## Lead Human Exposure and Health Risk Assessments and Ecological Risk Assessment for Selected Areas

Pilot Phase

EXTERNAL REVIEW DRAFT TECHNICAL REPORT

December 2006

Prepared for:

Office of Air Quality Planning and Standards U.S. Environmental Protection Agency Research Triangle Park, NC

Prepared by:

ICF International Research Triangle Park, NC

Work Funded Through:

Contract 68-D01-052, Work Assignment IV-5; and Contract EP-D-06-115, Wo rk Assignment 0-4

Zachary Pekar, Work Assignment Manager Nancy Riley, Project OfficerICF International i DRAFT – December 2006 

### DISCLAIMER

This report is being furnished to the U.S. Environmental Protection Agency (EPA) by ICF International in partial fulf illment of Contract No. EP-D-06-115, Work Assignment No. 0-4. Any opinions, findings, conclusions , or recommendations are thos e of the authors and do not necessarily reflect the views of the EPA or ICF In ternational. This document is being circulated to obtain review and comment from the Clean Air Scientific Advisory Committee (CASAC) and the general public. Comments on this document s hould be addressed to Zachary Pekar, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06, Research Triangle Park, North Caro lina 27711 (email: pekar.zachary@epa.gov).ICF International ii DRAFT – December 2006 

### PREFACE

This document has been prepared in support of the U.S. Envi ronmental Protection Agency’s (EPA) review of the National Ambient Air Qual ity Standards (NAAQS) fo r lead (Pb). This document describes the pilot phas e of t the human exposure, hea lth risk and ecological risk assessments. The purpose of the pilot phase is to test the exposure and risk assessment methodologies for the purposes of gaining insi ghts into their performance and to solicit comments from the Clean Air Scientific Advi sory Committee (CASAC) and the public on the use of these methodologies in the full-scale analys is. Accordingly, the ex posure and risk results presented in this document should be considered provisional and are subject to updating as part of the full-scale analysis.

These analyses have involved the implementation of multiple models and the evaluation of their performance is continuing beyond the release of this report. This is par ticularly the case for models not previously employed fo r applications such at those pertaining to this review (e.g., human blood Pb and risk models).ICF International iii DRAFT – December 2006 

### TABLE OF CONTENTS

1. Introduction............................................................................................................... ..............1-1

2. Overview of Risk Assessment ................................................................................................ 2-1 2.1 Case Studies.............................................................................................................. ........2-1 2.2 Human Exposure and Health Risk Assessments ..............................................................2-3 2.2.1 Conceptual Model......................................................................................................2- 3 2.2.2 Spatial Scale............................................................................................................ ...2-9 2.2.3 Exposure Assessment...............................................................................................2-10 2.2.3.1 Estimation of Media Concentrations .............................................................2-10 2.2.3.2 Blood Lead Modeling ....................................................................................2-12 2.2.4 Risk Assessment ......................................................................................................2-13 2.2.5 Uncertainty/Variability Analysis .............................................................................2-14 2.3 Ecological Risk Assessment ...........................................................................................2-16 2.3.1 Conceptual Model....................................................................................................2-16 2.3.2 Spatial Scale............................................................................................................ .2-22 2.3.3 Exposure Assessment...............................................................................................2-22 2.3.4 Effects Assessment ..................................................................................................2-23 2.3.5 Risk Characterization...............................................................................................2-23 2.3.6 Uncertainty/Variability Analysis .............................................................................2-24

3. Case Study Descriptions .................................................................................................... .....3-1 3.1 Primary Pb Smelter Case Study........................................................................................3-1 3.1.1 Description of Case Study Location ..........................................................................3-1 3.1.2 Description of Primary Pb Smelter............................................................................3-2 3.1.3 Human Exposure Measurements ...............................................................................3-4 3.1.4 Emissions ................................................................................................................ ...3-5 3.1.5 Summary of Environmental Data ..............................................................................3-5 3.1.6 Ecological Characterization.......................................................................................3-9 3.1.6.1 Description of Site and Surroundings..............................................................3-9 3.1.6.2 Description of Available Pb Environmental Data .........................................3-10 3.2 Secondary Pb Smelter Case Study..................................................................................3-11 3.2.1 Description of Case Study Location ........................................................................3-11 3.2.2 Description of Secondary Pb Smelter......................................................................3-12 3.2.3 Human Exposure Measurements .............................................................................3-14 3.2.4 Emissions ................................................................................................................ .3-14 3.2.5 Summary of Environmental Data ............................................................................3-14 3.2.6 Ecological Characterization.....................................................................................3-16 3.2.6.1 Description of Site and Surroundings............................................................3-16 3.2.6.2 Description of Available Data .......................................................................3-18 3.3 Near Roadway Urban Case Study...................................................................................3-18 3.3.1 Description of Case Study Location ........................................................................3-19 3.3.2 Human Exposure Measurements .............................................................................3-20 3.3.3 Emissions ................................................................................................................ .3-20ICF International iv DRAFT – December 2006 3.3.4 Summary of Environmental Data ............................................................................3-20 3.4 Near Roadway Non-Urban Case Study ..........................................................................3-21 3.5 Ecologically Vulnerable Case Study ..............................................................................3-22 3.6 National-Scale Surface Water and Se diment Screening Assessment.............................3-24

4. Estimates of Media Concentrations ........................................................................................4- 1 4.1 Primary Pb Smelter........................................................................................................ ...4-1 4.1.1 Spatial Template ........................................................................................................ 4-1 4.1.2 Air .................................................................................................................... ..........4-4 4.1.2.1 Study Area and Air Dispersion Modeling .......................................................4-4 4.1.2.2 Inhalation Exposure Concentrations................................................................4-4 4.1.2.3 Air and Deposition Results ..............................................................................4-5 4.1.2.4 Air Modeling Performance Assessment ..........................................................4-8 4.1.3 Outdoor Surface Soil................................................................................................4-12 4.1.4 Indoor Dust .............................................................................................................. 4-15 4.1.5 Surface Water and Sediment....................................................................................4-17 4.1.5.1 Surface Water ................................................................................................4-18 4.1.5.2 Sediment ........................................................................................................4-19 4.2 Secondary Pb Smelter.....................................................................................................4 -21 4.2.1 Spatial Template ......................................................................................................4- 21 4.2.2 Air .................................................................................................................... ........4-24 4.2.2.1 Air Dispersion Modeling ...............................................................................4-24 4.2.2.2 Inhalation Exposure Concentrations..............................................................4-24 4.2.2.3 Air and Deposition Results............................................................................4-25 4.2.2.4 Air Modeling Performance Assessment ........................................................4-27 4.2.3 Outdoor Surface Soil................................................................................................4-29 4.2.3.1 Performance Evaluation for So il Concentration Estimates ...........................4-30 4.2.4 Indoor Dust ............................................................................................................. .4-31 4.3 Near Roadway Urban Location Scenario .......................................................................4-32 4.3.1 Spatial Template ......................................................................................................4- 32 4.3.2 Air.. ................................................................................................................... .......4-33 4.3.2.1 Ambient Air Concentrations..........................................................................4-33 4.3.2.2 Inhalation Exposure Concentrations..............................................................4-34 4.3.3 Soil .................................................................................................................... .......4-35 4.3.4 Indoor Dust ............................................................................................................. .4-38 4.4 Near Roadway Non-Urban Locations.............................................................................4-39 4.4.1 Soil Concentrations at Corpus Christi, Texas Site...................................................4-39 4.4.2 Soil Concentrations at Atlee, Virginia Site..............................................................4-40 4.5 National-Scale Surface Water and Sediment Ecological Risk Screening Assessment ..4-41 4.5.1 Assessment of Available Data Sources for Surface Water Data .............................4-41 4.5.2 Dissolved Pb Surface Water Concentrations in the NAWQA Database .................4-45 4.5.3 Total Pb Sediment Concentrations from NAWQA Database..................................4-46

5. Human Exposure Assessment and Blood Pb Estimation........................................................5-1 5.1 Blood Pb Modeling Methods............................................................................................5-1 5.1.1 Overview of Human Exposure Assessm ent and Blood Pb Modeling Approach.......5-1ICF International v DRAFT – December 2006 5.1.2 Selection of Blood Pb Models ...................................................................................5-3 5.1.2.1 The IEUBK Model...........................................................................................5-3 5.1.2.2 The Leggett Model...........................................................................................5-5 5.1.3 Application of Blood Pb Models ...............................................................................5-8 5.1.3.1 Key Modeling Assumptions ............................................................................5-8 5.1.3.2 Adaptation of IEUBK Model...........................................................................5-8 5.1.3.3 Adaptation of Leggett Model...........................................................................5-9 5.1.3.4 Implementation of the Probabilistic Population Blood Pb Model.................5-11 5.1.4 Inputs to the Blood Pb Models.................................................................................5-12 5.1.4.1 Exposure Concentration Estimates for Policy-Relevant Sources..................5-12 5.1.4.2 Policy-Relevant Background Expos ure Pathway Concentrations and Pb Intake Estimates......................................................................................5-18 5.1.4.3 Factors Determining Pb Exposure, Intake, and Uptake.................................5-19 5.1.4.4 Inter-Individual Variability............................................................................5-22 5.2 Blood Pb Model Performance Evaluation ......................................................................5-24 5.2.1 Evaluation Versus General Popul ation and Site-Specific Blood Pb Measurements ...................................................................................................... .5-24 5.2.2 Stability of Percentile Blood Pb Estimates ..............................................................5-27 5.3 Limitations and Uncertainties in the Human Exposure Assessment and Blood Pb Modeling........................................................................................................... .......5-27 5.3.1 Exposure Concentrations and Intake Estimates.......................................................5-28 5.3.2 Exposure, Intake and Uptake Parame ters and Individual Variability......................5-31 5.4 Blood Pb Distribution Estimates for the Three Case Studies .........................................5-31 5.4.1 Primary Pb Smelter ..................................................................................................5-31 5.4.2 Secondary Pb Smelter ..............................................................................................5-32 5.4.3 Near Roadway Urban Case Study............................................................................5-33

6. Estimation of Human Health Risk ..........................................................................................6- 1 6.1 Methods ................................................................................................................... .........6-1 6.1.1 Overview of Approach...............................................................................................6-1 6.1.2 Blood Pb-IQ Relationships ........................................................................................6-1 6.2 Risk Assessment Results ..................................................................................................6 -3 6.2.1 Primary Pb Smelter Case Study.................................................................................6-3 6.2.2 Secondary Pb Smelter Case Study.............................................................................6-6 6.2.3 Near Roadway Urban Case Study..............................................................................6-9 6.3 Uncertainty and Variability in the IQ Loss Estimates .....................................................6-11 6.3.1 Sources of Uncertainty.............................................................................................6-11 6.3.2 Sensitivity Analysis Methodologgy.........................................................................6-12 6.4 Sensitivity Analysis Results............................................................................................6- 15 6.4.1 Alternative House Dust Pb Concentration Models..................................................6-15 6.4.2 Alternative Blood Pb Models...................................................................................6-17 6.4.3 Alternative Diet and Wa ter Absorption Fractions ...................................................6-19 6.4.4 Alternative Values for Soil and House Dust Ingestion and for Soil and Dust Absortion Fractions ...............................................................................6-21 6.4.5 Alternative Blood Pb Geomet ric Standard Deviation..............................................6-22ICF International vi DRAFT – December 2006 6.4.6 Alternative Blood Pb-IQ Models and Uncertainty Related to Statistical Fit of Models..................................................................................................... ....6-24 6.4.7 Alternative Blood Pb Metrics ..................................................................................6-26 6.4.8 Alternative Blood Pb Cutpoint for IQ Loss Estimation...........................................6-27 6.4.9 Summary of Sensitivity Analysis.............................................................................6-28

7. Ecological Risk Assessment ................................................................................................. ..7-1 7.1 Methods ................................................................................................................... .........7-1 7.1.1 Case Studies ............................................................................................................ ...7-1 7.1.2 National-Scale Surface Water and Sediment Screening Assessment ........................7-1 7.1.3 Ecotoxicity Screening Values ....................................................................................7-3 7.1.3.1 Soil Screening Values......................................................................................7-3 7.1.3.2 Water Quality Criteria......................................................................................7-5 7.1.3.3 Sediment Screening Values .............................................................................7-7 7.2 Exposure and Risk Estimates............................................................................................7-8 7.2.1 Primary Pb Smelter Case Study.................................................................................7-9 7.2.2 Secondary Pb Smelter Case Study...........................................................................7-11 7.2.3 Near Roadway Non-Urban Case Study ...................................................................7-12 7.2.4 National-Scale Surface Water and Sediment Screening Assessment ......................7-13 7.2.4.1 Surface Water Column Portion of the Screening Assessment.......................7-13 7.2.4.2 Sediment Portion of the Screening Assessment.............................................7-21 7.3 Limitations and Uncertainties.........................................................................................7-25 7.3.1 Primary Pb Smelter Case Study...............................................................................7-26 7.3.2 Secondary Pb Smelter Case Study...........................................................................7-26 7.3.3 Near Roadway Non-Urban Case Study ...................................................................7-26 7.3.4 National-Scale Surface Water and Sediment Screening Assessment ......................7-27 7.3.4.1 Surface Water Column Screen.......................................................................7-27 7.3.4.2 Sediment Screen.............................................................................................7-28 8. References................................................................................................................. ..............8-1ICF International vii DRAFT – December 2006 

### LIST OF EXHIBITS

Exhibit Page

2-1 Conceptual Model for Pb Human Expos ure and Health Risk Assessments....................2-5

2-2 Overview of Human Exposure and Health Risk Assessments’ Approach for the Pilot Phase................................................................................................ 2-7

2-3 Models and Measurements Used for Human Exposure and Health Risk Assessments in the Pilot Phase............................................................................ 2-8

2-4 Timeframe Reflected in the P ilot Phase Exposure Assessment.....................................2-10

2-5 Conceptual Model for Pb Ec ological Risk Assessment.................................................2-18

2-6 Overview of Ecological Ri sk Assessment Approach ....................................................2-19

2-7 Models and Measurements Used for the Ecological Risk Assessment .........................2-20

3-1 Case Studies Included in the Pilot Phase .........................................................................3-1

3-2 Facility Location Map – Primary Pb Smelter ..................................................................3-3

3-3 Summary of 2001 PbB Measurements for Herculaneum Residents................................3-4

3-4 Summary of 2002 PbB Measurements for Herculaneum Residents................................3-5

3-5 Summary of Environmental Data Sources for Primary Pb Smelter Case Study .............3-6

3-6 Distribution of Pb TSP Measurements Across the United States Relative to Monitors Near the Primary Pb Smelter................................................................3-7

3-7 Species of Concern Occurring in Jefferson County, Missouri ........................................3-9

3-8 Facility Location Map – Secondary Pb Smelter ............................................................3-13

3-9 Summary of Environmental Data Sources for Secondary Pb Smelter Case Study .......3-15

3-10 Distribution of Pb TSP Measurements Across the United States Relative to Monitors Near the Secondary Pb Smelter ......................................................... 3-16

3-11 Threatened and Endangered Specie s in Pike County, Alabama....................................3-18ICF International viii DRAFT – December 2006 3-12 Study Area for Near Roadway Case Study, Houston, Texas.........................................3-19

3-13 Vehicle Traffic Data for Road Segment of Interest.......................................................3-20

3-14 Summary of Environmental Data S ources for Near Roadway Urban Case Study.................................................................................................................. 3-20

3-15 Summary of Environmental Data S ources for Near Roadway Non-Urban Case Study ......................................................................................................... 3-22

3-16 Summary of Environmental Data Sources for HBEF....................................................3-23

3-17 Summary of Environmental Data Sour ces for National-Scale Surface Water and Sediment Screening Assessment ....................................................................... 3-24

3-18 USGS NAWQA Study Units Included in the National-Scale Surface Water Column Screening Assessment ......................................................................... 3-25

3-19 USGS NAWQA Study Units Included in National-Scale Sediment Screening Assessment ....................................................................................... 3-26

4-1 Modeled Block and Block Groups near Primary Pb Smelter Facility.............................4-3

4-2 Primary Pb Smelter Case Study: Mode led Air Concentrations and Deposition.............4-6

4-3 Primary Pb Smelter Case Study: Con centration and Deposition Isopleths .....................4-7

4-4 Primary Pb Smelter Case Study Air Concen tration Modeled Results Compared to Monitor Values ....................................................................................................4-9

4-5 Primary Pb Smelter Case Study De position Modeled Results Compared to Measured Values............................................................................................4-10

4-6 Wind Rose of Meteorological Data us ed for Primary Pb Smelter Case Study (Direction in which Wind is Blowing)...............................................................4-10

4-7 Air Monitor Locations near Primary Pb Smelter...........................................................4-11

4-8 Average Pre-excavation Soil Measur ements and Best-fit Trend Line...........................4-13

4-9 Summary of Primary Pb Smelter Case Study Soil Concentrations Extrapolated from Measurements......................................................................4-13

4-10 Soil Sample Clusters Used to Estimate Ecological Receptor Exposure via Soil and Corresponding Pb Anal ysis Results (ELM 2005) ......................... 4-14ICF International ix DRAFT – December 2006 4-11 Primary Pb Smelter Case Study: Mode led Indoor Dust Pb Concentrations.................4-17

4-12 Joachim Creek Measured Sediment Pb Concentration Data for the Primary Pb Smelter Case Study (ELM 2005)....................................................4-20

4-13 Mississippi River Measured Sediment Pb Concentration Data for the Primary Pb Smelter Case Study (ELM2005)..................................................... 4-21

4-14 Other Surface Water—Drainage Areas and U-Shaped Pond—Measured Sediment Pb Concentration Data for the Primary Pb Smelter Case Study (ELM 2005) ....................................................................................................... 4-21

4-15 Modeled Blocks and Block Groups at Secondary Pb Smelter Facility .........................4-23

4-16 Secondary Pb Smelter Case Study: Summ ary of Modeled Air Concentrations and Deposition.......................................................................................................... 4-25

4-17 Secondary Pb Smelter Case Study: C oncentration and Deposition Isopleths ..............4-26

4-18 Secondary Pb Smelter Case Study Air Concentration Modeled Results Compared to Monitor Values ............................................................................ 4-27

4-19 Wind Rose of Meteorological Data Used for Secondary Pb Smelter Case Study (Direction in which Wind is Blowing)...............................................................4-28

4-20 Dry Deposition Fluxes from Studi es in the United States .............................................4-28

4-21 Summary of Secondary Pb Smelte r Case Study Soil Concentrations ...........................4-30

4-22 Summary of Secondary Pb Smelter Case Study Soil Concentrations with Distance from Source Compar ed to Measurements .......................................... 4-30

4-23 Secondary Pb Smelter Case Study: M odeled Indoor Dust Pb Concentrations.............4-32

4-24 Enhancement Ratios and Air Concen trations for Near Roadway Urban Scenario..............................................................................................................4-34

4-25 Estimated Inhalation Exposure Concen trations for the Near Roadway Urban Scenario..............................................................................................................4-35

4-26 Pb Concentrations in Surface Soil, Corpus Christi, Texas ............................................4-36

4-27 Near Roadway Surface Soil Pb Concentr ations in Pacoima, California .......................4-37ICF International x DRAFT – December 2006 4-28 Estimated Soil Exposure Concentrations for the Near Roadway Urban Scenario..............................................................................................................4-38

4-29 Near Roadway Urban Case Study: Modele d Indoor Dust Pb Concentrations...............4-39

4-30 Pb Concentrations in Surface Soil, Cor pus Christi, Texas, for Ecological Risk Screening Assessment........................................................................................4-40

4-31 Pb Concentrations in Soil Collected from Potential Ecological Exposure Area, I-95 Interchange, Atlee, Virginia.......................................................................4-41

4-32 Map of NAWQA Study Units........................................................................................4-43

4-33 Distribution of Dissolved Pb Concentrat ions in Freshwater Surface Waters from the USGS NAWQA Database (USEPA 2006b) ................................................4-44

4-34 Number of Dissolved Pb Samples Less than the QL.....................................................4-46

5-1 Schematic Representation of the Human Exposure Assessment and Blood Pb Modeling Process............................................................................................5-1

5-2 Structure of the IEUBK Model (USEPA 1994; White et al. 1998).................................5-4

5-3 Structure of the Leggett Bi okinetic Model (Leggett 1993) .............................................5-6

5-4 Ages for IEUBK-Derived Blood Pb Estimates................................................................5-9

5-5 Population Distribution of Inhalation Exposure Concentrations for the Primary Pb Smelter Case Study.......................................................................................5-14

5-6 Population Distribution of Inhalation Exposure Concentrations for the Secondary Pb Smelter and Near Roadway Urban Case Studies........................5-15

5-7 Population Distribution of Soil E xposure Concentrations for the Three Case Studies.......................................................................................................5-16

5-8 Population Distribution of House Dust Exposure Concentrations for the Primary Pb Smelte r Case Study.........................................................................5-17

5-9 Population Distribution of House Dust Exposure Concentrations for the Secondary Pb Smelter and Near-Roadway Urban Case Studies .......................5-17

5-10 Summary of Non-Water Dietar y Pb Intake Estimates...................................................5-19

5-11 Input Parameter Values for the IEUBK Model..............................................................5-20ICF International xi DRAFT – December 2006 5-12 Summary of Blood Pb Model Evaluation......................................................................5-25

5-13 Distribution of Blood Pb Percentile Estimates, 100 Receptions of the Probabilistic Blood Pb Model............................................................................5-27

5-14 Average Contributions of Policy-Re levant Sources and Policy-Relevant Background Pathways to Overall Pb Uptake.....................................................5-28

5-15 Projected Blood Pb Levels (μg/dL) for Primary Pb Smelter Case Study......................5-32

5-16 Projected Blood Pb Levels (μg/dL) for Secondary Pb Smelter Case Study..................5-33

5-17 Projected Blood Pb Levels (μg/dL) fo r Near Roadway Urban Case Study...................5-33

6-1 Projected IQ Losses from the Primary Pb Smelter Current Conditions Scenario ...........6-4

6-2 Projected IQ Losses from the Primar y Pb Smelter Attainment Scenario ........................6-5

6-3 Average Exposure Pathway Contributi ons to Total Pb Uptake, Primary Pb Smelter Case Study..............................................................................................6-6

6-4 Projected IQ Losses from the Secondary Pb Smelter – Modeled (MPE) Soil Concentration Estimates ......................................................................................6-7

6-5 Projected IQ Losses from the Secondary Pb Smelter Case Study – Hybrid Soil Concentration Estimates...............................................................................6-8

6-6 Pathway Contributions to Total Pb Upta ke, Secondary Pb Smelter Case Study.............6-9

6-7 Projected IQ Losses from the N ear Roadway Urban Case Study..................................6-10

6-8 Average Exposure Pathway Contributions to Total Pb Uptake, Near Roadway Urban Case Study ..............................................................................................6-11

6-9 Baseline and Sensitivity Analysis Blood Pb and Risk Estimation Models and Assumptions................................................................................................6-14

6-10 Effects of Alternative House Dust Pb Concentration Models on Estimated Blood Pb Distributions.......................................................................................6-16

6-11 Effects of Alternative House Dust Pb Models on Estimated IQ Distributions..............6-16

6-12 Blood Pb Distributions Generated by th e Leggett and IEUBK Blood Pb Models ........6-18ICF International xii DRAFT – December 2006 6-13 IQ Loss Estimates for Primary Pb Smelter Current Conditions Derived Using the Leggett, IEUBK, and Lanphear et al. (1998) Blood Pb Models..................6-19

6-14 Impacts of Changing Diet and Drin king Water GI Absorption Fractions on Estimated Blood Pb Distributions......................................................................6-20

6-15 Impacts of Changing Diet and Drin king Water GI Absorption Fractions on Estimated IQ Loss Distributions........................................................................6-20

6-16 Effects of Von Lindern et al. (2003) Soil and Dust Inta ke Parameters on Estimated Blood Pb Distributions, Primary Pb Smelter Case Study..................................6-22

6-17 Effects of Von Lindern et al. (2003) Soil and Dust Inta ke Parameters on Estimated IQ Loss Distributions, Primary Pb Smelter Case Study ....................................6-22

6-18 Effects of Varying Individual Vari ability Estimates on Predicted Blood Pb Distributions.......................................................................................................6-23

6-19 Effect of Changing the GSD for Indi vidual Variability on Predicted IQ Loss Distributions.......................................................................................................6-24

6-20 Effects of Alternative Blood Pb-IQ M odels on Predicted IQ Distributions ..................6-25

6-21 Distributions of Concurrent, Lifetime Average, and Peak Blood Pb Distributions from the Primary Pb Smelter Case Study ..........................................................6-26

6-22 Effects of Using Different Blood Pb Metrics to Predict IQ Loss ..................................6-27

6-23 Effect of Reducing Blood Pb Cu toff Vlaues for IQ Estimation ....................................6-28

6-24 Impacts of Alternative Models a nd Parameters on Blood Pb and IQ Loss Estimates in the Sensitivity Analysis.................................................................6-30

7-1 Ecological Soil Screening Values for Pb.........................................................................7-4

7-2 Calculated Pb AWQC for Water Bodi es Analyzed for the Primary Pb Smelter Case Study..............................................................................................7-6

7-3 Pb Sediment Screening Values Evaluated for Use in This Ecological Risk Assessment ..................................................................................................7-8

7-4 HQs Calculated for Soils for Primary Pb Smelter Case Study ........................................7-9

7-5 HQs Calculated for Surface Waters for Primary Pb Smelter Case Study......................7-10ICF International xiii DRAFT – December 2006 7-6 HQs Calculated for Sediments in Surf ace Waters for Primary Pb Smelter Case Study ..................................................................................................................7-10

7-7 HQs Calculated for Soils for Secondary Pb Smelter Case Study ..................................7-11

7-8 Soil HQs Calculated for the Non-Urba n Near Roadway Case Study Location in Corpus Christi, Texas ........................................................................................ 7-12

7-9 Soil HQs Associated with Non-Urban Near Roadway Location in Atlee, Virginia......7-13

7-10 Results of Chronic Aquatic Risk Scre en –HQs for Measured Dissolved Pb Concentrations Compared to Chroni c AWQC (i.e., Criterion Continuous Concentration or CCC) for the Protec tion of Aquatic Life for Pb.....................7-15

7-11 Results of Acute Aquatic Risk Scr eening Assessment –HQs for Measured Dissolved Pb Concentrations Compared to Acute AWQC (i.e., Criterion Maximum Concentration or CM C) for the Protection of Aquatic Life for Pb ............................................................................................7-17

7-12 Results of Aquatic Risk Screeni ng Assessment – Mining Sites at which Dissolved Pb Measurements Exceed AWQC....................................................7-19

7-13 Results of Aquatic Risk Screening A ssessment - Locations at which Dissolved Pb Measurements Exceed AWQC, Excluding Mining Sites.............................7-20

7-14 Results of National-Scale Surface Water Risk Screening Assessment - Locations at which Dissolved Pb Measurements Exceed AWQC, Excluding Excluding Mining Sites......................................................................................7-21

7-15 Results of Sediment Risk Screening Assessment – Stations With Exact or Near Match to Surface Water Stat ions at which Dissolved Pb Measurements Exceed AWQC, Excluding Mining Sites ..................................7-22

7-16 Concentrations of Total Pb in Sediment s at Locations Near or Co-Located with the 15 Sites at which Dissolved Pb Concentrations Exceeded the AWQC, Excluding Mining Sites......................................................................................7-24Introduction

ICF International 1-1 DRAFT – December 2006 

### 1. Introduction 1

2 Sections 108 and 109 of the Clean Air Act (CAA) govern the establishment and periodic review 3 of the National Ambient Air Quality Standards (NAAQS) by the U.S. E nvironmental Protection 4 Agency (EPA). These standards are established for pollutants that may r easonably be anticipated 5 to endanger public health and welfare, and w hose presence in the ambient air results from 6 numerous or diverse mobile or stationary sour ces. The NAAQS are to be based on air quality 7 criteria, which are to accurately reflect the latest scientific kn owledge useful in indicating the 8 type and extent of identifiable e ffects on public health or welfare that may be expected from the 9 presence of the pollutant in ambient air. The EPA Administrator is to promulgate and 10 periodically review “p rimary” (health-based) and “secondary ” (welfare-based) NAAQS for such 11 pollutants. Based on periodic reviews of the air quality criteria and standards, the Administrator 12 is to make revisions in the criteria and standard s, and promulgate any new standards, as may be 13 appropriate. The CAA also requir es that an independent scientif ic review committee advise the 14 Administrator as part of this NAAQS review process, a function performed by the Clean Air 15 Scientific Advisory Committee (CASAC). 16 17 The EPA is presently conducting a review of the NAAQS for lead (Pb). The EPA’s overall plan 18 and schedule for this NAAQS review is presented in the Plan for the Review of the National 19 Ambient Air Quality Standards for Lead (USEPA 2006a). This plan discusses the preparation of 20 two key documents in the NAAQS review process: an Air Quality Criteria Document (CD) and 21 a Staff Paper. The CD provides a critical assessment of the latest available scientific information 22 upon which the NAAQS is to be based (USEPA 2006b). The Staff Paper evaluates the policy 23 implications of this information and presen ts staff conclusions and recommendations for 24 standard-setting options for the Administrator to consider. 25 26 In conjunction with preparation of the first draft of the Staff Pa per for Pb, EPA’s Office of Air 27 Quality Planning and Standards (OAQPS) has c onducted various policy-relevant assessments, 28 including pilot-scale quantitativ e human exposure and health risk assessments and an ecological 29 risk assessment for selected areas. The methods and results of these assessments are summarized 30 in Chapters 4 and 6 of the draft Staff Paper (U SEPA 2006c) and are described in detail in this 31 draft document. 32 33 Chapter 2 of this document provides an overview of the methods and data used for the pilot 34 phase human exposure and health risk assessments and ecological risk as sessment. An overview 35 of the selected case studies, including descri ptions of the locati ons, populations in the 36 surrounding areas, and available emissions estim ates and environmental measurements, is 37 provided in Chapter 3. The me thods and data used to estimate Pb concentrations in 38 environmental media for each case study are describe d in Chapter 4. Chapters 5 and 6 describe 39 the human exposure assessment and health ri sk characterization, re spectively, including 40 descriptions of the methods, models, and data used. The methods and re sults of the ecological 41 risk assessment are provided in Chapter 7. The references for this document are provided in 42 Chapter 8. 43Overview of Risk Assessment

ICF International 2-1 DRAFT – December 2006 

### 2. Overview of Risk Assessment 1

2 The human exposure and health risk assessments and ecological risk assessment being conducted 3 in support of U.S. EPA’s review of the Pb NAAQS focus on the estimation of risk resulting from 4 exposure to Pb released into ambient air. The preliminary design of these assessments was 5 described in the draft Analysis Plan (USEPA 200 6d). As described in the draft Analysis Plan, 6 these assessments are being conducted in two phas es: the pilot and the full-scale phases. The 7 pilot phase, described in this report, employed a case study based approach to estimate human 8 exposure and health risks and eco logical risks associated with the following two air quality 9 scenarios. 10 11

• Current Conditions. This scenario estimated risks asso ciated with current conditions at 12 each case study location. 13 14

• Attainment of Current NAAQS. This scenario, referred to as “attainment” in this 15 report, estimated risks assuming attainme nt of the current NAAQS at the case study 16 location that is curren tly not in attainment. 17 18 For both air quality scenarios, human and ecological exposures to Pb in air, as well as exposures 19 to Pb in other media (e.g., soil, indoor dust) resulting from atmospheric deposition of Pb, were 20 assessed. The sources associated with these exposu res are referred to in this report as “policy21 relevant” sources. This assessment also cons iders policy-relevant “b ackground” Pb exposures, 22 which include exposures that are not linked to Pb released to air, such as ingestion of drinking 23 water, ingestion of Pb paint, and other non-air-related exposures. 24 25 For the ecological risk assessm ent, the case study approach was supplemented by a national26 scale screening assessment of Pb concentration measurements in surface waters and sediments 27 across the United States. 28 29 The remainder of this chapter provides an overvie w of the selected case studies (Section 2.1) and 30 summaries of the approaches taken for the human exposure and health risk assessments (Section 31 2.2) and the ecological risk assessment (Section 2.3). 32

2.1 Case Studies 33
34 The selection of case study locati ons reflected consideration of factors presented in the draft 35 Analysis Plan, as well as comments receive d during consultation with CASAC (Henderson 36 2006). The selection criteria included, but were not limited to, the following: 37 38

• Coverage for a variety of ambient air s ources of Pb emissions ranging from large 39 industrial point sources (e.g., primary and secondary Pb smelters) to areas with 40 potentially significant reentrainmen t of historically deposited Pb; 41 42

• Availability of site-specific measurements for key environmental media (i.e., air, soil, and 43 indoor dust) and biomonitoring of Pb exposure (i.e., blood Pb levels); and 44Overview of Risk Assessment

ICF International 2-2 DRAFT – December 2006 1

• Consideration of demographics and socioeconom ic factors related to exposure (e.g., areas 2 with higher density of children in likely higher risk locations). 3 4 Five case studies were selected for the pilot pha se. Some of the selected case studies were 5 “shared” by the human and ecological assess ments (e.g., with one characterization of 6 environmental conditions serving the purposes of both assessments), while other case studies 7 were specific to one of the assessments. A brief description of each selected location is provided 8 below. 9 10

• Primary Pb Smelter Location. This case study involved a location associated with a 11 primary Pb smelting facility. This facility is located in Herculaneum, Missouri, and has 12 been in operation for more than 100 years. R ecent measured air concentrations from the 13 surrounding area and a robust data set of r ecent facility configuration information, 14 emissions estimates, measured media concentratio ns (soil, indoor dust, surface water, and 15 sediment), and exposure (i.e., blood Pb) measur ements were available for this location. 16 This case study was used for both the human ex posure and health risk assessments and 17 the ecological risk assessment. 18 19

• Secondary Pb Smelter Location. This case study focused on a location associated with 20 a secondary Pb smelting facili ty in Troy, Alabama. Recent measured air concentrations 21 from the area surrounding the facility and facility char acterization data (including 22 emission estimates) were readily available fo r this location. This case study was used for 23 both the human exposure and health risk asse ssments and the ecological risk assessment. 24 25

• Near Roadway Urban Location. This location in Housto n, Texas, was selected to 26 provide a case study that refl ected the influence of histor ically deposited Pb near 27 roadways in urban areas. It covers approximately 2.5 kilometers (km) of roadway near 28 an air monitoring site and includes residences within 200 meters (m) of this section of 29 roadway. The location is in a relatively dry region of the country where resuspension is 30 likely to occur, there are air measurements available from a monitor located close to a 31 roadway, there are numerous homes along the road ways near this monitor, and there is a 32 lack of identified point sources of Pb emi ssions in the surrounding area. This case study 33 was used for the human exposure and health risk assessments. 34 35

• Near Roadway Non-Urban Locations. Two locations were selected to evaluate the 36 ecological risks of historically deposited Pb in soils near roads in less developed areas, 37 where exposures to ecological receptors are expected to occur. One location was in 38 Corpus Christi, Texas, and th e other was in Atlee, Virginia , north of Richmond, both near 39 highly traveled interstates, but less deve loped than the near roadway urban location. 40 41

• Ecologically Vulnerable Location. The Hubbard Brook Experime ntal Forest, located in 42 the White Mountain National Forest, near North Woodstock, New Hampshire, was 43 selected as an ecological case study location. This location was selected because: (1) it 44 is located in an acidified watershed a nd is therefore expected to have higher 45 bioavailability of Pb; (2) there are no identi fied point sources of Pb in the surrounding 46Overview of Risk Assessment

ICF International 2-3 DRAFT – December 2006 area, which allows for an evaluation of impacts of regional background Pb 1 concentrations; (3) it is located in an elevat ed area, which is subject to comparatively 2 higher deposition of Pb due to wind speed a nd precipitation; and (4 ) there are available 3 data on trends (temporal, elevation, etc.) in Pb concentrations in various environmental 4 media. This assessment only focused on id entifying this site and did not involve 5 characterization of potential impacts associ ated with measured Pb concentrations. 6 7 The specific approach for each case study was ta ilored to the location, based on the available 8 data, the characteristics of the emission s ources, and the potentially affected populations. 9 10 For the ecological risk assessm ent, these case studies were supplemented by a national-scale 11 ecological screening risk assessment, consisting of two parts: the su rface water portion and the 12 sediment portion. This screening assessment wa s fundamentally different from the case study 13 analyses because it analyzed data from locati ons across the United States, instead of focusing on 14 one (or two) specific location(s). An overview of this screening assessment is provided below. 15 16

• National-Scale Screening Risk Assessment, Consisting of the Surface Water and the 17 Sediment Portions. This analysis utilized Pb concentration measurements for 18 freshwaters across the United States collected by the U.S. Geological Survey as part of 19 their National Ambient Water Quality A ssessment (NAQWA) program (USGS 2004). 20 Information on land use, Pb air emission sources, and surface water point source 21 discharges for a subset of these locations also played a role in this analysis. 22

2.2 Human Exposure and Health Risk Assessments 23

24 The pilot phase of the human exposure and heal th risk assessments was designed to estimate 25 neurological effects in young child ren (in terms of IQ loss) associated with exposures to Pb 26 emitted into ambient air for three case studies: primary Pb smelter, secondary Pb smelter, and 27 near roadway urban location. This section provi des an overview of the approach implemented 28 for these assessments, beginning with a descriptio n of the conceptual model (Section 2.2.1) and a 29 summary of the spatial s cale of the analysis (Section 2.2.2), fo llowed by brief descriptions of the 30 exposure assessment (Section 2.2.3), risk assessm ent (Section 2.2.4), and un certainty/variability 31 analysis (Section 2.2.5). 32

2.2.1 Conceptual Model 33
34 This section presents the conceptual model (Exhi bit 2-1) intended to illustrate the elements 35 pertinent to assessment of public health risks associated with environmental Pb exposures. 36 Elements included in the pilot phase are indicated with gray background and solid borders. 37 Exposure pathways involving Pb re leased into ambient air are s hown in bold text and pathways 38 involving background sources are shown in non-bold (regular) text. A desc ription of how the 39 different elements of the conceptual model were addressed in the pilo t phase, including any 40 differences from what was presented in th e draft Analysis Plan, is provided below: 41 42

• Sources. All sources in the conceptual model were addressed, either explicitly or 43 implicitly, in the pilot phase. As mentioned previously, the pilot phase estimated effects 44Overview of Risk Assessment

ICF International 2-4 DRAFT – December 2006 for two categories of sources: policy-relevant sources (whi ch include stationary and 1 mobile sources and outdoor soil which c ontains historically deposited Pb) and 2 background (which includes diet, 1 drinking water, indoor Pb paint, and other 3 miscellaneous sources). To the extent po ssible, estimates of blood Pb levels were 4 differentiated by the contri buting exposure pathways. 5 6

• Pathways. Exhibit 2-1 is intended to generally illustrate th e many pathways by which Pb 7 emitted into the environment becomes availabl e for human exposure. For the purposes of 8 this assessment, the pathways that pass through ambient air are of par ticular interest; all 9 other pathways are consider ed background. For the pilot phase, the contribution of 10 reentrained outdoor soil to ambi ent air concentrations was not estimated separately from 11 the contribution from other emission sources. 12 13

• Routes. The ingestion and inhalation routes are considered the primary routes of human 14 exposure to environmental Pb, with the i ngestion route expected to be the more 15 significant. Both routes were included in the pilot phase. 16 17

• Exposed Populations. For the purposes of the pilo t phase, population groups were 18 identified based primarily on age or life stage. Exposures were characterized in the pilot 19 phase for young children (ages 0 to 7). 20 21

• Internal Disposition. While Pb is distributed throughout the body, bone is an 22 established site of internal accumulation of Pb and blood is an established internal dose 23 metric for purposes of both exposure and risk assessment. The pilot phase relied on 24 blood Pb with corresponding dose-response func tions to estimate risks. The biokinetic 25 models used in this analysis recognize the role of bone as a re servoir with the potential to 26 act as a source and storage site. 27 28

