0095 0.0140 0.0192300530018 5 48.38417 -115.54806 MT Lincoln 1 3 12 35 0.0017 0.0029 0.0039300630031 5 46.87491 -113.99525 MT Missoula Missoula, MT 95,802 1 3 12 36 0.0020 0.0035 0.0051310550019 5 41.24722 -95.97556 NE Douglas Omaha-Council Bluffs, NE-IA 767,041 1 3 12 35 0.0030 0.0042 0.0055320030560 5 36.15861 -115.11083 NV Clark Las Vegas-Paradise, NV 1,375,765 1 1 5 15 0.0025 0.0039 0.0061320030561 5 36.16399 -115.11393 NV Clark Las Vegas-Paradise, NV 1,375,765 1 2 7 20 0.0025 0.0044 0.0086320310016 5 39.52508 -119.80772 NV Washoe Reno-Sparks, NV 342,885 1 3 12 36 0.0024 0.0040 0.0060330110020 5 43.00056 -71.46806 NH Hillsborough Manchester-Nashua, NH 380,841 1 3 12 34 0.0034 0.0053 0.0062330150014 5 43.07528 -70.74806 NH Rockingham Boston-Cambridge-Quincy, MA-N4,391,344 1 3 12 36 0.0024 0.0028 0.0036340070003 5 39.92304 -75.09762 NJ Camden Philadelphia-Camden-Wilmington,5,687,147 1 3 11 33 0.0042 0.0052 0.0069340230006 6 40.47279 -74.42251 NJ MiddlesexNew York-Northern New Jersey-Lo18,323,002 1 3 12 24 0.0045 0.0063 0.0114340273001 5 40.78763 -74.67630 NJ MorrisNew York-Northern New Jersey-Lo18,323,002 1 3 12 35 0.0027 0.0038 0.0059340390004 5 40.64144 -74.20836 NJ UnionNew York-Northern New Jersey-Lo18,323,002 1 3 12 36 0.0044 0.0059 0.0067350010023 5 35.13426 -106.58551 NM Bernalillo Albuquerque, NM 729,649 1 2 8 22 0.0013 0.0020 0.0027360050083 6 40.86586 -73.88075 NY BronxNew York-Northern New Jersey-Lo18,323,002 1 3 12 36 0.0040 0.0059 0.0067360050110 5 40.81616 -73.90207 NY BronxNew York-Northern New Jersey-Lo18,323,002 1 3 12 36 0.0047 0.0064 0.0079360290005 6 42.87684 -78.80988 NY Erie Buffalo-Niagra Falls, NY Metropol1,170,111 1 3 12 36 0.0106 0.0157 0.0192360310003 5 44.39309 -73.85892 NY Essex 3 12 34 0.0015 0.0021 0.0028360551007 5 43.14620 -77.54813 NY Monroe Rochester, NY 1,037,831 1 2 7 20 0.0031 0.0040 0.0048360556001 5 43.16100 -77.60357 NY Monroe Rochester, NY 1,037,831 1 1 5 15 0.0031 0.0037 0.0045360610062 1 40.72052 -74.00409 NY New YorkNew York-Northern New Jersey-Lo18,323,002 1 1 4 12 0.0070 0.0092 0.0190360632008 1 43.08216 -79.00099 NY Niagara Buffalo-Niagra Falls, NY Metropol1,170,111 1 1 4 11 0.0052 0.0063 0.0065360710002 1 41.49947 -74.00973 NY Orange Poughkeepsie-Newburgh-Middleto621,517 1 1 4 11 0.0034 0.0040 0.0053360810124 6 40.73620 -73.82317 NY QueensNew York-Northern New Jersey-Lo18,323,002 1 3 12 36 0.0038 0.0055 0.0068361010003 5 42.09071 -77.21025 NY Steuben Corning, NY 98,726 3 12 36 0.0028 0.0034 0.0042361030001 1 40.74583 -73.42028 NY SuffolkNew York-Northern New Jersey-Lo18,323,002 1 1 4 11 0.0032 0.0039 0.0051370210034 5 35.60972 -82.35083 NC Buncombe Asheville, NC 369,171 1 3 12 36 0.0019 0.0031 0.0052370350004 5 35.72889 -81.36556 NC Catawba Hickory-Lenoir-Morganton, NC 341,851 1 3 12 35 0.0025 0.0036 0.0060370510009 5 35.04142 -78.95311 NC Cumberland Fayetteville, NC 336,609 1 2 8 22 0.0021 0.0037 0.0057370570002 5 35.81444 -80.26250 NC Davidson Thomasville-Lexington, NC 147,246 1 2 8 23 0.0032 0.0047 0.0087370670022 5 36.11056 -80.22667 NC Forsyth Winston-Salem, NC 421,961 1 3 12 36 0.0026 0.0036 0.0063370810013 5 36.10917 -79.80111 NC Guilford Greensboro-High Point, NC 643,430 1 2 9 23 0.0028 0.0043 0.0064371070004 5 35.23146 -77.56879 NC Lenoir Kinston, NC 59,648 3 12 34 0.0026 0.0046 0.0062371190041 5 35.24028 -80.78556 NC Mecklenburg Charlotte-Gastonia-Concord, NC-S1,330,448 1 3 12 36 0.0029 0.0042 0.0052371590021 5 35.55187 -80.39504 NC Rowan Salisbury, NC 130,340 1 1 4 11 0.0032 0.0040 0.0057371830014 5 35.85611 -78.57417 NC Wake Raleigh-Cary, NC 797,071 1 3 12 34 0.0021 0.0038 0.0041380150003 5 46.82543 -100.76821 ND Burleigh Bismarck, ND 94,719 1 3 12 36 0.0012 0.0023 0.0036380171004 5 46.93375 -96.85535 ND Cass Fargo, ND-MN 174,367 3 12 36 0.0019 0.0027 0.0038380530002 5 47.58120 -103.29950 ND Mc Kenzie 3 12 35 0.0012 0.0026 0.00402B-16Table 2B-8. Pb-PM2.5 monitoring site informationqtrs years monthsannualmeanmaxquarterlymeanmaxmonthlymeansite poc lat longurbandata completeness(complete periods)3-year metricsstate county_name cbsa_name cbsa_pop00390171004 5 39.53000 -84.39250 OH ButlerCincinnati-Middletown, OH-KY-IN2,009,632 1 3 12 36 0.0092 0.0147 0.0273390350038 6 41.47694 -81.68194 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 1 3 12 35 0.0120 0.0163 0.0282390350060 5 41.49396 -81.67854 OH Cuyahoga Cleveland-Elyria-Mentor, OH 2,148,143 1 3 12 36 0.0123 0.0207 0.0270390490081 6 40.08778 -82.95972 OH Franklin Columbus, OH 1,612,694 1 3 12 34 0.0038 0.0052 0.0073390530003 5 38.94996 -82.10910 OH Gallia Point Pleasant, WV-OH 57,026 1 6 16 0.0043 0.0072 0.0085390610040 5 39.12861 -84.50417 OH Hamilton Cincinnati-Middletown, OH-KY-IN2,009,632 1 2 8 25 0.0056 0.0069 0.0113390610042 5 39.10500 -84.55111 OH Hamilton Cincinnati-Middletown, OH-KY-IN2,009,632 1 1 4 11 0.0079 0.0114 0.0286390810017 5 40.36610 -80.61500 OH Jefferson Weirton-Steubenville, WV-OH 132,008 1 1 5 11 0.0127 0.0150 0.0193390870010 5 38.51972 -82.66556 OH Lawrence Huntington-Ashland, WV-KY-OH 288,649 1 3 12 33 0.0059 0.0095 0.0137390930016 5 41.43944 -82.16167 OH Lorain Cleveland-Elyria-Mentor, OH 2,148,143 1 1 4 10 0.0157 0.0244 0.0450390933002 5 41.46306 -82.11444 OH Lorain Cleveland-Elyria-Mentor, OH 2,148,143 2 8 20 0.0238 0.0337 0.0465390950026 5 41.62056 -83.64139 OH Lucas Toledo, OH 659,188 1 3 12 36 0.0035 0.0053 0.0069390990014 5 41.09587 -80.65843 OH Mahoning Youngstown-Warren-Boardman,O602,964 1 3 12 35 0.0131 0.0253 0.0382391130031 5 39.75944 -84.14444 OH Montgomery Dayton, OH 848,153 1 3 11 25 0.0042 0.0079 0.0085391510017 5 40.78667 -81.39444 OH Stark Canton-Massillon, OH 406,934 1 1 4 10 0.0114 0.0148 0.0186391510020 5 40.80056 -81.37333 OH Stark Canton-Massillon, OH 406,934 1 2 8 24 0.0060 0.0082 0.0157391530023 5 41.08806 -81.54167 OH Summit Akron, OH 694,960 1 3 11 29 0.0050 0.0069 0.0098400450890 5 36.08518 -99.93494 OK Ellis 3 