• Endpoints. As illustrated in Exhibit 2-1, numer ous health endpoints are recognized in 29 the Air Quality Criteria Document (CD) as associated with Pb exposures. The endpoints 30 of interest for this assessment are those asso ciated with the range of exposures expected 31 to closely reflect current levels, and for whic h there is adequate information to develop 32 quantitative risk assessments. Recognizing th is, the pilot phase focused on neurological 33 effects in children. 34 35

• Metrics. The pilot phase used IQ decrement in children as the primary risk metric. 36 37

1 Note that exposure to Pb through the diet likely refl ects a combination of: (a) air-sourced Pb impact (e.g., deposition of Pb onto crops which in turn is reflected in Pb content in processed foods); and (b) background Pb (e.g., Pb introduced into food during packaging). However, for the pilot phase, dietary Pb ex posure was treated as purely background.Overview of Risk Assessment

ICF International 2-5 DRAFT – December 2006 Exhibit 2-1. Conceptual Mo del for Pb Human Exposure a nd Health Risk Assessments a 1

2Overview of Risk Assessment

ICF International 2-6 DRAFT – December 2006 An overview of the approach developed to implem ent the selected elements of the conceptual 1 model for the pilot phase is provided in Exhibi t 2-2. This exhibit s hows the key types of 2 information and models involved in each part of the pilot phase and how they are related to each 3 other and to the other phases of the analysis. Exhibit 2- 3 summarizes the use of these 4 information types and models for each case study. As indicated in these exhibits, the specific 5 approach for each case study differed based on the na ture of the case study (e.g., type of source, 6 locations of populations) and the site-specific measurements avai lable. In cases where the 7 available measurements were not sufficien t to characterize the study area (e.g., due to 8 insufficient spatial coverage), these data were used for performance evaluation. The following 9 sections describe each part of the pilot phase in more detail. 10Overview of Risk Assessment

ICF International 2-7 DRAFT – December 2006 Exhibit 2-2. Overview of Human Expo sure and Health Risk Assessments’ 1 Approach for the Pilot Phase 2

3Overview of Risk Assessment

ICF International 2-8 DRAFT – December 2006 Exhibit 2-3. Models and Measurements Us ed for Human Exposure and Health Risk Assessments in the Pilot Phase

Primary Pb Smelter Secondary Pb Smelter Near Roadway Urban

Location Herculaneum, Missouri Troy, Alabama Houston, Texas

Spatial resolution U.S. Census blocks, block groups U.S. Census blocks Distance ranges from roadways (0 to 12 m, 12 to 75 m, 75 to 200 m)
Exposure Assessment: Media Concentrations

Models ISC-Prime AERMOD n/a

Ambient air concentrations Measurements Site-specific air monitoring (13 locations) Site-specific air monitoring (2 locations) Site-specific, near roadway air monitoring, combined with estimated spatial gradients from literature (1 location)

Models ISC-Prime AERMOD n/a
Deposition to soil Measurements Site-specific measurements (10 locations) n/a n/a

Models n/a Multiple Pathways of Exposure (MPE) n/a

Soil concentrations Measurements Site-specific soil sampling combined with statistical extrapolation (909 locations) n/a Surrogate soil sampling data from Corpus Christi, Texas (3 locations)

Models Combination of sitespecific regressionbased model and pooled analysis model (the AGG model), both using ambient air and soil concentrations as inputs Pooled analysis model (the AGG model) using ambient air concentrations as inputs Pooled analysis model (the AGG model) using

ambient air and soil concentrations as inputs Indoor dust concentrations

Measurements Site-specific indoor dust sampling (17 locations) n/a n/a

Exposure Assessment: Internal Disposition
Blood Pb levels Models IEUBK, Leggett

Risk Characterization

Risk (IQ loss) Models Log-linear distribution concentration-response function (Lanphear et al. 2005)Overview of Risk Assessment

ICF International 2-9 DRAFT – December 2006 2.2.2 Spatial Scale 1

2 To capture the spatial variabili ty in media and exposure concen trations, a GIS-based “spatial 3 template” was created for each case study. This template defines the outer boundary and the 4 spatial units within this boundary for each case study. Due to the inherent differences between 5 the case studies, such as the environmental set ting, locations of populat ions, source types, and 6 availability of measurements, the spatial template for each case study was different. Detailed 7 descriptions of the templates are provided in Chapter 4. The spatial template for each case study 8 location is summarized below: 9 10

• Primary Pb Smelter Location. The outer boundary of this study area was set to 10 km, 11 which was expected to capture the populati on experiencing the most significant impacts 12 of the facility’s emissions. To provide sufficient spatial resolution to capture 13 concentration gradients, while minimizing the number of computations required for 14 estimating soil concentrations, blood Pb levels , and risks, the study area was divided into 15 a combination of U.S. Census blocks and block groups. This was done by first modeling 16 air concentrations at the centroid of each U.S. Census block within the study area. Based 17 on these estimated concentrations, locations with higher gradients in air concentrations 2 18 used blocks as the spatial unit. All other lo cations used block groups. Air concentrations 19 for block groups were estimated by calculati ng the area-weighted average of the block 20 values. Soil concentrations were estimated fo r the same combination of blocks and block 21 groups as the air concentrations. 22 23

• Secondary Pb Smelter Location. The outer boundary of this study area was also set to 24 10 km, which was expected to capture the population experiencing the most significant 25 impacts of the facility’s emissions. Air c oncentrations and deposition were modeled for 26 the centroid of each U.S. Census block within the study area. These estimated 27 concentrations indicated that relatively high gradient s exist across the blocks in nearly all 28 of the block groups in the study ar ea, and thus all locat ions used blocks as the spatial unit. 29 Soil concentrations were estimated for the sa me spatial units (i.e., blocks) as the air 30 concentrations. 31 32

• Near Roadway Urban Location. The boundaries of this study area were set based on 33 the presence of an approximately 2.5 km stre tch of roadway adjacent to the selected air 34 monitoring site. This section of roadway wa s selected based on data availability for 35 roadway traffic volume and the assumption that the measured air concentrations at the 36 monitoring site were a reasonable representati on of concentrations along that length of 37 roadway. The “depth” of the study area (i.e ., how far the study ar ea extends from the 38 roadway) was set based on published data (ICF 2005) for other locations suggesting that 39 emissions on roadways can contribute to ambi ent concentrations adjacent to the roadway 40 out to about 200 m. This study area was then subdivided into three z ones (i.e., 0 to 12 m, 41 12 to 75 m, and 75 to 200 m) on either side of the roadway, based on locations of soil 42

2 Gradients in air concentrations were estimated by calculating the ratio of maximum U.S. Census block concentration to average block concentration for each U.S. Census block group in the study area. U.S. Census blocks were defined as the spatial units for all block groups with ratios greater than 2.0.Overview of Risk Assessment

ICF International 2-10 DRAFT – December 2006 measurements and factors relating ambien t concentrations in these regions to 1 concentrations directly over the roadway. 2 3 2.2.3 Exposure Assessment 4 5 In the exposure assessment, blood Pb levels were estimated by first estimating Pb concentrations 6 in environmental media (i.e., air, soil, indoor dust) and then modeling blood Pb levels for the 7 populations exposed to these medi a. A summary of the timeframe associated with estimated 8 media concentrations and population data used in the pilot phase is provided in Exhibit 2-4. 9 10 Exhibit 2-4. Timeframe Reflected in the Pilot Phase Exposure Assessment 11

Data Primary Pb Smelter Secondary Pb Smelter Near Roadway Non-urban

Ambient outdoor air

Pb concentrations 2001 to 2005 (post-2001 SIP emissions data) 1997 to 2000 (stack test emissions data) 2001 (air monitoring data)

Soil Pb concentrations 2000 to 2005 (timeframe for soil sampling at site) 1997 to 2000 (modeled based on air deposition) 1998 (soil sampling)

Indoor dust concentrations 2000 to 2005 (estimated based on ambient air and soil

concentrations) 1997 to 2000 (estimated based on ambient air concentrations) 2001 (estimated based on ambient air and soil

concentrations)
Population data 2000 U.S. Census

Cumulative period

reflected in data 2000 to 2005 1997 to 2000 ~2001

The remainder of this section describes how medi a concentrations were estimated for each case 12 study and how blood Pb modeling was used in the assessment. 13

2.2.3.1 Estimation of Media Concentrations 14
15 This section summarizes the approach for estima ting media concentrations for each case study in 16 the pilot phase. In order to model the expos ure pathways identified in Exhibit 2-1, Pb 17 concentrations in the fo llowing media are required: 18 19

• Air, 20

• Outdoor soil, and 21

• Indoor dust. 22 23 For the pilot phase, concentrations in these media were estimate d for both the current conditions 24 and attainment air quality scenarios. The approa ch for estimating media concentrations for these 25 scenarios depended on the case study. 26Overview of Risk Assessment

ICF International 2-11 DRAFT – December 2006 Primary Pb Smelter: For the primary Pb smelter case study, a combination of measurements 1 and modeling was used to characterize media con centrations. Substantial analysis has been 2 performed for this location by U.S. EPA Region 7 a nd the facility. As a result, a robust data set 3 of ambient air monitoring, outdoor soil m easurements, surface water and sediment 4 measurements, and indoor dust measurements, as well as air modeling input values, were 5 available. Ambient air concentrations for the current conditions scenar io were characterized 6 using the Industrial Source Complex – Plume Risk Model Enhancements (ISC-Prime) model 7 (Schulman et al. 1997). This was the only case study with estimated ambient air concentrations 8 that exceeded the current NAAQS standard, thus requiring an additional assessment of ambient 9 air concentrations for the attainment scenario. Ambient air concentrations for the attainment 10 scenario were estimated by first calculating qua rterly average concentrations for the current 11 conditions scenario, replacing any estimated con centrations that exceeded the NAAQS with the 12 current standard (1.5 μg/m 3 ), and then calculating annual average concentrations from these 13 quarterly averages. 14 15 Inhalation exposure concentrations were estimated from these ambient air concentrations to 16 account for variability in daily mobility for the p opulation of interest (young children). In this 17 context, mobility refers to the tim e spent in different locations, such as outside at home, inside at 18 school, and in vehicles. These c oncentrations were estimated us ing ratios for Pb of estimated 19 exposure concentrations to ambi ent air concentrations (derived from the exposure and ambient 20 air concentrations estimated as part of the 1999 National-Scale Air Toxics Assessment [USEPA 21 2006e]). These ratios, which are age- and censu s-tract-specific, were applied to estimated 22 ambient concentrations for the current condition a nd attainment scenarios to calculate inhalation 23 exposure concentrations specific to ag e and location of the study population. 24 25 Part of this study area has undergone substant ial soil monitoring and remediation activities, 26 resulting in very different spatia l coverage of available soil meas urements within the remediation 27 zone compared to outside of it. For locations within the soil remediation zone, which extends 28 approximately 1.5 km from the facility, soil c oncentrations were estimated using available 29 measurements for the soil currently in those locat ions. Concentrations of Pb in soil outside of 30 the soil remediation zone were estimated us ing a statistical regr ession equation based on 31 available pre-remediation measurements. The same soil concentrations were used for the current 32 conditions and attainment scenarios. 33 34 As with soil concentrations, indoor dust concentrations of Pb were estimated using different 35 approaches for houses within the soil remediation zone than for those outside of the zone. For 36 residences within the soil remediation zone, an empirical model, developed from air monitoring 37 data and post-remediation indoor dust measurements available within the zone, was used. For 38 locations outside the remediati on zone, indoor dust concentrations were estimated using a pooled 39 analysis model, based on data from a variety of industrial and urban/indu strial studies (USEPA 40 1989, Appendix B). Both models estimated indo or dust concentrations for both the current 41 conditions and attainment scenarios based on ambient air and outdoor soil concentrations, 42 estimated as described above. 43 44Overview of Risk Assessment

ICF International 2-12 DRAFT – December 2006 Secondary Pb Smelter: For this case study, ambient air concentrations were estimated using 1 the American Meteorology Society/Environm ental Protection Agency Regulatory Model 2 (AERMOD) (USEPA 2004a). Based on these am bient air concentratio ns, inhalation exposure 3 concentrations were estimated using the same approach as for the primary Pb smelter case study 4 (described above). Outdoor soil concentrations were estimated using the soil modeling approach 5 from EPA’s Multiple Pathways of Exposure (MPE) methodology (USEPA 1998) and deposition 6 outputs from AERMOD. Indoor dust concentrati ons were estimated using a pooled analysis 7 model, based on data from a variety of indus trial and urban/industr ial studies (USEPA 1989, 8 Appendix B), which relates ambient air concentrations to indoor dust levels (outdoor soil was not 9 considered explicitly as an independent variable). 10 11 Near Roadway Urban Location: For this case study, air monitori ng data collected adjacent to 12 a roadway in Houston, Texas, were used to define the ambient air concentrations for the site. 13 These air monitoring data were combined with da ta from the literature characterizing the spatial 14 gradient in particulate matter con centrations near roadwa ys to estimate air concentrations in two 15 distance bands from the roadway: 0 to 75 m and 75 to 200 m (ICF 2005). Based on these 16 ambient air concentrations, inhalation exposure c oncentrations were calculated using the same 17 approach used for the primary Pb smelter case study (described above). No adequate site18 specific outdoor soil measurements were available fo r this site or elsewhere in Houston, and thus 19 Pb concentrations in outdoor soil were based on measurements obtained adjacent to a similar 20 roadway in Corpus Christi, Texas, taking into co nsideration other soil Pb measurements reported 21 in the literature and values obt ained from EPA’s SPECIATE data base. Based on these data and 22 the distance bands used to characterize air concen trations, soil concentrati ons were estimated for 23 three distance bands from the roadway: 0 to 12 m, 12 to 75 m, and 75 to 200 m. Indoor dust 24 concentrations were estimated using the pooled an alysis model used for the outer zone of the 25 primary Pb smelter case study that relates ambien t air and outdoor soil conc entrations to indoor 26 dust levels (USEPA 1989, Appendix B). 27

2.2.3.2 Blood Pb Modeling 28

Blood Pb levels were predicted for a child population at each case study location using two 29 different biokinetic models: the Integrated Exposure Uptake Biokinetic Model for Children 30 (IEUBK) (USEPA 2005a) and th e International Commission fo r Radiation Protection model 31 (Leggett 1993). 3 Inputs to these models included the estimated exposure media (air, soil, and 32 indoor dust) concentrations of Pb for each case st udy, as well as values for other parameters that 33 influence blood Pb levels. 34

The same fundamental approach was used to esti mate population distributions of blood Pb levels 35 at each of the three case study locations. This ap proach involved two steps. The first step 36 involved applying the IEUBK and Leggett models to predict central tendency blood Pb levels for 37 children within the study area for each case stud y location. The estimated blood Pb levels were 38 then parsed into the fraction associated w ith policy-relevant background and the fraction 39 associated with policy-relevant sources, with the latter category being further subdivided into 40

3 Although emphasis was placed on biokinetic models in predicting blood Pb levels for children in the pilot phase analysis, a statistical (regression)-based blood Pb model (Lanphear et al. 1998) was included as part of the sensitivity analysis (see Section 2.2.5).Overview of Risk Assessment

ICF International 2-13 DRAFT – December 2006 contributions from inhalation, so il ingestion and indoor dust inges tion exposure pathways. For 1 the purposes of this pilot phase assessment, drinking water and diet exposure pathways are 2 categorized as policy-relevant background, although it is recognized that a portion of Pb in both 3 pathways may derive from policy-relevant sources. 4

The second step involved implementing a probabilistic model to generate population 5 distributions of blood Pb levels for children in each case study location ba sed on consideration of 6 three key factors: 7

1. The central tendency blood Pb levels gene rated by the blood Pb model for each U.S. 8 Census block and block group in the study area (from the first step described above); 9 2. Demographic data (i.e., distri bution of children 0 to 7 years of age across the blocks and 10 block groups comprising a given study area); and 11 3. A geometric standard deviation characterizi ng inter-individual va riability in blood Pb 12 level (e.g., reflecting differences in behavi or and biokinetics related to Pb). 13 14 This approach produced a distribution of mode led children’s blood Pb levels for each case study 15 location. Each of the individual modeled blood Pb levels was further differentiated to show the 16 relative contribution of polic y-relevant background and sources to total blood Pb. 17

2.2.4 Risk Assessment 18 19 As discussed above, IQ impacts in exposed children have been selected as the endpoint for risk 20 quantification in the pilot phase assessment, on the basis of a large body of evidence establishing 21 the occurrence of the IQ effects at low exposure and blood Pb le vels. The characterization of the 22 IQ loss impacts for the pilot phase assessment involved generating a distribution of IQ loss 23 estimates for the set of children simulated in the exposure assessment using the estimated blood 24 Pb levels for these children and a blood Pb concen tration-response relationship for IQ loss. The 25 selected concentration-respons e relationships are the log- normal concentration-response 26 functions for concurrent 4 and lifetime average blood Pb concen trations from a pooled analysis of 27 epidemiology studies focusing on IQ loss in children (Lanphe ar et al. 2005). 28 29 For the pilot phase, cutpoints of 2.4 μ g/dL (concurrent exposure metric) and 6.1 μ g/dL (lifetime 30 average exposure metric) were selected as blood Pb levels below which IQ loss was not to be 31 predicted. The selection of thes e cutpoints is based on consideration for the blood Pb level at 32 which confidence in being able to characterize the shape of the concentration-response functions 33 diminishes significantly. For example, for th e concurrent blood Pb concentration response 34 function, less than five percent of the children from the pooled analysis had concurrent blood Pb 35 levels below 2.4 μ g /dL (Lanphear et al. 2005), which suggests an increased uncertainty in the 36 functional form below this level of exposure. 37 38

4 In the epidemiological studies, “conc urrent” refers to a blood Pb measurement taken concurrently with the IQ assessment. The children included in the pooled analysis on which the concentration-response function was based ranged from five to 10 years old (refer to Section 6.1.2 for mo re details). In these studies, “lifetime average” refers to the average of blood Pb measurements taken over the child’s life from six months until the IQ assessment.Overview of Risk Assessment

ICF International 2-14 DRAFT – December 2006 As with the exposure analysis, th e contributions of policy-releva nt sources and policy-relevant 1 background exposure pathways to overall IQ loss are differentiated. These results are presented 2 using two primary risk metrics, as described below: 3

• Population risk percentiles. This metric characterizes th e degree of IQ loss associated 4 with policy-relevant sources for specific percentiles of the child population (e.g., the 50 th

, 5 90 th , 95 th , 99 th percentile modeled child) . This category of metric provides perspective on 6 the distribution of IQ loss resulting from po licy-relevant sources in each study area, 7 ranging from the typical or average child (50 th percentile or mean) to children 8 experiencing higher exposures (e.g., 90 th and 99 th percentiles). 9

• Child frequency counts associated with specific risk percentiles. This metric 10 characterizes the number of children, for a gi ven study area, associated with each of the 11 population percentiles (e.g., 25 children in a particular study area population are 12 predicted to have risk le vels at or above the 99 th percentile). It provides a perspective on 13 the number of children associated with various degrees of IQ loss for a particular case 14 study. 15

2.2.5 Uncertainty/Variability Analysis 16

For the pilot phase assessment, sensitivity analysis techniques were used to examine the issue of 17 uncertainty and its impact on exposure and risk estim ates. This sensitivity analysis consisted of a 18 series of “one-at-a-time” analyses conducted to evaluate the impacts of modeling methods and 19 parameter inputs on estimated blood Pb and IQ loss distributions. The general approach 20 employed was to evaluate changes in risk metr ics associated with changing single models or 21 input parameter values away from th ose used in a “baseline” analysis. 22

The baseline risk analysis focused on the curren t conditions scenario for the primary Pb smelter 23 case study. Note that while the baseline models and parameter values are believed to provide a 24 credible estimate of blood Pb and IQ changes, it is not suggested that the baseline model is 25 uniquely the “best,” or that it provides dem onstrably “central tendency” risk estimates. 26

The sensitivity analysis completed for the p ilot phase focused on those modeling elements 27 (including input datasets and modeling steps) believed to have the greatest potential for 28 significantly impacting exposure and ri sk modeling, including the following: 29

• Indoor dust prediction approach . Given its notable contribu tion to blood Pb levels, the 30 sensitivity of the blood Pb and IQ change es timates to the selected approach used to 31 estimate indoor dust concentrations was analyze d. This analysis evaluated the effect of 32 using different regression models on di stributions of estimated indoor dust Pb 33 concentrations. 34

• Blood Pb model selection. Blood Pb metrics developed usin g IEUBK were compared to 35 those based on the Leggett biokinetic model, as well as an “empirical” blood Pb model 36 developed by Lanph ear et al. (1998). 37Overview of Risk Assessment

ICF International 2-15 DRAFT – December 2006 • Exposure, intake, and update value selection. Two variations in gastrointestinal 1 absorption and soil and dust ingestion parame ters were evaluated. In one set of 2 sensitivity runs, the gastrointestinal abso rption fraction for diet ary and drinking water 3 ingestion was varied above and below the cen tral tendency value used in the baseline 4 assessment. For the second set of sensitivity runs, an alternat ive set of inputs for soil and 5 dust absorption and relative ingestion amounts of soil versus dust were evaluated for their 6 impacts on estimated blood Pb and IQ loss dist ributions. These values for the second set 7 of runs were derived by Von Linde rn et al. (2003) from a statis tical analysis of soil, dust, 8 and blood Pb distributions at a la rge mining/smelting “Superfund” site. 9

• Blood Pb GSD selection

. The probabilistic exposure model was run with two alternative 10 values of the blood Pb GSD and the results were compared to the results using the 11 baseline blood Pb GSD. 12

• Concentration-response function for IQ loss. Three alternative concentration-response 13 functions for IQ loss were evaluated. The first two consisted of the upper and lower 95 14 percent confidence limits on the log-linear re gression model, which were derived based 15 on a reanalysis of the Lanphear et al. model results (USEPA 2006a). The third 16 alternative consisted of the piecewise linear version of the Lanphear et al. (2005) model. 17

• Blood Pb metrics. This analysis evaluated the ma gnitude of impact on estimated IQ 18 losses associated with using the following alte rnative blood Pb metrics: lifetime average 19 and peak (defined as the highest annual aver age). These results were compared to the 20 results estimated using concurre nt blood Pb level distributions. 21

• Blood Pb cutpoint for IQ loss . The sensitivity of estimat ed IQ change to 50 percent 22 reductions in the blood Pb cutpoints for th e concurrent and lifetime average blood Pb 23 levels was evaluated. 24

Ultimately, this sensitivity analysis serves two primary purposes: 25 26 1. Determining which of the modeling elements included in the sensitivity analysis has the 27 greatest impact on risk modeling, which may he lp guide future efforts to refine the 28 overall assessment; and 29 2. Obtaining a semi-qualitative feel for the poten tial magnitude of overall uncertainty in the 30 risk results. 31 32 This type of sensitivity an alysis does not, however, allo w for a rigorous assessment of 33 probabilistically-defined confidence in specific risk percentiles to be quantified (that only being 34 possible with a formal uncertainty analysis). 35Overview of Risk Assessment

ICF International 2-16 DRAFT – December 2006 2.3 Ecological Risk Assessment 1

2 The ecological risk assessment involved four case studies and a natio nal-scale screening 3 assessment. These analyses were designed to estimate the potential for ecological risks 4 associated with exposures to Pb emitted into ambient air for three case studies: primary Pb 5 smelter, secondary Pb smelter, and near road way non-urban location. Activities for the fourth 6 case study (ecologically vulnerable location) focu sed on identification an d description and did 7 not include risk analyses. 8 9 This section provides an overvie w of the approaches implemented for these analyses, beginning 10 with a description of the concep tual model (Section 2.3.1) and a su mmary of the spatial scale of 11 the analysis (Section 2.3.2), followed by brief de scriptions of the exposure assessment (Section 12 2.3.3), effects assessment (Section 2.3.4), risk ch aracterization (Section 2.3.5), and uncertainty 13 and variability assessment (Section 2.3.6). 14

2.3.1 Conceptual Model 15
16 This section presents the conceptual model (Exhi bit 2-5) intended to illustrate the elements 17 pertinent to assessment of ecol ogical risks associated with environmental Pb exposures. 18 Elements addressed either explicitly or implicitly in the pilot phase are indi cated with black text. 19 Components with gray text were not addressed in the pilot phas e due to uncertainty regarding 20 available data and modeling tools. A description of how the differe nt elements of the conceptual 21 model were addressed in the pilo t phase, including any differences from what was presented in 22 the draft Analysis Plan, is provided below: 23 24 Sources. Sources associated with Pb released into ambient air were addre ssed, either explicitly 25 or implicitly, in this assessment. These sources are indicated in black text in Exhibit 2-5. When 26 measurements were used, the analysis attempte d to focus on those pertaining to Pb initially 27 released into ambient ai r, isolating contributions from policy-relevant background (i.e., non-air) 28 sources to the extent possible. 29 30 Pathways. This assessment focused on policy-rele vant exposure pathways involving Pb 31 released to ambient air and subse quently transported to soil, surface water, and sediment. Direct 32 inhalation exposures for birds and mammals were not addressed because ecological exposures 33 are expected to be primarily th rough ingestion of Pb in and on th eir foods and through incidental 34 ingestion of Pb in soils. Because Pb accumulates in soils, the ingestion exposures are expected 35 to be much higher than any inhalation exposures . In addition, the human exposure and health 36 risk assessments are expected to be protective of inhalation exposures for wildlife because the 37 critical effect under evaluation for humans (i.e., IQ loss) is more sensitive to Pb exposure than 38 the effects of Pb on growth, reproduction, or surv ival that are relevant to wildlif e population 39 risks. 40 41 Organisms. Potential ecological impacts for both terres trial receptors (e.g., populations of birds, 42 mammals, soil invertebrates, plants) and fr eshwater aquatic organisms (e.g., benthic 43 communities, water-column communities) we re considered in the pilot phase. 44 45Overview of Risk Assessment

ICF International 2-17 DRAFT – December 2006 Assessment Endpoints. The assessment endpoints for assess ing ecological risks associated with 1 Pb deposition from air are adve rse effects on the survival, grow th, and reproduction of exposed 2 ecological receptors. 3 4 Risk Metrics. Assessment of ecological risk for this assessment involves assessing the potential 5 for Pb exposures to be toxic to ecological recepto rs at the organism level and to adversely affect 6 growth and reproductive rates at the population level. These e ffects will be evaluated in each 7 case study (and in the national-scale screening assessment) using the hazard quotient (HQ) 8 approach, in which HQs are calculated by divi ding environmental medium-specific exposure 9 estimates by the appropriate ecotoxicity screening values. 10

An overview of the approach developed to implem ent the selected elements of the conceptual 11 model for the ecological risk assessment is provide d in Exhibit 2-6. This exhibit shows the key 12 types of information and models involved in each part of the assessment and how they are related 13 to each other and to the other pa rts of the analysis. Exhibit 2-7 summarizes the use of these 14 information types and models for each case study. As indicated in these exhibits, the specific 15 approach for each case study differed based on the na ture of the case study (e.g., type of source, 16 locations of populations) and the site-specific measurements avai lable. In cases where the 17 available measurements were not sufficien t to characterize th e study area (e.g., due to 18 insufficient spatial coverage), these data were used for performance evaluation. The following 19 sections describe each of the parts of the assessment in more detail. 20

21Overview of Risk Assessment

ICF International 2-18 DRAFT – December 2006 Exhibit 2-5. Conceptual Model for Pb Ecological Risk Assessment a, b 1

2Overview of Risk Assessment

ICF International 2-19 DRAFT – December 2006 Exhibit 2-6. Overview of Ecol ogical Risk Assessment Approach 1

2Overview of Risk AssessmentICF International 2-20 DRAFT – December 2006Exhibit 2-7. Models and Measurements Used for the Ecological Risk AssessmentPrimary Pb Smelter CaseStudySecondary PbSmelter CaseStudyNear Roadway Non-UrbanCase StudyEcologicallyVulnerable CaseStudyNational-ScaleScreeningAssessment:Surface Water andSediment PortionsLocation Herculaneum, Missouri Troy, AlabamaCorpus Christi, TexasAtlee, VirginiaHubbard BrookExperimental Forest,New HampshireSurface waterbodies in the UnitesStatesSpatial extent and resolutionApproximately 6 kmdiameter, centered on pointsourceU.S. Censusblocks•Corpus Christi: singletransect perpendicular toroad; 0.5 to 4 m fromroad•Atlee: 140 m section ofroad; 2 to 30 m from roadoblong basin about 8km long by 5 km wide47 basin study unitsfrom all regions ofthe United States,covering approx. 50percent of U.S. landbaseExposure Assessment: Estimating Media ConcentrationsModels n/a AERMODan/a n/a n/aDepositionto soilMeasurements n/a n/an/a 12 samples n/aModels n/a MPEbn/a n/a n/aSoil conc.MeasurementsSite-specific conc. of totalPb in soil samples(26 locations)n/aSite-specific conc. of totalPb in soil samples(Corpus Christi:2 locations;Atlee: 26 locations)Site-specific conc. oftotal Pb in soilsamples(124 locations)n/aModels n/a n/a n/a n/a n/aSurfacewater conc.MeasurementsSite-specific conc. ofdissolved Pb in watercolumn samples from eightwater bodies/drainage areas(30 locations)n/a n/aSite-specific conc. ofdissolved Pb in watercolumn samples(locations notreported)Site-specific conc.of dissolved Pb insurface watersamples (430samples)Models n/a n/a n/a n/a n/aSedimentconc.MeasurementsSite-specific conc. of totalPb in sediment samplesfrom five waterbodies/drainage areas (69locations)n/a n/a n/aSite-specific ornearby water bodyconc. of total Pb insediment samples(15 locations)Overview of Risk AssessmentICF International 2-21 DRAFT – December 2006Exhibit 2-7. Models and Measurements Used for the Ecological Risk AssessmentPrimary Pb Smelter CaseStudySecondary PbSmelter CaseStudyNear Roadway Non-UrbanCase StudyEcologicallyVulnerable CaseStudyNational-ScaleScreeningAssessment:Surface Water andSediment PortionsEcological Risk AssessmentSoilSoil screening values developed based onU.S. EPA Superfund Ecological SoilScreening Level (Eco-SSL) methodologycn/a n/aFreshwater –water columnU.S. EPA Pb freshwaterAWQC for aquatic lifederived based on sitespecific measured waterhardness (conc. of CaCO3)cn/a n/a n/aU.S. EPA Pbfreshwater AWQCfor aquatic lifederived based onsite-specific ornearby water bodymeasured waterhardness (conc. ofCaCO3)cEcotoxicityScreeningLevelsFreshwater –sedimentSediment screening valuesbased on MacDonald et al.(2000) sediment qualityassessment guidelinescn/a n/a n/aSediment screeningvalues based onMacDonald et al.(2000) sedimentquality assessmentguidelinescaAmerican Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) (USEPA 2004a)1bMultiple Pathways of Exposure (MPE) (USEPA 1998)2cThese values are based on measured ecotoxicity data and other types of measured data (e.g., soil screening levels evaluate measured food and soil ingestion3rates for birds and mammals).4Overview of Risk Assessment

ICF International 2-22 DRAFT – December 2006 2.3.2 Spatial Scale 1

2 To capture the spatial variabili ty in media and exposure concen trations, a GIS-based “spatial 3 template” was created for each case study. This template defines the outer boundary and the 4 spatial units within this boundary for each case study. Due to the inherent differences between 5 the case studies, such as the environmental set ting, locations of populat ions, source types, and 6 availability of measurements, the spatial template for each case study was different. Detailed 7 descriptions of the templates are provided in Chapter 4. The spatial template for each case study 8 location is summarized below: 9 10

• Primary Pb Smelter Location. Overall, the spatial templa te for this case study is the 11 same as that used for the human exposure and health risk assessments of this location. 12 For the ecological risk assessm ent, however, the spatial temp late also included a surface 13 water body within 2.5 km of the facility. 14 15

• Secondary Pb Smelter Location. The spatial template for this case study is the same as 16 that used for the human exposure and hea lth risk assessments of this location. 17 18

• Near Roadway Non-Urban Locations. The characterization of the two locations 19 selected for this case study relied upon av ailable soil measurements. For the Corpus 20 Christi, Texas location, the template includes a single transect of soil perpendicular to the 21 road extending approximately 4 m from the edge of the road. For the Atlee, Virginia 22 location, the template includes a band of so il approximately 30 m wide located adjacent 23 to a 140 m section of I-95. 24 25

• Ecologically Vulnerable Location. This location was selected and described as part of 26 the assessment, but not subjected to any further analysis . Thus, no spatial template was 27 developed. 28 29

• National-Scale Ecological Screening Assessment: Surface Water and Sediment 30 Portions. The spatial scale of this analysis incl uded parts of watershed basins from all 31 regions of the United States, accounting for drainage from approxi mately 50 percent of 32 the United States land base. 33

2.3.3 Exposure Assessment 34
35 The measures of exposure for the pilot phase are total Pb concentrations in soil, dissolved Pb 36 concentrations in fresh surface waters (water colu mn), and total Pb concentrations in freshwater 37 sediments. Exposure concentrations were estima ted for the three case studies and the national38 scale screening assessment as described below. 39 40

• For the primary Pb smelter case study, measur ed concentrations of total Pb in soil, 41 dissolved Pb in surface waters, and total Pb in sediment were used to develop point 42 estimates for locations with Pb thought to be associated with atmospheric Pb deposition, 43 rather than with non-air sources, such as runoff from waste storage piles. 44Overview of Risk Assessment

ICF International 2-23 DRAFT – December 2006 1

• For the secondary Pb smelter case study, soil concentrations of Pb were estimated using 2 fate and transport modeling based on EP A’s MPE methodology (USEPA 1998). The 3 pilot phase of the assessment was limite d to soil pathways for this case study. 4 5

• For the near roadway non-urban case study, so il concentration measurements collected 6 for two locations adjacent to interstate highw ays, one in an area of fairly high-density 7 development (Corpus Christi, Texas) and another in an area of medium-density 8 development (Atlee, Virginia), were used to develop point estimate s of Pb associated 9 with historical deposition. The pilot phase of the assessment was limited to soil pathways 10 for this case study. 11 12

• In the national-scale surface wa ter and sediment screening assessment, measurements of 13 dissolved Pb concentrations in surface water and total Pb concentrations in sediments for 14 locations across the United States were use d. Air emissions, water discharge, and land 15 use data for the areas surroundi ng these locations were asse ssed to identify locations 16 where atmospheric Pb deposition may be expect ed to contribute to potential ecological 17 impacts. The exposure assessment focused on these locations. 18

2.3.4 Effects Assessment 19
20 The tools used in the ecological risk assessm ent for soils, fresh surface water bodies, and 21 freshwater sediments included the follow ing screening-level ecotoxicity values. 22 23

• For terrestrial organisms, soil screening va lues were developed for the NAAQS review 24 based on the methodology for Ecological Soil Screening Levels (Eco-SSLs) developed 25 by EPA’s Office of Solid Waste and Em ergency Response (USEPA 2005b; USEPA 26 2005c) 27 28

• For aquatic organisms in the water colum n, EPA-recommended ambient water quality 29 criteria (AWQC), expressed as concentrations of Pb in the water co lumn, were used. 30 31

• For benthic organisms, the sediment screen ing values used were sediment quality 32 assessment guidelines developed by MacDonald et al. (2000; reporte d in MacDonald et 33 al. 2003). 34

2.3.5 Risk Characterization 35
36 The HQ approach was used for three case studies (primary Pb smelter, secondary Pb smelter, and 37 near roadway non-urban) and the national-scale ecological screeni ng risk assessment, consisting 38 of the surface water portion and the sediment portion. Measures of exposure for each location 39 were divided by ecotoxicity screening values for each environmental medium (i.e., point 40 estimates of Pb concentrations in soil, su rface water, and sediment were divided by the 41 corresponding ecotoxicity screeni ng value). For each case study, as well as for the national-scale 42 ecological screening assessment’s surface wate r and sediment portions, HQ values were 43 calculated where estimates of media Pb concentrations were available. 44Overview of Risk Assessment

ICF International 2-24 DRAFT – December 2006 2.3.6 Uncertainty/Variability Analysis 1

2 For the ecological risk assessment, a qualitative evaluation of uncer tainties was conducted and is 3 described in Section 7.3. 4Case Study Descriptions

ICF International 3-1 DRAFT – December 2006 

### 3. Case Study Descriptions 1 2 As discussed in Chapter 2, a case study-based approach was employed for the pilot phase to 3 estimate human exposure and health risks and ecol ogical risks. This chapter, in sections 3.1 4 through 3.5, provides descriptions of each of the case study locations, accompanied by an 5 overview of the available information relevant to and considered in the human exposure and 6 health risk assessments and the ecological risk assessment. The surface water and sediment data 7 used for the national-scale sc reening assessment are discusse d in Section 3.6. Exhibit 3-1 8 provides a list of the case study and national-scale evaluations performed for the pilot phase 9 analysis and indicates the a pplicability of each to the human exposure and health risk 10 assessments and/or the ecological risk assessment. 11 12 Exhibit 3-1. Case Studies Included in the Pilot Phase 13 Case Study Applicability Primary Pb smelter location Human and ecological Secondary Pb smelter location Human and ecological Near roadway urban location Human Near roadway non-urban locations Ecological Ecologically vulnerable location Ecological National-scale surface water and sediment screening Ecological 14 3.1 Primary Pb Smelter Case Study 15 16 The Herculaneum Lead Smelter (HLS) is one of th e largest primary Pb smelters in the world and 17 is the only currently operating Pb smelter in th e United States. The HLS facility represents a 18 relatively large point source that has been activ e for over a century and has a large amount of 19 site-specific data characterizi ng both media concentrations (so il, indoor dust, and ambient air) 20 and human blood Pb levels. Pb contaminant cond itions for the area surrounding this facility are 21 dominated by emissions from this facility, with older historical auto and other point source 22 emissions being of relatively lesser importance. Environm ental sampling conducted around the 23 primary Pb smelter has shown Pb contamin ation throughout the community surrounding the 24 smelter. Available environmental data are di scussed in Section 3.1.5 and presented in Appendix 25 A. 26 27 3.1.1 Description of Case Study Location 28 29 The HLS facility is located in Herculaneum, Missouri. The city of Herculaneum is in Jefferson 30 County, about 42 kilometers (km) [26 miles (mi)] southwest of St. Louis, and its approximate 31 area is 9 km 2 . As of 2000, there were estimated to be 37,562 people living within a 10-km radius 32 of the HLS facility (2,064 within 2 km; 14,237 between 2 and 5 km; and 21,261 between 5 and 33 10 km). Of this population in 2000, 3,880 were children ages 7 years old and younger (171 34 within 2 km; 1,545 between 2 and 5 km; and 2,164 between 5 and 10 km) (U.S. Census Bureau 35 2005). 36 37Case Study Descriptions

ICF International 3-2 DRAFT – December 2006 3.1.2 Description of Primary Pb Smelter 1 2 The HLS facility is located at 881 Main Street in Herculaneum (see E xhibit 3-2). The HLS 3 property is 52 acres and consists of three main areas: (1) the smelter plant, which is located on 4 the east side of Main Street; (2) office buildings located on the west side of Main Street; and (3) 5 a 40- to 50-feet high slag storage pile that covers 24 acres. The f acility is bordered on the east by 6 the Mississippi River, on the southeast by Jo achim Creek, on the west and north-northwest by 7 residential areas, and on the south- southwest by the slag pile. A la rge part of the slag pile is 8 located in the floodplain wetlands of Joach im Creek and the Mississippi River. 9 10 The principal processing occurring at the facility includes: (1 ) sintering, smelting, and refining 11 of Pb ore; (2) sulfuric acid production from waste sulfur-c ontaining gases generated by the 12 sintering operation; and (3) wastew ater treatment. Sources at the facility include various stacks 13 and vents from plant processes, fugitive emissi ons from ore handling operations, wind erosion 14 from the slag pile, and fugitive emissions from transport of Pb concentrate over local roads. A 15 Pb ore concentrate, consisting of approximately 80 percent Pb sulfide, is processed at the 16 smelter. The ore is transported by truck fr om eight Pb mines near Viburnum, Missouri, 17 approximately 42 km (75 mi) south-southwest of Herculaneum. The smelting operation 18 generates a molten slag, 20 percent of which is sent to the slag stor age pile as waste. Stack and 19 fugitive emissions from the facility and deposit ion of these emissions to soil and surface water 20 have resulted in elevated Pb concentrations in the surroundi ng areas (Missouri DNR 1999, as 21 cited in ATSDR 2003). 22Case Study Descriptions