12 34 0.0012 0.0019 0.0027401091037 5 35.61278 -97.47222 OK Oklahoma Oklahoma City, OK 1,095,421 1 3 12 36 0.0022 0.0033 0.0046401431127 5 36.20490 -95.97654 OK Tulsa Tulsa, OK 859,532 1 3 12 36 0.0031 0.0045 0.0056410170120 5 44.06390 -121.31258 OR Deschutes Bend, OR 115,367 1 1 4 11 0.0014 0.0018 0.0021410290133 5 42.31408 -122.87924 OR Jackson Medford, OR 181,269 1 3 12 35 0.0019 0.0029 0.0035410390060 5 44.02631 -123.08374 OR Lane Eugene-Springfield, OR 322,959 1 3 12 35 0.0015 0.0025 0.0041410510246 6 45.56130 -122.67878 OR Multnomah Portland-Vancouver-Beaverton, OR1,927,881 1 3 12 36 0.0075 0.0182 0.0398410610119 5 45.33897 -117.90480 OR Union La Grande, OR 24,530 2 8 20 0.0012 0.0020 0.0026420010001 5 39.92000 -77.31000 PA Adams Gettysburg, PA 91,292 3 12 35 0.0037 0.0070 0.0082420030008 6 40.46556 -79.96111 PA Allegheny Pittsburgh, PA 2,431,087 1 3 12 36 0.0112 0.0141 0.0252420030021 5 40.41361 -79.94139 PA Allegheny Pittsburgh, PA 2,431,087 1 1 3 9 0.0073 0.0083 0.0129420030064 6 40.32361 -79.86833 PA Allegheny Pittsburgh, PA 2,431,087 1 2 9 23 0.0143 0.0239 0.0356420270100 5 40.81139 -77.87703 PA Centre State College, PA 135,758 1 3 12 35 0.0032 0.0043 0.0061420290100 5 39.83444 -75.76861 PA Chester Philadelphia-Camden-Wilmington,5,687,1473 12 32 0.0046 0.0086 0.0105420430401 5 40.24500 -76.84472 PA Dauphin Harrisburg-Carlisle, PA 509,074 1 3 12 34 0.0063 0.0122 0.0190420450002 5 39.83556 -75.37250 PA Delaware Philadelphia-Camden-Wilmington,5,687,147 1 3 12 34 0.0042 0.0057 0.0073420490003 5 42.14175 -80.03861 PA Erie Erie, PA 280,843 1 3 12 34 0.0057 0.0153 0.0323420692006 5 41.44278 -75.62306 PA Lackawanna Scranton--Wilkes-Barre, PA 560,625 1 3 12 33 0.0054 0.0087 0.0115420710007 5 40.04667 -76.28333 PA Lancaster Lancaster, PA 470,658 1 3 12 35 0.0073 0.0175 0.0231420950025 5 40.62806 -75.34111 PA Northampton Allentown-Bethlehem-Easton, PA-740,395 1 3 12 36 0.0065 0.0095 0.0152420990301 5 40.45694 -77.16556 PA Perry Harrisburg-Carlisle, PA 509,074 3 12 36 0.0035 0.0056 0.0084421010004 7 40.00889 -75.09778 PA Philadelphia Philadelphia-Camden-Wilmington,5,687,147 1 3 12 36 0.0052 0.0071 0.0090421010136 5 39.92750 -75.22278 PA Philadelphia Philadelphia-Camden-Wilmington,5,687,147 1 3 12 34 0.0038 0.0061 0.0104421255001 5 40.44528 -80.42083 PA Washington Pittsburgh, PA 2,431,087 3 12 36 0.0050 0.0067 0.0084421290008 5 40.30469 -79.50567 PA Westmoreland Pittsburgh, PA 2,431,087 1 3 12 35 0.0051 0.0070 0.0097421330008 5 39.96528 -76.69944 PA York York-Hanover, PA 381,751 1 3 12 34 0.0058 0.0112 0.0169440070022 5 41.80795 -71.41500 RI Providence Providence-New Bedford-Fall Rive1,582,997 1 3 12 36 0.0065 0.0432 0.1103440071010 5 41.84092 -71.36094 RI Providence Providence-New Bedford-Fall Rive1,582,997 1 1 5 14 0.0030 0.0037 0.0051450190046 5 32.94275 -79.65718 SC Charleston Charleston-North Charleston, SC 549,033 2 6 17 0.0019 0.0026 0.0039450190049 5 32.79098 -79.95869 SC Charleston Charleston-North Charleston, SC 549,033 1 3 12 36 0.0022 0.0035 0.0048450250001 5 34.61712 -80.19879 SC Chesterfield 3 12 36 0.0021 0.0035 0.0044450450009 5 34.90105 -82.31307 SC Greenville Greenville, SC 559,940 1 3 12 36 0.0026 0.0050 0.0060450790019 5 33.99330 -81.02414 SC Richland Columbia, SC 647,158 1 3 12 34 0.0048 0.0092 0.0122460990006 5 43.54429 -96.72644 SD Minnehaha Sioux Falls, SD 187,093 1 3 12 36 0.0022 0.0031 0.0052470370023 5 36.17633 -86.73890 TN DavidsonNashville-Davidson--Murfreesboro1,311,789 1 3 12 34 0.0038 0.0065 0.0107470654002 5 35.05093 -85.12631 TN Hamilton Chattanooga, TN-GA 476,531 1 3 12 34 0.0038 0.0050 0.0071470931020 5 36.01944 -83.87361 TN Knox Knoxville, TN 616,079 1 3 11 29 0.0040 0.0052 0.0059470990002 5 35.11611 -87.47000 TN Lawrence Lawrenceburg, TN 39,926 3 12 35 0.0021 0.0030 0.00402B-17Table 2B-8. Pb-PM2.5 monitoring site informationqtrs years monthsannualmeanmaxquarterlymeanmaxmonthlymeansite poc lat longurbandata completeness(complete periods)3-year metricsstate county_name cbsa_name cbsa_pop00471570047 5 35.16895 -90.02157 TN ShelbyMemphis, TN-MS-AR1,205,204 1 3 12 36 0.0033 0.0045 0.0076471631007 5 36.54065 -82.52167 TN Sullivan Kingsport-Bristol-Bristol, TN-VA 298,484 1 3 12 33 0.0031 0.0049 0.0086471650007 5 36.29778 -86.65278 TN SumnerNashville-Davidson--Murfreesboro1,311,789 1 3 12 32 0.0027 0.0051 0.0068480430002 5 30.36580 -103.64910 TX Brewster 3 11 20 0.0014 0.0025 0.0043480430101 5 29.30250 -103.16782 TX Brewster 3 12 25 0.0009 0.0018 0.0028481130050 5 32.77417 -96.79778 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 1 3 12 34 0.0027 0.0041 0.0055481130069 5 32.81995 -96.86008 TX Dallas Dallas-Fort Worth-Arlington, TX 5,161,544 1 3 12 36 0.0036 0.0077 0.0169481390015 5 32.43694 -97.02500 TX Ellis Dallas-Fort Worth-Arlington, TX 5,161,544 3 11 31 0.0029 0.0057 0.0085481410044 5 31.76567 -106.45523 TX El Paso El Paso, TX 679,622 1 3 12 34 0.0036 0.0060 0.0090481410053 5 31.75852 -106.50105 TX El Paso El Paso, TX 679,622 1 3 12 34 0.0078 0.0148 0.0236481670014 5 29.26332 -94.85657 TX Galveston Houston-Sugar Land-Baytown, TX4,715,4073 11 32 0.0021 0.0028 0.0041482010024 5 29.90111 -95.32694 TX Harris Houston-Sugar Land-Baytown, TX4,715,407 1 3 12 35 0.0041 0.0066 0.0087482010026 5 29.80250 -95.12555 TX Harris Houston-Sugar Land-Baytown, TX4,715,407 1 3 11 31 0.0028 0.0038 0.0056482010055 5 29.69574 -95.49924 TX Harris Houston-Sugar Land-Baytown, TX4,715,407 1 3 11 32 0.0020 0.0026 0.0037482011034 5 29.76799 -95.22058 TX Harris Houston-Sugar Land-Baytown, TX4,715,407 1 3 11 31 0.0026 0.0073 0.0160482011039 7 29.67005 -95.12849 TX Harris Houston-Sugar Land-Baytown, TX4,715,407 1 2 10 30 0.0023 0.0042 0.0072482030002 5 32.66900 -94.16745 TX Harrison Marshall, TX 62,110 3 11 32 0.0019 0.0027 0.0035482430004 5 30.66938 -104.02463 TX Jeff Davis 3 11 25 0.0008 0.0014 0.0028482450022 5 29.86395 -94.31776 TX Jefferson Beaumont-Port Arthur, TX 