ICF International 3-3 DRAFT – December 2006 Exhibit 3-2. Facility Locati on Map – Primary Pb Smelter 1

2Case Study Descriptions

ICF International 3-4 DRAFT – December 2006 3.1.3 Human Exposure Measurements 1 2 Blood Pb levels at or above 10 μ g/dL have been recorded for He rculaneum residents, including 3 children less than 72 months of age (ATSDR 2002; ATSDR 2003) . The U.S. Department of 4 Health and Human Services (DHSS) and the Je fferson County Health Department (JCHD), in 5 cooperation with the Agency for Toxic Substances and Disease Registry (ATSDR), have offered 6 blood Pb (PbB) testing to the residents of He rculaneum and surrounding communities. Results 7 of two such testing events conducted in 2001 and 2002 have been documented in DHSS/ATSDR 8 health consultation reports (ATSDR 2002; ATSDR 2003) and are summarized here. 9 10 A total of 935 Herculaneum residents were te sted in 2001. A summary of PbB results by age 11 group is provided in Exhibit 3-3. Of the children less than 72 months old that were tested in 12 2001, 33 (28 percent) had PbBs of 10 μ g/dL or greater. In the area closest to the smelter, 30 out 13 of 67 (45 percent) of the children under 72 mont hs of age who were tested in 2001 had PbBs 14 equal to or above 10 μ g/dL (ATSDR 2002). 15 16 Exhibit 3-3. Summary of 2001 PbB Measurements 17 for Herculaneum Residents 18 PbB ( μ g/dL) Number of Individuals Children less than 72 Months 0 – 9 85 10 – 19 27 20 – 29 5 30 or Higher 1 Children between 6 and 17 Years 0 – 9 149 10 – 19 13 20 – 29 0 30 or Higher 0 Adults 18 Years or Older 0 – 24 653 25 – 39 1 40 – 49 0 50 or Higher 1 Source: ATSDR 2002 19 20 In September 2002, DHSS and JCHD conducted a vol untary community-wide PbB testing event, 21 and a total of 340 Herculaneum residents were tested. Results by age group for Herculaneum 22 residents are summarized in Exhibi t 3-4. As shown in Exhibit 3- 4, of the children less than 72 23 months old that were tested in 2002, 8 (14 percent) had PbBs 10 μ g/dL or higher. 24 25Case Study Descriptions

ICF International 3-5 DRAFT – December 2006 Exhibit 3-4. Summary of 2002 PbB Measurements 1 for Herculaneum Residents 2 PbB ( μ g/dL) Number of Individuals

Children less than 72 Months

0 – 9 50

10 – 19 6

20 – 29 2 30 or Higher 0

Children between 6 and 17 Years

0 – 9 127

10 – 19 2

20 – 29 0 30 or Higher 0

Adults 18 Years or Older

0 – 24 147

25 – 39 5

40 – 49 1 50 or Higher 0 Source: ATSDR 2003 (Tables 1-3) 3 4 3.1.4 Emissions 5 6 The Pb emissions estimates used for the primar y Pb smelter case study were obtained from EPA 7 Region 7 and reflect the 2000 Revisi on of the State Implementation Plan (SIP) developed for the 8 facility (MDNR 2000). Cumulative Pb emission s from processes at the facility, fugitive 9 emissions from transferring of materials, fugitiv e emissions from storage at the slag pile, and 10 emissions associated with dust from roadways in the vicinity of the smelter totaled 266 11 tons/year. Particle sizes for emissions from road segment emission points around the primary Pb 12 smelter ranged from 44 to 210 μ m. Particle sizes for emissions from all other emission points at 13 the primary Pb smelter ranged from 6.3 to 45.5 μ m. EPA Region 7 is currently in the process of 14 reviewing source characterization data to en hance modeling conducted as part of the SIP 15 planning. Consequently, the dispersion model ru ns completed for the pilot phase using these 16 emissions should be considered il lustrative only. Emissions and re lease parameters, particle size 17 inputs, and other inputs used fo r fate and transport modeling of the primary Pb smelter are 18 provided in Appendix B. 19 20 3.1.5 Summary of Environmental Data 21 22 The environmental data sets available for the primary Pb smelter case study are summarized in 23 Exhibit 3-5. 24 25Case Study Descriptions

ICF International 3-6 DRAFT – December 2006 Exhibit 3-5. Summary of Environmental Data Sources for Primary Pb Smelter Case Study 1

Media Data Set Timeframe Locations Comments

Human Health a
EPA-operated high volume samplers 2001 – 2003 4 locations Located along roads; see Exhibits A-1, A-2
Ambient air EPA’s Air Quality System (AQS)/AirData 2001 - 2005 9 locations Monitors within 10 km of facility; see Exhibits A-1, A-3

Pre-excavation 2000 - 2004 Over 900 locations around primary Pb smelter Locations within approximately 2.4 km (1.5 mi) of facility; see Exhibit A-4

Post-excavation 2000 - 2004 Approximately 300 locations around primary Pb smelter Locations within approximately 2.4 km (1.5 mi) of facility; see Exhibit A-5 Residential soil

Recontamination Assessment 2002 - 2006 31 residences Locations within approximately 1.6 km (1 mi) of facility; see Exhibits A-6, A-7

Indoor dust Recontamination Assessment 2002 - 2006 17 residences Locations within approximately 1.6 km (1 mi) of facility; See Exhibit A-8

Deposition to soil Soil boxes 2003 – 2004 b 10 locations See Exhibits A-9, A-10

Deposition to air Filters 2003 – 2004 b 10 locations See Exhibits A-9, A-11

Ecological c

Non-residential soil Site Characterization Investigation (ELM 2005) 2000 61 locations, 26 of which were included in the assessment See Chapters 4 and 7 for more information

Surface water Site Characterization Investigation (ELM 2005) 2000 62 locations, 30 of which were included in the assessment d See Chapters 4 and 7 for more information

Sediment Site Characterization Investigation (ELM 2005) 2000 82 locations, 69 of which were included in the assessment e See Chapters 4 and 7 for more information

a Several data sources existed, including analyses conducte d by EPA, the facility, ATSDR, Missouri Department of 2 Natural Resources (DNR), and various consultants. Aside from EPA’s AQS air monitoring data, the data 3 represented in this table were obtained electronically from EPA Region 7 (2006a). These EPA data are the only 4 environmental data discussed and summarized for the primary Pb smelter in this chapter and in the associated 5 appendices. Attempts were made to obtain environmental data from sources outside EPA (such as those listed 6 above), but no additional data were received w ithin the time available for this assessment. 7

b These are the most recent data available from EPA Region 7. 8

c Some sampling locations were excluded from the ecological risk assessment b ecause of their close proximity to the 9 slag storage area; it was assumed that th ese areas had significant Pb contributions from pathways other than the air 10 (e.g., runoff and flooding). 11

d The sampling locations were from eight water bodies/dra inage areas including Joachi m Creek, Mississippi River, 12 U-shaped pond approximately 1.7 km NW of the facility. 13

e The sampling locations were from five water bodies/dra inage areas including Joachim Creek, Mississippi River, U14 shaped pond approximately 1.7 km NW of the facility. 15Case Study Descriptions

ICF International 3-7 DRAFT – December 2006 1 Air Monitoring: As shown in Exhibit 3-5, th ere are two air monitoring da ta sets available from 2 EPA for the primary Pb smelter. Appendix A, Exhibit A-1 shows the lo cations of the 13 air 3 monitoring locations relative to the facility. Air monitoring data for the four EPA high-volume 4 sites are provided by year in Exhibit A-2. In general, average annual Pb concentrations in 5 ambient air declined from 2001 to 2003 at these lo cations. Most notable are the reductions in 6 measurements at the monitor with Pilot Analys is ID 102. The maximum Pb concentration (65 7
μ g/m 3 ) occurred at this monitor in 2001. In 2003, th e maximum at this monitor decreased to 10 8

μ g/m 3 (a decrease of 85 percent). Likewise, the average annual Pb concentration at the monitor 9 decreased 84 percent from 2001 to 2003. 10 11 Air monitoring data for the nine AirData monito rs are provided by year in Exhibit A-3. Based 12 on these data, decreases in average annual Pb co ncentrations have been observed since 2001. 13 The largest difference was observed for M onitor ID 290990005, where average annual Pb 14 concentrations decreased from 2.11 μ g/m 3 in 2001 to 0.278 μ g/m 3 in 2005 (a decrease of 87 15 percent). 16 17 For comparison purposes, the average annual Pb c oncentrations for the year 2005 from AirData 18 monitors located around the primary Pb smelter were compared to AirData monitor results 19 across the United States. Exhibit 3-6 shows the di stribution of average annual Pb concentrations 20 in total suspended particulat e matter (TSP) for 208 monitors across the United States (with 21 average annual monitored Pb concentrations sorted in ascending order). The 2005 monitor 22 results for the nine AirData monitors located in the vicinity of the primary Pb smelter are 23 indicated using a solid square ( ■ ). The average monitored Pb c oncentrations detected at the 208 24 monitors ranged from 0.001 to 1.56 μ g/m 3 . The 1.56 μ g/m 3 maximum is associated with 25 Monitor 290990015 , one of the monitors iden tified within 10 km of the primary Pb smelter. Of 26 the 208 locations, the nine monitors located within 10 km of the primary Pb sm elter all fall in the 27 top 30 percent of all monitored ambient levels for these 208 monitors, wi th four of the nine 28 monitors in the top 10 percent. 29 30 Exhibit 3-6. Distribution of Pb TSP Measurements 31 Across the United States Relative to Monitors Near the Primary Pb Smelter 32

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

Monitors Across the U.S. (N=208)Lead TSP Concentration (ug/m3)

■ - Monitor Around Hercualneum

33 34Case Study Descriptions

ICF International 3-8 DRAFT – December 2006 Soil: As shown in Exhibit 3-5, there are three soil data sets availa ble from EPA for the primary 1 Pb smelter: pre-excavation, post-excavation, and recontamination assessment data. Pre2 excavation soil samples were collected from reside ntial locations around the smelter prior to soil 3 removal activities. Pre-excavation soil sample results for over 900 residential locations around 4 the primary Pb smelter are presented in Appendi x A, Exhibit A-4. Average soil concentrations 5 at these sampling locations ranged from 53 to 23,350 mg/kg. 6 7 Based on pre-excavation sampling results, Pb-contaminated soil in residential yards near the 8 smelter was removed, replaced with clean backfill, and re-seeded with grass. Post-excavation 9 soil data were available for over 300 residential locations. It is unclear from the available 10 information whether post-excavation soil samp les were collected immediately following 11 excavation or after the yards were backfilled. Po st-excavation results are presented in Appendix 12 A, Exhibit A-5. Average soil c oncentrations at these properti es ranged from 70 to 2,757 mg/kg. 13 14 The EPA has recently conducted post-remediation resi dential yard soil sampling at 31 locations 15 within approximately a 1.6 km (1 mi) radius of the primary Pb smelter to determine whether 16 residential soils previously excavated are becoming recontaminated. Results for the 17 recontamination assessment samples are provided in Appendix A, Exhibit A-6. For most of the 18 31 recontamination assessment locations, average Pb concentrations in the replacement “clean” 19 soil increased between 2002 and 2006. Refer to A ppendix A, Exhibit A-7 for a summary of the 20 pre-excavation, post-excavation, an d recontamination assessment data for these 31 residential 21 locations. 22 23 Indoor Dust: The interiors of 17 of th e 31 residential properties identified for the soil 24 recontamination assessment were also assessed fo r Pb levels in indoor dust. Interior dust 25 removal (in which areas inside homes were wi ped and/or vacuumed) was performed at these 26 residences prior to recontamination sampling. Appendix A, Exhibit A-8 provides a summary of 27 recontamination indoor dust sample results fo r these 17 properties. Carpet dust samples 28 collected during recontamination sampling ev ents at these residences contained Pb 29 concentrations that ranged from 122 to 4,350 mg/kg. Pb loadings in window sill wipe samples 30 ranged from 5.6 to 1,385 μ g/ft 2 . There were no general patterns identified at homes during 31 successive sampling events. Pb concentrations and/or loadings may have increased, decreased, 32 or remained generally the same (see Appendix A, Exhibit A-8). This lack of pattern may be 33 attributed in part to inconsistent hous e cleaning protocols within the homes. 34 35 Deposition: As shown in Exhibit 3-5, soil boxes 1 were set up at 10 locations around the primary 36 Pb smelter. Deposition monitoring locations ar e shown in Appendix A, Exhibit A-9. From 2003 37 to 2004, samples were collected monthly to meas ure Pb deposition on soil; results for these 38 locations are presented in Appendix A, Exhib it A-10. Maximum concentr ations at the nine 39 locations (excluding the control site) ranged from 25 to 406 mg/kg in 2003 and from 25.3 to 527 40 mg/kg in 2004. The overall average Pb concentrati on in these soil boxes acr oss all nine locations 41 increased from 49 mg/kg in 2003 to 96.5 mg/kg in 2004, an increase of almost 100 percent. 42 43

1 Clean soil is placed in containers that measure approximately 2 feet by 3 feet . Soil boxes were intended to provide a repeatable means of measuring Pb deposition on soil that would be less likely to be disturbed than soil in residential yards.Case Study Descriptions

ICF International 3-9 DRAFT – December 2006 Air deposition monitoring data were available for the same 10 locations around the primary Pb 1 smelter for which soil box monitoring data were available (see Appendix A, Exhibit A-9). Dry 2 deposition samples were collected monthly at tw o levels (1 foot and 10 feet) above the ground 3 surface from April 2003 through April 2004. Data coll ected at each level for these locations are 4 presented in Appendix A, Exhibit A-11. The annual Pb deposition rates at a height of 1 foot for 5 the nine monitoring locations (excluding the control site) ranged from 0.34 to 22 mg/ft 2 , and the 6 overall average Pb deposition rate across all nine locations at the height of 1 foot was 4.8 mg/ft 2

. 7 The annual Pb deposition rates at a height of 10 feet for the nine monitoring locations ranged 8 from 0.26 to 33 mg/ft 2 , and the overall average Pb deposition ra te across all nine locations at the 9 height of 10 feet was 5.0 mg/ft 2 . The average annual Pb deposition rates at each level by 10 location are provided in Exhibit A-11. 11 12 3.1.6 Ecological Characterization 13 14 3.1.6.1 Description of Site and Surroundings 15 16 Ecologically relevant features near the primary Pb smelter facility include the Mississippi River, 17 Joachim Creek, emergent and scrub-shrub wetla nds, and successional and mature bottomland 18 hardwood forest tracts (ELM 2005). Bottomland hardwood forests and agri cultural fields are 19 present to the west, south, and east of the characte rization area between the smelter’s slag storage 20 area (SSA) and Joachim Creek. The most mature bottomland hardwood forest is adjacent to 21 Joachim Creek. Immediately south of the facili ty is a mixture of floodplain forest, emergent 22 marsh, and scrub-shrub wetland habita t that is populated by willow ( Salix spp. ) trees (ELM 23 2005). 24 25 The Missouri Department of Conservation (MDC) lists the plant and animal species that are 26 endangered, vulnerable, or imper iled by county in the Heritage Database (MDC 2006). Exhibit 27 3-7 lists the species of concern for Jefferson Co unty; species that have acquired the state or 28 federal status of “endangered” are identified. As shown in Exhibit 3-7, the pink mucket, 29 scaleshell, gray bat, Indiana ba t, and pallid sturgeon are on both the state and federal endangered 30 lists. 31 32 Exhibit 3-7. Species of Concern O ccurring in Jefferson County, Missouri a

Animals Plants
Lake sturgeon (SE) Acipenser fulvescens Wild leek Allium burdickii
Elktoe Alasmidonta marginata Wild sarsaparilla Aralia nudicaulis
Alabama shad Alosa alabamae Forked aster Aster furcatus
Brown bullhead Ameiurus nebulosus A moss Campylium polygamum
Western sand darter Ammocrypta clara A sedge Carex texensis
Rock pocketbook Arcidens confragosus Fremont's leather flower Clematis fremontii
Great egret Ardea alba Nuttall tick-trefoil Desmodium nuttallii Swamp metalmark Calephelis muticum A lichen Dibaeis absoluta Highfin carpsucker Carpiodes velifer Pale avens Geum virginianum

Eastern collared lizard Crotaphytus collaris collaris Fir clubmoss Huperzia porophila
Crystal darter (SE) Crystallaria asprella Weak rush Juncus debilisCase Study Descriptions

ICF International 3-10 DRAFT – December 2006 Exhibit 3-7. Species of Concern O ccurring in Jefferson County, Missouri a

Animals Plants

Blue sucker Cycleptus elongatus A moss Mnium thomsonii
Eastern hellbender (SE) Cryptobranchus alleganiensis A liverwort Nardia lescurii

Spectaclecase (FCE) Cumberlandia monodonta Stemless evening primrose Oenothera triloba

Elephantear (SE) Elliptio crassidens A panic grass Panicum dichotomum var. nitidum
Snuffbox (SE) Epioblasma triquetra Missouri cliffbrake Pellaea glabella var. missouriensis
Proserpine cavesnail Fontigens proserpina Wild sweet william Phlox maculata ssp. Pyramidalis
Ebonyshell (SE) Fusconaia ebena A liverwort Preissia quadrata

Mooneye Hiodon tergisus Sullivantia Sullivantia sullivantii Pink mucket (SE; FE) Lampsilis abrupta

Scaleshell (SE; FE) Leptodea leptodon

Black sandshell Ligumia recta

Silver chub Macrhybopsis storeriana Gray bat (SE; FE) Myotis grisescens Indiana bat (SE; FE) Myotis sodalist
Ghost shiner Notropis buchanani Hickorynut Obovaria olivaria Belted crayfish Orconectes harrisonii
River darter Percina shumardi Sheepnose (SE; FCE) Plethobasus cyphyus
Paddlefish Polyodon spathula Wood frog Rana sylvatica Eastern spadefoot Scaphiopus holbrookii Pallid sturgeon (SE; FE) Scaphirhynchus albus

a Abbreviations: SE = state endangered status; FE = fe derally endangered status; FCE = federal candidate for 1 endangered status; Source: MDC (2006). 2 3 Throughout much of the year, migratory birds such as the red-tailed hawk, belted kingfisher, and 4 great blue herons utilize the habitat near the primary Pb smelter. The state and federally 5 endangered bald eagle has been spotted on-site at the primary Pb smelter facility, which is 6 known to be within the habitat for the bird. The f acility is also within th e habitat of the Indiana 7 bat, which, as mentioned above, is also on the st ate and federal endangered species lists. None 8 of these species, however, feed on soil inverteb rates. In addition, the state and federally 9 endangered pallid sturgeon has been identified in the Mississippi River adjacent to and 10 downstream of the facili ty (USEPA Region 7 2000). 11 12 3.1.6.2 Description of Available Pb Environmental Data 13 14 As shown in Exhibit 3-5, the data set fo r the ecological risk assessment includes Pb 15 concentration data in surface soil, surface water, and sediment collected from non-residential 16 locations near the primary Pb smelter (ELM 200 5). The data set sampling locations include 17Case Study Descriptions

ICF International 3-11 DRAFT – December 2006 areas within a 2.1 km (1.5 mi) radi us of the facility and two “refer ence areas” that are 6 to 7 km 1 south of the facility (ELM 2005). 2 3 Soil: Total Pb concentration data are available for soil samples occurrin g in six clusters of 4 locations in forested areas within 0.3 km of Joachim Creek that are remote from residential 5 development (ELM 2005, Figure 7): the western a nd southern slopes of the slag storage area 6 (SSA); sites along central, east, and west transects from the SSA; and sites located along the 7 historic railroad grade on the west side of Joachim Creek. Soil samples near Joachim Creek may 8 reflect Pb contribution from r unoff from the SSA, and flooding from Joachim Creek (ELM 9 2005). The soil sampling locations w ithin a 2.1 km radius were all in areas that might have been 10 subject to Pb inputs from Joachim Creek during fl ooding events. As such, the stations might not 11 represent the concentrations of Pb in soils that result from direct air emissions from the smelter. 12 The reference soil samples were taken from ar eas approximately 5.6 and 6.5 km south of the 13 facility. Soil samples were collected from 0 to 3 inches below the surface (ELM 2003). 14 15 The average total Pb soil concentration for th e entire ELM (2005) data set was 269 mg/kg. The 16 minimum concentration detected was 20.4 mg/kg, and the maximum was 752 mg/kg. 17 18 Surface water and sediment: Dissolved Pb concentration data in surface water samples and 19 total Pb concentration data for sediment samp les are available for sites along the Mississippi 20 River, including one reference station more th an 2.1 km upstream of the facility, and sampling 21 stations within approximately 2.5 km upstream and downstream of the confluence with Joachim 22 Creek. Data are also available for sites in Joachim Creek along the approximately 4.4 km of 23 Joachim Creek that lie within 2.1 km of the smelte r, as well as two locations farther upstream. 24 Pb measurements are also availa ble in nearby wetlands (ELM 2005). 25 26 Dissolved Pb was not detected by ELM in sample s from surface water locations included in the 27 pilot phase, and the detection limit for the analysis was 3 μ g/L. Total Pb concentrations in 28 surface water for the full data set averaged 14.5 μ g/L and ranged from below the detection limit 29 to 105 μ g/L. Total Pb concentrations in the sedime nt samples for the full data set averaged 263 30 mg/kg and ranged from 6.2 to 3,040 mg/kg. 31 32 3.2 Secondary Pb Smelter Case Study 33 34 The secondary Pb smelter case study focused on the impacts of emissions from a smaller point 35 source (compared to the primary Pb smelter) locate d in Alabama. There were less site-specific 36 data characterizing media concentrations and hu man exposure levels available for this study area 37 than for the primary Pb smelter case study. Howe ver, recent air concentration data from the area 38 surrounding the facility and facili ty characterization data (inc luding emission estimates) were 39 readily available. 40 41 3.2.1 Description of Case Study Location 42 43 The secondary Pb smelter case study location is in Troy, Alabama. Troy is a city located in Pike 44 County, positioned in the south cen tral portion of the state, and its approximate area is 68 km 2

. 45 As of 2002, there were estimated to be 17,910 peopl e living within a 10-kilo meter radius of the 46Case Study Descriptions

ICF International 3-12 DRAFT – December 2006 facility (2,186 within 2 k ilometers; 10,634 between 2 and 5 km; and 5,090 between 5 and 10 1 km). Of this population, 1,672 are children ag es 7 years and under (187 within 2 km; 896 2 between 2 and 5 km; and 589 between 5 and 10 km) (U.S. Census Bureau 2005). 3 4 3.2.2 Description of Secondary Pb Smelter 5 6 This facility is one of 15 secondary Pb smelte rs currently operating in the United States (ECR 7 2006). US-231 borders the facility to the north -northeast and a railroad line and Henderson 8 Highway run along the north-northwest and west boundaries of the facility. The area located 9 directly west of Henderson Highway is foreste d. To the south and south-southwest are other 10 industries and businesses. Big Creek appear s to be the closest major water body, located 11 approximately 0.8 km (0.5 mi) sout h-southeast from the center of the facility. The City of Troy 12 is located north and east of the facility and north of US-231 (see Exhibit 3-8). 13 14 Secondary Pb smelters produce Pb from scrap an d provide the primary means for recycling Pb15 acid automotive batteries. Approxi mately 95 percent of all Pb-aci d batteries are recycled at 16 secondary Pb smelters. Secondary Pb smelters perform three basic unit operations: battery 17 breaking, smelting, and refining and alloying. Battery breaking is accomplished by either 18 crushing or cutting battery cases into pieces. The plastic, spent acid, and Pb-bearing materials 19 are then separated. Pb-bearing materials are processed in one of three types of smelting 20 furnaces: blast, reverberatory, or rotary. Molten Pb from these furnaces is further processed in 21 refining kettles and subsequently cast into molds. The waste stream from the furnaces (i.e., slag) 22 is either returned to the primary smelting furn ace or treated in a separate furnace dedicated to 23 slag cleaning to recover additional Pb. There are three types of emission s ources at secondary Pb 24 facilities – process sources, process fugitive sources, and fugitive dust sources. The types of 25 sources at the secondary Pb smelter analyzed in these assessments include: blast furnace, 26 agglomeration furnace, alloying kettles and heat ing system, flue dust storage bins, and slag 27 treatment furnace. Stack emissions from the f acility and fugitive emissions associated with 28 materials storage and handling and roadway dust have resulted in re leases of Pb to the air and 29 soil (ECR 2006). 30 31 32Case Study Descriptions

ICF International 3-13 DRAFT – December 2006 1 Exhibit 3-8. Facility Location Map – Secondary Pb Smelter 2

3 4Case Study Descriptions

ICF International 3-14 DRAFT – December 2006 3.2.3 Human Exposure Measurements 1 2 No information on children’s PbBs specific to the area around the secondary Pb smelter was 3 identified. However, the Lead Poisoning Pr evention Branch of the CDC collected PbB 4 surveillance data for children less than 72 mont hs of age in Pike County, Alabama in 2003. Of 5 the 407 children tested by the CDC, there were 33 (approximately 8 percent) confirmed cases of 6 elevated PbB (i.e., PbB above 10 μ g/dL). For children less than 72 months of age in the state of 7 Alabama and in the United States as a whole, the confirmed elevated PbBs as a percent of 8 children tested in 2003 was 2.4 percent and 1.9 percent, resp ectively (CDC 2003). Note, 9 however, that the statis tics for children in Pike County do not necessarily re present PbBs for 10 children living in Troy, Alabama. 11 12 3.2.4 Emissions 13 14 As of June 9, 1994, when EPA proposed the sec ondary Pb smelter MACT standard (59 FR 15 63941), there were 23 secondary Pb smelters opera ting in the United States. As of 2002, there 16 were 15 operating facilities in 11 states. Of these 15 facilities, the secondary Pb smelter 17 analyzed in this study is the thir d highest emitter of Pb (ECR 2006). 18 19 The estimates for process emissions for the secondary Pb smelter analyzed in this study were 20 calculated from Pb emissions measured during stack tests performed December 1997, November 21 1999, and February 2000 (ECR 2006). Fugitive emissions were estimated by comparing the 22 modeled concentrations from the stack emi ssions to background Pb concentrations and 23 monitored concentrations. The cumulative Pb em issions from this facili ty, including facility 24 process and fugitive emissions, were estimated to be 4.56 tons/year. Particle sizes for emissions 25 from point sources at the facility ranged from 0.5 to 10 μ m, and particle sizes for emissions from 26 area sources at the facility ranged from 1.25 to 22.5 μ m. Emissions and release parameters, 27 particle size inputs, and other i nputs for fate and transport modeli ng for the facility are provided 28 in Appendix C. 29 30 3.2.5 Summary of Environmental Data 31 32 The environmental data sets available for the secondary Pb smelter cas e study are summarized in 33 Exhibit 3-9. 34Case Study Descriptions

ICF International 3-15 DRAFT – December 2006 1 Exhibit 3-9. Summary of Environmental Data Sources for 2 Secondary Pb Smelter Case Study a 3
Media Dataset Timeframe Locations Comments

Human Health

Ambient air EPA’s AQS/AirData 1997 – 2000 b 2 locations Located 400 and 680 meters from the facility; see Exhibits A-12, A-13.

Residential soil No data identified.
Indoor dust No data identified. Deposition No data identified. Ecological
Non-reidential soil No data identified.
Surface water No data identified.
sediment No data identified.

a In general, site characterization information was lacking fo r this secondary Pb smelter. Data, with the exception of 4 limited air monitoring data, were not available based on information from EPA Region 4. Information from the 5 Alabama Department of Environmental Management (ADEM) indicates there may be relevant soil data available 6 from the facility (ADEM 2006); however, no data have been obtained to date. 7

b AirData monitor values from 1997 - 2000 were obtained for the purpose of comparing monitored values to 8 modeled air concentrations (see Chapter 4). Because emi ssions data for the secondary Pb smelter were based on 9 stack tests for 1997, 1999, and 2000, monitoring results from similar years were used. 10 11 Air Monitoring: As shown in Exhibit 3-9, average annual Pb concentrations in the vicinity of 12 the secondary Pb smelter were available from EPA’s AQS/AirData ne twork (USEPA 2006f) for 13 two air monitors located near th e facility (see Appendix A, Exhibit A-12). Data from these two 14 air monitoring sites for 1997 through 2000 are presen ted in Appendix A, Exhibit A-13. Over this 15 period, average annual Pb concentrations at the monitor closer to the facility ranged from 0.383 16 to 0.474 μ g/m 3 , with the lowest average annual concentr ation in year 2000. Average annual Pb 17 concentrations at the second monitor ranged from 0.132 to 0.198 μ g/m 3

. 18 19 For comparison purposes, the average annual Pb c oncentrations for the year 2005 from AirData 20 monitors located around the second ary Pb smelter case study location were compared to AirData 21 monitor results across th e United States Exhibit 3-10 shows the distribution of average annual 22 Pb concentrations in total su spended particulate matter (TSP) for 208 monitors across the United 23 States (with average annual monitored Pb concen trations sorted in as cending order). The 2005 24 monitor results for the tw o AirData monitors located in the vi cinity of the secondary Pb smelter 25 are indicated using a solid square ( ■ ). The average monitored Pb concentrations from the 208 26 monitors ranged from 0.001 to 1.56 μ g/m 3 . Both of the monitors lo cated near the secondary Pb 27 smelter fall into the top 15 percent of the 208 locations. 28Case Study Descriptions

ICF International 3-16 DRAFT – December 2006 1 Exhibit 3-10. Distribution of Pb TSP Measurements Across the 2 United States Relative to Monitors Near the Secondary Pb Smelter 3

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

Monitors Across the U.S. (N=208)Pb TSP Concentration (ug/m3)

■ - Monitor Around Sanders

4 5 Soil: No soil measurement data for Pb were identi fied in the vicinity of the secondary Pb 6 smelter case study location. For the human e xposure and health risk assessments, soil 7 concentrations were estimated using two me thods – a model-only approach and a hybrid 8 approach using model results and surrogate measur ements from a similar facility. See Section 9 4.2.3 for details. 10 11 Indoor Dust: No indoor dust data for Pb were available from homes located in the vicinity of 12 the secondary Pb smelter. Indoor dust concentrat ions were estimated using an empirical model 13 that relates ambient air concentrations to indoor dust concen trations, as discussed in Chapter 4. 14 15 Deposition: No Pb deposition monitoring data were iden tified in the vicinity of the secondary 16 Pb smelter case study location. For the human exposure and health risk assessments and the 17 ecological risk assessment, Pb de position arising from emissions fr om the secondary Pb facility 18 was modeled using EPA’s AERMOD air disper sion model, as discussed in Chapter 4. 19 20 3.2.6 Ecological Characterization 21 22 3.2.6.1 Description of Site and Surroundings 23 24 The secondary Pb smelter location falls within the Alabama Coastal Plain in Pike County, 25 Alabama. It is located in an area of disturbed forests, and is less than 1.6 km (1 mi) from Big 26 Creek, which is part of the Pea River watershed. Big Creek is located approximately 0.8 km (0.5 27 mi) south-southeast from the cente r of the facility. The surrou nding area includes emergent and 28 scrub-shrub wetlands, forests, freshwater creeks, ponds, rivers , croplands, pastureland, and 29 developed urban areas. The Pea Ri ver watershed drains into the Gu lf of Mexico. The watershed 30 is underlain by coastal plain sediments, includ ing sand, clay, and limes tone; and the topography 31 can be characterized as gentle to mo derate rolling hills (CPYRWMA 2006). 32 33Case Study Descriptions

ICF International 3-17 DRAFT – December 2006 Diversity of terrestrial and aquatic animal species is relatively high. Th e Choctawhatee and Pea 1 River basins, in which the secondary Pb sme lter is located, contain 43 species of marine, 2 estuarine, and freshwater fish species (Cook and Kopaska-Merkel 1996). Anadromous fish 3 species (i.e., saltwater fish that must spawn in freshwater) found in the Pea River basin include 4 the following: the threatened Gulf sturgeon ( Acipenser oxyrhynchus desotoi ), Alabama shad 5 ( Alosa alabamae ), striped bass ( Morone saxatalis ), and skipjack herring ( Alosa chrysochloris ). 6 The Pea River basin also provides habitat fo r 20 species of freshwater mussels (Cook and 7 Kopaska-Merkel 1996), as well as numerous specie s of snails, snakes, and other invertebrates. 8 9 Terrestrial species supported in this region include a variety of birds, mammals, invertebrates, 10 and vascular plants. A recent floristic survey of the Pike County Pocosin Nature Preserve 11 indicated that there were a total of 348 species of vascular flora (Alvin et al. 2002). The preserve 12 is approximately 11 km (7 mi) northeast of the sec ondary Pb smelter facility. In a recent survey 13 of birds spotted by citizens of Troy, Alabama, 29 species were reported, including American 14 robins (which consume soil invertebrates), th e northern cardinal, American goldfinch, and 15 various sparrows, doves, woodpeckers, and wa rblers (GBBC 2003). Othe r terrestrial fauna 16 found in the region include small mammals and invert ebrate species. A total of 34 vascular flora 17 from Pike County are listed by the Alab ama Natural Heritage Inventory Program as endangered, 18 threatened, or of special concern in the stat e (Alabama Natural Heri tage Inventory 2001). 19 20 Exhibit 3-11 displays the current listing of threatened and endangered species for Pike County, 21 Alabama (NatureServe 2006). According to Na tureServe and the USFWS, no species in Pike 22 County are on the state or fe deral endangered species list (Outdoor Alabama 2003). A few 23 species, however, are candidates for the fede ral list, as designated in Exhibit 3-11. 24 25Case Study Descriptions

ICF International 3-18 DRAFT – December 2006 Exhibit 3-11. Threatened and Endangered Species in Pike County, Alabama a 1
Animals
Invertebrates Plants

Delicate spike Elliptio arctata Apalachicola Wild Indigo Baptisia megacarpa Variable spike Elliptio icterina Baltzell's Sedge Carex baltzellii Purple pigtoe Fusconaia succissa Southern Twayblade Listera australis Southern sandshell (FCE) Hamiota australis American Pinesap Monotropa hypopithys

Southern fatmucket Lampsilis straminea claibornensis Alabama Black Cherry Prunus alabamensis

Fuzzy pigtoe (FCE) Pleurobema strodeanum Arkansas Oak Quercus arkansana

Giant floater Pyganodon grandis Eared Coneflower Rudbeckia auriculata
Tapered pigtoe (FCE) Quincuncina burkei Silky Camellia Stewartia malacodendron Southern creekmussel Strophitus subvexus Sessile-leaved Warea Warea sessilifolia Lilliput Toxolasma parvus

Pondhorn Uniomerus tetralasmus Paper pondshell Utterbackia imbecillis Choctaw bean (FCE) Villosa choctawensis Little spectaclecase Villosa lienosa
Southern rainbow Villosa vibex Vertebrates- Amphibians Two-toed amphiuma Amphiuma means Vertebrates-Reptiles Coachwhip Masticophis flagellum

a Abbreviation: FCE = Federal candidate for endangered status 2 Source: NatureServe (2006) 3 4 3.2.6.2 Description of Available Data 5 6 Pb data in environmental media other than ai r were not available ar ound the vicinity of the 7 secondary Pb smelter facility. Therefore, for esti mates of Pb concentrations in outdoor soils, the 8 ecological risk screening assessment for the seco ndary Pb smelter case study relied on fate and 9 transport modeling and input data collected for th e human exposure and health risk assessments. 10 11 3.3 Near Roadway Urban Case Study 12 13 The location for the near roadway case study was se lected primarily based on the availability of 14 suitable air monitoring data. Spec ifically, measurements were sought that were inclusive of Pb 15 concentrations and particle sizes, reported for an urban site adjacent to a roadway, and located in 16 a dry climate where there was historical use of le aded gasoline. Several days of air monitoring 17 data for ambient measurements of particle-bound Pb were available for an urban site adjacent to 18 a roadway in Houston, Texas (ICF 2006a). This location was selected fo r the pilot phase, and 19 the section of land adjacent to a segment of a well-traveled road near the monitoring site was 20 defined as the specific study area. This road has been in use as a thoroug hfare since at least the 21 1950s; therefore, it seems likely that the historical use of leaded gasoline in automobiles during 22 this time has resulted in an accumulation of Pb in the near roadway soil. 23 24Case Study Descriptions

ICF International 3-19 DRAFT – December 2006 3.3.1 Description of Case Study Location 1 2 The setting for the near roadway case study loca tion is a small area of mixed residential and 3 commercial use about 14 km northwest of dow ntown Houston (see Exhibit 3-12). The study 4 area is the narrow strip of land that extends 200 meters from either side of a well-traveled 5 roadway that is located south of the selected ai r monitoring station. The area is focused on a 2.3 6 km stretch of this street that runs roughly east-west, with the monitoring st ation situated about 7 115 m north of the street. This length was defi ned as the segment for the case study based on the 8 subdivision of the street into “li nks.” Roadways are typically di vided into links for the purposes 9 of organizing data by departments of transpor tation and other municipal offices; the study area 10 comprises two links of this street. Relevant vehi cle traffic data for these links are presented in 11 Exhibit 3-13. 12 13 The land adjacent to the segment of road that makes up the study area was subdivided into three 14 zones for this case study: one extending from the edge of the roadway out to 12 meters; one that 15 includes the region between 12 to 75 meters from the roadway e dge; and one that includes the 16 region between 75 to 200 meters from the roadway edge (see Exhibit 3-12). Additional 17 information regarding the spat ial characteristics of the st udy area and the rationale for 18 subdividing the area into three zones is included in Section 4.3.1. 19 20 21 Exhibit 3-12. Study Area for Near Roadway Case Study, Houston, Texas 22

23Case Study Descriptions

ICF International 3-20 DRAFT – December 2006 1 Exhibit 3-13. Vehicle Traffic Da ta for Road Segment of Interest 2 Road Link Section Link Length (km) Approx. Annual Average Daily Throughput (AADT), Vehicles a Segment east of site 0.7 22,000 Segment west of site 1.6 17,000

a Length and vehicle throughput from Highway Performance Monitoring System (U.S. Department of 3 Transportation 2006); data are representative of 2002 and 2003. 4 5 As of 2000, about 1,950 people resided in 620 house holds within the boundaries of this study 6 area, including about 320 children between the ages of 0 and 7 years. Demographic data for the 7 case study were estimated using information from the U.S. Census by area-weighting population 8 data for census blocks overlapping with the study area. More information on these calculations 9 and relevant demographics for each of the three zones in the study area is included in Appendix 10 D. 11 12 3.3.2 Human Exposure Measurements 13 14 No human exposure measurements were identif ied for the near roadway urban study area. 15 16 3.3.3 Emissions 17 18 No emissions data were collected for th e near roadway case study. Soil and air Pb 19 concentrations were derived from measuremen ts; and no modeling of emissions was conducted 20 for this case study. 21 22 3.3.4 Summary of Environmental Data 23 24 The environmental data sets identified for th e near roadway case study are summarized in 25 Exhibit 3-14. 26 27 Exhibit 3-14. Summary of Environmental Data Sources for 28 Near Roadway Urban Case Study 29

Media Data Set Timeframe Location Comments DRI: 1 monitor (PM 2.5 and PM 10 ) February 2001; 3 daily averages (ICF 2006b) 115 m from road segment of interest Used to characterize near roadway air concentrations

Ambient air AQS/AirData: 3 monitors (PM 2.5 , PM 10 , TSP) 1996 and 2006; multiple observations (USEPA 2006f) Houston (15-30 km from road segment) Distance to nearest roadway not specified

Soil Turer and Maynard (2003): near roadway measurements October 1998 a Corpus Christi; I-37 entrance ramp near downtown; 2, 3, and 12 m from roadway Adjacent to roadway; higher traffic volume than study area

Indoor dust No data identified. Deposition No data identified.