385,090 3 11 32 0.0019 0.0030 0.0049482570005 5 32.56917 -96.31583 TX Kaufman Dallas-Fort Worth-Arlington, TX 5,161,544 1 3 11 30 0.0024 0.0063 0.0128482730314 5 27.42694 -97.29861 TX Kleberg Kingsville, TX 31,963 3 11 29 0.0010 0.0017 0.0024483030001 5 33.59085 -101.84759 TX Lubbock Lubbock, TX 249,700 1 2 10 28 0.0010 0.0024 0.0062483390078 5 30.35030 -95.42514 TX Montgomery Houston-Sugar Land-Baytown, TX4,715,4073 11 32 0.0031 0.0042 0.0058483550034 5 27.81180 -97.46563 TX Nueces Corpus Christi, TX 403,280 1 3 12 36 0.0013 0.0021 0.0033483611100 5 30.19417 -93.86694 TX Orange Beaumont-Port Arthur, TX 385,090 3 11 32 0.0020 0.0028 0.0041490110004 5 40.90297 -111.88447 UT Davis Ogden-Clearfield, UT 442,656 1 2 10 29 0.0035 0.0059 0.0071490353006 5 40.73639 -111.87222 UT Salt Lake Salt Lake City, UT 968,858 1 3 12 36 0.0042 0.0077 0.0131490494001 5 40.34139 -111.71361 UT Utah Provo-Orem, UT 376,774 1 3 12 36 0.0034 0.0072 0.0095500070012 5 44.48028 -73.21444 VT Chittenden Burlington-South Burlington, VT 198,889 1 3 12 35 0.0023 0.0029 0.0037510870014 5 37.55833 -77.40028 VA Henrico Richmond, VA 1,096,957 1 2 8 24 0.0030 0.0042 0.0064511390004 5 38.66333 -78.50472 VA Page 2 8 24 0.0027 0.0045 0.0081515200006 5 36.60778 -82.16444 VA Bristol (City) Kingsport-Bristol-Bristol, TN-VA 298,484 1 3 12 35 0.0036 0.0057 0.0083517600020 5 37.51056 -77.49833 VA Richmond (City) Richmond, VA 1,096,957 1 1 4 12 0.0027 0.0033 0.0064517700014 5 37.25611 -79.98500 VA Roanoke (City) Roanoke, VA 288,309 1 2 8 24 0.0074 0.0140 0.0283530330024 6 47.75333 -122.27722 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 35 0.0030 0.0046 0.0073530330032 6 47.54556 -122.32222 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 2 7 22 0.0078 0.0134 0.0201530330048 6 47.61846 -122.32972 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 33 0.0032 0.0052 0.0089530330057 6 47.56333 -122.33833 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 33 0.0074 0.0150 0.0260530330080 6 47.57027 -122.30860 WA King Seattle-Tacoma-Bellevue, WA 3,043,878 1 3 12 36 0.0034 0.0055 0.0075530630016 5 47.66083 -117.35722 WA Spokane Spokane, WA 417,939 1 1 4 11 0.0038 0.0062 0.0087540390011 5 38.44861 -81.68389 WV Kanawha Charleston, WV 309,635 2 9 25 0.0026 0.0043 0.0048540391005 5 38.36806 -81.69361 WV Kanawha Charleston, WV 309,635 1 2 8 25 0.0043 0.0067 0.0077540511002 5 39.91597 -80.73406 WV Marshall Wheeling, WV-OH 153,172 1 1 6 19 0.0065 0.0081 0.0124550270007 5 43.43500 -88.52778 WI Dodge Beaver Dam, WI 85,897 3 12 36 0.0036 0.0059 0.0083550590019 5 42.50472 -87.80930 WI Kenosha Chicago-Naperville-Joliet, IL-IN-W9,098,3163 12 36 0.0038 0.0057 0.0073550710007 5 44.13861 -87.61611 WI Manitowoc Manitowoc, WI 82,887 2 8 25 0.0039 0.0060 0.0113550790026 5 43.06111 -87.91250 WI Milwaukee Milwaukee-Waukesha-West Allis, 1,500,741 1 3 12 36 0.0058 0.0115 0.0245551198001 5 45.20389 -90.60000 WI Taylor 3 12 36 0.0020 0.0030 0.0050551330027 