a Sample data for soil confirmed by Maynard (2006). 30Case Study Descriptions

ICF International 3-21 DRAFT – December 2006 1 Two air monitoring data sets were identified for locations in the vicinity of the road segment that 2 is the focus of this case study. Air monitoring of Pb was conducted by Desert Research Institute 3 (DRI) at a single monitor locate d about 115 m north of the road segment. Daily average air 4 concentrations of Pb in the PM 10 fraction collected over three da ys in February 2001 ranged from 5 0.0045 to 0.0056 μg/m 3 . In addition, EPA’s AirData databa se includes measurements of Pb for 6 three air monitors located in H ouston. None of these monitors is located within 15 km of the 7 road segment of interest, and the distance to the nearest roadway was not specified for any 8 monitor. Mean concentrations of Pb in the TSP fraction ranged from 0.001 to 0.02 μg/m 3 for 9 1996 to 2006 across the three monitors. M ean concentrations of Pb in the PM 10 fraction ranged 10 from 0.006 to 0.012 μg/m 3 for 1999 to 2006 for the one monitor with PM 10 measurements. 11 12 Although no suitable soil Pb measurements were id entified for locations in the vicinity of the 13 road segment of interest or elsewhere in Houst on, one set of near roadway soil measurements in 14 Corpus Christi was identified in the lite rature. Turer and Maynard (2003) reported 15 concentrations of Pb in surface soil samples taken adjacent to an interstate entrance ramp; 16 concentrations ranged from 214 mg/kg at 12 m fro m the roadway to 766 mg/kg at 3 m from the 17 roadway. See Appendix D for additional detail on the air and soil data sets. No measurements 18 of Pb in dust inside residences or measurements of deposition were identified in the vicinity of 19 the near roadway urban case study location. 20 21 3.4 Near Roadway Non-Urban Case Study 22 23 From the literature search for studies of Pb in near roadway soils (des cribed in Section 3.3.4), 24 two non-urban sites for which soil Pb levels ar e available were iden tified for use in the 25 ecological risk assessment. These locations are: (1) Interstate 37 near oil refineries in Corpus 26 Christi, Texas (Turer and Maynard 2003) and (2) Interstate 95 north of Richmond, Virginia, at 27 Exit 86 which connects to a moderately trav eled, two-lane road (Speiran 1998). 28 29 Land cover data from 1992 within 1.6 km (1 mi ) of the Corpus Christi, Texas study location 30 showed 59 percent industrial, 10 pe rcent low intensity re sidential, and 25 per cent high intensity 31 residential (Vogelmann et al. 2001). The rema ining 5 percent of the surrounding area included 32 shrubland, row crops, pasture, grasses, and forested upland, including evergreen forest and 33 deciduous forest. The 1992 land cover data within approximately 1.6 km (1 mi) of the Atlee, 34 Virginia, study locations was 26 percent developed: 2 percent low-intensity residential and 35 commercial and 24 percent industrial and transp ortation. The remaining 74 percent included 25 36 percent deciduous forest, 14 per cent woody wetlands, and 12 percen t pasture (Vogelmann et al. 37 2001). Smaller proportions of mixed forest, ev ergreen forest, row cr ops, and transitional 38 (barren) areas were also found. 39 40 Exhibit 3-15 lists the environmental data sour ces for the near roadway non-urban case study. 41 42Case Study Descriptions

ICF International 3-22 DRAFT – December 2006 Exhibit 3-15. Summary of Environmental Data Sources for 1 Near Roadway Non-Urban Case Study 2

Media Data Set Timeframe Locations Comments Ecological
Corpus Christi, Texas Site No. 2 (Turer and Maynard 2003) ~1998 a 4 locations taken from a transect perpendicular to the road (0.5, 1.2, 2, and 4 m from the road) Soil

Atlee, Virginia (Speiran 1998) 1994 27 locations taken along a 140-m section of interstate, ranging from 2 to 30 m from road See Chapters 4 and 7 for more information

a Sample data for soil confirmed by Maynard (2006). 3 4 The Pb soil data for the Corpus Christi, Texas location 2 meters from the roadway ranged from 5 15 mg/kg at 20-cm depths to 650 mg/kg at 10-cm depths. The Pb soil concentrations at the 6 Atlee, Virginia location ranged from 17 mg/ kg 15 meters from the roadway to 540 mg/kg 2 7 meters from the roadway; both of these Atlee samples were collected from 7.5-to 15-cm soil 8 depths. 9 10 3.5 Ecologically Vulnerable Case Study 11 12 The Hubbard Brook Experimental Forest (HBEF) is located in central New Hampshire within 13 the White Mountain National Forest. HBEF is approximately 210 km north of Boston, MA, and 14 vehicle access is limited. HBEF soils are mostly well-drained Spodosols w ith pH levels at or 15 below 4.5 and feature the Berkshire, Skerry, Becket, and Lyman soil series (Hubbard Brook 16 Information Oversight Committee 2001). A unique ch aracteristic of the S podosol soil order is 17 that the soils horizons are not nece ssarily parallel to the soil surface, as is the case in most orders 18 (Soil Survey Staff 1999). An important feature of HBEF surface topograp hy is the soil layer 19 movement caused by the uprooting of trees, which cr eates mixtures of the lower mineral surface 20 soil layers as well as buried horizons where mixt ure does not occur. This surface disturbance 21 influences weathering and biogeochemical cycles. HBEF is completely fore sted and consists of 22 80 to 90 percent hardwoods and 10 to 20 percent conifers. Sugar maple ( Acer saccharum ), 23 beech ( Fagus grandifolia ), and yellow birch ( Betula allegheniensis ) are prevalent at low to mid 24 elevations, while red spruce ( Picea rubens ), balsam fir ( Abies balsamea ), and white birch ( Betula 25 papyrifera var. cordifolia ) are predominant at higher elevati ons. Commercial logging at HBEF 26 ended between 1915 and 1917; the present forestis an even-aged, second-growth forest. 27 (Hubbard Brook Information Oversight Committee 2001) . HBEF consists of a number of study 28 watersheds that have been subjec t to experimental manipulation; Watershed 6 (W6) serves as the 29 reference watershed and has not be en experimentally disturbed. 30 31Case Study Descriptions

ICF International 3-23 DRAFT – December 2006 Pb concentration data in forest soils, surf ace water, and bulk precipitation are available for 1 HBEF; Pb bulk deposition data are also av ailable (see Exhibit 3-16). Ambient air Pb 2 concentration data were not located. The presented data largely focus on W6 and areas 3 proximate to W6, although data from othe r areas of HBEF are also included. 4 5 Exhibit 3-16. Summary of Environmental Data Sources for HBEF 6

Media Data Set Timeframe Locations Comments Atmospheric Inputs - - - -

Precipitation Wang et al. (1995) 1993 HBEF; at least 12 samples Sample location not specified.

Bulk deposition Johnson et al. (1995) 1976 – 1989 HBEF Used available data (from W6 and areas immediately adjacent) to construct regression model, which was

used to predict Pb inputs

Yanai et al. (2004) 1976 – 2000 HBEF (Watershed 6, Watershed 1); 100

samples were collected from elevations ranging from 490 to 790 m See Appendix E

Soil

Wang and Benoit (1996) 1993 – 1994 HBEF (a watershed immediately west of

Watershed 6); 24 lysimeters sampled

monthly at three elevations (600, 730, and 750 m) See Appendix E

Surface water (stream) Wang et al. (1995) 1993 Bear Brook (west of HBEF Watershed 6); samples were collected at elevations

ranging from about 400 to 800 m Sample size not reported.

7 Yanai et al. (2004) analyzed forest floor 2 soil samples (this data set also includes samples from 8 Watershed 1 [W1]) and reported a 32 percent decline in forest floor Pb concentrations at HBEF 9 from 1976 to 2000. Pb concentrations for this data set ranged from about 38 mg/kg collected in 10 2000 to 115 mg/kg collected in 1978. Soil profile analysis shows that Pb has become more 11 concentrated at lower depths over time. Johnson et al. (1995) also no ted a downward movement 12 of Pb in soil layers. Wang et al. (1995) repor ted stream dissolved Pb concentrations ranging 13 from about 5 to 15 parts per trilli on (ppt) for samples collected in 1993. 14 15 For the pilot phase, the ecologically vulnerable case study assessment was limited to identifying 16 the case study location (i.e., HBEF) and identify ing associated available media concentration 17 data sets (as described fu rther in Appendix E). 18 19

2 Yanai et al. (2004) define “forest floor” as the O-A soil horizons.Case Study Descriptions

ICF International 3-24 DRAFT – December 2006 3.6 National-Scale Surface Water and Sediment Screening Assessment 1 2 The geographic coverage of th e national-scale screening asse ssment includes locations in 3 freshwater watershed basins from all regions of the United States. The assessment was limited to 4 freshwaters. Monitoring data reviewed by EPA’s Office of Rese arch and Development indicated 5 that Pb concentrations in larg e lakes and oceans are lower than in smaller freshwater bodies 6 (USEPA 2006b). Exhibit 3- 17 lists the data sets used in this screening assessment. Exhibit 3-18 7 lists the 47 basins for which the National Water Quality Assessment (NAWQA) database 8 provided dissolved Pb surface water concentration data. Approximately 50 percent of the land 9 base of the United States is covered by these 47 basins. The basins for which sediment Pb 10 concentration were evaluated are listed in Exhibit 3-19. 11 12 Exhibit 3-17. Summary of Environmental Data Sources for 13 National-Scale Surface Water and Sediment Screening Assessment 14

Media Data Set Timeframe Locations Comments

Ecological

Surface

water column National Water Quality Assessment (NAWQA) Database (USGS 2004) 1991 to 2004 (Analysis limited to 1994 through 2004) 47 basin locations across the United States See Chapters 4 and 7 and Appendix H

Sediment National Water Quality Assessment (NAWQA) Database (USGS 2004) 1992 to 2000 12 basin locations See Chapters 4 and 7

15Case Study Descriptions

ICF International 3-25 DRAFT – December 2006 Exhibit 3-18. USGS NAWQA Study Units a Included in the National-Scale 1 Surface Water Column Screening Assessment 2

Study Unit/Basin Name (Year Sampling Started) Study Unit ID

No. Study Unit/Basin Name (year Sampling Started) Study Unit ID

No.

Northeast Midwest New England Coastal Basins (1997) 1 Central Nebraska Basins (1991) 35
Connecticut - Housatonic - and Thames River Basins (1991) 2 South Platte River Basin (1991) 40

Hudson River Basin (1991) 3 Upper Colorado River Basin (1994) 44 Long Island and New Jersey Coastal Drainage (1994) 4 Northern Rockies Intermontane Basins (1997) 46

Delaware River Basin (1997) 5 Rio Grande Valley (1991) 45 Allegheny and Monongahela (1994) 9 Northwest
Middle Atlantic Yellowstone Basin (1997) 43 Lower Susquehanna River Basin (1991) 6 Upper Snake River Basin (1991) 48
Potomac River Basin & Delmarva Peninsula (1991, 1999) 8 7 Central Columbia Plateau & Yakima River Basin (1991, 1999) 50 51

Kananha-New River Basin (1994) 10 Puget Sound Basin (1994) 52
North-central Williamette Basin (1991) 53 Lake Erie - Lake St. Clair Drainage (1994) 11 Southwest
White River & Great and Little Miami River Basins (1991, 1997) 13 12 Great Salt Lake Basins (1997) 47

Upper Illinois River Basin (1997) 14 Central Arizona Basins (1994) 49 Lower Illinois River Basin (1994) 15 Sacramento River Basin (1994) 54 Western Lake Michigan Drainage (1991) 16 Nevada Basin and Range (1991) 55

Upper Mississippi River Basin (1994) 17 San Joaquin-Tulare Basins (1991) 56 Red River of the North Basin (1991) 18 Santa Ana River Basin (1997) 57 Albemarle-Pamlico Drainage (1991) 19 Hawaii Tennessee River Basin (upper & lower; 1994, 1997) 20 29 Oahu (1997) 58

Southeastern Alaska Santee Basin and Coastal Drainages (1994) 21 Cook Inlet Basin 59

Apalachicola-Chattahoochee-Flint River Basin (1991) 22

Georgia-Florida Coastal Plain (1991) 23 Southern Florida (1994) 24
South-central Mobile River and Tributaries (1997) 26

Mississippi Embayment (1994) 27 Acadian-Pontchartrain (1997) 28

Eastern Iowa Basins (1994) 30

Ozark Plateaus (1991) 31 Trinity River Basin (1991) 33 South Central Texas (1994) 34

a See Section 4.5 for a discussion of these Study Units. 3 4Case Study Descriptions

ICF International 3-26 DRAFT – December 2006 Exhibit 3-19. USGS NAWQA Study Units 1 Included in National-Scale Sediment Screening Assessment 2

Study Unit/Basin Name Study Unit ID No.

New England Coastal Basins 1

Connecticut - Housatonic - and Thames River Basins 2

Ozark Plateaus 31

Rio Grande Valley 45

Northern Rockies Intermontane Basins 46

Great Salt Lake Basins 47

Oahu 58

3 There are temporal trends associ ated with whether dissolved Pb was measured at given station. 4 Most notably, at many stations for which multiple years of data are available, if the first year of 5 sampling indicated all dissolved Pb measurements to be less than the quantitation limit (QL) of 1 6 μg/L, no further measurements of dissolved Pb were attempted (e.g., dissolved Pb measured in 7 1999 less than 1 μg/L; no further measurements in 2000 through 2004). 8 9 Most dissolved Pb concentrations reported in the NAWQA database are below the QL. There 10 are only 2,558 samples analyzed for dissolved Pb in surface waters for the NAWQA database 11 from 1994 to 2004. 3 Of the 2,558 samples examined, 83 percent (2,116) were below the QL 12 (USEPA 2006b). Of the 442 dissolved Pb concentra tions reported in the da ta set, 17 percent (74 13 of 442) were estimated. 4 14 15 The NAWQA database had only one measurement of total Pb sediment concentrations for each 16 of the 12 locations evaluated in the sediment por tion of the screening a ssessment. Of these 12 17 total measurements, seven total Pb concentratio ns were between 23 and 68 mg/kg; two were at 18 239 and 240 mg/kg, and three were between 1,620 and 2,900 mg/kg. 19

3 Note that EPA/ORD’s review of the NAWQA database identified 3,445 sample s; however, their review included samples from 1991 forward (USEPA 2006b, Table 7-3).

4 Of the 74 estimated values, 68 were for concentrations above detection limit of 0.04 μg/L but below the QL of 0.08 μg/L.Estimates of Media Concentrations

ICF International 4-1 DRAFT – December 2006 

### 4. Estimates of Media Concentrations 1 2 This chapter discusses methods, results, limitation s, and uncertainties associated with the media 3 concentrations used for exposure assessment in th e human exposure and health risk assessments 4 and ecological risk assessment. The context fo r these media concentrations within the risk 5 assessments is described in Chapter 2 and illu strated in Exhibits 2-2 and 2-3 for the human 6 exposure and health risk assessments and in Exhibits 2-5 and 2-6 for the ecological risk 7

assessment. 8 9 4.1 Primary Pb Smelter 10 11 The media concentrations for the primary Pb smelter case study were estimated using a 12 combination of modeling approaches and monitoring data. The timeframe of the data used in the 13 study is from 2000 to 2005. The emissions inputs ar e representative of potential emissions in 14 2001, and the soil sample data used was collect ed from 2000 to 2005. Recent air monitoring 15 data indicated that this facility is not in attainment with the current Pb NAAQS; therefore, both a 16 current conditions and an attain ment scenario were considered. The inhalation exposure and 17 indoor dust concentrations are different between the two scenarios for the modeled locations 18 where the current conditions air concentrations exceed the current Pb NAAQS. This is because 19 the inhalation exposure and indoor dust concen trations are calculated from the modeled air 20 concentrations. For the pilot phase, the same soil concentrations of Pb were used for both 21 scenarios. 22 23 4.1.1 Spatial Template 24 25 As discussed in Section 2.2.2, the outer boundary of the study area for the primary Pb smelter 26 case study was set to approximately 10 kilometers (km), which was expected to capture the 27 population experiencing the most si gnificant impacts of the facil ity’s emissions. Modeled air 28 concentrations of Pb from the primary Pb sm elter were compared to estimated background Pb 29 concentrations from the Interagency Monitoring of Protected Visual Environments (IMPROVE) 30 database (IMPROVE 2006) to determine at what distance the air concentr ations resulting from 31 the facility emissions reached approximately 50 percent of the background air concentration. 32 From this comparison, it was found that a radius greater than 50 km woul d be required to reach 33 air concentrations that would be 50 percent of background. Due to model run times, this would 34 not be feasible, so the decisi on was made to model the facilit y’s impacts in a study area that 35 extended 10 km from the facility. This smalle r study area is expected to capture the highest 36 exposures associated with the facility’s emissions. 37 38 A combination of U.S. Census blocks and block groups (U.S. Census Bureau 2005) were used as 39 the spatial units at which to model air c oncentrations. The 29 block groups that are 40 predominantly within 10 km of the facility were se lected to define the sp atial extent of the study 41 area. Because of the irregular sh ape of block groups, not all of th e block groups with area within 42 10 km were included, and some that were included have area outside 10 km. The selection was 43 made to create a coverage area that would in clude block groups whose average concentration 44 would predominantly come from air within 10 km of the source. All census block centroids 45 within these 29 block groups were included as receptors in the ISC model runs (i.e., air 46Estimates of Media Concentrations

ICF International 4-2 DRAFT – December 2006 concentrations and deposition fl uxes were modeled at each block centroid). There are 1,321 1 blocks within these block groups. Of these blocks , 14 were identified as fa lling within facility 2 boundaries, or adjacent to the faci lity in the Mississippi River. 1 These 14 blocks were removed 3 from the assessment; other zero population blocks within the study area were still included. A 4 total of 1,307 census block centroids were includ ed as receptors in the air dispersion model 5 simulations, including blocks with in the study area with zero popul ation. The blocks with zero 6 population were included in the modeling simulati ons to aid in understandi ng the patterns of air 7 concentrations in the study area. These locatio ns were not included in the exposure assessment 8 and are not included in exhibits summarizing modeling results (w ith the exception of isopleths 9 diagrams). The elevation of each block centroi d was generated using USGS digital elevation 10 model files and the AERM AP preprocessor model. 11 12 In order to limit the locations modeled for the ex posure assessment, a combination of block and 13 block group-level results were used for the expo sure assessment. The average concentration and 14 deposition in each block group was calculated by spatially-weighting the values at the block 15 level. The area of each block was obtained fr om the U.S. Census (Census Bureau 2005). The 16 decision of whether to use the block or block gr oup was made by consider ing the range of block17 level concentrations wi thin a block group. If the ratio of the maximum block-level air 18 concentration in the block group to the average air concentration in th e block group was greater 19 than 2.0, the blocks in the block group were mode led individually. All other block groups were 20 modeled as units. This method generally resulted in blocks near the facility, as well as some 21 blocks far from the facility that fall within ve ry large block groups, being modeled individually 22 (see Exhibit 4-1). 23 24 Six census blocks near the facility for whic h there are non-zero population counts for the year 25 2000 were removed from the analysis because curre nt information indicate s that as of 2004 they 26 have zero population (Gradient Corporation 2004 ). A total of 22 census block groups and 201 27 census blocks (all with non-zero population counts ) in other block groups were included in the 28 exposure assessment. 29

1 All territory in the United States is delineated into Cens us blocks (U.S. Census Bureau 2005). Therefore, large water bodies like the Mississippi River often contain Census blocks, although there is no population associated with these blocks.Estimates of Media Concentrations

ICF International 4-3 DRAFT – December 2006 1 Exhibit 4-1. Modeled Block and Block Gr oups near Primary Pb Smelter Facility 2

3Estimates of Media Concentrations

ICF International 4-4 DRAFT – December 2006 4.1.2 Air 1 2 The air concentrations and deposit ion of Pb resulting from emissions at the primary Pb smelter 3 facility were estimated using the ISC-PR IME (Schulman et al. 1997; USEPA 1995) air 4 dispersion model and compared to the air concentration and deposition measurements from 5 nearby monitors. The emissions used for the ai r quality modeling are described in Section 3.1.4. 6 The air concentration and depos ition monitoring data used for comparison are described in 7 Section 3.1.5. 8 9 4.1.2.1 Study Area and Air Dispersion Modeling 10 11 The air dispersion model ISC-PRIME was used for the primary Pb smelter air quality modeling. 12 The meteorological data used for the model simu lations included 25 consecutive months (April 13 1, 1997 to April 30, 1999) of on-site data. These meteorological data were also used for an 14 analysis by U.S. EPA Region 7 (2006b) of the facility. Meteorologi cal data are currently being 15 collected at the facility, but problems due to da ta gaps prevented their use in this analysis 16 (USEPA Region 7 2006b). As described in Secti on 3.1.4, emissions, release parameters, particle 17 size parameters, and building downwash inputs were all provided by Region 7 (in the form of an 18 input runstream file). The emissions and source parameters represent the 2000 Revision of the 19 SIP developed for the facility (MDNR 2000). 20 21 Hourly air concentrations and deposition were ou tput from the dispersion model at each receptor 22 (i.e., block or block group, as described in S ection 4.1.1) and monitor lo cation. The hourly air 23 concentration and deposition model results were averaged over the modeling time period to 24 obtain annual average air concentration and annua l average deposition for the current conditions 25 scenario. The concentrations were also averag ed quarterly to determine whether any of the 26 current conditions scenario’s air quarterly averag e concentrations were gr eater than the current 27 Pb NAAQS. Modeled Pb air concentrations in some blocks exceeded the NAAQS for at least 28 one quarter. For the current attainment scenari o, these concentrations were set to the current 29 NAAQS value (1.5 μ g/m 3 ), assuming attainment, and the annual averages were then recalculated 30 using the revised quarterly averages. All other m odeled concentrations for the current attainment 31 scenario were set to the same values as the current conditions scenario. 2 32 33 4.1.2.2 Inhalation Exposure Concentrations 34 35 Inhalation exposure concentrations of Pb were estimated for the population of interest (young 36 children) from the estimated ambient air concen trations using age group- and location-specific 37 relationships for Pb developed from modeli ng performed for U.S. EPA’s 1999 National-Scale 38 Air Toxics Assessment (USEPA 2006e), one of U.S. EPA’s National Air Toxics Assessment 39 (NATA) activities. These relationships account for air concentration di fferences indoors and 40 outdoors and mobility or time spent in different lo cations (e.g., outdoors at home, inside at home, 41 etc.) for the population of interest. 42

2 As described in Section 2.2, this method is recognized to be a simplification of how air concentrations would respond.Estimates of Media Concentrations

ICF International 4-5 DRAFT – December 2006 The NATA national-scale assessment produced air c oncentrations of Pb (and other hazardous air 1 pollutants) for each U.S. Census tract (using the Assessment System for Population Exposure 2 Nationwide model, ASPEN), and co rresponding exposure concentrations of Pb for each of five 3 age-groups at each U.S. Census tract. The relationships (or ratios) between ambient Pb 4 concentration and Pb exposure concentration fr om the NATA national-scale assessment for the 0 5 to 4 age group (the closest age group for which outputs are available to th e age group of interest 6 for this assessment) ranged from 0.37 to 0.42 for the U.S. Census tracts within the study area for 7 the primary Pb smelter location. The ratios ar e presented in Appendix F, Exhibit F-1. 8 9 Use of ratios for the 0 to 4 age group (rather than for 0 to 7) contributes some uncertainty in the 10 estimate of children inhalation expo sure concentrations. In addition, there is some uncertainty in 11 the magnitude of the air concentrations ge nerated using the ASPEN model for the NATA 12 assessment. In a comparison to monitoring da ta across the country, the ASPEN modeled air 13 concentrations generally underest imate monitored concentrations (see section on Comparison to 14 Monitored Values, USEPA 2006e). However, the relationship between ambient concentrations 15 and exposure concentrations (which is the comparis on used here) is not exp ected to be affected 16 by underestimated ambient concentrations from the NATA study. 17 18 4.1.2.3 Air and Deposition Results 19 20 Annual average Pb air concentrations and deposi tion fluxes were calculate d at the centroid of 21 each census block group, block, and monitor receptor point for th e two years of meteorological 22 data. To take into account variations in mete orological data, the annual average concentrations 23 and depositions for each of the two years were av eraged to generate one set of representative 24 annual average concentration and deposition results for the current conditions scenario. For the 25 attainment scenario, concentration estimates were adjusted at the eight census blocks at which 26 one or more quarterly average air concentrations exceeded the current NAAQS. At these blocks 27 any quarterly averages that exceeded the NAAQ S were set equal to the NAAQS and the annual 28 average was calculated from the quarterly averag es. All other block and block groups for which 29 a quarterly average air concentration did not exceed the current NAAQS were kept at the 30 modeled values for the attainment scenari o. Using this method, it is likely that the 31 concentrations at blocks that did not exceed the current NAAQS are slightly overestimated for 32 the attainment scenario. However, because th ere may be multiple met hods that the facility 33 would use to obtain attainment (i .e., different controls on different processes), th e exact pattern 34 of concentration reductions could vary depending upon which location in the facility has reduced 35 emissions. Thus, it is likely to underestimate re ductions in ambient air concentrations for the 36 current NAAQS attainment scenario relativ e to the current conditions scenario. 37 38 All concentration, deposition, and inhalation exposure estimates for the census blocks and block 39 groups modeled are presented in Appendix F, Exhi bits F-2 and F-3, for both scenarios. Exhibit 40 4-2 presents the range of air concentration, i nhalation exposure, and deposition values generated 41 from the modeling for both the current conditions and current attainment scenarios. The values 42 are summarized from the model results at the 223 blocks and block groups with non-zero 43 population selected for further analysis in th e exposure assessment. Exhibit 4-3 shows the 44 isopleths of the block-level mode led air concentration results. 45 46Estimates of Media Concentrations

ICF International 4-6 DRAFT – December 2006 Exhibit 4-2. Primary Pb Smelter Case Study: 1 Modeled Air Concentrations and Deposition a 2
Current Conditions Current Attainment

Statistic b Average Annual Pb Air Concentratio

n ( μ g/m 3 ) Children 0 to 4 Average Annual Pb Air Exposure

Concentration ( μ g/m 3 ) Average Annual Pb Air Concentration ( μ g/m 3 ) Children 0 to 4 Average Annual Pb Air Exposure Concentration ( μ g/m 3 ) Dry Deposition (g/m 2 /year)

Maximum 2.73 1.14 1.50 0.628 5.20 95 th Percentile 0.662 0.277 0.662 0.277 1.28 Median 0.0221 0.00895 0.0221 0.00895 0.0938 5 th Percentile 0.00845 0.003 29 0.00845 0.00329 0.0266 Minimum 0.00541 0.00210 0.00541 0.00210 0.0163

a The 223 blocks and block groups with non-zero population selected for analysis were used to create this summary. 3
Note that in some of these blocks the 2000 U.S. Census indicates there are no children. 4

b The statistic (e.g., 95 th percentile, median) may not be at the same lo cation for each of the da ta results presented 5
here. 6 7Estimates of Media Concentrations

ICF International 4-7 DRAFT – December 2006 Exhibit 4-3. Primary Pb Smelter Case Study : Concentration and Deposition Isopleths 1

2 3Estimates of Media Concentrations

ICF International 4-8 DRAFT – December 2006 4.1.2.4 Air Modeling Performance Assessment 1 2 As described in Section 3.1.5, nine air monito rs from AQS/AirData (USEPA 2006f) and four 3 EPA high-volume air monitoring stations that ar e not in the AQS/AirData database (USEPA 4 Region 7 2006a) were identified wi thin 10 km of the primary Pb smelter facility. Pb 5 concentrations measured at all of these monitors are for Pb in total suspen ded particulates (TSP). 6 Modeled air concentrations were generated from ISC-PRIME at each of these locations and are 7 presented in Exhibit 4-4. There is no specific year of monitori ng data that can be directly 8 correlated with the emissions ch aracterization because the emissi ons represent those from the 9 2000 Revision of the SIP developed for the facilit y, rather than a specif ic year’s emissions 10 (MDNR 2000). Therefore, the most recent 5 year s of monitoring data were compared to the 11 modeled air concentrations. The range of the ratios of monitored to modeled concentrations is 12 presented for each monitor location in Exhibit 4-4. A more detailed comparison is presented in 13 Appendix F, in Exhibit F-4. 14 15 As presented in Exhibit 4-4, it va ries whether the model or monito r Pb air concentration is higher 16 at each monitoring point. It appears that roughly with increasing distance from the facility, the 17 ratio of monitored-to-modeled c oncentrations increases. This suggests that the model may be 18 overestimating concentrations at close distan ces, and underestimating concentrations with 19 increasing distance. This may be related to depo sition parameters, emissions estimates at certain 20 locations at the facility (the facility span s approximately 1 km), or meteorological data. 21 22 Exhibit 4-5 presents a comparison of the depos ition modeled at 10 locations for which dry 23 deposition measurements are av ailable (USEPA Region 7 2006a). Measured deposition was 24 available for 2003-04 at two heights (1 foot and 10 feet). The m odeled deposition fluxes at those 25 same locations are compared to the all measured values and a range of the ratio of monitored to 26 modeled deposition is presented in Exhibit 4-5. A more detailed comparison table is provided in 27 Appendix F, Exhibit F-5. For most locations, the predicted dry deposition is less than the 28 measured dry deposition. 29 30 A wind rose created from the on-site meteorological data at the primary Pb smelter (Exhibit 4-6) 31 shows that the predominant wind directions are to the east and north. The air monitors and 32 facility are mapped in Exhibit 4- 7 to show the relative locations. The shaded area on the map 33 indicates the area with modeled emi ssion release points, and the red ci rcle is the main stack at the 34 facility. Because the Mississippi River form s a natural boundary on the eastern side of the 35 facility, the air monitors are all to the north, we st, and south. There are no close-by air monitors 36 to the east, in the predominant wind direction (the closest monitor in an eastern direction is more 37 than 40 km to the northeast). However, because of the width of the river, the monitor network is 38 expected to capture the high-end human inhalation exposure values from th e facility emissions. 39 In addition, the land on the eastern side of the ri ver is farmland, and not as densely populated as 40 the area with the monitors (see Exhibit 3-2). The highest monitored values are at monitors 102, 41 290990015, 290990004, and 290990005, which are all north (and sl ightly west) of the facility. 42 This corresponds with what may be expected fr om the wind rose. Of the modeling results, one 43 of the highest concentrations was modeled at mo nitor 101 to the southwest of the main stack. 44 This likely is a result of the wi de spread of emission sources modeled. Monitor 101 is located 45 amongst modeled emission sources, resulting in th e high modeled concentration. Overall, the 46Estimates of Media Concentrations

ICF International 4-9 DRAFT – December 2006 modeled concentrations and depositions are fair ly representative of c onditions over the last 5 1 years based on comparison with the monitors. 2 3 Exhibit 4-4. Primary Pb Smelter Case Study Air Concentration Modeled Results 4 Compared to Monitor Values 5
Pilot Phase Analysis ID or Monitor ID Distance to Main Stack (km) a 2-year Average Modeled Air Conc ( μ g/m 3 ) Range of Monitored to Modeled Ratio for 2001 to 2005 b
U.S. EPA Region 7 Da tabase HiVols Values c
100 0.39 1.06 0.7 to 1.2 (n = 3) 101 0.45 4.04 0.1 to 0.3 (n = 2) 102 0.53 2.17 0.4 to 2.8 (n = 3) 103 0.73 0.186 2.1 to 5.4 (n = 3)

AirData Monitored Values d
290990015 0.29 1.71 0.8 to 1.9 (n = 5) 290990004 0.34 2.46 0.4 to 0.5 (n = 2) 290990005 0.78 0.463 0.6 to 4.6 (n = 5) 290990011 0.89 0.323 0.9 to 4.7 (n = 5) 290990016 0.92 0.151 1.3 to 2.0 (n = 2) 290990013 1.5 0.135 1.4 to 6.7 (n = 5) 290990008 1.5 0.044 1.5 to 6.2 (n = 5) 290990010 2.2 0.043 0.8 to 1.8 (n = 5) 290990009 2.7 0.048 1.0 to 7.0 (n = 5)

a The facility spans roughly 0.2 by 1 km. Therefore, so me monitors will be closer to other emission release 6 points than they are to the main stack. 7

b The number indicated in parentheses after each ra nge indicates how many annual average monitored 8 concentrations are compared to the modeled results (i.e., 2, 3, or 5 annual averag e values are available from 9 the monitor). 10

c The Pb air concentration detections (which appear to be TSP Pb) reported in the HiVols data set of the 11 U.S. EPA Region 7 database were averaged for each year (see Exhibit A-2). All non-detects were assigned 12 half of the detection limit for averaging. Averages for 2001 are from measurements taken between October 13 and December; averages for 2002 ar e from measurements taken all year ; and averages fo r 2003 are from 14 measurements taken between January and July. 15

d AirData values are for TSP Pb and are averages obta ined from the AirData website monitor reports. In 16 some cases, there is more than one monitor at a site. 17 18Estimates of Media Concentrations

ICF International 4-10 DRAFT – December 2006 Exhibit 4-5. Primary Pb Smelter Ca se Study Deposition Modeled Results 1 Compared to Measured Values 2
Pilot Phase Analysis ID Distance to Main Stack (km) a Modeled Average Pb Deposition (g/m 2 /yr) Range of Monitored to Modeled Deposition Ratio for 2003-04 b

1071 0.38 2.62 0.53 to 0.65

181 0.49 1.23 0.71 to 0.79

286 0.56 2.55 0.30 to 0.80

1073 0.70 0.45 1.44 to 1.71

444 0.71 1.42 0.24 to 0.54

240 0.76 0.37 0.82 to 1.30

207 0.85 0.65 0.40 to 0.83

576 1.0 0.53 0.31 to 0.69

531 1.3 0.35 0.31 to 0.87

1072 1.9 0.21 0.26 to 1.04

a The facility spans roughly 0.2 by 1 km. Therefore, some monitors will be closer to other 3 emission release points than they are to the main stack. 4

b For each set of ratios, four sets of averaged deposition values were compared – deposition 5 measurements at 1 ft and 10 ft in 2003 and 2004. See Exhibit A-11 for individual deposition 6

measurements. 7 8 Exhibit 4-6. Wind Rose of Meteorological Da ta used for Primary Pb Smelter Case Study (Direction in which Wind is Blowing) a

a Wind rose for 18,240 hours of dataEstimates of Media Concentrations

ICF International 4-11 DRAFT – December 2006 Exhibit 4-7. Air Monitor Loca tions near Primary Pb Smelter 1 2

3Estimates of Media Concentrations

ICF International 4-12 DRAFT – December 2006 4.1.3 Outdoor Surface Soil 1 2 For the human exposure and health risk assessm ents, outdoor surface soil concentrations were 3 estimated for each spatial unit (i.e., census bloc ks and block groups) in the study area from the 4 soil sample measurements in the area. The exte nt and types of soil data sets used for the 5 calculations are described in S ection 3.1.5. Many of the yards within 1.5 km of the primary Pb 6 smelter facility have been excavated and filled wi th clean soil in the last 10 years. EPA has 7 taken soil samples from 31 of these sites since 2002. Th ese measurements are called 8 “recontamination” samples. The EPA database also contains soil samples for more than 900 9 locations labeled as “pre-excavation.” Thes e samples were taken from November 2000 to 10 August 2004. Not all of the locations with pre-ex cavation samples have b een filled with clean 11 soil, only select locations within 1.5 km of the facility. Documentation available for the 12 “recontamination” soil samples indicates that sample s were taken to a depth of less than an inch 13 (EPA 2001), and it is likely the “pre-excavation” samples were taken the same way. Depending 14 on the location of the modeled block or block gro up in this study (within or outside of the soil 15 cleanup area), the soil concentra tions for this assessment were calculated using either the 16 recontamination or pre-excavation data set. 17 18 All census blocks within the so il cleanup area (approximately 1.5 km) were identified from the 19 Gradient Corporation report (2004 ). For these 28 blocks with population, soil concentrations 20 were estimated from the recontamination soil samples taken in 2005. For blocks for which there 21 were one or more soil measurements available, the block soil concentration was set to the 22 average (arithmetic mean) of those measurem ents. For blocks for which there were no 23 measurements, but for which there were nearby meas urements (i.e., across the street), the soil 24 concentration was set to the nearby measurement aver age. For other blocks, the average of all of 25 the recontamination soil measurements within 5 00 m was calculated and set as the value for the 26 block. 27 28 Outside the soil cleanup area, soil concentrations were estimated using a regression equation of 29 the pre-excavation soil concentrations. The dist ance of each pre-excavation soil sample to the 30 main stack was measured using a geographical information system (GIS). The measurements 31 were grouped according to distance from the main stack, with separate groups for each 500 meter 32 (m) increment. The arithmetic mean for each group was calculated, resulting in five arithmetic 33 mean average values for soil concentration whic h were plotted versus distance. A regression 34 power equation (R 2 of 0.92) was calculated from the sample s (see Exhibit 4-8). Note that pre35 excavation soil samples taken within 1.5 km of the facility were included to develop the 36 regression equation; however, the equation was not used to estimate soil concentrations at blocks 37 within the 1.5 km soil clean-up area (as indicated in Exhibit 4-8). The distance of each census 38 block and block group centroid from the main stac k was measured in GIS. Soil concentrations 39 for the blocks and block groups outside the so il cleanup area were then calculated using the 40 regression equations based on distance from the stack. 41 42Estimates of Media Concentrations

ICF International 4-13 DRAFT – December 2006 Exhibit 4-8. Average Pre-excavation Soil Measurements and Best-fit Trend Line 1

y = 2E+07x -1.4417

R 2 = 0.9233

0 500 1000 1500 2000 2500 3000 3500 4000

0 500 1000 1500 2000 2500
Distance from main stack (m)Lead soil concentration (mg/kg) Average of measurements

Best-fit line

Line indicating distance beyond which regression equation was used to estimate soil concentrations

2 3 All calculated soil concentrations at the block and block group locations are summarized in 4 Appendix F, Exhibit F-2 and F-3, with an indication of which me thod was used to calculate the 5 values. A summary of the soil conc entrations is presented in Exhi bit 4-9. Note that because of 6 the soil cleanup within 1.5 km of the stack, the calculated soil concentrations, as well as the 7 measured concentrations, near the facility are in many cases lower than the soil concentrations 8 calculated or measured in locations without soil cleanup. Soil concentr ations throughout the 9 modeled region range from 16 to 976 mg/kg. 10 11 Exhibit 4-9. Summary of Primary Pb Smelter Case Study Soil Concentrations 12 Extrapolated from Measurements 13

Statistic Calculated Average Soil Concentration (mg/kg) Distance from Main Stack (m) a
Maximum 976 980 95 th Percentile 426 1,700 Median 84.0 5,400 5 th Percentile 23.6 13,000 a Minimum 15.9 17,000 a

a Receptors at blocks greater than 10 km from the f acility were included because of the irregular shape 14 of block groups (see Section 4.1.1 and Exhibit 4-1). 15 16 For the ecological risk screening assessment, surf ace soil concentration data were extracted from 17 data sets provided by ELM (2005) in its Characterization Area Investigation Report for the 18Estimates of Media Concentrations