5 43.02028 -88.21500 WI Waukesha Milwaukee-Waukesha-West Allis, 1,500,741 1 3 12 36 0.0097 0.0185 0.0217720610001 5 18.42472 -66.11639 PR Guaynabo San Juan-Caguas-Guaynabo, PR 2,509,007 1 3 12 36 0.0018 0.0026 0.0058780100012 5 17.71444 -64.78528 VI St Croix 1 1 5 12 0.0003 0.0007 0.00092B-18Table 2B-9. Pb_PM2.5 monitoring site distribution statisticsAll sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean271 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 0.0051 0.0060 0.0074 0.0112 0.0450max quarter mean271 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 0.0083 0.0101 0.0140 0.0175 0.1674max monthly mean271 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 0.0133 0.0180 0.0228 0.0302 0.3091Source-oriented sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean8 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 0.0114 0.0114 0.0180 0.0180 0.0180max quarter mean8 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 0.0157 0.0157 0.0296 0.0296 0.0296max monthly mean8 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 0.0286 0.0286 0.0475 0.0475 0.0475Not source-oriented sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean263 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 0.0048 0.0057 0.0073 0.0097 0.0450max quarter mean263 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 0.0080 0.0092 0.0128 0.0172 0.1674max monthly mean263 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 0.0124 0.0163 0.0204 0.0283 0.3091Urban sitesn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean216 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 0.0056 0.0065 0.0079 0.0118 0.0450max quarter mean216 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 0.0092 0.0115 0.0148 0.0182 0.1674max monthly mean216 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 0.0160 0.0191 0.0244 0.0329 0.3091Urban sites, located in MSA's > 1 million populationn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean99 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 0.0075 0.0090 0.0097 0.0123 0.0450max quarter mean99 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 0.0126 0.0147 0.0182 0.0239 0.0967max monthly mean99 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 0.0201 0.0245 0.0282 0.0413 0.2091Urban sites, located in MSA's < 1 million populationn min pct5 pct10 pct15 pct20 pct25 pct30 pct35 pct40 pct45 median mean pct55 pct60 pct65 pct70 pct75 pct80 pct85 pct90 pct95 maxannual mean117 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 0.0046 0.0051 0.0063 0.0078 0.0257max quarter mean117 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 0.0079 0.0087 0.0112 0.0150 0.1674max monthly mean117 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 0.0112 0.0131 0.0186 0.0283 0.30912B-19