ICF International 4-14 DRAFT – December 2006 primary Pb smelter facility in Herculaneum, Missouri. Sample results were aggregated into 1 geographic clusters to average media concentrati ons that will be used in the ecological risk 2 characterization. To ensure that data reflect contributions primarily via air deposition, the 3 sample locations were reviewed to exclude samp les designed to assess contributions from other 4 pathways (e.g., surface drainage pathways and groundwater flow pathways). The samples on the 5 central, east, and west transects of the smelter’s slag storage area (SSA) were excluded from this 6 assessment because the ELM (2005) report states that the floodwaters of Joachim Creek have 7 been visually observed to come into contact with the SSA, thereby depositing slag materials into 8 the floodplains as the water flow subsides. The soil measurements for the west and southern 9 slope of the SSA were also excluded because the sampling transects were located close to water 10 seeps from the SSA. 11 12 Soil samples farthest away from the SSA, where th e possibility of direct contact with Pb from 13 the SSA during flooding events wa s the lowest, were chosen in this analysis. This set of 14 sampling sites was along the historic railroad grade on the west side of Joachim Creek, where 15 each site was at a progressively greater distance fr om the SSA. Also included in this analysis 16 were samples from the two “reference” areas. ELM collected samples from approximately 5.6 17 km and 6.5 km south of the facil ity, respectively, to measure “bac kground” concentrations of Pb 18 in the soil and to establish a baseline for comparis on to the measurements closer to the facility. 19 The closer reference area was s outh of Crystal City and the fart her reference area was south of 20 the Festus Memorial Airport. ELM chose these ar eas because they have a similar habitat to areas 21 surrounding the facility in terms of past and cu rrent land use and regiona l geology. There are no 22 known industrial sources of Pb near these references sites. 23 24 To develop soil concentration estimates for the eco logical risk screening assessment, the surface 25 soil data was grouped into three geographic clusters. Cluster 1 comprised of ten samples taken 26 from the west side of Joachim Creek (approxima tely 1 km west of the facility); Cluster 2 27 contained six samples taken from Crystal City (a pproximately 5.6 km south of the facility); and 28 Cluster 3 contained ten samples taken from near the Festus Memorial Airport (approximately 6.5 29 km south of the facility ). Soil samples were taken from depths of 0 to 3 inches (ELM 2003). 30 The soil sampling results for the three soil sample clusters are summari zed in Exhibit 4-10 and 31 presented in more detail in Appendix F. 32 33 Exhibit 4-10. Soil Sample Clusters Used to Estimate Ecological Receptor Exposure 34 via Soil and Corresponding Pb Analysis Results (ELM 2005) 35

Sample Cluster Mean Measured Pb Conc. (mg/kg) Standard Deviation of Measured Pb Conc. Mean Detection Limit (mg/kg)
Sample Cluster 1: soil samples from West of Joachim Creek 425 45.7 0.35
Sample Cluster 2: soil samples from Crystal City locations (south of facility) 64.7 10.0 0.41
Sample Cluster 3: soil samples from 'Near Festers Airport' locations (south of facility) 48.5 14.4 0.39
36 37Estimates of Media Concentrations

ICF International 4-15 DRAFT – December 2006 4.1.4 Indoor Dust 1 2 For estimating indoor dust concentrations for resi dences at the primary Pb smelting facility, two 3 dust prediction models were used: 4 5

• For locations within 1.5 km of the facility: a site-specific regression model; and 6

• For receptors more than 1.5 km away from the facility: a pooled analysis model (referred 7 to as the “AGG” or “aggregate” model) identifi ed from the literature which predicts Pb 8 dust concentrations given soil a nd ambient air Pb levels base d on data from a variety of 9 industrial and urban and industr ial studies (USEPA 1989). 10 11 The site-specific model is based on data collect ed within the residential remediation zone 12 characterizing yard soil Pb leve ls (post-remediation) and ind oor dust levels. The AGG, or non13 site-specific model, was selected for zones outside of the remedi ation area because available soil 14 and indoor dust data did not ex tend to these more distant area s and the site-specific model 15 derived for the remediated zone was deemed not representative for the non-remediated zone. 16 17 The site-specific regression model was developed for predicting indoor dust levels within the 18 remediation zone immediately adjace nt to the primary Pb smelter. The data set used to “fit” the 19 model was based on indoor dust samples (dependent variable) collected fo r 17 houses within the 20 remediation zone. Indepe ndent variables included: 21 22

• Modeled annual-average air Pb concentrations from census block centroids located 23 within 200 m of each of the 17 houses; 24 25

• Road dust measurements located within 300 m of each house 3 ; and 26 27

• Post-remediation residential soil Pb measuremen ts collected from the yard of each house. 28 29 The pre-remediation soil Pb concentrations collected during 2001 were not included in 30 regression modeling because they might not be representative of current conditions at the site 31 (i.e., it was felt that soil samples collected post- remediation would be more representative of 32 current conditions given the level of remediati on within this zone and ongoing recontamination 33 which was occurring). Because the focus is on predicting indoor dust Pb concentrations 34 reflecting longer-term trends, multiple samples take n over time for a given medium at a specific 35 house were averaged to produce a “temporally-avera ged” value reflecting longer-term trends in 36 Pb concentrations. For a more detailed summ ary of the regression analysis see Appendix F. 37 38 Multiple regression models were tested for this analysis, which are listed in Exhibit F-17 and F39 18 of Appendix F. During the testing, the road dust samples were found to not have significant 40 predictive power for indoor dust Pb. This may refl ect the fact that the indoor dust sample data 41 set does not provide significant coverage for hom es located near to the truck haul routes, 42 resulting in a poor correlation be tween road dust levels and indoor dust levels. Yard soil samples 43

3 Road dust measurements were compiled by EPA Region 7 (2006a) from 2002 to 2006 at 21 locations ranging from 160 to 1,700 m from the main stack at the facility.Estimates of Media Concentrations

ICF International 4-16 DRAFT – December 2006 were found to be slightly (but statistically significan tly) negatively correlat ed with indoor dust 1 levels. This counter-intuitive finding may reflect the process of yard remediation which has 2 likely resulted in a patchwork of remediated yards with varyi ng degrees of recontamination by 3 air-deposited Pb (i.e., remediati on activity has interfered with a ny correlation between yard soil 4 Pb levels, ambient air Pb levels, and indoor dust Pb levels that might have existed prior to 5 remediation efforts). Because of this negative correlation, the relationship to soil Pb levels was 6 excluded from the selected regression mode l, resulting in a model that only uses air 7 concentrations as input but has a fairly large in tercept, which likely refl ects a number of factors 8 not correlated with ambient air or distance fr om the facility, including generalized soil Pb 9 contamination in the area as well as contri butions of indoor Pb paint to indoor dust. 10 11 Although multiple regression models were tested, ultimately, the H6 model was selected for use 12 in the pilot phase because it has one of the highest goodness-of-fit measures (R 2 is 0.701), it 13 tracks non-linearities of the data even at low leve ls, and fits longer-term trends better than other 14 models. The H6 indoor dust model presented as follows relates the natu ral log of indoor house 15 dust to the natural log of ambient air Pb: 16 17 ln( Pb dust ) = 8.3884 + 0.73639 × ln( Pb air ) 18 19
CCC = exp{1.273 [ln( hardness )] – 4.705} × CF 20
CMC = exp{1.273 [ln( hardness )] – 1.460} × CF 21 where: 22 23
CF = conversion factor = 1.46203 -[ ln( hardness ) × 0.145712] hardness is 24 expressed as mg/L of calcium carbonate (CaCO 3 ) 25 Pb dust = concentration of Pb in indoor dust (mg/kg) 26 Pb air = concentration of Pb m odeled in the ambient air ( μ g/m3). 27 28 For the remainder of the study area, the pooled analysis AGG model based on data collected 29 during the 1970s and 1980s at a number of activ e primary Pb smelters, including the primary 30 smelter analyzed here, was used (USEPA 1989). The AGG model predicts indoor dust Pb based 31 on both outdoor soil and ambient air Pb levels. The model is appropriate for the non-remediation 32 portion of the primary Pb smelter case study area because this area has not been subjected to 33 extensive remediation and is therefore likely to resemble the locations included in the pooled 34 analysis used in deriving this model (i.e ., areas not having undergone extensive yard soil 35 remediation). Furthermore, because the non-reme diation portion of the study area is likely to 36 have soil Pb gradients reflecti ng long-term atmospheric deposition of Pb, it is likely that indoor 37 dust would be partially dependent on soil P b. Therefore, the AGG model presented here 38 (including both the soil and air factors) was selected for this portion of the study area: 39 40 Pb dust = 31.3 + (638 × Pb air ) + (0.364 × Pb soil ) 41 42 where: 43 44 Pb soil = concentration of Pb in outdoor soil (mg/kg). 45 46Estimates of Media Concentrations

ICF International 4-17 DRAFT – December 2006 Exhibit 4-11 presents a summary of the Pb indoor dust concentrations generated in the primary 1 Pb smelting study area for the current conditions and current attainment scenarios. Indoor dust 2 concentrations were only estimated for census blocks with children. Exhibit 4-11 shows the 3 number of census block or block groups and children modeled with increasing levels of Pb house 4 dust. All estimated indoor dust Pb concentrations for residences in the primary Pb smelter case 5 study are presented in Appendix F, Exhibit F-14. 6 7 8 9 Exhibit 4-11. Primary Pb Smel ter Case Study: Modeled Indoor Dust Pb Concentrations a,b 10
Current Conditions Current Attainment House dust concentration ( μ g/g) Census blocks/ block groups with dust concentrations greater than value Children living in area with dust concentration greater than value Census blocks/ block groups with dust concentrations greater than value Children living in area with dust concentration greater than value 30 137 3,880 137 3,880 50 61 970 61 970 100 26 104 26 104 500 23 97 23 97 1,000 14 47 14 47 3,000 5 9 4 8 5,000 2 6 0 0

a The 137 blocks and block groups with children aged 0 to 7 in the 2000 U.S. Census (U.S. Census Bureau 2005) 11 were used to develop this summary. Note that Exhibits 4-2 used all blocks with population, even blocks without 12
children. 13
b Number of children ages 0 to 7 from U.S. Census 2000 were used in this analysis (U.S. Census Bureau 2005). 14 15 Studies summarized in the 1990 review of the Pb NAAQS contained measurements of house 16 dust ranging from 10 to 35,000 ppm. A high va lue of 100,000 ppm was measured in one home 17 within 2 km of a Pb smelting f acility (USEPA 1989). The Pb indoor dust concentrations for this 18 primary Pb smelter case study fall within the ra nge presented by U.S. EPA (1989), but they are 19 not in the high-end of the range. Within a distance of 2 km from the modeled primary Pb 20 smelting facility, the maximum dust concentrat ion at a receptor location is 5,300 parts per 21 million (ppm) at 300 m from the main stack. In a study of Pb concentrations in household dust 22 near a facility that has operat ed as a secondary Pb smelter since 1972 and as a primary smelter 23 for the previous 200 years in the Czech Republic , Rieuwerts et al. ( 1999) measured household 24 dust Pb concentrations in houses in neighborhood adjacent to the facility (the neighborhood 25 ranges from approximately 0 to 500 m away from the facility according to a figure). Measured 26 Pb concentrations in household dust from 14 homes ranged from 861 to 5890 ppm with a 27 geometric mean of 1668 ppm. Indoor dust concentr ations in the primary Pb smelter pilot phase 28 assessment are similar, ranging from 1,500 to 5,300 ppm out to 500 m from the facility, with a 29 geometric mean of 3,100 ppm. 30 31 4.1.5 Surface Water and Sediment 32 33 Measurements of sediment and surface water Pb concentrations were obtained from ELM’s 34 Characterization Area Investigation Report (2005). Sediment samples chosen for this 35 assessment were co-located with surface water samples at all lo cations. ELM sampled Joachim 36Estimates of Media Concentrations

ICF International 4-18 DRAFT – December 2006 Creek, the Mississippi River, a U-shaped ephemeral pond approximately 1.7 km northwest of the 1 primary Pb smelter facility, and drainage areas in the watershed. 2 3 4.1.5.1 Surface Water 4 5 Results of all surface water samples collected from the Joachim Creek, Mississippi River, and 6 the nearby pond and backwater flow areas showed that levels of dissolv ed Pb were below the 7 detection limit for Pb of 3.0 μ g/L (note that the detection limit for dissolved Pb in surface waters 8 for the U.S. Geological Survey since 2001 is 1.0 μ g/L). The surface water sample data analyzed 9 by ELM (2005) was reviewed and aggregated, and th e results for sampling lo cations used in this 10 assessment are presented in Appendix F. 11 12 ELM (2005) collected surface water samples from four backwater and low flow areas within a 13 1.6 km radius to characterize impacts of aerial de position of historical smelter emissions. ELM 14 collected samples from two locations on Joachim Creek and its feeder streams (CHRDDP and 15 DAMUP), based on the assumption that even tho ugh most of the direct deposition of particles 16 from the emissions would be in the direction of the prevailing winds, minor gusts and winds 17 along with aerodynamic effects may take the contam inants in any direction. Additional samples 18 were taken from the unnamed pond north of Joachi m Creek (UPOND) and from a slow flow area 19 along the western shore of the Mississippi downstream of the facility (RRDP). 20 21 The other ELM (2005) surface water samples were reviewed and the ones that indicated likely 22 contributions of Pb from sources other than air deposition because they bo rdered the storage slag 23 area (SSA) were excluded. Sampling sites were chosen from among those taken along Joachim 24 Creek to represent sections of the creek appr oximately 800 m, 1.6 km, and 3.2 km to the west 25 and southwest of the primary Pb smelter based on aerial maps showing the sampling locations. 26 Transects were established by ELM in the area of point bars, backwaters, and low flow areas for 27 sediment sampling as well as locations perpendicu lar to stream flow at each sampling location. 28 ELM collected three surface water samples at each transect, one at the thalweg (i.e., the deepest 29 water area of the creek, generally in the middl e) and the other two equidistant between the 30 thalweg and the banks of the Joachim Creek. The water samples were coll ected in the center of 31 the vertical water column (i.e., equidistant from the water surface and the top of the sediment) at 32 each of the three locations of each transect. 33 34 The sampling locations identified in the Missi ssippi River were approximately 2.7 km upstream 35 from the facility, in the immediate vicinity of the facility, and approximately 2.5 km downstream 36 from the facility (data for sampling stations further downstream on the Mississippi River were 37 not reported). ELM chose the farther downstream lo cations to assess the potential impact on the 38 Mississippi River surface waters from the fac ility’s SSA downstream, as well as deposition of 39 smelter emissions. 40 41 Data were aggregated for the ecological risk screening assessment as follows: 42 43

• Joachim Creek. Samples were clustered geographically and then averaged. Cluster 1 44 corresponds to the set collected farthest upstream from the facility, while Cluster 5 was 45 nearest upstream from the facility, and the remaining clusters fill the range between. 46Estimates of Media Concentrations

ICF International 4-19 DRAFT – December 2006 Results are provided in Exhib it F-7. Although total Pb con centrations were typically 1 above the detection limit of 3 μg/L, with values up to 106 μg total Pb per liter of water, 2 dissolved Pb concentrations were not above detection limits. 4 Therefore, all measured 3 surface water concentrations of dissolved Pb at the sampling locations of interest were 4 below the detection limits used in the study. 5 6

• Mississippi River . Samples from upstream, at th e facility, and downstream of the 7 facility are presented and will be evaluated separately. Sample results are presented in 8 Exhibit F-8. Although some of the measured total Pb concentrations exceeded the 9 detection limit, all measured dissolved Pb con centrations were below the detection limit. 10 11

• Other surface water samples from U- shaped pond and drainage areas. Sample 12 results are presented in Exhibit F-9. Again, a ll measured concentrations of dissolved Pb 13 in these surface waters were belo w the detection limit of 3 μg/L. 14 15 4.1.5.2 Sediment 16 17 Sediment sample data were extracted and aggr egated into geographic clusters based on ELM’s 18 Characterization Area Investigation Report (2005) at the same locations as the surface water 19 concentrations presented in the previous secti on. Mean Pb concentrations in dried sediment 20 sample clusters for Joachim Creek ranged from 34.5 to 78.7 mg/kg; for the Mississippi River 21 from 12.1 mg/kg downstream to 30.1 mg/kg near th e facility; and in th e U-shaped pond mean 22 sediment concentrations were 170 mg/kg. Exhi bits 4-12 through 4-14 present a summary of the 23 clustered sediment concentrations considered in this analysis. More de tailed tables showing 24 measured sediment concentrations at each location are presented in Appendix F. 25 26 Sediment samples to 10 cm in depth from the Mi ssissippi River were collected from the back27 eddies located behind wing dams and jetties, beca use ELM expected signif icant sedimentation in 28 these areas due to the engineering of the Mississi ppi River by the Army Corps of Engineers. In 29 Joachim Creek, ELM focused sediment sampling locations at point bars and other depositional 30 areas. Joachim Creek exhibited sinu ous meanders in the vicinity of the facility, which lies within 31 one of the large loops of one of th e meanders of the creek. Since th e series of meanders lead to a 32 slower and steadier current of the water, one woul d expect inorganic const ituents associated with 33 facility-related operations to sorb onto silt a nd organic matter particles and mobilized facility34 related constituents to collect at the point bars and other depositional zones. ELM collected 35 sediment samples from these areas to assess the potential for impacts from deposition of smelter 36 emissions, possibly from both slag piles and air emissions, on the sediments of Joachim Creek. 37 38 ELM also collected sediment samples from b ackwater and low flow pond areas, ephemeral 39 ponds, and drainage pathways to characterize imp acts of air deposition of historical smelter 40 emissions. They collected surface water and sedi ment samples from the lowest points in the 41 watershed and drainage pathways within 2.4 km from the facility. This choice of four sampling 42

4 Appendix B-2 of ELM (2005) reports surface water summary statistics in units of mg/L; this contradicts the units reported in Appendices C-5, C-6, C-7, and C-8 which pr esent the measured constituent concentration results for the characterization area surface water samples. The Appendix C data tables all indi cate that the measured values are in μg/L unless otherwise noted; therefore, μg/L was assumed to be the correct unit.Estimates of Media Concentrations

ICF International 4-20 DRAFT – December 2006 areas represented the areas of direct deposition from smelte r emissions and deposition of 1 impacted particles by surface water run-offs. 2 3 Given the focus of this assessment on impacts from air emissions from the smelter, sediment 4 samples that were taken very close to the wate r seeps at the toe of the SSA were excluded, 5 because the ELM reports states that the wa ters from Joachim Creek, on occasion, had been 6 observed to come into contact with the SSA during flooding events. 7 8 Data were aggregated for the risk screening assessment as follows: 9 10

• Joachim Creek . Samples were clustered geographical ly and then averaged. Cluster 1 11 corresponded to the samples collected furthest upstream from the faci lity, while Cluster 5 12 was nearest upstream from the facility. Meas urements are presented in Exhibit 4-12 and 13 Exhibit F-10. 14 15

• Mississippi River . Samples from upstream, at th e facility, and downstream of the 16 facility are presented and will be evaluated separately. Measurements are listed in 17 Exhibit 4-13 and Exhibit F-11. 18 19

• Other surface waters including U-shaped pond and watershed drainage areas

. 20 Measurements are presented in Exhibit 4-14 and Exhibit F-12. 21 22 Exhibit 4-12. Joachim Creek Measured Sediment Pb Concentration Data 23 for the Primary Pb Smelter Case Study (ELM 2005) 24

Sample Cluster Number of Sample Sites Mean Pb Concentration in Dry Sediment (mg/kg) Standard Deviation of Pb Concentration in Dry Sediment

Cluster 1 3 36.5 14.9
Cluster 2 4 56.0 20.0
Cluster 3 6 78.7 55.3
Cluster 4 11 30.0 8.5
Cluster 5 3 34.5 5.2 25Estimates of Media Concentrations

ICF International 4-21 DRAFT – December 2006 Exhibit 4-13. Mississippi River Measured Sediment Pb Concentration Data 1 for the Primary Pb Smelter Case Study (ELM 2005) 2

Location with Respect to Facility Number of Sample Sites Mean Pb Concentration in Dry

Sediment (mg/kg) Standard Deviation of Pb Concentration in Dry Sediment
Upstream 5 14.7 8.1 Near facility 20 30.1 25.6
Downstream 11 12.1 3.2 3 Exhibit 4-14. Other Surface Water—Drainage Areas and U-Shaped Pond—Measured 4 Sediment Pb Concentration Data for the Primary Pb Smelter Case Study (ELM 2005) 5

Sampled Surface Water Body and Sample ID No. Number of Sample Sites Pb Concentration in Dry Sediment (mg/kg) Standard Deviation of Pb Concentration in Dry Sediment
Drainage area CHRDDP 1 110 NA Drainage area RRDP-02 1 14.8 NA U-shaped pond 4 170.1 90.6 6 4.2 Secondary Pb Smelter 7 8 The media concentrations for the secondary Pb smelter case study were estimated using 9 modeling and then compared to available measur ed values. For the time-period represented by 10 the emissions (1997 to 2000), air monitoring data near the facility did not indicate quarterly 11 average air concentrations exceeding the current NAAQS 5 , and modeled quarterly air 12 concentrations were also all less than the current NAAQS (USE PA 2006f). Therefore, only a 13 current conditions scenario was performed for this case study. 14 15 4.2.1 Spatial Template 16 17 The study area extent was defined out to the dist ance where the concentrations associated with 18 the secondary Pb smelter facility emissions r eached approximately 50 percent of the background 19 concentration of Pb in the area. Air concentr ations generated from a preliminary AERMOD run 20 with a radial grid of recept ors placed at seven distances out to 50 km were compared to 21 background Pb concentrations from the Inte ragency Monitoring of Protected Visual 22 Environments (IMPROVE) database (IMPROVE 2006) . The average (arithmetic mean) of all 23 background Pb measurements taken between 2000 a nd 2004 at two nearby sites that were used 24 for comparison to the secondary Pb smelter results were 0.0012 μ g/m 3 (from 411 samples) and 25 0.0019 μ g/m 3 (from 481 samples). Results of the prel iminary air quality m odel run showed that 26 at a radius of 10 km, estimated air concentratio ns were closest to 50 percent of the background 27 average air concentrations. The deposition gradient was not consid ered for selecting the extent 28 of the modeling region because background deposition measurements were not available. 29

5 In 2003, the quarterly average Pb air concentration at one of th e monitors near the sec ondary Pb smelter exceeded the current NAAQS (measured concentration was 1.9 μ g/m 3 ) (USEPA 2006f). This time period was not included in this phase analysis due to availability of emissions data , but this monitor value indicates that emission rates may have changed between 2000 and 2003 at the facility.Estimates of Media Concentrations

ICF International 4-22 DRAFT – December 2006 1 Using GIS software, the 12 census block groups th at are predominantly within 10 km of the 2 facility were identified. Because of the irre gular shape of block groups, not all of the block 3 groups with area within 10 km were included, and some that were included have area outside 10 4 km. The selection was made to include block groups whose average exposure would 5 predominantly come from air within 10 km of th e source. Model receptors were placed at all 6 block centroids within the 12 bloc k groups of interest. This resu lted in 665 block centroid points 7 being modeled. Some of these blocks had zer o population in the 2000 U.S. Census. Two air 8 monitors from AQS/AirData (USEPA 2006f) we re identified between 400 and 700 m of the 9 facility. The locations of these 2 monitors we re modeled as discrete receptors, for direct 10 comparison purposes between mode l results and monitoring data. 11 12 There are 435 census blocks with non-zero population in the 2000 U.S. Census within the model 13 region, and air dispersion model re sults were generated at all of these blocks. An attempt was 14 made to limit the locations modeled for the expo sure assessment, as was done for the primary Pb 15 smelting case study (see Section 4.1.1). The averag e concentration and deposition in each of the 16 12 block groups was calculated by sp atially-weighting the values at the block level. The area of 17 each block was obtained from the U.S. Census (C ensus Bureau 2005). The range of block-level 18 concentrations within each block group was cons idered by calculating the ratio of the maximum 19 block-level air concentration in the block group to the average air concentration in the block 20 group. For the primary Pb smelter case study, wh en these results were analyzed, the blocks 21 groups with ratios greater than 2.0 were modeled at the block level and others modeled as block 22 groups. For the secondary Pb smelter, although five block groups had maximum to average 23 ratios less than 2.0, U.S. EPA and ICF decided to model these block groups as blocks because of 24 the small size of the block groups and their proxim ity to the facility (see Exhibit 4-15). A total 25 of 435 blocks with non-zero population were included in the assessment. 26 27Estimates of Media Concentrations

ICF International 4-23 DRAFT – December 2006 Exhibit 4-15. Modeled Blocks and Block Groups at Secondary Pb Smelter Facility 1 2

3Estimates of Media Concentrations

ICF International 4-24 DRAFT – December 2006 4.2.2 Air 1 2 The air concentrations and deposit ion of Pb from the secondary Pb smelter facility were modeled 3 with AERMOD and compared to the air concentr ations from nearby monitors. The emissions 4 used for the air quality modeling are described in Section 3.2.4 and repr esent emissions from 5 December 1997, November 1999, and February 2000. 6 7 4.2.2.1 Air Dispersion Modeling 8 9 The air dispersion model AERMOD was used for the secondary Pb smelter modeling (USEPA 10 2004a; USEPA 2004b). The meteorological data used includes five consecutive years (1990 to 11 1994) of nearby measurements. Surface-level an d upper air meteorological data were obtained 12 for weather stations located in Montgomery, Alabama (NOAA 1997a) and Centerville, Alabama 13 (NOAA 1997b), respectively, and processed using the meteorological pre-processor, AERMET 14 (USEPA 2002a). These stations represent loca tions close in proximity and geography to Troy, 15 Alabama, and for which five consecutive years of surface and upper air meteorological data were 16 available. Obtaining five consecutive years of weather observations for use in AERMOD was 17 desirable because it allowed for the natural variab ility in weather conditio ns be captured in the 18 air modeling. 19 20 AERMOD was configured to model the same emissi ons for the five years of meteorological data 21 (1990 to 1994). Hourly air concentration and de position results were averaged over this time 22 period to obtain average air co ncentration and annual average deposition. As described in 23 Section 3.2.4, emissions, release parameter, part icle size parameters, and building downwash 24 inputs were all obtained from the residual risk study of the facility (ECR 2006). Receptors and 25 the study area boundary were sel ected specifically for this m odel application. Due to the 26 relatively flat terrain in Alabama, terrain cal culations were not used in this application. 27 28 4.2.2.2 Inhalation Exposure Concentrations 29 30 As in the primary Pb smelting case study, inha lation exposure concentrations of Pb were 31 estimated for the population of interest (young children) from the estimated ambient air 32 concentrations using age group- and location-sp ecific relationships for Pb developed from 33 modeling performed for U.S. EPA’s 1999 Natio nal-Scale Air Toxics Assessment (USEPA 34 2006c), one of EPA’s NATA activities. See Section 4.1.2.2 for more information. 35 36 The relationships (or ratios) be tween ambient Pb concentration and Pb exposure concentration 37 from the NATA national-scale assessment for th e 0 to 4 age group (the closest age group for 38 which outputs are available to th e age group of interest for this assessment) ranged from 0.44 to 39 0.46 for the census tracts in the st udy area for the secondary Pb sm elter location. The ratios are 40 presented in Appendix G, Exhibit G-1. Use of ratio s for the 0 to 4 age gro up (rather than for 0 to 41 7) contributes some uncertainty in the estimate of children’s inhalation ex posure concentrations. 42 Uncertainty related to the magnitude of the ASPE N modeled air concentrati ons is also discussed 43 in Section 4.1.2.2. 44 45Estimates of Media Concentrations

ICF International 4-25 DRAFT – December 2006 4.2.2.3 Air and Deposition Results 1 2 Annual average Pb air concentrations and deposi tion fluxes were calculate d at each block and 3 monitor receptor point for the five years of mete orological data. To take into account variations 4 in meteorological data, the annual average concen trations and depositions for each of the five 5 years were averaged to generate one set of representative an nual average concentration and 6 deposition results for the cu rrent conditions scenario. 7 8 All concentration, deposition, a nd inhalation exposure estimates for the blocks modeled are 9 presented in Appendix G, Exhibit G-2. In a ddition, Exhibit 4-16 presents the range of air 10 concentration, inhalation exposure, and depositio n values generated from the modeling. The 11 values are summarized from the model resu lts at the 435 blocks with non-zero population. 12 Exhibit 4-17 shows isopleths of the bl ock-level modeled air concentrations. 13 14 Exhibit 4-16. Secondary Pb Smelter Case Study: 15 Summary of Modeled Air Con centrations and Deposition a 16 Current Conditions

Statistic b Average Annual Pb Air Concentration ( μ g/m 3 ) Children Ages 0 to 4 Years Average Annual Pb Air Exposure Concentration ( μ g/m 3 ) Total Deposition (g/m 2 /year)

Maximum 0.536 0.238 0.435 95 th Percentile 0.0178 0.008 0.0066 Median 0.0047 0.0021 0.0013 5 th Percentile 0.0009 0.0004 0.0002 Minimum 0.0005 0.0002 0.0001

a The 435 blocks with non-zero population selected fo r analysis were used to create this summary. Note 17
that in some of these blocks the 2000 U. S. Census indicates there are no children. 18

b The statistic (e.g., 95 th percentile, median) may not be at the same location for each of the data results 19 presented here. 20 21 22Estimates of Media Concentrations

ICF International 4-26 DRAFT – December 2006

Exhibit 4-17. Secondary Pb Smelter Case St udy: Concentration and Deposition Isopleths 1

2 3Estimates of Media Concentrations

ICF International 4-27 DRAFT – December 2006 4.2.2.4 Air Modeling Performance Assessment 1 2 The monitoring data at the two ai r monitor locations near the fac ility were compared to modeled 3 concentrations at the same locations (see Exhibit 4-18). Overall, the modeled concentrations at 4 the monitor locations are approximately three times lower than the monitor values. Both of the 5 monitors are located to the northwest of the facil ity. Because the meteorological data is not site6 specific, there is likely some uncer tainty with its use in estimating air concentrations at specific 7 points. It is possible that the local predominan t wind direction is different from that of the 8 meteorological data. Therefore, the monitored air concentrati ons were also compared to 9 modeled air concentrations within similar distances to the facilit y, in all directions modeled on a 10 radial grid (Exhibit 4-18). When compared to concentrations in all directions, the monitored 11 values fall within the range of modeled results. A wind rose created from the five years of 12 Montgomery, Alabama, wind data (Exhibit 4-19) shows that the predominant wind direction 13 used in the model runs is direc tly to the west. The northwest (t he direction of the monitors) is 14 not a predominant wind direction in the metrologi cal data set used for modeling; however, it is 15 unknown whether this is the predominan t wind direction at this site. 16 17 The potential difference between actual site me teorological data and the meteorological data 18 used may help explain why the modeled concen trations are not closer to the monitored 19 concentrations at the exact monitor locations, but modeled concentrations in all directions are 20 within the range of monitored con centrations at similar distances. Because the monitors are both 21 to the northwest of the facility (see Exhibit A-12), it is unknown whether modeled concentrations 22 are underestimated in all directions. Thus, ther e is the potential that all air and deposition 23 concentrations are underestimated from the mo deling or that the degree of over- or under24 prediction of concentrations from modeling is dependent upon dire ction. A directional 25 difference between modeled and actual air concentr ations can impact risk results (either under26 or over-predicting) because the number of mode led children varies by census block and each 27 census block is in a specific direction from the facility. 28 29 Exhibit 4-18. Secondary Pb Smelter Case Study Air Concentration 30 Modeled Results Compared to Monitor Values 31
Modeled Results U.S. EPA AirData Monitor Distance from Midpoint of Facility (m) Range of Monitor Air Concentrations from AirData 1997 to 2000 ( μ g/m 3 ) Range of Modeled Distances for Comparison Range of Modeled Concentrations a ( μ g/m 3 ) Modeled Concentration at Monitor Location ( μ g/m 3 )

11090003 400 0.383 to 0.474 300 to 500 m (54 Points) 0.08 to 6.0 0.11

11090006 680 0.132 to 0.198 500 to 800 m (72 Points) 0.038 to 0.17 0.050

a The modeled concentrations presented here were generate d from a model run with a radial receptor grid. This 32 summary is not from census block centroid results. 33 34 No local measurements of Pb deposition were found to compare to the modeled results. Studies 35 were summarized in the U.S. EPA Pb criteria document (USEPA 2006b) that provided ranges of 36 Pb dry deposition fluxes in various locations acr oss the United States. None of these are 37 specifically for deposition near a secondary Pb sm elter, but they provide a range of deposition 38 values for comparison. Exhibit 4-20 summarizes these deposition values. 39Estimates of Media Concentrations

ICF International 4-28 DRAFT – December 2006 1 The maximum annual estimated deposition flux from the secondary Pb smelter emissions at a 2 census block centroid (0.435 g/m 2 /year) is higher than any of th e deposition fluxes presented in 3 Exhibit 4-20. This is expected because none of those studies m easured deposition directly next 4 to a facility. The median es timated deposition flux (0.0013 g/m 2 /year) does fall within the range 5 of some of the measurements in Exhibit 4-20 (i.e ., New York City, Detroit, and sites near Lake 6 Michigan). The lower estimated deposition fluxes are at the low end of most of the measured 7 ranges, which is expected because the locations of those fluxes could be described as urban 8 background. Comparison of the modeled deposi tion fluxes to these measurements throughout 9 the country provides some confidence that the m odeled deposition is within the expected range. 10 11 Exhibit 4-19. Wind Rose of Meteor ological Data Used for Secondary Pb Smelter Case Study (Directi on in which Wind is Blowing) a

a Wind rose for 43,824 hours of data 12 Exhibit 4-20. Dry Deposition Fluxes from Studies in the United States 13

Location Mean Value or Range (g/m 2 /year) Source
New York City, building exterior plate collecting deposition (weekly values from 2003 to 2005 averaged) 0.0098 Caravanos et al. 2006
Two sites on Chesapeake Bay in 1990 – 1991 0.00037 to 0.001 Wu et al. 1994 New York-New Jersey Harbor Bight ar ea 0.00015 to 0.00076 Gao et al. 2002 Urban site in metropolitan Detroit 1982 – 1991 0.0004 to 0.004 Pirrone et al. 1995 Sites near Lake Michigan 1993 – 1995 0.0084 to 0.014 Yi et al. 2001 Lake Michigan 0.00095 Sweet et al. 1998 Lake Superior 0.00092 Sweet et al. 1998 Lake Erie 0.00078 Sweet et al. 1998 14Estimates of Media Concentrations

ICF International 4-29 DRAFT – December 2006 4.2.3 Outdoor Surface Soil 1 2 Outdoor soil concentrations were estimated us ing two methods for the secondary Pb smelter 3 facility – a model-only appro ach and a hybrid approach usi ng model results and surrogate 4 measurements. The modeled soil concentrations were calculated for each census block analyzed 5 for this case study using the AERMOD deposition estimates and EPA’s Multiple Pathways of 6 Exposure (MPE) methodology (USEPA 1998). The MPE methodology represents the update of 7 the Indirect Exposure Methodol ogy, or IEM (USEPA 1990a). MP E consists of a set of 8 multimedia fate and transport algorithms developed by EPA’s Office of Research and 9 Development, including a soil mixing model which wa s used in this assessment to calculate the 10 soil concentrations resulting from depositi on at the secondary Pb smelter location. 11 12 In the MPE algorithms, cumulative soil concentrati on is calculated as a function of particle 13 deposition, soil mixing depth, bulk density, and a so il loss constant. The soil loss constant (in 14 this case) was set up to be a f unction of loss due to leaching, er osion, and runoff. Concentration 15 in the soil was calculated in the top 1 cm of soil assuming constant deposition of Pb for the entire 16 operating period of the facility ( 37 years). Although it is likely that the deposition has changed 17 in the last 37 years, possibly ev en declining with reduced emissions, this method was used due to 18 lack of soil samples and historical emissions data. With this assumption in mind, other 19 assumptions in the soil concentration calcula tions were made conservatively. All input 20 parameters used for the soil mixing model are lis ted in Appendix G, Exhibit G-4. Site-specific 21 input parameters were used when feasible, but assumptions were made for some parameters, in 22 many cases based on suggested values in the Human Health Risk Assessment Protocol 23 (HHRAP), which contains a database of input parameters for the MPE equations (USEPA 24 2006g). 25 26 Soil concentrations estimated using the mode ling approach for each census block are 27 summarized in Exhibit 4-21, and presented in mo re detail in Appendix G, Exhibit G-2. Across 28 the census blocks in the study area, the modeled t op 1 cm soil concentrations from the facility’s 29 deposition ranged from 0.05 to 368 mg/kg. A b ackground soil concentration of 15 mg/kg was 30 added to these values for use in the exposure assessment (based on Gustavsson et al. 2001) to 31 account for natural soil concentrations of Pb and general anthropogenic background. 32 33 As described in the next section (Section 4.2.3.1), a set of soil measurements at a similar facility 34 was used to develop the hybrid so il concentration result set for the secondary Pb smelter case 35 study (using modeled and surrogate data). Soil measurements taken in surface soil from 100 to 36 1000 m from a similar facility (Kimbrough and Suffet 1995) were co mpared to the modeled soil 37 concentrations from this assessment. The modeled soil concentrations at the census block 38 receptors are approximately three times lower th an the measured concentrations. Thus, the 39 modeled results were scaled up th ree times to account for the pot ential under-prediction of the 40 soil model when the results were compared to the surrogate measurements. The hybrid soil 41 concentrations are presented in Exhibit 4-21 along with the modeled soil concentrations. 42 Consideration of these two approaches for ch aracterizing soil Pb represents an important 43 sensitivity analysis consideration for this case study and consequently, parallel sets of risk 44 estimates (using each approach) are presented. 45 46Estimates of Media Concentrations

ICF International 4-30 DRAFT – December 2006 Exhibit 4-21. Summary of Secondary Pb Smelter Case Study Soil Concentrations a 1

Statistic Modeled Average Soil Concentration from Deposition (mg/kg) Background Concentration (mg/kg) Modeled Approach Pb Soil Concentration: Modeled plus Background (mg/kg) Hybrid Approach Pb Soil Concentration (mg/kg)

Maximum 368 15 383 1150 95 th Percentile 6.16 15 21.2 63.5 Median 1.23 15 16.2 48.7 5 th Percentile 0.15 15 15.2 45.5

Minimum 0.05 15 15.0 45.2

a Surface soil concentrations were calculated to a depth of 1 cm. 2 3 4.2.3.1 Performance Evaluation for Soil Concentration Estimates 4 5 The modeled soil concentrations were compared to measured soil concentrations near another 6 secondary Pb smelter because soil measurements are not available near the secondary smelter 7 used in this assessment. Kimbrough and Suffet ( 1995) measured Pb in soil samples out to from 8 100 to 1000 m from a secondary Pb smelter. Pe rsonal communication with Kimbrough indicated 9 that the soil samples taken were from surf ace soil (information on the specific depth was not 10 available) and in an urban area. Exhibit 4-22 presents the comparison of modeled soil 11 concentrations at similar distances from the f acility as presented in the Kimbrough and Suffet 12 (1995) study. 13 14 Exhibit 4-22. Summary of Secondary Pb Smelter Case Study Soil Concentrations 15 with Distance from Source Compared to Measurements a 16 Kimbrough and Suffet (1995) Secondary Pb Smelter Case Study

Distance from

Facility (m) Measured concentrations (mg/kg) Number of measured locations compared b Modeled concentration +

background

(mg/kg) Number of modeled locations compared c

100 140 to 1900 4 200 770 1 190 1 250 400 1 340 to 380 79 to 380 2 500 220 to 380 2 620 to 680 34 to 51 5 750 150 to 490 4 800 96 1 830 to 880 31 to 34 2 925 31 1 1000 49 to 250 5 1060 to 1090 25 to 29 4

a Soil concentrations were modeled to a depth of 1 cm, and the measurements represent surface soil (according to the 17 study’s author). 18

b Kimbrough and Suffet (1995) measured Pb concentratio ns in soil at different directions from the facility. 19

c Modeled soil concentrations compared here were generate d from deposition estimates at census block centroid and 20
air monitor locations. Some of the census blocks near th e facility had zero population in 2000 and are therefore not 21 included in other parts of this study. 22 23Estimates of Media Concentrations

ICF International 4-31 DRAFT – December 2006 The comparison between the modeled soil concen trations and the Kimbrough and Suffet (1995) 1 soil concentrations varies, mostly as a result of direction from the source. On average, the 2 measured concentrations are approximately th ree times higher than the modeled results, 3 suggesting an underprediction of so il concentrations from the mode l results for this case study. 4 Because of this potentially significant impact (d ownward bias) on exposure and risk results, an 5 alternative soil concentration for the study area was calculated by combining the modeling 6 results (used to characterize spatial variability in soil levels across the study area) with surrogate 7 data from Kimbrough and Suffet (1995) to adjust th e absolute soil Pb levels for the study area. 8 The result is the “hybrid” soil c oncentration set presented in Exhi bit 4-21, in which the modeled 9 soil concentrations are scaled up three times. 10 11 4.2.4 Indoor Dust 12 13 Indoor dust sampling data were not availabl e for the secondary Pb smelter case study, 14 necessitating the use of modeling to characterize indoor dust Pb leve ls within the study area. A 15 version of the AGG pooled analysis (USEPA 1989) model that uses ambient air Pb levels for 16 predicting dust levels was chosen. This is a si milar model as used for the primary Pb smelter 17 case study at distances greater th an 1.5 km from the source (see Section 4.1.4); however, in the 18 case of the secondary Pb smelter, an “air-only” version of the model was employed reflecting the 19 reduced overall confidence associated with so il characterization at this case study. 20 21 The air-only AGG model does reflec t (implicitly) some considerat ion for the soil-to-indoor dust 22 mechanism in the air signal. Specifically, the larg er air factor for the ai r-only model (relative to 23 the air plus soil dust model's air factor) reflects the fact that, in this version of the model, air 24 measurements are used to cover both the direct lo ading of indoor dust from air and the loading of 25 outdoor soil by air with subseque nt impacts of that soil on indoor dust through such mechanisms 26 as indoor tracking of soil (USEPA 1989). Th e AGG model used for the secondary smelter was 27 based on a number of studies focusing mainly on primary Pb smelters. This basis carries an 28 uncertainty into indoor dust pred ictions generated using this mode l related to th e potential for 29 differences between primary and secondary Pb sm elters that may relate to indoor dust loading 30 (e.g., particle size profiles and nature of the entrained Pb co mpounds). The air-only AGG model 31 used in this analysis is presented below: 32 33 Pb dust = 60 + (844 × Pb air ) 34 35 where: 36 Pb dust = concentration of Pb in indoor dust (mg/kg), and Pb air is the concentration of Pb 37 modeled in the ambient air ( μ g/m 3 ). 38 39 Exhibit 4-23 presents a summary of the Pb indoor dust concentrations generated in the secondary 40 Pb smelting study area for the current conditions scenario. Indoor dust co ncentrations were only 41 estimated for census blocks with children. Th e numbers of census block or block groups and 42 children modeled with increasing levels of Pb house dust are pr esented in Exhibit 4-23. All 43 estimated Pb dust concentrations for the secondar y Pb smelter facility are presented in Appendix 44 G, Exhibit G-3. 45 46Estimates of Media Concentrations

ICF International 4-32 DRAFT – December 2006 Exhibit 4-23. Secondary Pb Smelter Case Study: Modeled Indoor Dust Pb 1 Concentrations a 2
Current Conditions

House dust concentration ( μ g/g) Census blocks/ block groups with dust concentrations greater than value a Children living in area with dust concentration greater than value b
60 296 1,672 70 40 172 80 6 16 100 2 3 120 1 2

a The 296 blocks with children aged 0 to 7 in the 2000 U.S. Census were used to develop this 3 summary. Note that Exhibits 4-21 includes all blocks with population, even blocks without 4
children. 5
b Number of children ages 0 to 7 from the 2000 U.S. Census were used in this analysis (U.S. 6 Census Bureau 2005). 7 8 Studies summarized in the 1990 review of the Pb NAAQS contained measurements of house 9 dust ranging from 10 to 35,000 ppm. A high va lue of 100,000 ppm was measured in one home 10 within 2 km of a Pb smelting f acility (USEPA 1989). The Pb indoor dust concentrations for this 11 case study fall within the range presented by U.S. EP A (1989), but they are at the low-end of the 12 range. In addition, all of the concentrations are much lower than the 100,000 ppm concentration 13 cited within 2 km of a Pb smelter. The fact that this facility is a sec ondary, and not primary, Pb 14 smelter may explain some of the difference. In the study mentioned in Section 4.1.4, household 15 dust Pb concentrations were measured at 14 homes within approximately 500 m (approximated 16 from a figure) from a secondary Pb smelter in the Czech Republic (Rieuwerts et al. 1999). 17 Measured Pb concentrations ranged from 861 to 5890 ppm with a geometric mean of 1668 ppm. 18 Indoor dust concentrations in this secondary Pb smelter pilot phase assessment were not 19 estimated within 500 m. At 590 m from the facili ty in this pilot phase of the analysis, the 20 estimated indoor dust concentrati on is 130 ppm. In addition to th e close distance, the secondary 21 Pb smelter studied by Rieuwerts et al. (1999) used to be a primary Pb smelter, perhaps 22 explaining why the indoor dust co ncentrations were higher than in this pilot phase of the 23 analysis. 24 25 4.3 Near Roadway Urban Location Scenario 26 27 4.3.1 Spatial Template 28 29 As described in Section 3.3.1, the location of the study area for the near roadway urban case 30 study was selected based on the availability of air monitoring data. Of the streets in the vicinity 31 of the near roadway monitoring site in Houston, Texas, a thoroughfare to the south was selected 32 as the street around which the scenario was design ed, due to its proximity to the monitoring site 33 and the length of the road links for that street. 6 The distance from the monitoring site to another 34 major road is about the same as the distance from the site to the street th at was selected (i.e., 100 35

6 Although it was not a strict requirement that the selected roadway section used for th is case study correspond to road link delineations, the road link was a convenient unit given the availability of road data, especially vehicle throughput data, by link. Matching the delineations of selected links may provide benefits in analyses beyond the pilot scale (e.g., in scaling up results to wider geographic areas).Estimates of Media Concentrations

ICF International 4-33 DRAFT – December 2006 m vs. 115 m for the selected roadway), but the roadwa y links for the selected street are shorter in 1 this area. A shorter roadway segment was judged to be more desirable because it was assumed 2 that less uncertainty would be associated with the assumption that the measured air 3 concentrations are representative of conditions along the entire length of the roadway segment. 4 5 The subdivision of the study area into smalle r regions for characte rizing risks (i.e., the 6 development of the spatial template) was based in part on research c onclusions regarding the 7 impacts of roadway proximity on the spatial gradie nt of pollutant concentr ations near roads. 8 Cohen et al. (2005) have conduc ted analyses using the CALPU FF non-steady state Gaussian puff 9 model to estimate the dimensions of a “zone of influence” for the nea r-ground-level ambient air 10 concentrations of several air pollu tants, including diesel PM, in the vicinity of roadways. The 11 zone of influence was defined as the area near a roadway in which ambient air concentrations are 12 affected (i.e., enhanced) by emissions that occu r in the roadway. Their results suggest that a 13 steep spatial gradient exists adjacent to roadwa ys, with a zone of influence existing out to 14 somewhere between 200 and 400 m from the roadway. 15 16 In a related analysis of the da ta published by Cohen et al. (2005) as well as other air modeling 17 results, ICF (2005) calculated the enhancem ent ratio (i.e., the ratio of near-roadway 18 concentrations to concentrations more distant from the roadway) for several air pollutants, 19 including diesel PM. 7 These enhancement ratios were developed for a 0 to 75 m zone and a 75 20 to 200 m zone adjacent to the roadway. Therefore, these same intervals formed the basis for the 21 spatial template for air concentrations. 22 23 The inner 0 to 75 m zone was further divided in to two areas, with one comprising the area 0 to 24 12 m from the roadway and the other encompassi ng the area between 12 and 75 m from the road. 25 The inner region at 0 to 12 m was developed to facilitate the use of soil measurements available 26 for a surrogate location (see below) that incl uded measurements at and within 12 m of the 27 roadway. This allowed an apparently steep initia l decrease in near-roadway soil Pb levels to be 28 captured by the exposure modeling for this case st udy. Aerial photographs of the area indicated 29 that relatively small residentia l yards are situated immediately adjacent to the road, suggesting 30 that this initial 12 m band may serve as a play area for children. C onsequently, the spatial 31 template for this case study included exposure ba nds parallel to the ro ad and on either side, 32 extending from 0 to 12 m, 12 to 75 m, and 75 to 200 m. Although six separate zones could be 33 defined – the exposure bands are located on both si des of the road – only three sets of media 34 concentrations were necessary for the blood Pb modeling, as soil and air concentrations were 35 assumed to be the same on both sides of the road. 36 37 4.3.2 Air 38 39 4.3.2.1 Ambient Air Concentrations 40 41 For this scenario, 0.005 μg/m 3 , the average value for Pb in the PM 10 fraction available from the 42 measurements collected for this location (ICF 2006b), was assumed to be a reasonable estimate 43

7 Literature reviews did not identify any research specifically focused on spatial gradients associated with Pb sorbed to particulates near roadways or reentrained roadside du st. In the absence of more specific data, diesel PM was assumed to be a reasonable surrogate for Pb with regard to near-roadway concentrations.Estimates of Media Concentrations

ICF International 4-34 DRAFT – December 2006 of the ambient Pb concentration at the monitoring station site. 8 To derive media concentrations 1 for the zones adjacent to the roadway, geometric means of the enhancement ratios for near2 roadway concentrations of diesel PM from ICF (2005) were used to adjust the concentration at 3 the monitoring station. As described in the pr evious description of the spatial template, 4 enhancement ratios were derived for the interval s of 0 to 75 m from the road and 75 to 200 m 5 from the road. Because the near roadway mon itoring station in Houston was located about 115 6 m from the selected street, the average measured Pb concentration at this station was assumed to 7 be representative of ambient Pb in the 75 to 200 m zone. Using the geometric mean of the 8 enhancement ratios, the concentration for th e 0 to 75 m band was then estimated to be 9 approximately 0.008 μg/m 3 . Ambient air concentr ations of Pb are summarized in Exhibit 4-24. 10 11 Exhibit 4-24. Enhancement Ratios and Air Concentrations for 12 Near Roadway Urban Scenario 13

Zone (Distance from Roadway) Enhancement Ratio a Estimated Ambient Pb Concentration,

μ g/m 3 Method for Deriving Pb Concentration

0 to 12 m and

12 to 75 m 2.4653 0.008 Calculated based on relative values of enhancement ratios

75 to 200 m 1.6204 0.005 Average of measured values from DRI Houston site

a Geometric means of enhancement ra tios for diesel PM; from ICF (2005). 14 15 A comparison of the ambient air concentrations us ed for this scenario to other data can help 16 evaluate the validity of the selected media concentrati ons. Although the average Pb 17 concentration at the monitoring station was estimat ed based on only three days of measurements, 18 the average value of 0.005 μg/m 3 is reasonably consistent with the ambient Pb measurements for 19 Houston reported in AQS (USEPA 2006f). Sp ecifically, the average Pb measurements for PM 10 20 at the industrial/suburban site iden tified in AQS were 0.006 and 0.007 μg/m 3 for 1999 and 2005 21 (see Section 3.3.4). The average of the Houston DRI Pb measurements is also near the middle of 22 the range of averages of ambient measuremen ts obtained by DRI (ICF 2006b) for the other five 23 near-roadway monitoring locations included in their study, which range from 0.0024 to 0.0164 24 μg/m 3

. 25 26 4.3.2.2 Inhalation Exposure Concentrations 27 28 As in the primary Pb smelting case study, inha lation exposure concentrations of Pb were 29 estimated for the population of interest (young children) from the estimated ambient air 30 concentrations using age group- and location-sp ecific relationships for Pb developed from 31

8 It is noted that the determination of air concentrations for the other two human health scenarios used air levels associated with total suspended particulates (TSP) rather than PM10. No TSP data for Pb were available for the Houston monitoring site. As summarized in the Pb crite ria document (USEPA 2006b), r eentrained Pb in surface soil is generally associated with smaller particle size fractio ns; thus, it seems likely that the PM10 Pb measurements presented here represent the bu lk of Pb exposure (if the source of Pb is indeed resuspended surface soil). It is possible, however, that actual Pb levels may be slightly underrepresented by the Pb air concentrations derived for this scenario from the Houston data. At another air monito r in Houston (labeled as commercial, urban/center city location), mean annual TSP Pb measured between 1996 and 2006 ranged from 0.02 μg/m 3 to 0.005 μg/m 3

.Estimates of Media Concentrations

ICF International 4-35 DRAFT – December 2006 modeling performed for U.S. EPA’s 1999 Natio nal-Scale Air Toxics Assessment (USEPA 1 2006e), one of U.S. EPA’s NATA activities. See Section 4.1.2.2 for more information. 2 3 For the near roadway urban scenario, the study area was encompassed by one census tract (FIPS 4 ID 48201520500). The relationships (or ratios) be tween ambient Pb concentration and Pb 5 exposure concentration from the NATA national-s cale assessment for the 0 to 4 age group (the 6 closest age group to that of interest for this assessment for which outputs are available) is 0.4603 7 for this census tract. The exposure-to-concentrati on ratios for this tract for children 0 to 4 years 8 old were used to calculate inhalation exposure co ncentrations for this case study (see Exhibit 4- 9 25). Note that the use of ratios for the 0 to 4 age group (rather than for 0 to 7) contributes some 10 uncertainty in the estimate of children inhalation exposure concentrations. Uncertainty related to 11 the magnitude of the ASPEN modeled air concen trations is also discussed in Section 4.1.2.2 12 13 Exhibit 4-25. Estimated Inhalation Exposure Concentrations for 14 the Near Roadway Urban Scenario 15
Zone Ambient Concentration (μg/m 3 ) Age Group Exposure-toconcentration Ratio Inhalation Exposure Concentration (μg/m 3 )
0 to 12 m and

12 to 75 m 0.008 0 to 4 yrs 0.4377 0.0033

75 to 200 m 0.005 0 to 4 yrs 0.4377 0.0022 16 4.3.3 Soil 17 18 As described in Section 3.3.4, although no measuremen ts of Pb in surface so il were identified in 19 the vicinity of the case study lo cation in Houston, a range of da ta collected at roadsides and 20 similar sites at other locales were identified in th e literature. Using thes e data, surrogate soil Pb 21 values were developed for the Houston DRI mon itoring site to estimate soil concentrations for 22 this case study. The soil concentrations derive d for this scenario are intended to represent Pb 23 levels in surface soil that are primarily attributed to Pb em itted from vehicles during the 24 historical use of leaded gasoline and deposited to the near roadway soils (rather than attributed to 25 other urban sources of Pb, such as deposition of Pb from industrial sources or Pb paint). As 26 such, the near roadway case study is intended to address the con cern described in the criteria 27 document (USEPA 2006b) that Pb deposited in su rface soil over time derived from exhaust from 28 automobile traffic burning leaded gasoline represents a potential source via direct exposure to the 29 soil and through resuspension of dust to ambient air. 30 31 Measurements of Pb in roadside surface soil co llected by Turer and Maynard (2003) in Corpus 32 Christi, Texas, were identified as the most useful measurements for this scenario, primarily 33 because of their proximity and geographic similar ity to the case study location. Corpus Christi, 34 located about 300 km southwest of Houston, is closer to the coas t than Houston, but has roughly 35 the same annual average air temperature and precipitation (NCDC 2006) . Turer and Maynard 36 collected samples at two locations, including a site adjacent to the entrance ramp to Interstate 37. 37 The AADT for that site in 1998 was reported to be 48,000 vehicles per day. This is a higher 38 traffic volume than the volume reported for the se lected street in Houston near the case study 39 (AADT about 20,000). This difference in traffi c volume was judged to be acceptable for the 40Estimates of Media Concentrations

ICF International 4-36 DRAFT – December 2006 pilot phase. Although elevated Pb concentrations in soil have b een observed to be statistically 1 correlated with increased traffic volume on adjacent roadways in many cases (see, for example, 2 Sanchez-Martin et al. (2000) a nd Fakayode and Olu-Owolabi (2003)), there appear to be other 3 confounding variables that can result in weak correlations between tr affic counts and Pb levels in 4 near roadway soil in some cases (Hafen and Brin kman 1996). In other wo rds, Pb is generally 5 more elevated in soils near heavily-traveled road ways, but the exact impact of traffic volume can 6 vary between locations and under different conditi ons. Total Pb concentrations in surface soil 7 for three samples taken near Interstate 37 are summarized in Exhibit 4-26. 8 9 Exhibit 4-26. Pb Concentrations in Surface Soil, Corpus Christi, Texas a 10 Sample Location as Distance from Roadway Sample Depth Total Pb Concentration (mg/kg)

2 m 0 cm 731

3 m 0 cm 766

12 m 0 to 5 cm 214

a From Turer and Maynard (2003); sampling site near I-37 and Martin Luther King Drive at 11
North Port Avenue close to downtown. 12 13 Before using the measurements presented by Turer and Maynard (2003) to develop media 14 concentrations for this case study, other relevant da ta from the literature were reviewed. In their 15 discussion, Turer and Maynard refer to Pb meas urements by Harrison (19 87) at the “edges of 16 roadways and freeways in Corpus Christi” with concentrations of 250 mg/kg; no description of 17 the specific sampling locations or distance from the roadway was provided. Other relevant data 18 include records in EPA’s SPECIATE database of four samples in Houston. Although these 19 samples are in the same town as this case study, they are judged to have greater uncertainty 20 because it is not known when the samples were ta ken or where the sampling occurred, especially 21 with regard to proximity to roadways. The sa mpling is described as “r eentrained soil dust” (no 22 sampling depth was specified). All four of these SPECIATE samples were reported to be 0.04 23 percent Pb by weight, or 400 mg/kg. 24 25 In another recent study, Lejano and Ericson (2005) sampled surface soil for Pb in Pacoima, 26 California (near Los Angeles), collecting a to tal of 176 measurements across eight sampling 27 categories. Four of these categ ories include exclusively sample s taken within 150 m of a major 28 roadway, with each of the four categories focusi ng on a different road in the Pacoima area. 29 Mean total Pb concentrations for each of these four sampling categories are reported here (see 30 Exhibit 4-27). Traffic volume at each locati on as reported by Lejano and Ericson is also 31 presented. Specific sampling loca tions (i.e., distance from the roadway) were not reported; 32 however, mean Pb concentrations for three of the four road s were above 100 mg/kg. These 33 results suggest that the near-roadway concentrations of Pb in soil within 50 m of the road could 34 be substantially higher than the reported means. 35 36Estimates of Media Concentrations

ICF International 4-37 DRAFT – December 2006 Exhibit 4-27. Near Roadway Surface Soil Pb Concentrations in Pacoima, California a 1 Sampling Category (Adjacent Roadway) Mean Pb Concentration (mg/kg) AADT (Vehicles/day)

San Fernando Rd 171 10,250 I-5 119 292,000 S-118 102 142,000 S-210 43 116,000

a From Lejano and Ericson (2005). 2 3 There is substantial evidence that Pb concentrati ons in surface soil decrease rapidly with distance 4 from the roadway. Sutherland and Tolosa (2001) reported that the relationship is approximately 5 linear when the log of concentra tion is plotted against the log of distance from the roadway. 6 Similarly, Filipelli et al. (2005) have reported an exponential decay in Pb concentration with 7 increasing distance from the roadway. Hafen and Brinkmann (1996) surveyed results from 8 several other studies and observe d a generally exponen tial decrease in Pb concentration with 9 distance from the road. In general, based on the c onclusions of these studies, it appears that Pb 10 concentrations adjacent to a road decrease down to the local background le vel within a distance 11 of about 50 m from the roadway. 12 13 Local background in an urban environment, how ever, can vary depending on proximity to 14 current and previous sources of Pb emissions, imp acts from Pb-based paint, and other factors. 15 No representative “urban measurements” for Houston were identified, but several studies 16 reporting other relevant measuremen ts were identified as follows: 17 18

• Mean concentration of total Pb in surf ace soil for “random” non-vehicular-impacted 19 locations in Pacoima, California was reporte d as 111 mg/kg (Lejano and Ericson 2005). 20 21

• Soils at parks, schools, and roadside embank ments in Corpus Christi were sampled in 22 1984. The arithmetic mean surface soil concentr ation of Pb across all 485 samples was 23 208 mg/kg (Harrison 1987, c ited in Battelle 1996). 24 25

• Median Pb concentration in surface soil in sa mples from a range of land-use categories in 26 Miami, Florida was reported to be 98 mg/kg (Chinreje et al. 2004). 27 28

• An approximate level of “background” for Pb in US soils is about 15 mg/kg, taking into 29 account natural soil concentra tions of Pb and general anthropogenic contributions 30 (Gustavsson et al. 2001). 31 32 Based on the available data and considering the relevant details of the near roadway urban 33 scenario, the measurements near I-37 in Californi a reported by Turer and Maynard were selected 34 as the primary data source for developing surrogate soil concentrations fo r this scenario. As 35 described in Section 4.3.1, the zone extending ou t to 75 m was subdivided into two zones, a 36 near-roadway zone from 0 to 12 m and another zone encompassing the area between 12 and 75 37 m. These boundaries were defined based on the avai lability of Pb measurements in surface soil 38 from Turer and Maynard (2003) out to 12 m. Fo r estimating the average concentration within 39Estimates of Media Concentrations

ICF International 4-38 DRAFT – December 2006 the 0 to 12 m zone, the relationshi p between log of Pb concentrati on and log of distance from the 1 roadway within this near roadway range was assu med to be linear (Sutherland and Tolosa 2001). 2 Based on this relationship and using the data poi nts for samples taken 3 and 12 m from the road, 3 intermediate concentrations between 3 and 12 m were estimated. The mean concentration for 4 this zone was calculated using these inte rmediate values to be about 388 mg/kg. 5 6 For the study area between 12 and 75 m from th e roadway, it seemed reasonable that Pb 7 concentrations in surface so il would decrease more slowly until reaching local background 8 levels. Based on the available literature (and especially Lejano and Er icson 2005), a surrogate 9 background level of 100 mg/kg was defined for the case study location. For simplicity and 10 consistency with the spatial template, it was a ssumed that the soil concentration at 75 m and 11 beyond was at this level. In the absence of other, more specific data on near-road soil 12 concentration patterns, the Pb surface soil levels for this case study were assumed to decrease 13 linearly between 12 and 75 m. Given the soil concentrations defined for 12 and 75 m, the 14 average concentration fo r this interval was calculated to be 157 mg/kg. Estimated soil 15 concentrations for this case st udy are summarized in Exhibit 4-28. 16 17 Exhibit 4-28. Estimated Soil Exposure Concentrations 18 for the Near Roadway Urban Scenario 19
Zone Surface Soil Concentration (mg/kg)

0 to 12 m 388

12 to 75 m 157

75 to 200 m 100 20 4.3.4 Indoor Dust 21 22 Because indoor dust sampling data were not ava ilable for the near roadway urban case study, an 23 empirical model was used to describe the relati onship between Pb concen trations in indoor dust 24 and in outdoor soil. Specifically, a pooled analysis model (the AGG model) identified relates 25 indoor dust levels to ambient air and soil Pb levels was used (USEPA 1989). In this case, unlike 26 the secondary Pb smelter case study, soil Pb levels were considered because surrogate empirical 27 data on Pb levels near roadways were avai lable, thereby increasing confidence in the 28 characterization of soil Pb levels for this case study. The same version of the “AGG” model 29 (soil plus air regression model) (USEPA 1989) used for the primary Pb smelter was also used for 30 the near roadway urban case study: 31 32 Pb dust = 31.3 + (638 × Pb air ) + (0.364 × Pb soil ) 33 34 where: 35 36 Pb dust = concentration of Pb in indoor dust (mg/kg) 37 Pb air = concentration of Pb m odeled in the ambient air ( μ g/m 3 ) 38 Pb soil = concentration of Pb in outdoor soil (mg/kg). 39 40Estimates of Media Concentrations

ICF International 4-39 DRAFT – December 2006 Exhibit 4-29 presents a summary of the Pb i ndoor dust concentrations generated in the near 1 roadway urban study area for the current conditio ns and current attainment scenarios. Indoor 2 dust concentrations were only estimated for censu s blocks with children. In addition, because 3 only three sets of soil and air co ncentrations were estimated for the near roadway urban case 4 study (corresponding to the three zo nes surrounding the road of in terest), only three indoor dust 5 concentration estimates were derived. Exhib it 4-29 shows the number of census blocks and 6 children modeled with increasing le vels of Pb house dust. Estimated Pb dust concentrations for 7 all census blocks included in or partially ove rlapping with the near roadway study area are 8 presented in Appendix D. 9 10 Exhibit 4-29. Near Roadway Urban Case Study: 11 Modeled Indoor Dust Pb Concentrations a 12

House Dust Concentration ( μ g/g) Census Blocks with Dust Concentrations Greater than Value Children Living in Area with Dust Concentration

Greater than Value a
71 50 319 94 25 91 178 7 7

a Number of children ages 0 to 7 from U.S. Census 2000 were used in this analysis (U.S. 13 Census Bureau 2005). 14 15 As noted in Section 4.3.2.1, Pb measurements in outdoor air were only identified for Pb 16 associated with PM 10 and not TSP. Because concentrations of Pb in indoor dust were estimated 17 using outdoor ambient Pb measurem ents that (for this case stu dy) were reflective of the PM 10 18 fraction only, modeled indoor dust levels may be underestimated. The degree to which the 19 modeled indoor dust levels are underpredicted (if at all) would depend on: (1) the extent that the 20 PM 10 fraction represents total Pb in ambient air, and (2) the sensitivity of predicted indoor dust 21 Pb concentrations to outdoor ambient Pb concentr ations (especially with respect to the relative 22 influence of soil concentrati on inputs on this estimate). 23 24 4.4 Near Roadway Non-Urban Locations 25 26 For the ecological risk screening assessment, measured soil concentrations reported in the 27 literature were extracted for use in the risk charact erization for this scenari o. In contrast to the 28 human exposure and health risk assessments, data were sought for areas not located in downtown 29 areas. Less developed locations were evaluate d to increase the likelihood of proximity to 30 ecological receptors. Soil concentrations fo r two non-urban locations near roadways are 31 summarized in this section. 32 33 4.4.1 Soil Concentrations at Co rpus Christi, Texas Site 34 35 Turer and Maynard (2003) analyzed samples for Pb from roadside surf ace soil collected in 36 Corpus Christi, Texas. The data summarized here are from an industrial site at I-37 and Upriver 37 Road, which had a traffic volume of 45,000 ve hicles per day. Exhibit 4-30 shows the 38 concentrations of Pb measured at various di stances and sample depths from the roadside. 39 40 41 42Estimates of Media Concentrations

ICF International 4-40 DRAFT – December 2006 1 2 Exhibit 4-30. Pb Concentrations in Surface Soil, Corpus Christi, Texas, 3 for Ecological Risk Screening Assessment a 4 Sample Location – Distance from Roadway (m) Sample Depth (cm) Total Pb Concentration (mg/kg)

2 2.5 340

2 10 650

2 20 15

4 2.5 140

a From Turer and Maynard (2003); sampling site near I-37 and Upriver Road in refinery operations area. 5 6 4.4.2 Soil Concentrations at Atlee, Virginia Site 7 8 As described in Speiran (1998), th e Virginia study site is loca ted at the northbound Atlee/Elmont 9 exit (exit 86) from Interstate 95 (I-95), about 14.5 km north of Richmond, Virginia. The 10 northbound exit ramp at exit 86 is separated fr om the highway lanes by a triangular-shaped 11 grassy area. The grassed area has been mowed periodically, but there has been no historical 12 application of pesticides or fertilizers. Chemical characteri stics of the soil in the grassed area 13 were investigated in 1994, prior to planned constr uction modifications to In terstate travel lanes 14 and drainage ways and installation of a detentio n basin. Soil samples in the grassed area were 15 collected at various depth inte rvals and analyzed for heavy me tals and other constituents. 16 17 Samples from seven soil sampling locations were initially collected in the grassy area in June 18 1994. Samples were collected at 3- inch intervals up to a depth of 12 inches and were analyzed 19 for metals. An additional 11 soil locations were sampled in the grassy area in October 1994 and 20 were analyzed in the same manner as the J une 1994 samples. Five of these 11 sampling 21 locations were sampled at the 0 to 3 and 9 to 12- inch intervals. Two locations were sampled at 22 the 0 to 3 and 3 to 6-inch intervals. The remaining four locations were sampled at the 0 to 3 inch 23 interval only. 24 25 Samples used for this ecological screening risk assessment were limited to those located at a 26 distance 2 m or greater from I-95 and the exit ra mp at exit 86 and collected at a depth of 20 27 centimeters or less. Therefore, only samples coll ected at 0 to 3 inches and 3 to 6 inches and 28 samples that appear to be at least 2 m from eith er I-95 or the exit ramp (using figures provided in 29 Speiran [1998]) were used in the evaluation of pot ential ecological risks for this near roadway 30 non-urban scenario. Samples at th ese locations were considered to be representative of potential 31 ecological exposures. Pb analytical results for these samples are presente d in Exhibit 4-31. Pb 32 concentrations at these locations and depths ranged from 17 to 540 mg/kg, with an average 33 concentration of 123 mg/kg. 34 35 36 37 38 39Estimates of Media Concentrations

ICF International 4-41 DRAFT – December 2006 1 2 Exhibit 4-31. Pb Concentrations in Soil Collected from Potential 3 Ecological Exposure Area, I- 95 Interchange, Atlee, VA a 4
Location ID Pb Concentration 0 to 3 Inches in Depth (mg/kg) Pb Concentration 3 to 6 Inches in Depth (mg/kg)

1 46 35

2 73 23

3 46 17

4 70 31

5 94 24

6 510 540

10 99 --

16 140 --

22 100 --

a From Speiran 1998 (see Figures 2 & 3 and Table 1). 5 6 7 4.5 National-Scale Surface Water and Sediment Ecological Risk Screening Assessment 8 9 This section provides a description of the media c oncentrations used in the national-scale surface 10 water and sediment screening assessment. Secti on 4.5.1 describes the select ion of a data set to 11 represent Pb concentrations in freshwaters across the United States resulting from atmospheric 12 sources of Pb. Section 4.5.2 provides information on the selected data set for dissolved Pb water 13 column concentration data that were used in the surface water portion of the screening 14 assessment. Section 4.5.3 describes the freshwater sediment data set compiled for the sediment 15 portion of this screening assessment. These sedi ment data were selected based on results from 16 the surface water portion of the screening assessment. 17 18 4.5.1 Assessment of Available Data Sources for Surface Water Data 19 20 To identify measured dissolved Pb concentrations in surface waters of the United States for use 21 in the exposure analysis of the national-scale surface water and sediment screening assessment, 22 readily available summaries of Pb concentrations in surface waters and database compilations of 23 trace metal concentrations meas ured in surface waters were examined. The most promising 24 databases are those developed and maintained on-line by U.S. EPA and the U.S. Geologic 25 Survey (USGS). They include U.S. EPA’ s STORET database, the USGS National Water 26 Information System (NWIS) database, and th e USGS National Water-Quality Assessment 27 (NAWQA) Data Warehouse. Each of these data bases was evaluated to select the one most 28 appropriate for the surface water portion of the national-scale surface water and sediment 29 screening assessment. The evaluation is briefly summarized below. 30 31 STORET: The STORET database from the EPA’s O ffice of Water includes the water quality 32 sampling data that states develop to determine compliance with water quality standards and that 33 states report to EPA annually. STORET also includes sampling data from Superfund sites and 34Estimates of Media Concentrations

ICF International 4-42 DRAFT – December 2006 other sources (e.g., U.S. Army Corps of Engin eers). The EPA BASINS interactive software 1 program uses the data from STORET. The Leg acy Data Center (LDC) part of STORET is a 2 static, archived database that cont ains historical water qu ality data dating back to the early part of 3 the 20th century and collected up to the end of 1998. The main STORET database is an 4 operational system actively being po pulated with water quality data. It contains data collected 5 beginning in 1999, along with older data that have been properly documented and migrated from 6 the LDC. Both the LDC and STORET contain raw biological, chemical , and physical data on 7 surface and ground water collected by federal, stat e, and local agencies, Indian Tribes, volunteer 8 groups, academics, and others. All 50 states, territo ries, and jurisdictions of the United States are 9 represented; however, few stations are present in some states (e .g., Texas). For an analysis of 10 current water conditions, the operational STORET system is more appropriate than the LDC. 11 12 In a download of surface water concentrations for Pb from STORET for the months January 13 2004 to May 2006, it was observed that detection or reporting limits appeared to range from less 14 than 0.04 to 10 μg/L based on the values reported by different states, with a quantitation limit of 15 1 μg/L being common. A total of 30,993 samples were found for Pb from the United States’ 16 surface waters over that time peri od. Of the total reported Pb concentrations included in that 17 download, only 32 percent (9,910 of 30,993) were for dissolved Pb; the remaining concentrations 18 were for total recoverable Pb. Of the 9,910 samp les analyzed for dissolved Pb, only 21.5 percent 19 (2,134) had dissolved Pb concentrations above th e detection or reporting limit. Thus, only 7 20 percent of all samples analyzed for Pb (2,134 of 30,993) provided dissolved Pb measurements 21 above the quantitation limit. 22 23 Limitations of STORET data incl ude differences in sampling dens ity per unit area or length of 24 stream, differences in frequency of sampling, di fferences in detection and quantitation limits 25 across states, and inconsistencies in how samp les are collected, analyzed, and reported across 26 states, particularly when estima ting concentrations between the de tection and quantitation limits. 27 For Pb, another limitation is that many stat es do not attempt to measure dissolved Pb 28 concentrations, only total Pb c oncentrations, because measurement of dissolved Pb generally 29 results in non-detects. 30 31 USGS Databases: USGS maintains water quality data in two separate data sets: the National 32 Water Information System (NWIS) Network and the National Water Quality Assessment 33 (NAWQA) Data Warehouse (USGS 2004). The NWIS system was originally developed for 34 water flow data, and includes data from 1.5 milli on sites across all 50 states, the District of 35 Columbia, and Puerto Rico. Chemical quality data have been added to it in recent years, but still 36 are limited. The data have been compiled throug h 2004 from a variety of projects ranging from 37 national-level studies to small watershed projects; therefore, the sampling methods, density of 38 sampling sites, and detection limits are variable across the data se t. Data for dissolved Pb are 39 available from 1972 to 2001; however, the vast ma jority of records are from the 1980s when the 40 quantitation limit for Pb at most locations was 5 μg/L. 41Estimates of Media Concentrations

ICF International 4-43 DRAFT – December 2006 The NAWQA Data Warehouse includes the sa mpling data from the NAWQA program that 1 started systematically collecting chemical, biolog ical, and physical wate r quality data from a 2 large number of water basins and aquifers—referred to by NAWQA as “Study Units”—across 3 the nation in 1991. As of July 2006, data have been compiled for samples collected through 4 September 30, 2004, and cover 47 Study Units. Basins from all regions of the United States are 5 included; however, only approximately 50 percent of the land base is co vered by these basins 6 (see Exhibit 4-32). NAWQA Study Unit boundaries frequently cross state boundaries, and their 7 size usually exceeds 10,000 square kilome ters (about 3,900 square miles). 8

9 Pb is one of the analytes in the program, butsimilar to the case for STORET, dissolved Pb 10 measurements are available for fewer than 20 per cent of the sampling locations. In an analysis 11 of the data in NAWQA, U.S. EPA found a tota l of 3,445 measurements of dissolved Pb in 12 surface waters, for which 86 percent were nondet ects (see Exhibit 4-33) (USEPA 2006b). When 13 looking at a subset of those data determined to be “natural” (i.e., fo rest or rangeland) or 14 “ambient” (background) areas, 88 percent of the 430 samples for dissolved Pb in surface waters 15 were non-detects. A mean concentration of 0.66 μg/L was calculated for all sites. A mean 16 concentration of 0.52 μg/L was calcu lated for samples considered to represent natural areas. The 17 relatively low dissolved Pb concen trations associated with the 95 th percentile values (Exhibit 4- 18 33) indicate that relatively few water samples are likely to exceed the initial aquatic screening 19 Exhibit 4-32. Map of NAWQA Study UnitsEstimates of Media Concentrations

ICF International 4-44 DRAFT – December 2006 value of 1.2 μg/L. Data for dissolved Pb from NAWQA from 1994 to 2004 was examined and it 1 was found that the quantita tion limit for dissolved Pb changed for this program from 1 μg/L to 2 0.08 μg/L in the fall of 2000. 3 4 Exhibit 4-33. Distribution of Dissolved Pb Concentrations in Freshwater Surface Waters 5 from the USGS NAWQA Database (USEPA 2006b) 6

Statistic “Ambient” Locations “Natural” Locations Sample size 3445 430 % Non-detect 86 88 Minimum (μg/L) 0.04 0.04 Arithmetic mean (μg/L) 0.66 0.52

95 th Percentile (μg/L) 1.1 0.50 Maximum (μg/L) 29.78 8.40 7 It was concluded that of the th ree available surface water quality databases, the NAWQA data set 8 is most appropriate for the surface water portion of the national-scale su rface water and sediment 9 screening assessment for several reasons. The incl usion of dissolved Pb as an analyte is limited 10 in all of the databases (total Pb is measured mo re often). None of the databases provide the co11 located measurements of water hardness in the sa me records as the measurements of dissolved 12 Pb. STORET and NWIS include samples from more locations in the United States than does the 13 NAWQA data set, but the sampling and reporting protocols represented in STORET and NWIS 14 are less consistent from site to site. Data for dissolved Pb in NWIS are predominantly from the 15 1980s, and therefore do not represent current co nditions. The NAWQA data set, on the other 16 hand, provides representative (eve n though not complete) coverage of the United States, with 17 samples through 2004 included in the database. The NAWQA data se t also provides a consistent 18 approach to sampling and analysis of the elemen ts using consistent quantitation limits across the 19 country. Given the sampling design for NAWQA and the consistency of the data across the 20 country, it is considered to be more appropriate for a national-scale surface water and sediment 21 screening assessment than th e other two data sets. 22 23 Even with using the NAWQA database, for which the quantitation limit for dissolved Pb was 24 1 μg/L from 1994 to 2000, a difficulty for the as sessment is that the chronic Ambient Water 25 Quality Criterion (AWQC) for the protection of aqua tic life in soft waters (i.e., for waters with 26 CaCO 3 concentrations less than 45 mg/L), is less th an 1 μg/L, thus the detection limit is too high 27 to resolve the chronic AWQC for soft wate r. The most recent quantitation limit of 0.08 μ g/L 28 (starting in the fall of 2000) is adequate to identify concentrations of dissolv ed Pb that exceed the 29 chronic AWQC benchmark in wate r as soft as 4.7 mg/L CaCO 3 . Also, since the fall of 2000, 30 dissolved Pb concentrations between the de tection (0.04 μg/L) and quantitation (0.08 μg/L) 31 limits were estimated in some cases. 32 33Estimates of Media Concentrations

ICF International 4-45 DRAFT – December 2006 4.5.2 Dissolved Pb Surface Water Concentrations in the NAWQA Database 1 2 To download dissolved Pb concentrations from the USGS NAWQA Data Warehouse, the 3 NAWQA Study Units, trace elements in water, a nd the years 1994 to 2004 were specified (see 4 Appendix H) to avoid earlier years when leaded gasoline in some loca tions might have still 5 influenced air Pb concentrations . Once the data were downloaded, the values for dissolved Pb 6 (analyte # 01049 - Pb, water, filtered, micrograms per liters) were examined. 7 8 The 49 Study Units for which sampling data curr ently are included in the USGS NAWQA Data 9 Warehouse database are listed in Appendix H, E xhibit H-1. The different sampling stations 10 within the unit were identified with an unique st ation ID number. Some units contained some 11 recent series of sampling stations very close to ea ch other along a river or stream (evident from 12 the latitude and longitude reported for the stations); those were cons idered to be separate units. 13 14 It is important to note that th e intensity of sampling at differe nt locations within a Study Unit 15 varied substantially. Sampling at some Study Un its began in 1991, while sampling of others did 16 not begin until 1999. In general, a few of the loca tions were sampled at least monthly for several 17 years. In contrast, a single sample had been taken at many other locations, perhaps as an 18 exploratory exercise. Some loca tions were sampled a few times a year at three year intervals, 19 while other locations were sampled for only a couple years. 20 21 The percentage of the Study Unit sampling stations for which dissolved Pb was an analyte during 22 one or more sampling times from 1994 to 2004 was 16 percent overall (Exhibit H-1), which is 23 quite low. As is true for EPA’s STORET databa se, measurements of total Pb are much more 24 common than measurements of dissolved Pb. However, because the AWQC for Pb are 25 expressed as dissolved Pb, total Pb measuremen ts were not evaluated. The distribution of 26 dissolved Pb measurements was not uniform ove r the Study Units or across sampling stations 27 within Study Units. Dissolved Pb was not measured for any of the stations for some of the Study 28 Units, while for other Units, dissolved Pb was m easured at least once at a majority of the 29 Sampling Stations. 30 31 There are temporal trends in wh ether dissolved Pb was measured at given station. Most notably, 32 at many stations for which multiple years of data are available, if the first year of sampling 33 indicated all dissolved Pb measurements to be less than the quantitation limit (QL) of 1 μg/L, no 34 further measurements of dissolved Pb were at tempted (e.g., dissolved Pb measured in 1999 less 35 than 1 μg/L; no further measurements in 2000 through 2004). 36 37 Most dissolved Pb concentrations reported in the NAWQA database are below the QL. There 38 are only 2,558 samples analyzed for dissolved Pb in surface waters for the NAWQA database 39 from 1994 to 2004. The 3,445 samples included in EP A’s review of the database (Exhibit 4-33) 40 include samples from 1991 forward (USEPA 2006b). Of the 2,558 samples examined, 83 41 percent (2,116) were below the QL. Of the 442 di ssolved Pb concentrations reported in the data 42 set, 17 percent (74 of 442) were estimated. 9 Exhibit 4-34 summarizes the number of samples 43 that were below the QL for dissolved Pb according to the reported QL. 44