## APPENDIX 5A:

PREDICTED PERCENT OF COUNTIES WITH A MONITOR NOT LIKELY TO MEET ALTERNATIVE STANDARDS AND ASSOCIATED PERCENT POPULATIONAppendix 5.A. Predicted percent of counties with a monitor notlikely to meet alternative standards & associated percentpopulation.The values below were derived from the Pb-TSP dataset described in Chapter 2 and in Appendix 2.B.Total Northeast SoutheastIndustrialMidwestUpperMidwest Southwest NorthwestSouthernCaliforniaOutlyingareasNumber of countieswith monitors(population in thosecounties)►125 (58,035) 14 (7,285) 43 (13,298) 43 (17,166) 7 (2,184) 3 (1,015) 7 (3,002) 6 (12,774) 2 (1,311)level / form▼0.02, max quarterly 53% (55%) 64% (83%) 35% (29%) 72% (41%) 29% (25%) 33% (67%) 57% (78%) 67% (88%) 0% (0%)0.02, max monthly 66% (75%) 64% (83%) 51% (58%) 86% (83%) 43% (49%) 67% (81%) 86% (78%) 67% (88%) 0% (0%)0.02, 2nd max monthly 55% (62%) 64% (83%) 40% (32%) 74% (74%) 14% (16%) 67% (81%) 71% (78%) 50% (75%) 0% (0%)0.05, max quarterly 39% (36%) 43% (21%) 35% (29%) 44% (15%) 14% (16%) 33% (67%) 57% (78%) 50% (75%) 0% (0%)0.05, max monthly 45% (42%) 43% (21%) 35% (29%) 56% (25%) 29% (25%) 33% (67%) 57% (78%) 67% (88%) 0% (0%)0.05, 2nd max monthly 38% (18%) 43% (21%) 35% (29%) 44% (15%) 14% (16%) 0% (0%) 57% (78%) 33% (0%) 0% (0%)0.10, max quarterly 23% (6%) 29% (11%) 21% (6%) 30% (5%) 14% (16%) 0% (0%) 29% (31%) 0% (0%) 0% (0%)0.10, max monthly 34% (32%) 43% (21%) 26% (23%) 40% (11%) 14% (16%) 0% (0%) 57% (78%) 50% (75%) 0% (0%)0.10, 2nd max monthly 27% (10%) 43% (21%) 23% (6%) 30% (5%) 14% (16%) 0% (0%) 57% (78%) 0% (0%) 0% (0%)0.20, max quarterly 17% (3%) 21% (1%) 16% (5%) 23% (4%) 0% (0%) 0% (0%) 14% (12%) 0% (0%) 0% (0%)0.20, max monthly 23% (9%) 21% (1%) 23% (23%) 30% (5%) 14% (16%) 0% (0%) 29% (31%) 0% (0%) 0% (0%)0.20, 2nd max monthly 20% (5%) 21% (1%) 19% (6%) 26% (5%) 14% (16%) 0% (0%) 29% (31%) 0% (0%) 0% (0%)0.3, max quarterly 14% (3%) 7% (0%) 16% (5%) 19% (3%) 0% (0%) 0% (0%) 14% (12%) 0% (0%) 0% (0%)0.3, max monthly 18% (3%) 21% (1%) 16% (5%) 26% (4%) 0% (0%) 0% (0%) 14% (12%) 0% (0%) 0% (0%)0.3, 2nd max monthly 14% (2%) 7% (0%) 16% (5%) 21% (2%) 0% (0%) 0% (0%) 14% (12%) 0% (0%) 0% (0%)0.4, max quarterly 10% (1%) 0% (0%) 14% (1%) 14% (2%) 0% (0%) 0% (0%) 14% (12%) 0% (0%) 0% (0%)0.4, max monthly 14% (3%) 7% (0%) 16% (5%) 21% (4%) 0% (0%) 0% (0%) 14% (12%) 0% (0%) 0% (0%)0.4, 2nd max monthly 14% (2%) 7% (0%) 16% (5%) 19% (2%) 0% (0%) 0% (0%) 14% (12%) 0% (0%) 0% (0%)0.50, max quarterly 10% (1%) 0% (0%) 14% (1%) 12% (1%) 0% (0%) 0% (0%) 14% (12%) 0% (0%) 0% (0%)0.50, max monthly 14% (3%) 7% (0%) 16% (5%) 21% (4%) 0% (0%) 0% (0%) 14% (12%) 0% (0%) 0% (0%)0.50, 2nd max monthly 11% (1%) 7% (0%) 14% (1%) 16% (2%) 0% (0%) 0% (0%) 0% (0%) 0% (0%) 0% (0%)Percent of counties with a monitor not likely to meet stated standard and level(percent of total or regional population in counties with monitors that reside in non-meeting counties)United StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and StandardsAir Quality Strategiesand Standards DivisionResearch Triangle Park, NCPublication No. EPA 452/R-07-013November 2007