9 Of the 74 estimated values, 68 were for concentrations above detection limit of 0.04 μg/L but below the QL of 0.08 μg/L.Estimates of Media Concentrations

ICF International 4-46 DRAFT – December 2006 1 Exhibit 4-34. Number of Dissolved Pb Samples 2 Less than the QL 3

QL Number of Sample Less Than QL a
2 11 1 2010 0.5 1 0.2 10 0.16 1 0.08 83 Total 2,116

a A total of 2,558 samples were examined, of which 2,116 were 4 below the QL. 5 6 Limiting the exposure assessment to the year 200 0 to present was considered, given that fall 7 2000 was when the QL for dissolved Pb dropped to 0.08 μg/L, low enough to determine if the 8 chronic surface water screening value is exceeded for most waters (i.e., for waters with hardness 9 > 4.7 mg/L as CaCO 3 ). However, less than 14 percent of the total samples for dissolved Pb 10 between 1994 and 2004 were analyzed with the lower dissolved Pb QL of 0.08 μg/L. To 11 determine that percentage, the number of samp les reported as “< 0.08” μg/L (N = 83), the 12 number reported as estimated (E) with a value less than 0.08 μg/L (N = 68), and the number for 13 which the measured value was equal to or gr eater than 0.08 but less than 1.0 μg/L (N = 205) 14 were added. A few of the last group might have been measured with a QL between 0.08 and 1.0 15 (see Exhibit 4-34); however, no attemp ts were made to adjust for that. Thus, the percentage of 16 samples analyzed for dissolved Pb in the NAWQA data set between 1994 to 2004 for which the 17 QL limit was 0.08 μg/L is approximately 14 pe rcent (i.e., 356 of 2,558). Thus, limiting the 18 surface water and sediment water screening asse ssment to the years for which the QL was 0.08 19 μg/L would have severely limited the exposure data for the assessment. 20 21 Further descriptions of the di ssolved Pb concentrations from 1994 to 2004 in the NAWQA Data 22 Warehouse for the surface water po rtion of the screening assessment are provided in Section 23 7.2.4 (Risk Characterization) as they relate to the aquati c screening levels (AWQC). 24 25 4.5.3 Total Pb Sediment Concentrations from NAWQA Database 26 27 The surface water portion of this national-scale surface water and sediment ecological screening 28 assessment identified 15 locations at which at le ast one measurement of dissolved Pb in the 29 water column exceeded the water-hardness-specif ic Pb chronic AWQC. These locations are 30 likely to represent a fraction of locations that actually exceed the Pb chronic AWQC. 31 Nonetheless, those 15 locations were made the focus of the sediment portion of the screening 32

assessment. 33 34 The NAWQA database also includes total Pb sedi ment concentration data measured for water 35 bodies across the United States. Sediment sampling locations in the NAWQA database co36 located with the surface water samples evaluate d for the water column screening assessment 37 were identified. Co-located sediment and surf ace water data were available for 9 of the 15 38 locations. There were an additional three “nea r-matches,” with a sediment sampling station on 39Estimates of Media Concentrations

ICF International 4-47 DRAFT – December 2006 the same creek or river, but somewhat upstr eam or downstream of the surface water sampling 1 location. For the sediment sampling sites that were categorized as near matches, however, it is 2 important to note that the surface water sample s at the same location had not exceeded the 3 chronic AWQC in the surface water column portion of the screening assessm ent. Therefore, the 4 value of the “near-match” locations is limited. Finally, there were three locations for which even 5 a near-match was not found. 6 7 The sediment total Pb concentration data are presented in Chapter 7. Exhibit 7-16 summarizes 8 the total Pb sediment concentrations measured at the nine locations with co-located sediment and 9 surface water data and the three “near-match” locations. The NA WQA database appears to have 10 only one measurement of Pb sediment concentrati ons for each of these 12 locations. Several of 11 those samples were taken in the early 1990s, ear lier than the bulk of the surface water Pb 12 concentrations used in the surface water screenin g assessment. The concentrations ranged from 13 23 to 2,900 mg Pb/kg dry sediment. The high concen tration was detected at a location that had 14 an upstream NPDES permit for metals; thus the Pb contamination may have come from a source 15 other than air. 16Human Exposure Assessment and Blood Pb Estimation

ICF International 5-1 DRAFT – December 2006 

### 5. Human Exposure Assessment and Blood Pb Estimation 1 2 This chapter describes the approaches and methods that were used to evaluate children’s Pb 3 exposures to policy-relevant s ources and policy-relevant back ground pathways, and to predict 4 the changes in individual blood Pb levels and popul ation blood Pb distribu tions associated with 5 these exposures. Section 5.1 provides a descript ion of the human exposure assessment and blood 6 Pb modeling approaches, and presents the deriva tion of the model inputs . Section 5.2 reviews 7 the results of the blood Pb mode l performance evaluation. Section 5.3 discusses the limitations 8 and uncertainties associated with the human e xposure assessment and blood Pb estimation, and 9 Section 5.4 summarizes the result s of the blood Pb modeling as applied to the primary Pb 10 smelter, secondary Pb smelter, and near roadway urban case studies. 11 12 5.1 Blood Pb Modeling Methods 13 14 5.1.1 Overview of Human Exposure Assessment and Blood Pb Modeling Approach 15 16 Exhibit 5-1 (which is an excerpt from Exhib it 2-2) summarizes the human exposure assessment 17 and blood Pb modeling methods used in this asse ssment. Exposure media (air, soil, and indoor 18 dust) concentrations and Pb exposure and inta ke assumptions associated with both policy19 relevant sources and policy-relevant background pathways serve as inputs to two biokinetic 20 blood Pb models (discussed in Section 5.1.2). Fo r the purposes of the pilot phase assessment, 21 drinking water and diet pathways are categorized as policy-re levant background, although it is 22 recognized that a portion of Pb in both pathways may derive from policy-relevant sources. 23 24 Exhibit 5-1. Schematic Representation of the Human Exposure 25 Assessment and Blood Pb Modeling Process 26

27 Exposure concentrations are the primary inputs to the blood Pb modeling process. In each 28 exposure pathway, the relationship between expo sure concentration and Pb uptake (absorbed 29 dose) is defined by a range of factors related to physiological processes a nd to the chemical and 30 physical properties of the exposure media. Th ese factors include resp iratory volume, soil and 31 dust ingestion rates, and gastrointestinal (GI) absorption fracti ons for diet, water, and soil/dust, 32 which determine how much Pb is absorbed from each medium. Values for these factors differ 33 with age and across exposure medi a, as discussed in Section 5.1.4.3. 34Human Exposure Assessment and Blood Pb Estimation

ICF International 5-2 DRAFT – December 2006 1 As discussed in Chapter 4, exposure concentratio ns in air, soil, and indoor dust have been 2 estimated for census blocks and block groups with in the case study areas. Application of a 3 specific set of exposure concentrations and the physiological and behavi oral inputs to the blood 4 Pb models yielded a lifetime (bir th to 84 months [or 7 years] of age) blood Pb profile for the 5 child receiving that combination of exposures , from which two blood Pb metrics have been 6 derived. The first metric is the lifetime average (age six to 84 months) and the second is an 7 estimate of “concurrent” blood Pb concentration, which has been defined as the average over 8 ages 73 to 84 months). 1 The blood Pb models yield central tendency estimates of a child’s blood 9 Pb concentrations for specified simulation periods (with the temporal precision varying 10 depending on the specific model) and for specific patterns of exposure. Estimates for a single 11 child (representing central tenden cy exposure) do not provide in formation about how individual 12 responses to Pb exposure might vary among the exposed children or how changes in individual 13 blood Pb levels would affect the population exposure for a given case study. Thus, a 14 probabilistic approach has been implemented to capture both the effects of inter-individual 15 variability in blood Pb levels and the population distribution of exposures on the resultant 16 population distribution of blood Pb statistics. 17 18 Development of population distribu tions of blood Pb levels for each of the case studies involved 19 the following steps: 20 21 1. Blood Pb model was used to generate central tendency estimates of blood Pb per census 22 block or block group. 23 2. The number of children (0 to 7 years of age) residing in each block and block group was 24 determined from the U.S. Census Bur eau data (U.S. Census Bureau 2005). 25 3. A lognormal distribution reflect ing inter-individual variab ility in both behavior and 26 biokinetics related to Pb exposure was devel oped using a geometric standard deviation 27 (GSD) obtained from the literature which was ce ntered on a value of 1.0. Values selected 28 from this distribution represent "adjustmen t factors" reflecting specific patterns of 29 behavior and biokinetics in chil dren related to Pb exposure. 30 4. Population-weighted random sampling was then us ed to first select a blood Pb level from 31 the results of Step 1 above. The probability for sampling each census block or block 32 group is set proportional to the number of young children (0 to 7 years of age) in 33 residence (obtained in Step 2). The data set generated in Step 1 was sampled 10,000 34 times in this way. Each of these 10,000 blood Pb levels was then multiplied by an 35 adjustment factor selected from the lognorma l distribution (describ ed in Step 3) to 36 produce a blood Pb level for a simulated child. These 10,000 simulated child blood Pb 37 levels were then used to characterize (via pe rcentiles) the distribu tion of blood Pb levels 38 in the population. 39 40 The predicted blood Pb distribution in the expos ed population reflects variability contributed by 41 both the population-weighted distri butions of exposure concentra tions and by the inter-individual 42 responses to Pb exposures. 43

1 The rationale for defining the average blood Pb for ages 73 to 84 months as concurrent reflects the fact that the average age of IQ testing in the Lanphear et al. (2005) study of blood Pb-IQ relationships was approximately seven years (see also Chapter 6).Human Exposure Assessment and Blood Pb Estimation

ICF International 5-3 DRAFT – December 2006 1 The following sections discuss in detail the bl ood Pb models used for this assessment, the 2 selected model inputs, and how the models were implemented to estimate case study-specific 3 blood Pb levels for children 0 to 7 years of age. 4 5 5.1.2 Selection of Blood Pb Models 6 7 Two biokinetic models were applie d to predict blood Pb levels in the pilot phase assessment. 8 These two models were the Integrated Exposure Uptake Biokinetic (IEUBK) Model for Children 9 (hereafter referred to as the IEUBK model) and the International Commission for Radiation 10 Protection (ICRP) model (hereafter referred to as the Leggett mode l). Both are well11 documented, widely used, and have been subject to a range of testing and calibration exercises 12 (see Section 4.4 of USEPA 2006b). A third “empi rical” (regression-based) model developed by 13 Lanphear et al. (1998) was included in the sensi tivity analysis, as described in Chapter 6 and 14 Appendix K. 15 16 5.1.2.1 The IEUBK Model 17 18 This section presents a general overview of EPA’s IEUBK model, the primary blood Pb 19 estimation tool in the pilot phase assessment. The IEUBK Lead Model Version 1.0 Build 263 20 (USEPA 2005a) was used to model blood Pb levels for all three case stud ies. The IEUBK model 21 consists of three main modules: the exposure module, the uptake module, and the biokinetic 22 module (see Exhibit 5-2). The exposure module accep ts inputs related to six exposure media: 23 air, food (excluding water), water, soil, indoor dust, and other. Expos ure, intake, and uptake 24 factors were combined with expos ure concentrations in the vari ous media to estimate Pb uptake 25 through two pathways: inhalation and ingestion. The IEUBK mode l provides default values for 26 the various model input parameters that can be adjusted by the us er for a specific application. 27 These parameters include those used by the model to estimate Pb uptake, including absorption 28 and inhalation rates, water inta ke, dietary intakes of specific food classes, and soil and dust 29 ingestion rates. The selection of model input parameter values fo r the pilot phase assessment is 30 discussed in Section 5.1.4. 31 32 The exposure module also includes default age-speci fic estimates of time spent outdoors, as well 33 as along with age-specific estimates of outdoor a nd indoor air Pb concentrations, inhalation rate, 34 and respiratory tract absorption fraction, all of which are used to estimate age-specific Pb 35 inhalation uptakes. The absorption fraction imp licitly reflects both deposition of inhaled Pb in 36 the respiratory tract and absorpti on of deposited Pb, either from th e respiratory tract or from the 37 GI tract. The model also contains an option fo r calculating indoor dust Pb concentrations based 38 on an empirical relationship between air, soil, and dust Pb levels (a variation of the AGG models 39 discussed in Chapter 4). Inge stion uptake is calculated usi ng absorption fractions that are 40 specific to the ingested media (f ood, water, soil, or dust). Tota l GI Pb uptake is modeled as 41 being composed of a saturable and an unsaturable component. 42 43 In the biokinetic component of the model, abso rbed Pb (from ingesti on and inhalation) is 44 assumed to appear immediately in the plasma -extracellular fluid (ECF) compartment. The 45 plasma-ECF compartment constitutes the central compartment in the biokinetic model from 46Human Exposure Assessment and Blood Pb Estimation

ICF International 5-4 DRAFT – December 2006 which exchange to all the other compartments o ccurs. Cortical and trabecular bone (which are 1 not directly coupled in the IE UBK model) constitute the main long-term storage compartments, 2 with the estimated turnover in other compartmen ts being more rapid. The binding capacity of 3 the red blood cell compartment is modeled as being saturable, simulating the limited capacity of 4 ALAD and other Pb binding proteins. Pb excr etion occurs through a urine pathway (distinct 5 from the kidney compartment); hepatobiliary secr etion is coupled with the liver compartment, 6 with a minor component of excretion from “other soft tissues” (i.e., skin, hair, and nails). The 7 model is termed “biokinetic,” rather than “pharmacokinetic,” becau se transfers between 8 compartments are controlled by firs t-order transfer coefficients (e quivalent to first-order rate 9 constants), rather than being perf usion-controlled. A more comple te description of the derivation 10 and structure of the IEUBK model can be f ound in USEPA (1994) and White et al. (1998). 11 12 Exhibit 5-2. Structure of the IEUBK Model 13 (USEPA 1994; White et al. 1998) 14

15Human Exposure Assessment and Blood Pb Estimation

ICF International 5-5 DRAFT – December 2006 The structure of the IEUBK model is determinis tic. As configured, the model generates point 1 estimates of blood Pb concentrations in response to a given set of exposure parameters. Using 2 the batch mode, the user may calculate the imp acts of changing Pb concentrations in various 3 media, and the model also allows the user to plot the impact of continuously changing the Pb 4 concentration in a specified expos ure medium between two values. The user can specify the age 5 at the start of exposure, an ini tial blood Pb level, and the age at which the simulation ends. For 6 each case study in the pilot phase assessment, the batch mode of the IEUBK model was used to 7 generate blood Pb levels for th e different age intervals for chil dren living in each census block 8 and block group. 9 10 The IEUBK model also allows the user to calculate blood Pb dist ributions based on the 11 assumption of lognormality (USEPA 2002b). In this mode, the model uses the point estimate of 12 blood Pb as the GM of a lognormal distribution w ith a defined GSD. Graphs of the projected 13 blood Pb distributions can be generated, along with estimates of the proportions of exposed 14 children with blood Pb levels above use r-specified levels of concern. 15 16 5.1.2.2 The Leggett Model 17 18 Leggett (1993) published a sophistic ated biokinetic model for Pb exposures, intended primarily 19 for use in radiological dosimetry, that has since been adopted as the standard model for this 20 purpose by the International Commission for Radiat ion Protection (ICRP). The model predicts 21 changes in blood Pb levels for exposed individual s over their entire lifespan (birth to 90 years 22 old). The compartmental structure of the Leggett model (Exhibit 5-3) is more complex than that 23 of the IEUBK model, and was patterned after si milar models developed by the ICRP to model 24 the age-specific biokinetics of calcium -like radionuclides (Leggett 1993). 25
26 27Human Exposure Assessment and Blood Pb Estimation

ICF International 5-6 DRAFT – December 2006 Exhibit 5-3. Structure of the Le ggett Biokinetic Model (Leggett 1993) 1

2 Differences between the structures of the Leggett model and the IEUBK model include: 3 4

• The published version of the Leggett model lacks the multipathway exposure module of 5 the IEUBK. The Leggett model accepts to tal respiratory and ingestion intakes 6 (administered doses) as inputs and calculates Pb uptake using age-specific absorption 7 factors. 8
9

• The Leggett model lacks a “pr obabilistic” component; all predictions are deterministic 10 for a single individual recei ving a given set of exposures, with no capability for 11 generating graphical outputs. 12 13

• The central exchange compartment in the Leggett model is “diffusible plasma,” rather 14 than the plasma-ECF compartment used in th e IEUBK model. Extra-vascular fluid, red 15 blood cells (RBCs), and a bound plasma fracti on are the other blood/fluid compartments 16Human Exposure Assessment and Blood Pb Estimation

ICF International 5-7 DRAFT – December 2006 that exchange directly with plasma in the Leggett model, with di fferent transfer rates 1 reflecting differences in estimated exchange rates. 2 3

• The trabecular and cortical bone compartments in the Leggett model are divided into two 4 subcompartments each, one exchangeable, and one “non-exchangeable.” Pb in the “non5 exchangeable” compartments of both types of bone can be remobilized, but only 6 relatively slowly as a result of bone remodeling. 7 8

• Urinary excretion is modeled in the Leggett model as part of an integrated kidney 9 compartment, rather than separate ly as in the IEUBK model. 10
11

• In the Leggett model, the liver is modele d as two compartments with rapid and 12 moderately rapid Pb exchange, respectively. Other soft tissues are modeled as having 13 three compartments with differing exchange rates. Pb in brain tissue is explicitly 14 modeled. 15
16 On the whole, the Leggett model is somewhat mo re technically sophistic ated than the IEUBK 17 model, but lacks a built-in facil ity to convert exposure concentr ations into Pb uptake and to 18 integrate uptakes from multiple exposure media. 19 20 Like the IEUBK model, the Leggett model is bi okinetic, with exchange between compartments 21 modeled using first-order “transfer rates” (equiva lent to rate constants). The values for the 22 transfer rates were estimated us ing a range of data from adult human radioactive tracer studies, 23 autopsy data from adults and children, and data from animal studies re lated to the absorption, 24 deposition, and excretion of Pb and chemically similar elements (Leggett 1993). The Leggett 25 model differs from the IEUBK model in that data from short-term studies (on the time-scale of 26 hours to days) are used to estimate parameter values for the most rapid of the uptake and 27 exchange processes and thus the time resolution of the Leggett model is much finer than that of 28 the IEUBK model. The user may specify any step length, depending on the degree of time 29 resolution required in the blood Pb predictions. Like the IEUBK model, the binding capacity of 30 Pb in RBCs is assumed to be saturable. 31 32 Leggett developed estimates of biokinetic paramete rs for six age categories: newborn (0 to 100 33 days), 1 year, 5 years, 10 years, 15 years, and 25 years and older, with age-specific transfer 34 parameters for children estimated by interpolatio n between the nearest values. Transfer factors 35 for children were adjusted to take into acc ount the more rapid bone turnover (calcium/Pb 36 addition and resorption) in childr en compared with adults. 37 38 Predictions from the Leggett model have been co mpared with the deterministic predictions of 39 blood Pb levels generated by the IEUBK model, using the IEUBK default inputs (Pounds and 40 Leggett 1998). In that comparison, the Leggett mode l predictions were substantially higher than 41 those of the IEUBK model. 2 42

2 Blood Pb levels generated using the Leggett model for th is exposure analysis (Sectio n 5.4) are systematically lower than the IEUBK-based blood Pb levels. This trend is the opposite of that reported in the Pounds and Leggett (1998) article. Given that a customized version of the Leggett model was developed for the pilot phase assessment, additional model evaluation activities are being completed for the pilot phase (see Section 5.1.3.3).Human Exposure Assessment and Blood Pb Estimation

ICF International 5-8 DRAFT – December 2006 1 5.1.3 Application of Blood Pb Models 2 3 5.1.3.1 Key Modeling Assumptions 4 5 For all of the case studies, exposure concentratio ns were assumed to be constant throughout the 6 seven-year duration of the exposure scenario. Exposure concentrations associated with policy7 relevant sources were estimated and modeled ba sed on data from the rece nt past (1997 to 2005), 8 as summarized in Exhibit 2-4. 9 10 Data from the U.S. Census provided estimates for the year 2000 of the numbers of children 11 living in each block or block group (U.S. Census Bureau 2005; see Chapter 3); the numbers of 12 exposed children were assumed to be constant through the entire exposure period (to 7 years). 13 In- and out-migration to and from the case study areas were not considered. To the extent that 14 the case study exposures were hi gher than might occur at typica l residences outside the study 15 areas, this assumption may have overestimated bl ood Pb impacts compared to those that might 16 actually occur, and vice versa. 17 18 As discussed further below, maternal blood Pb levels during pregnancy were assumed to be 19 identical for all children at a level consistent w ith nationally representati ve values for women of 20 childbearing age (see Section 5.1.4.3). Thus, all children were assumed to start with the same 21 body burden of Pb at birth. Similarly, all expo sed children were assumed to receive the same 22 pattern of nationally represen tative policy-relevant backgrou nd (non-air related) exposures 23 throughout the exposure period. 24 25 5.1.3.2 Adaptation of IEUBK Model 26 27 As discussed above, the IEUBK model was used in batch mode to generate blood Pb estimates at 28 different ages for children exposed from birth in each block or block group for each case study 29 location. Inputs to the IEUBK mo del included exposure parameters and intake and uptake factor 30 values (see Section 5.1.4 and Appendix I) and th e exposure concentrations for each pathway. 31 The input data also include age-specific Pb exposure concentrations for policy-relevant 32 background pathways (e.g., drinking water and non-water diet), which were assumed to be the 33 same for children at all locations. 34 35 As described in Section 5.1.1, for th e pilot phase assessment, blood Pb metrics were calculated to 36 match the lifetime average and concurrent blood Pb estimates used in deriving the concentration37 response (blood Pb-IQ loss) functions that are used in this analysis (described in Section 6.1.2). 38 For this analysis, lifetime average is derived as the average blood Pb level for the exposure 39 interval 6 months through 84 months, and concurre nt is derived as the av erage blood Pb level for 40 the exposure interval 73 through 84 months. 3 To derive these metr ics, first IEUBK blood 41 estimates were generated for nine specific ages (see Exhibit 5-4) for each block or block group; 42 these estimates represented the central-tendency blood Pb levels experienced by children of those 43

3 The rationale for defining the average blood Pb for ages 73 to 84 months as concurrent reflects the fact that the average age of IQ testing in the Lanphear et al. (2005) study of blood Pb-IQ relationships was approximately seven years (see Chapter 6).Human Exposure Assessment and Blood Pb Estimation

ICF International 5-9 DRAFT – December 2006 ages in each block or block group. The lifetime av erage blood Pb metric is derived as the time1 weighted average of the blood Pb values for the nine ages. The concurrent blood Pb metric is 2 derived as the average of the last two ages (6 years 4 mont hs and 6 years 10 months). 3 4 Exhibit 5-4. Ages for IEUBK-Derived Blood Pb Estimates 5 Ages (Months) for Output IEUBK Blood Pb Estimates Age Period (Months) Represented by IEUBK Blood Pb Estimate a
9 7 to 12 15 13 to 18 21 19 to 24 31 25 to 36 43 37 to 48 55 49 to 60 67 61 to 72 76 73 to 78 82 79 to 84

a Modeling periods run from the first day of the first month to the last day of the last month. 6 7 The nine age periods for which the point estimates were obtained using IE UBK were selected to 8 capture those periods of childhood exposure expected to produce si gnificant variab ility in blood 9 Pb (i.e., exposures occurring under 2 years of ag e). Consequently, exposure intervals covering 10 the first two years of life (i.e ., 7 to 12 months, 13 to 8 months , and 19 to 24 months) are six 11 months long, while the remainder of the simulati on period, up to the last year (see below), are 12 simulated with year-long exposure intervals. 13 14 The lifetime average and concurrent (average during age 6) estimates were stored in spreadsheets 15 to serve as inputs to the proba bilistic population blood Pb model (s ee below). Each time the 16 Monte Carlo sampling algorithm chose a particular block or block group, th e appropriate lifetime 17 average and concurrent blood Pb le vels served as the GM values for that block or block group 18 from which the individual blood Pb estimates were derived. 19 20 5.1.3.3 Adaptation of Leggett Model 21 22 For the pilot phase assessment, rather than us ing the existing FORTRAN-based Leggett model, a 23 version of the Leggett model implemented in Visu al Basic® was developed. The need for this 24 new version of the Leggett model was based on two factors: 25 26 1. The need for a version of the model capable of batch-mode runs to support estimating 27 blood Pb levels for a relatively large number of blocks and block groups associated with 28 each case study location (s ee Section 5.1.1), and 29 2. The need for the ability to efficiently link the Leggett model (by coding the Leggett 30 model in Visual Basic®) to the multipathway intake and uptake module that was 31 developed for tracking pathway-specific c ontributions to total Pb uptake. 32 33 The development of a Visual Basic® version of the Leggett model involved contacting Dr. Joel 34 Pounds (Pounds 2005) and obtaining the FORTRAN c ode for the Leggett model (DOS version) 35 together with input files contai ning the biokinetic parameter values . The biokinetic algorithms of 36 the model were recoded in Visual Basic® and a user interface and multipathway intake and 37 uptake module were added (as noted above). The intake and uptake module was designed to 38Human Exposure Assessment and Blood Pb Estimation

ICF International 5-10 DRAFT – December 2006 accept the same exposure, intake, and uptake factors as the IEUBK, and to calculate inhalation 1 and ingestion Pb uptake in the sa me way as the IEUBK model. 2 3 Inhalation pathway uptake wa s calculated as follows: 4 5
BRTCRN = RSVOL × BRETH × AFLNG 6 7 where: 8 9 BRTCRN = Pb uptake by inhalation ( μ g/day) 10 RSVOL = age-specific respiratory volume (m 3 /day) 11
BRETH = modeled exposure concentration ( μ g/m 3 ) 12 AFLNG = inhalation absorption fraction (unitless) 13 14 Ingestion pathway Pb uptake was calculated as: 15 16 EATCRN = FOODUP + H2OUP + SOILUP + DUSTUP 17
FOODUP = EAT × FI 18 H2OUP = H2OCNS × H2OCONC × H2OA 19
SOILUP = SLING × SOILPB × SLAF 20
DUSTUP = DSTING × DUSTPB × DSTAF 21 22 where: 23 24 EATCRN = total uptake th rough ingestion ( μ g/day) 25 FOODUP = uptake from food ( μ g/day) 26 EAT = daily Pb intake from non-water diet ( μ g/day) 27
FI = GI absorption fracti on for diet (unitless) 28 H2OUP = uptake from drinking ( μ g/day) 29 H2OCNS = direct residential drin king water consumption (l/day) 30 H2OCONC = average Pb concentr ation in drinking water ( μ g/day) 31 H2OA = GI absorption fraction for water (unitless) 32 SOILUP = daily Pb uptake from soil ( μ g/day) 33 SLING = daily soil ingestion (g/day) 34 SOILPB = average soil Pb concentration ( μ g/g) 35 SLAF = absolute GI absorption fraction for soil Pb (unitless) 36 DUSTUP = daily Pb uptake from indoor dust ( μ g/day) 37 DSTING = daily indoor dust ingestion (g/day) 38 DUSTPB = average indoor dus t Pb concentration ( μ g/g) 39 DSTAF = absolute GI absorption fraction for dust Pb (unitless) 40 41 Model code was tested for logical correctness ag ainst the code supplied in Pounds (2005). The 42 model algorithms were tested for numerical stabil ity and precision, and the impacts of different 43 iteration time step durations on predicted blood Pb le vels were evaluated. In addition, the results 44 of test exposure scenarios were compared to population blood Pb statisti cs (as discussed in 45 Section 5.2). Age-specific parameter values used to estimate Pb input parameters were specified 46Human Exposure Assessment and Blood Pb Estimation

ICF International 5-11 DRAFT – December 2006 to conform as closely as possible to those used in the IEUBK modeling; that is, so that they 1 yielded the same daily Pb uptake (absorbed dose) from each pathway as went into the IEUBK 2 biokinetic component. Although co mprehensive model evaluation of the version of the Leggett 3 model developed for the pilot ph ase assessment was not completed in time for this report, the 4 Leggett-based exposure analysis ha s been included here to allow c onsideration for the use of this 5 particular blood Pb model as pa rt of the full-scale assessment. 4 The results (both exposure and 6 risk) generated using Leggett should, as is the case with all of the results in the pilot phase 7 assessment, be considered provisional and subject to updating as part of the full-scale assessment 8 and in light of additiona l model evaluation findings. 9 10 For the case study blood Pb modeling, exposure c oncentrations for each block or block group 11 and exposure scenario were input to the model in batch mode. Lifetime (6 to 84 months) blood 12 Pb profiles were generated from the daily Pb mo del outputs, and lifetime average and concurrent 13 blood Pb estimates were calculated using the appr opriate averaging period s (i.e., averaging the 14 individual daily values from 6 to 84 months fo r the lifetime average metric and averaging daily 15 values for the last year (months 73 to 84) for the concurrent metric). The model iteration time 16 was set at 0.1 day throughout the modeling perio d. Test runs indicated that modeled daily, 17 concurrent, and lifetime average blood Pb con centrations from age six months onward were 18 identical to those obtained using much shorter time steps. 19 20 Outputs from the blood Pb modeling (lifetime av erage and concurrent blood Pb estimates for 21 each census block or block group) were saved in spreadsheets (as were the IEUBK results) and 22 used as inputs to the probabilistic m odel, as described in Section 5.1.3.4. 23 24 5.1.3.4 Implementation of the Probab ilistic Population Blood Pb Model 25 26 The probabilistic population blood Pb and health risk model was implemented in an Excel® 27 spreadsheet using spreadsheet functions and Visu al Basic for Applications® code. The input 28 sheets for the case studies and exposure scenario s contained one row pe r block or block group 29 having one or more resident children. The data elements in each row included: the block or 30 block group number; the number of resident children 0 to 7 years old; the exposure 31 concentrations in air, soil, and indoor dust; a nd the estimated lifetime average and concurrent 32 GM blood Pb concentrations for each block or block group, as derived using either the IEUBK 33 or Leggett models described above. Also included in the table was a weighting factor value for 34 each block or block group that indicated the cumu lative proportion of exposed children residing 35 in the block or block groups and all of the bloc ks or block groups in the preceding rows of the 36 spreadsheet. The model used th e cumulative weights to select cen sus blocks or block groups in a 37 population-weighted fashion during the samp ling iterations, as discussed below. 38 39 The model included a spreadsheet and code algorithm that calculated the approximate 40 proportions of total Pb uptake (absorbed dose) from each pathway for each census block or block 41 group. This algorithm was run before the main s imulation and the results were placed in the 42 appropriate rows of the input shee t. Proportional contributions to total Pb uptake were calculated 43 for inhalation, soil ingestion, indoor dust inges tion, and combined policy-relevant background 44

4 Evaluation of the Leggett model as applied for this exposure and risk assessment is continuing beyond completion of this report. Additional findings of that evaluation will be conveyed by separate cover.Human Exposure Assessment and Blood Pb Estimation

ICF International 5-12 DRAFT – December 2006 pathways. The model calculated indoor dust contributions separa tely for the intercept of the 1 indoor dust model (assumed to be policy-relevant bac kground from Pb paint and other exposure 2 sources not explicitly modeled fo r the pilot phase), and for air- and soil-related terms in the AGG 3 model, as appropriate. The source contributions to total Pb uptake were calculated 4 deterministically for each block or block group us ing the same intake and uptake factors as used 5 in the blood Pb models. It is important to note that the model calculates the contributions of the 6 various exposure pathways to total Pb uptake for each block or block group, not for individuals. 7 Thus, the results discussed in Section 5.3.1 re present only approximate central tendency 8 estimates of pathway-specific contributions associ ated with various blood Pb percentile estimates 9 from the probabilistic model. 10 11 The probabilistic blood Pb/IQ loss model was imp lemented by sampling blood Pb levels 10,000 12 times in a population-weighted manner from the bl ocks and block groups included in each case 13 study, adding a lognormally distributed term repres enting individual variability to each of the 14 10,000 GM lifetime average and concurrent blood Pb values, and extracting population blood Pb 15 statistics from the resulting i ndividual blood Pb estimates. Indi vidual variability was modeled by 16 sampling from a 0 to 1 uniform variate and us ing the Excel® LOGINV fu nction to generate a 17 lognormal variate with the appropriate block or block group GM, and a defined population GSD 18 (see Section 5.1.4.3). 19 20 The pathway contributions to total Pb uptake as sociated with each block or block group were 21 also carried through the analysis. Each iteration of the m odel was implemented on a single row 22 of a spreadsheet, and standard spreadsheet func tions were used to extract population blood Pb 23 statistics (percentile values) af ter the sampling was complete. For the primary Pb smelter and 24 near roadway case studies, diet and drinking water were the only policy-relevant background 25 pathways. In the secondary Pb smelter cas e study, diet, water, and an estimated 15 μ g/g natural 26 soil exposure were identified as policy-relevant background pathways. As noted in Chapter 4, 27 the “AGG” models used to estimate house dust con centrations include intercepts that presumably 28 represent the contri bution to house dust from paint and othe r indoor sources of exposure. When 29 the AGG (Air-only) model is use d, the intercept contributes 60 μ g/g to the total estimated house 30 dust Pb levels. When the AGG (Air + Soil) version was used, the house dust intercept 31 concentration was 30.5 μ g/g. In all of the case studies, th e “intercept” contributions were 32 included as being associated w ith policy-relevant sources b ecause it was not possible to 33 unconditionally rule out these elemen ts of the house dust Pb models as being indirectly related to 34 air Pb levels. 35 36 5.1.4 Inputs to the Blood Pb Models 37 38 5.1.4.1 Exposure Concentration Estimates for Policy-Relevant Sources 39 40 Exposure concentrations in media impacted by po licy-relevant sources were estimated for each 41 census block or block group in each of the three case studies as described in Chapter 4. In the 42 primary Pb smelter case study, two scenarios were evaluated: the curren t conditions scenario, 43 representing estimates of exposure based on rece nt site-related data and modeling; and the 44 attainment scenario, wherein all of the exposure concentration es timates were the same as under 45 current conditions, with the exception of seve n census blocks near the facility, where all 46Human Exposure Assessment and Blood Pb Estimation

ICF International 5-13 DRAFT – December 2006 estimated quarterly averaged ambient air concentrations above the NAAQS (1.5 μ g/m 3 ) were 1 reduced to the level of standard and annual aver age air concentrations then derived from these 2 adjusted quarterly averages. Exposure concentrat ions that were estimated from the ambient air 3 concentrations (for example, indoor dust Pb c oncentrations) were like wise reduced in these 4 seven high-exposure blocks. In the secondary Pb smelter case study, two se ts of soil and indoor 5 dust exposure concentrations were devel oped, one based on EPA’s MPE methodology (see 6 Section 4.2.3) and one based on a hybrid method in which the MPE estimates were scaled to be 7 more congruent with environmental sa mpling data from similar facilities. 8 9 For the inhalation exposure modeling, as de scribed above, each block or block group was 10 characterized by an estimated inhalation exposure concentration and by an estimated number of 11 resident children. The inhalation exposure concen trations together with exposure concentrations 12 for other key media (e.g., soil, indoor dust, diet ) and numbers of children exposed in each block 13 or block group define the dist ribution of population exposures th at go into the calculation of 14 individual blood Pb levels in the probabilis tic model. Exhibit 5-5 shows the cumulative 15 population distribution of inhalati on pathway exposure concentrations for the primary Pb smelter 16 case study under the current conditio ns and attainment scenarios. 5 Important features of this 17 graph include the wide range of e xposures (note that the y-axis is logarithmic) and the fact that 18 the great majority of the expos ed populations experience relati vely low inhalation exposures 19 (less than approximately 0.015 μ g/m 3 ). Only a small “tail” of the distribution (a small proportion 20 of the exposed children) experiences much highe r exposures. Note that the cumulative exposure 21 distributions are identica l for the two exposure scenarios, ex cept for the seven highest exposure 22 blocks, as discussed above. 23

5 The cumulative distribution is derived by ranking the census blocks from lowest to highest based on inhalation exposure concentration and plotting the exposure concentrations versus the number of children exposed at or below that concentration.Human Exposure Assessment and Blood Pb Estimation

ICF International 5-14 DRAFT – December 2006 1 Exhibit 5-5. Population Di stribution of Inhalation Exposure Concentrations 2 for the Primary Pb Smelter Case Study 3

0.001 0.010 0.100 1.000 10.000

0% 20% 40% 60% 80% 100%
Cumulative Proportion of Population ExposedInhalation Exposure Concentration,μg Pb/m3 Primary Pb Smelter, Current Conditions

Primary Pb Smelter, Attainment

4 5 Exhibit 5-6 shows the population distributions of inhalation e xposure concentrations for the 6 secondary Pb smelter and near roadway urban case studies. For the s econdary Pb smelter case 7 study, the basic shape is the same, although less ex treme, than that seen for the primary Pb 8 smelter. Estimated inhalation exposures are ve ry low in the secondary Pb smelter case study, 9 and increase gradually, for most of the population. There is a small “hook” at the high end of the 10 curve representing a relatively small proportion of the population in census blocks near the 11 facility experiencing higher exposures. In c ontrast, the exposure di stribution for the near 12 roadway urban case study is a step function; this pattern shows the single-value estimated 13 inhalation concentrations for census blocks in the 75 to 200 meters (m) and 0 to 75 m bands, 14 respectively. 15 16Human Exposure Assessment and Blood Pb Estimation

ICF International 5-15 DRAFT – December 2006 Exhibit 5-6. Population Distribution of Inhalation Exposure Concentrations 1 for the Secondary Pb Smelter and Near Roadway Urban Case Studies 2

0.0001 0.0010 0.0100 0.1000

0% 20% 40% 60% 80% 100%
Cumulative Proportion of Population ExposedInhalation Pb Exposure,μg Pb/m3 Secondary Pb Smelter
Near Roadway Urban

3 4 The distribution of soil exposure concentrations for all thr ee case studies (including both the 5 MPE and hybrid approaches for soil for the secondar y Pb smelter case study) is shown in Exhibit 6 5-7. In this case, it is the e xposure concentration distributions for the secondary Pb smelter case 7 study that are flatter, with more extreme high-e nd “tails” than the primary Pb smelter. The 8 distribution of exposure concentrations for the near roadway urban case study is again a step 9 function, in this case with three “steps” represen ting the soil concentrations in the 75 to 200 m, 10 12 to 75 m, and 0 to 12 m exposure “bands,” respectively. 11 12Human Exposure Assessment and Blood Pb Estimation

ICF International 5-16 DRAFT – December 2006 Exhibit 5-7. Population Distribution of Soil Exposure Concentrations 1 for the Three Case Studies 2

10 100 1,000

0% 20% 40% 60% 80% 100%

Cumulative Proportion of Population ExposedSoil Pb Concentration,μg Pb/g Near Roadway Urban
Secondary Pb Smelter, MPE

Secondary Pb Smelter, Hybrid

Primary Pb Smelter

3 4 The population distributions of indoor dust exposur e concentrations are shown for the primary 5 Pb smelter case study in Exhibit 5-8, and for th e secondary Pb smelter and near roadway urban 6 case studies in Exhibit 5-9. Th e pattern of indoor dust exposure concentrations closely matches 7 the pattern seen for inhalation expo sure concentrations, a consequence of the fact that the air Pb 8 concentration in air is a major determinant of indoor dust Pb concentrations estimates by the 9 AGG models (described in Chapter 4 and 6). Th e estimated indoor dust concentrations for the 10 secondary Pb smelter and near roadway urban case studies also follow the general patterns seen 11 for the other media, again because of the correl ation introduced between ai r, soil, and indoor dust 12 exposure by the use of the AGG model. The sk ewed nature of the exposure concentration 13 distributions for the primary and secondary Pb smelter case studies strongly affects the nature of 14 the estimated blood Pb distributions, as will be discussed in Section 5.4. 15 16Human Exposure Assessment and Blood Pb Estimation

ICF International 5-17 DRAFT – December 2006 Exhibit 5-8. Population Distribution of Ho use Dust Exposure Concentrations for the 1 Primary Pb Smelter Case Study 2

10 100 1,000 10,000

0% 20% 40% 60% 80% 100%

Cumulative Proportion of Population ExposedHouse Dust Pb Concentration,μg Pb/g

Primary Pb Smelter, Current Conditions

Primary Pb Smelter, Attainmen t

3 4 Exhibit 5-9. Population Distribution of Ho use Dust Exposure Concentrations for the 5 Secondary Pb Smelter and Near -Roadway Urban Case Studies 6

0 50 100 150 200

0% 20% 40% 60% 80% 100%

Cumulative Proportion of Population ExposedHouse Dust Pb Concentration,μg Pb/g Near Roadway Urban

Secondary Pb Smelter, MPE Secondary Pb Smelter, Hybrid

7 8Human Exposure Assessment and Blood Pb Estimation

ICF International 5-18 DRAFT – December 2006 5.1.4.2 Policy-Relevant Background Exposure Pathway Concentrations and 1 Pb Intake Estimates 2 3 As noted above, the exposure concentrations and Pb intake from policy-relevant background 4 pathways (drinking water and non-wate r diet) were also parameter i nputs to the blood Pb model. 5 For the purposes of the pilot ph ase assessment, as stated in Section 5.1.1, exposure through these 6 pathways was categorized as policy-relevant ba ckground, and all of the exposed children were 7 assigned the same age-specific estimates. 8 9 All of the exposed populations we re assigned the same Pb concen tration in drinking water. 10 While there is a rather large am ount of data in the literature, in many cases, the data are from 11 “first-draw” samples, non-random (“priority”) sa mples, or from communities where Pb levels 12 were known to be elevated. After reviewi ng the literature, the average drinking water 13 concentration was estimated to be 4.61 μg/L, base d on data from two recent studies of residential 14 water concentrations in homes and apartments in the United States and Canada (Moir et al. 1996, 15 Clayton et al. 1999). The range of values seen in these studies (0.84 to 16 μg/L) was considered 16 to be representative of randomly sampled reside ntial water in houses cons tructed since Pb pipe 17 and solder were banned for residential use. The se lected value is close to the “default” value (4.0 18

μ g/L) provided with the IEUBK model (USEPA 1994). Much higher values have been 19 encountered in homes with Pb piping and/or very corrosive water. 20 21 In addition to drinking water, it is expected th at young children will be exposed to Pb in the 22 foods they consume. In this assessment, all exposed children were assumed to receive the age23 specific estimates of dietary Pb intake de veloped by EPA’s Office of Solid Waste and 24 Emergency Response (USEPA 2006h). EPA de veloped these estimates by analyzing food 25 consumption data from the third National Hea lth and Nutrition Examination Survey (NHANES 26 III) conducted by the National Center for Health Statistics (CDC 1997), and food residue data 27 from the U.S. Food and Drug Administration’s (FDA) Total Dietary Study (USFDA 2001). The 28 daily intake values shown in Exhibit 5-10 are co nsiderably lower than those developed using the 29 same methodology in the 1980s and 1990s. Pb concentrations in food have decreased 30 dramatically since the prohibi tion of Pb solder in food c ontainers in 1982 (USEPA 2006b, 31 Section 3.4). 32Human Exposure Assessment and Blood Pb Estimation

ICF International 5-19 DRAFT – December 2006 1 Exhibit 5-10. Summary of Non-Water Dietary Pb Intake Estimates 2

Age Category (months) Updated Dietary Pb Intake Estimate (μg/day)

0 to 11 3.16

12 to 23 2.6

24 to 35 2.87

36 to 47 2.74

48 to 59 2.61

60 to 71 2.74

72 to 84 2.99 3 There is some potential for double-counting of wa ter and dietary Pb inta ke because some food 4 categories (e.g., baby formula, soup) are prepared using domestic wate r. As discussed in Section 5 5.1.4.3, this double-counting is minimized by limiting th e estimated intake of domestic water to 6 “direct ingestion” (i.e., consump tion directly from the tap). 7 8 5.1.4.3 Factors Determining Pb Exposure, Intake, and Uptake 9 10 As discussed previously, ther e is a range of model inputs that govern how the exposure 11 concentrations are converted to absorbed Pb dose (uptake). These factors represent the 12 physiological and behavioral char acteristics of the exposed p opulation and the chemical and 13 physical properties of the exposure media that govern exposure and absorption by inhalation and 14 ingestion. 15 16 Because substantial data have become availabl e since the IEUBK default values were last 17 updated, a literature review was conducted to identify and evaluate recent information related to 18 Pb exposures, absorption, and bioavailability (USEPA 2006b). Expert s in OSWER and OAQPS 19 were also consulted in an effort to derive exposure, intake, and uptake values for the pilot phase 20 assessment. Exhibit 5-11 presents the paramete r values that were sel ected as inputs to the 21 IEUBK in the pilot phase assessment. 22 23 A number of the values in Exhibit 5-11 differ fr om the suggested default values in the most 24 current version of the IEUBK (USEPA 2005a). Ch ildren’s daily ventilati on rate estimates were 25 based on the ICRP Publication 89 (ICRP 2002). Pending a re-analysis pertaining to the child 26 respiratory absorption fraction values, the values used in the pilot ph ase for the primary and 27 secondary Pb smelter case studies were those developed for point source locations for the 28 assessment described in the 1990 Staff Paper (USEPA 1990b; USEPA 1989). For the near 29 roadway urban case study, the value used in the pilot phase assessment was 0.32, which falls 30 within the range of 0.25 to 0.45 es timated in the past for areas not influenced by point sources 31 (USEPA 1989), and is the IEUBK default. 32 33Human Exposure Assessment and Blood Pb Estimation

ICF International 5-20 DRAFT – December 2006 Exhibit 5-11. Input Parameter Values for the IEUBK Model 1

Parameter Value

IEUBK Default Age Ranges (Years)

Parameter IEUBK Parameter

Name0.5 to 11 to 22 to 33 to 44 to 55 to 66 to 7 Basis/Derivation

Inhalation

Daily ventilation rate (m3/day) Ventilation rate 4 5.1 6 6.8 7.8 8.8 10 ICRP (2002), with interpolation for intermediate ages.

Absolute inhalation absorption

fraction (unitless) Lung absorption 0.42 (Primary, secondary smelter), 0.32 (Near Roadway Urban) Smelter value: USEPA (1989), Appendix A IEUBK value: USEPA (1989), central tendency value from analysis of deposition data.

Indoor air Pb concentration Indoor air Pb concentration (percentage of outdoor) 100%

Time spent

outdoors Time spent outdoors (hours/day) Not used Time spent indoors/outdoors is not considered because the input air concentrations are already long-term weighted averages of indoor and outdoor concentrations.

Drinking Water Ingestion

Water consumption (L/day) Water consumption (L/day) 0.34 0.31 0.31 0. 33 0.36 0.39 0.42 Based on value for infants, 1 to 3 yr olds, 1 to10 yr olds (with trend lines used to interpolate intermediate age ranges) (USEPA 2002c).

Water Pb concentration ( μ g/L) Pb concentration

in drinking water ( μ g/L) 4.61 GM of values reported in studies of United States and Canadian populations (residential water) (Moir et al. 1996, Clayton et al. 1999, as cited in USEPA (2006b), Section 3.3 Table 3-10).

Absolute absorption (unitless) Total percent accessible 50% (Single value used across all age ranges) Assumed similar to dietary absorption (see "Total percent accessible" under Ingestion-Diet below).Human Exposure Assessment and Blood Pb Estimation

ICF International 5-21 DRAFT – December 2006 Exhibit 5-11. Input Parameter Values for the IEUBK Model 1

Parameter Value

IEUBK Default Age Ranges (Years)

Parameter IEUBK Parameter

Name0.5 to 11 to 22 to 33 to 44 to 55 to 66 to 7 Basis/Derivation

Diet

Dietary Pb intake ( μ g/day) Daily Pb intake ( μ g/day) 3.16 2.6 2.87 2. 74 2.61 2.74 2.99 Estimates based on (a) Pb food residue data from U.S. Food and Drug Administration Total Diet Study (USFDA 2001), and (b) food consumption data from NHANES III (CDC 1997).

Absolute absorption (unitless) Total percent accessible 50% Alexander et al. (1974) and Ziegler et al. (1978), as cited in USEPA 2006 (Section 4.2.1). These two dietary balance studies suggest that 40-50% of ingested Pb is absorbed by children (2 weeks to 8 years of age). Soil/Indoor Dust Ingestion

Soil/dust weighting

factor (unitless) Soil/dust ingestion weighting

factor (percent soil) 45% This is the percent of total ingestion that is soil. Value reflects best judgment and consideration (results published by van Wijnen et al. (1990), as cited in (USEPA 1989). The van Wijnen et al. study looked at tracer studies of ingestion rates for rainy days and non –rainy days. It was assumed that rainy days were associated with all soil ingestion and non-rainy days were associated with a combination of soil and dust with the delt a representing soil.

Total dust + soil ingestion

(mg/day) Amount of soil/dust ingested daily (mg) 85 135 135 135 100 90 85 USEPA 1989, which was based on multiple studies focusing on children.

Absolute gastrointestinal

absorption (soil and dust) (unitless) Total percent accessible · Primary Pb smelter case study: 0.48 for soil and 0.26 for dust

· Secondary Pb smelter and near roadway: 0.30 for both soil and dust · Site specific absorption factors for soil and indoor dust were derived for the primary Pb smelter case study using relative bioavailabi lity (RBA) estimates generated based on swine studies involving soil and dust samples collected in the study area (Casteel et al. 2005). These RBAs were converted to absolute bioavailability factors (i.e., total percent accessible values) by applying the absolute bioavailability factor for the control material (Pb acetate water solution also fed to the animals).

· Secondary Pb smelter and near roadway values: USEPA (1989) reflects evidence that Pb in dust and soil is as accessible as dietary Pb and that dust/soil ingestion may occur away from mealtimes (resulting in enhanced absorption relative to exposure during meal events).Human Exposure Assessment and Blood Pb Estimation

ICF International 5-22 DRAFT – December 2006 Exhibit 5-11. Input Parameter Values for the IEUBK Model 1

Parameter Value

IEUBK Default Age Ranges (Years)

Parameter IEUBK Parameter

Name0.5 to 11 to 22 to 33 to 44 to 55 to 66 to 7 Basis/Derivation

Other

Maternal blood

Pb ( μ g/dL) Maternal blood Pb concentration at childbirth, μg/dL 1.94 NHANES IV, national GM for adult women – all nationalities (CDC 2004).

2 Estimated children’s direct water ingestion values were interpolated from values in EPA’s 3 Children-Specific Exposure Factors Handbook (USEPA 2002c); the GI absorption fraction of Pb 4 from water (and diet) was retained at the IEUBK default value of 50 percent, and is consistent 5 with OAQPS previous analyses of Pb uptake (USEPA 1989). As noted in Section 5.1.4.2, age6 specific dietary intake values for Pb were revi sed to reflect the latest analyses of FDA and 7 NHANES III data on food consumption pattern and Pb residue levels. (USEPA 2006h). 8 9 Age-specific soil and indoor dust ingestion rate s were left at the IEUBK default values. 10 Similarly, the weighting factor fo r soil and indoor dust ingestion was also left at 45 percent soil, 11 despite limited data supporting this specific value (USEPA 1989; USEPA 1994). The impacts of 12 changes in the weighting factor and other variables related to soil and dust ingestion were 13 investigated through sensitivity analys es, as discussed in Section 6.3. 14 15 Casteel et al. (2005) evaluated th e GI absorption of Pb and other metals from soil samples taken 16 from the primary Pb smelter study area in juvenile swine. Results of these experiments were 17 used to derive estimates of absolute GI ab sorption fractions (the IEUBK inputs are called 18 “Percent Available”) of 0.48 (48 pe rcent) for soil and 0.26 (26 per cent) for indoor dusts. For the 19 other case studies, the IEUBK generic default valu e for GI absorption of Pb from soil and indoor 20 dust (0.30, or 30 percent) was used. This value is generally consistent with more recently 21 reported values, although estimates vary widely . As was the case with the soil-indoor dust 22 weighting factor, the impacts of changes in absorption fractions for soil and indoor dust were 23 investigated in se nsitivity analyses. 24 25 For the case study blood Pb modeling, the IEUBK de fault value for maternal blood Pb level was 26 updated using data from the most recent NHANES survey. NHANES IV data from 2002 to 27 2004 indicate that the GM blood Pb value for reproductive age women has dropped to about 1.94 28

μ g/dL (Maddaloni et al. 2005). 29 30 5.1.4.4 Inter-Individual Variability 31 32 The last major input to the probabilistic bl ood Pb model is the GSD reflecting individual 33 variability in response to Pb exposures. A GS D value of 1.6 has been used as the central 34 tendency estimate for individual blood Pb variabili ty in the pilot phase assessment. This value 35Human Exposure Assessment and Blood Pb Estimation

ICF International 5-23 DRAFT – December 2006 was provided by EPA (USEPA 1994) based on the di stribution of blood Pb levels measured in 1 children exposed to smelter emissions at the Midvale, UT, smelter (White et al. 1998). 2 Subsequent studies suggest that this is a reasonable central tendency GSD for populations of 3 children living in relatively small, defined ar eas where the sources and relative importance of 4 different exposure pathways and media are simila r across the exposed popul ation. The selection 5 of this value is supported by the fact that expos ure concentrations used in this analysis are 6 expected to be relatively consis tent within each block and bl ock group. Similarly, using a GSD 7 reflecting the distri bution of blood Pb levels for children re siding near a primary Pb smelter (i.e., 8 a dominant Pb source such as the Midvale, UT, smelter) seems appropriate for modeling blood 9 Pb levels near primary and secondary Pb sme lters. It is also worth noting that the pre10 remediation blood Pb GSD for children participat ing in the Baltimore Urban Pb Soil Abatement 11 Project was estimated at 1.5 (White et al. 1998), which adds support to using the GSD of 1.6 for 12 the near roadway urban case study, which can be interpreted as being so mewhat similar to a 13 general urban scenario in terms of Pb exposure. 14 15 Recent surveys of blood Pb levels in children at the national-le vel (NHANES IV data for years 16 1999 to 2002) have found GSD values in the ra nge of 2.03 to 2.23 (CDC 2004). These GSDs, 17 which are considerably larger than the values used in the pilot phase assessment, likely reflect 18 the fact that, while blood Pb levels for the majority of children in the United States have 19 decreased significantly over the last one to two decades, a small fraction of children still retain 20 relatively elevated blood Pb levels due to c ontinued exposure to Pb paint and other artifact 21 sources. Consequently, as the median and m ean blood Pb levels have dropped, the extreme 22 upper tail of the distribution is still somewhat anchored by these high-exposure children, 23 resulting in an increased GSD for the overall population (CDC 2004) . It is expected that the 24 variation in exposure concentra tions within the case study bloc ks or block groups is much 25 smaller than that seen in th ese national population surveys, fu rther supporting the use of the 26 smaller GSD values. 27 28 The small amount of publicly av ailable blood Pb data from child ren living near the primary Pb 29 smelter site (see Exhibits 3-3 and 3-4), while no t sufficient by itself to establish reliable GSD 30 estimates for this group, appear to be consistent with a low to mode rate GSD value (less than 2). 31 This conclusion was based on a review of summary blood Pb data from the site (numbers of 32 samples within specified concentration ranges) ; individual blood Pb measurements were not 33 available. The impact of assuming different GSD values has a major impact on the blood Pb 34 distribution results; the results of sensitivity an alysis involving this variable are provided in 35 Section 6.4. 36 37Human Exposure Assessment and Blood Pb Estimation

ICF International 5-24 DRAFT – December 2006 5.2 Blood Pb Model Performance Evaluation 1 2 5.2.1 Evaluation Versus General Population and Site-Specific Blood Pb 3 Measurements 4 5 As noted previously, the IEUBK and Leggett blood Pb models were evaluated with regard to 6 their predictions relative to each other and relative to population blood Pb statistics from well7 studied populations believed to have experien ced exposures similar to those estimated for 8 populations in the case studies. In addition, the upper percentile blood Pb estimates from the 9 primary Pb smelter case study were compared to children’s blood Pb sampling results taken near 10 the facility in 2001. Exhibit 5-12 prov ides a summary of these analyses. 11Human Exposure Assessment and Blood Pb Estimation

ICF International 5-25 DRAFT – December 2006 1 Exhibit 5-12. Summary of Blood Pb Model Evaluation 2

Case Study Description of Performance

Evaluation Results of the Performance Evaluation Implications for Overall Analysis

All Case Study Populations For both the lifetime average and average at age 6 (concurrent) blood metrics, the 50th percentile (i.e., median) value for each of the three study populations was compared to the GM value for the 0 to 5 year old age group from NHANES IV (2001- 2002) (CDC 2004). Modeled median lifetime average blood Pb modeled with IEUBK ranged from 1.2 to 1.9 μ g/dL (average 1.7) for the three case studies. Median concurrent blood Pb modeled with IEUBK ranged from 1.0 to 1.4 μ g/dL (average 1.2) for the three case studies.

Leggett median lifetime average blood Pb ranged from 0.8 to 1.2
μ g/dL (average 1.0) for the three case studies Median concurrent blood Pb ranged from 0.5 to 0.8
μ g/dL (mean 0.7) for the three case studies. The relatively close match between the modeled median lifetime average levels from IEUBK and the national GM value from NHANES IV (for children ages 0 to 5 years old) suggests that the IEUBK model with the given set of exposure, intake, and uptake factors is neither significantly over- or underestimating exposures for the study population. Because concurrent blood Pb in this analysis is defined as the average blood Pb from age 6 to 7 years, when blood Pb levels are known to decline from values seen in younger children, the median concurrent IEUBK values appear to also generally consistent with population data. The median blood Pb estimates are insensitive tothe relatively small number of highexposure block groups in the primary and secondary Pb smelter case studies; thus the lower exposure experienced by the large majority of the exposed populations are dominating this metric. The Leggett model, however, with the selected exposure, intake, and uptake factors, appears to be underestimating the GM blood Pb statistics somewhat, compared to the national population. The reason for this is not clear, although it is possible that the Pb exposure levels of the NHANES population are actuallylower than the combined policyrelevant background and policyrelevant source levels used in the case studies. Recent studies (USEPA 2006b) indicate that the soil and house dust Pb concentrations seen in all but the most exposed blocks in the primary Pb smelter case study fall well within the range of typical national values.Human Exposure Assessment and Blood Pb Estimation

ICF International 5-26 DRAFT – December 2006 1 Exhibit 5-12. Summary of Blood Pb Model Evaluation

Case Study Description of Performance

Evaluation Results of the Performance Evaluation Implications for Overall Analysis

Primary Pb smelter Comparison of upper-bound end of range of modeled blood Pb levels against the set of site-specific measured blood Pb levels collected for children <6 years of age in 2002 58 site-specific blood Pb level measurements from children ages 0 to 72 months old in 2001 yield the following percentiles:

> 95th is 20 to 29 μ g/dL > 90th is 10 to 19 μ g/dL > 50th is 0 to 9 μ g/dL

Percentile results generated from our site-specific modeling (Leggett and IEUBK combined):

> 99.9th% is 13 to 29 μ g/dL > 99.5th% is 6 to 17 μ g/dL > 99th% is 4 to 11 μ g/dL > 95th% approaches 5 μ g/dL It should be noted that the blood Pb levels measured at the primary Pb smelter location co rrespond to highlycontaminated areas and were focused on children believed to be at risk of adverse effects. Thus, it is to be expected that the percentile values seen in the blood Pb data should correspond to higher percentile estimated blood Pb values at the primary Pb smelter location, because the latter include many children far from the site, who experience relatively low exposures.

Numerically, the highest percentile values measurable from the data are in the same range as the highest percentiles in the modeled data.

These results indicate that the modelpredicted higher percentile blood Pb values from the secondary Pb case study are generally consistent with the high-end measured values.

2 With regard to the median blood Pb levels, the IEUBK lifetime average blood Pb predictions 3 from the three case studies rather closely match the GM estimate from the NHANES survey. As 4 expected, the IEUBK concurrent blood Pb estim ates are somewhat lower than the NHANES GM 5 values, likely because the former data are deri ved from younger children, who are expected to 6 have somewhat higher blood Pb levels. Median blood Pb estimates from the Leggett model are 7 systematically lower than those from the IEUBK, and lower than the NHANES GM. 6 The 8 reason for this difference is not clear, a lthough it is possible that the NHANES population may 9 have experienced lower exposures than the expos ed populations in the cas e studies; this would 10 imply that the IEUBK values were slight overe stimates and would support the argument that the 11 Leggett model “underestimates” were not as large as they appear. 12 13 The upper percentile blood Pb estimates from the IEUBK and Leggett models appear to be in the 14 same general range as the upper perc entile data from the blood Pb samples taken near the facility 15 in 2001. This suggests that the models are not dramatically overestimating blood Pb levels for 16 this subpopulation that would be most strongly affected by Pb exposures from policy-relevant 17

sources. 18 19

6 As noted earlier, the trend between IEUBK-based and Leggett-based blood Pb levels generated for the pilot phase is opposite to the trend reported in the Pounds and Leggett (1998) study. This apparent contradiction elevates the importance of model evaluation and performance evaluation related to the IEUBK and Leggett models, which was not completed in time for inclusion in this report. Additional findings from that evaluation will be conveyed by separate cover.Human Exposure Assessment and Blood Pb Estimation

ICF International 5-27 DRAFT – December 2006 5.2.2 Stability of Percentile Blood Pb Estimates 1 2 In addition to the performance evaluation of the blood Pb model described above, the stability of 3 the various blood Pb percentile estimates was also evaluated. Because these estimates are 4 probabilistic, they can be expected to vary as th e model is run multiple ti mes or with different 5 numbers of iterations. 6 7 To examine the variability in the percentile blood Pb estimates, the probabilistic blood Pb model 8 for the primary Pb smelter current conditions scenario was run 100 times, with 10,000 iterations 9 per run. The distributions of the blood Pb percentile estimate s are summarized in Exhibit 5-13. 10 11 Exhibit 5-13. Distributions of Blood Pb Percentile Estimates, 12 100 Repetitions of the Probabilistic Blood Pb Model 13

Blood Pb Percentile Mean Estimate of Statistic,
μ g/dL Standard Deviation Coefficient of Variation Average Difference a Average Difference/Mean Estimate
99.9th 20.9 2.02 10% 2.24 11% 99.5th 11.2 0.87 8% 0.98 9% 99th 7.6 0.41 5% 0.46 6% 95th 3.7 0.065 2% 0.074 2% 90th 2.9 0.034 1% 0.038 1% 75th 2.0 0.018 1% 0.020 1% Mean 1.7 0.018 1% 0.021 1% Median 1.3 0.010 1% 0.012 1% 25th 0.93 0.007 1% 0.008 1% First 0.40 0.007 2% 0.008 2%

a Average difference = expected value of difference between two successive si mulation estimates (10,000 iterations). 14 15 The results indicate that the pr ecision of the model is quite good for the mid-range blood Pb 16 percentile estimates; the coefficient of variati on (the standard deviation divided by the mean, 17 multiplied by 100) is one percent or less for the median and all percentiles between the 25 th and 18 the 90 th , inclusive. Above the 95 th percentile, the precision of th e estimates drops rapidly, such 19 that the coefficient of variation is about five percent for the 99 th percentile, 8 percent for the 20 99.5 th percentile, and 10 percent for the 99.9 th percentile. These statistics should be kept in mind 21 when interpreting the blood Pb distribu tion results presented in Section 5.4. 22 23 5.3 Limitations and Uncertainties in the Human Exposure Assessment and Blood Pb 24 Modeling 25 26 A number of factors affect the degree of un certainty associated with the human exposure 27 assessment and blood Pb modeling. These factor s, discussed in the following subsections, 28 include the estimated exposure concentrations associated with policy -relevant sources and 29 policy-relevant background, the expo sure, intake, and uptake factor values, the differences in the 30 blood Pb model themselves, the approach used to characterize individual variability, and the 31 demographics of the exposed population. 32 33Human Exposure Assessment and Blood Pb Estimation

ICF International 5-28 DRAFT – December 2006 5.3.1 Exposure Concentrations and Intake Estimates 1 2 Exposure concentration estimates have a varying affect on the results of the blood Pb modeling, 3 depending on the extent to which they contribute to total Pb uptake (absorbed dose). Exhibit 5- 4 14 summarizes the general pattern of how po licy-relevant sources and policy-relevant 5 background pathways contribute to total Pb intake for different pe rcentile estimates of blood Pb 6 for the three case studies. As discussed in S ection 5.1.3.4, the values in the exhibit are the Pb 7 uptake contribution estimates for the block or block group whose geometric mean blood Pb was 8 used to estimate the specified individual blood Pb percentiles. In addition, the values represent 9 the averages of the pathway contributions calcul ated from the distributions of lifetime average 10 and concurrent blood Pb percentile values, and the averages of the percentile values calculated 11 from the different exposure scenarios for each case study location. For the most part, the 12 pathway contributions to Pb upt ake do not differ substantially across the exposure scenarios or 13 blood Pb metrics. Exceptions to this pattern ar e discussed in Sections 6.1 and 6.2. Detailed 14 tables of pathway contributions are provided in Appendix J. 15 16 Exhibit 5-14. Average Contributions of Policy-Relevant Sources and Policy-Relevant 17 Background Pathways to Overall Pb Uptake 18
Primary Pb Smelter (Current Conditions) Secondary Pb Smelter Near Roadway Urban Blood Pb Percentile Estimate PolicyRelevant Background PolicyRelevant Sources PolicyRelevant Background PolicyRelevant Sources PolicyRelevant Background PolicyRelevant Sources Maximum 2% 98% 68% 32% 20% 80%

99.9 th 2% 98% 65% 35% 44% 56%

99.5 th 4% 96% 67% 33% 44% 56%

99 th 4% 96% 66% 34% 44% 56%

95 th 22% 78% 68% 32% 44% 56%

90 th 34% 66% 67% 33% 44% 56%

75 th 45% 55% 67% 33% 44% 56%
Median 56% 44% 68% 32% 35% 65%

25 th 56% 44% 68% 32% 35% 65%

1 st 54% 46% 68% 32% 44% 56%
19 As illustrated in Exhibit 5-14, the policy-relevant background pa thways tend to contribute a 20 relatively large proportion of total Pb uptake for individuals with lower blood Pb levels, and the 21 importance of policy-relevant background pathways then drops with increasing blood Pb. This 22 pattern is especially pronounced for the primary Pb smelter because air, soil, and indoor dust 23 exposures are relatively low for the bulk of th e exposed population, and th en become extremely 24 high for the highest exposure bl ocks and block groups. For the secondary Pb smelter case study, 25 the contribution of policy-rele vant background exposure pathways (diet, water, and background 26 soil) remains above 50 percent for all of the expos ure percentiles. In the near roadway urban 27 case study, the contribution from policy-relevant background pathways is lower for less exposed 28 groups than for the other case st udies, but it is noted that 100 μ g/g “urban background” soil Pb is 29 included as “policy-related” in this tabulation. 30Human Exposure Assessment and Blood Pb Estimation

ICF International 5-29 DRAFT – December 2006 1 Overall, among the pathways for which individual contributions were derived (see Appendix J), 2 the inhalation pathway contribute s the least to total Pb uptake, ranging from 0.01 percent in the 3 lowest exposure blocks for the secondary Pb smelter case study to 4.3 percent in the highest 4 exposure blocks for the primary Pb smelter. As noted previously, how ever, the impact of 5 ambient air is assumed to be amplified by its impact on indoor dust Pb concentrations through 6 the AGG models. The proportional contribution from indoor dust to total Pb intake is 7 consistently in the range of 25 to 35 percent in the secondary Pb smelter and near roadway case 8 studies; however, for the primary Pb smelter cas e study, it ranges from ab out 10 percent in the 9 low-exposure blocks and block groups to 87 to 88 percent in the highest exposure block groups. 10 As noted previously, the contribution from house dust includes an “intercept” which accounts for 11 a large, sometimes dominant, proportion of the to tal dust contribution in census blocks with low 12 air and/or soil concentrations. 13 14 Soil exposure contributes about 6 to 11 percent of total Pb intake in the secondary Pb smelter 15 case study, 30 to 53 percent in the near roadway urban case study, and from about 7 to 63 16 percent in the primary Pb smelter case study. Because of the relativ ely widespread soil 17 contamination at the primary Pb smelter location, soil contributions to total Pb uptake tend to be 18 greater for individuals in the lower blood Pb percentiles, whose air and indoor dust exposure 19 concentrations are relatively low. 20 21 The factors that contribute to uncertainties in the exposure con centrations are discussed below 22 for the primary Pb smelter, the secondary Pb smelter, and the near roadway urban case study 23 locations: 24 25 Primary Pb Smelter 26 27

• Emissions estimates from the facility processes were based on maximum permitted 28 emissions. This may not appropriately repres ent current emissions (could be higher or 29 lower). In addition, source characteristics of area and volume sources are uncertain, and 30 emissions from roadways not directly adjacent to the facility were not included. 31 32

• The particle size distributions of the emissions from the prim ary Pb smelter are uncertain. 33 These distributions impact air and dust concentr ations (but not soil, as soil concentrations 34 were estimated from measurements for this case study). 35 36

• Inhalation exposure concentratio ns for the 0 to 7 age group were estimated from the pilot 37 phase assessment ambient air concentrations using location-specifi c ratios of ambient 38 concentration to exposure concentrations for the 0 to 4 age group from the NATA 39 national-scale assessment (s ee Section 4.1.2.2). This contributes to uncertainty, 40 particularly because the 5 to 7 age group is school-aged and may have different activity 41 patterns from the 0 to 4 group. 42 43

• Soil concentration measurements were availa ble out to approximately 2.5 km from the 44 facility. Beyond that distance, the regr ession equation used to calculate soil 45 concentration was extrapolated, l eading to some uncertainty. 46Human Exposure Assessment and Blood Pb Estimation

ICF International 5-30 DRAFT – December 2006 1

• To generate air concentrations for the atta inment scenario, all quarterly average air 2 concentrations exceeding the NAAQS were set to the attainment level (1.5 μ g/m 3 ). All 3 other air concentrations were kept at the cu rrent conditions modeled concentrations. This 4 is a simplification of how ai r concentrations would be affected by emission changes 5 necessary to meet the NAAQS, and likel y results in an overestimation of air 6 concentrations for the attainment scenario. 7 8

• Soil concentration estimates for the attainment scenario are the same as those used for the 9 current conditions scenario. This may contribute to a more conservative soil 10 concentration, although soil remediation activ ities make this difference difficult to 11 predict. 12 13 Secondary Pb Smelter 14 15

• Facility Pb emissions estimates were based on stack tests from 1997, 1999, and 2000. 16 Current emissions may be different. In addition, estimating annual emissions from 17 single-day stack tests may not a ppropriately account for true trends in emissions over an 18 entire year. 19 20

• The particle size distributions of the emissions from th e secondary Pb smelter are 21 uncertain and were based on general pr ocess information from AP-42. These 22 distributions lead to uncertainties in air, soil, and dust concentrations. 23 24

• The meteorological data used for the air disp ersion modeling were not local, but from a 25 nearby station. The use of these data result in less precision in di rectional estimates of 26 air, soil, and dust concentrations, which is cons istent with the results of the comparison to 27 air monitoring data at specifi c locations (see Section 4.2.2.4). 28 29

• Inhalation exposure concentratio ns for the 0 to 7 age group were estimated from the pilot 30 phase analysis ambient air concentrations using location-specific ratios of ambient 31 concentration to exposure concentrations for the 0 to 4 age group from the NATA 32 national-scale assessment (see Section 4.1.2.2). This leads to uncertainty, particularly 33 because the 5 to 7 age group is school-aged a nd may have different activity patterns from 34 the 0 to 4 group. 35 36

• Soil concentrations of Pb were calculate d assuming the deposition (derived from the 37 recent emissions estimates [see Appendix L]) has been constant for the last 37 years 38 (facility operating time). This is likely not tr ue, and it is quite possible this results in an 39 underestimation of soil concentra tions. In addition, because of lack of site-specific data, 40 modeled soil concentrations are uncerta in. In a comparison to measured soil 41 concentrations at a similar facility, the modeled results were much lower (Section 42 4.2.3.1). Thus, a second soil scenar io was created with soil con centrations scaled up by a 43 factor of three. There is still uncertainty over whether this scaled-up set of Pb soil 44 concentrations is an appropriate upper-bound because the measurements were not taken 45 at the same location. 46Human Exposure Assessment and Blood Pb Estimation

ICF International 5-31 DRAFT – December 2006 1 Near Roadway Urban Case Study 2 3

• Compared to the other case studies, there were relatively little site-specific data available 4 upon which to base estimates of air, soil, a nd indoor dust concentratio ns. These estimates 5 are therefore very uncertain, and only indi cative of potential e xposure magnitudes. 6 7 5.3.2 Exposure, Intake, and Uptake Para meters and Individual Variability 8 9 The exposure, intake, and uptake factors used in this analysis, partic ularly those affecting 10 exposure pathways accounting for a large proportion of total Pb uptake, ma y strongly affect the 11 results of the blood Pb modeling for specific indi viduals. Section 6.4 presents the results of a 12 sensitivity analysis in which the variables evaluated were selected based on the relative 13 importance of the various pathways. The vari ables evaluated include d dietary and drinking 14 water GI absorption fractions, soil and dust ingest ion rates, and soil and GI absorption fractions. 15 As discussed in Chapter 6, variation of these va lues over credible ranges tended to have much 16 less impact on the blood Pb distri butions than differences in the blood Pb models that were used, 17 and between the blood Pb metrics (lifetim e average, concurrent, and peak). 18 19 The single variable with by far the largest impact on blood Pb distributions was, not 20 unexpectedly, the GSD estimate representing indi vidual responses to Pb exposures. 21 22 5.4 Blood Pb Distribution Estimate s for the Three Case Studies 23 24 5.4.1 Primary Pb Smelter 25 26 The results of the blood Pb modeling for the prim ary Pb smelter case study are summarized in 27 Exhibit 5-15. The median concurrent blood Pb estimates are 1.3 μ g/dL and 0.7 μ g/dL, 28 respectively, when estimated with the IEUBK an d Leggett models. The median lifetime average 29 blood Pb estimates from the two models are 2.7 and 1.6 μ g/dL, respectively. Comparing the two 30 sets of estimates, it can be seen that the re duction of exposure in the highest exposure blocks 31 under the attainment scenario has essentially no impact on the estimated blood Pb distributions 32 except for the very highes t percentiles. The 99.9 th percentile values for the four blood Pb metrics 33 are reduced between about eight percent and 18 pe rcent under the attainment scenario, compared 34 to the current conditions estimates. 35 36Human Exposure Assessment and Blood Pb Estimation

ICF International 5-32 DRAFT – December 2006 Exhibit 5-15. Projected Blood Pb Levels ( μ g/dL) for Primary Pb Smelter Case Study 1 Concurrent Blood Pb Lifetime Average Blood Pb

Statistic IEUBK Leggett IEUBK Leggett Average Proportionfrom PolicyRelevant Sources a Current Conditions Exposure Scenario 99.9 th 22 14 29 23 98% 99.5 th 12 6.7 17 11 95% 99 th 7.4 4.2 11 6.8 91% 95 th 3.7 2 5.3 3.1 67% 90 th 2.9 1.5 4.1 2.3 70% 75 th 2.0 1.0 2.7 1.6 59% Median 1.3 0.7 1.8 1.1 54%

25 th 0.9 0.5 1.2 0.7 49% 1 st 0.4 0.2 0.5 0.3 46% Attainment Exposure Scenario 99.9 th 18 13 25 21 96% 99.5 th 11 6.4 16 11 93% 99 th 7.9 4.3 11 7.1 93% 95 th 3.7 2 5.4 3.1 76% 90 th 2.9 1.5 4.1 2.3 68% 75 th 2.0 1.0 2.8 1.6 58% Median 1.4 0.7 1.9 1.1 52%

25 th 0.9 0.5 1.2 0.7 45% 1 st 0.4 0.2 0.5 0.3 44%

a Pathways impacted by policy-relevant sources incl ude inhalation, soil and indoor dust ingestion (and 2 exclude policy-relevant background sources , such as diet and drinking water). 3 4 The last column of Exhibit 5-15 summarizes th e estimated average proportions of Pb uptake 5 contributed by policy-relevant s ources at the various percentile blood Pb estimates. Under both 6 exposure scenarios, it can be seen that the proportion of Pb uptak e from policy-relevant sources 7 increases from around 40 percent for the lowest blood Pb percentiles, to around 98 percent for 8 the very highest percentiles. The contributions of individual exposure pathways to total blood 9 uptake in the three case studies ar e discussed in more detail in Section 6.2 and tabulated in 10 Appendix J. 11 12 5.4.2 Secondary Pb Smelter 13 14 Exhibit 5-16 summarizes the blood Pb modeling resu lts for the secondary Pb smelter case study. 15 The median blood Pb levels (and the higher percen tile estimates) are lowe r than those calculated 16 for the primary Pb smelter, for both sets of so il estimates. Median concurrent estimates are 0.9 17

μ g/dL and 0.5 μ g/dL, based on the IEUBK and Leggett models, respectively. The median 18 lifetime average blood Pb estimates are 1.1 and 0.7 μ g/dL. It can be seen from the data in 19 Exhibit 5-16 that the higher soil concentrati ons estimated using the hybr id approach result in 20 increases in both the blood pe rcentile estimates and in the estimated proportions of Pb uptake 21 from policy-relevant sources. 22 23Human Exposure Assessment and Blood Pb Estimation

ICF International 5-33 DRAFT – December 2006 Exhibit 5-16. Projected Blood Pb Levels ( μ g/dL) for 1 Secondary Pb Smelter Case Study 2 Concurrent Blood Pb Lifetime Average Blood Pb

Statistic IEUBK Leggett IEUBK Leggett Average Proportionfrom PolicyRelevant Sources a MPE Approach for Characterizing Soil Concentrations 99.9 th 3.7 2.1 4.7 3.0 25% 99.5 th 3.0 1.6 3.9 2.3 27% 99 th 2.7 1.4 3.5 1.9 24% 95 th 1.9 1.0 2.5 1.4 28% 90 th 1.7 0.9 2.1 1.2 23% 75 th 1.2 0.6 1.6 0.9 24% Median 0.9 0.5 1.1 0.7 25% 25 th 0.7 0.3 0.8 0.5 24% 1 st 0.3 0.2 0.4 0.2 24% Hybrid Approach (Model + Surrogate Data) for Characterizing Soil Concentrations 99.9 th 4.7 2.3 6.3 3.3 38% 99.5 th 3.7 1.9 4.9 2.7 39% 99 th 3.3 1.7 4.4 2.5 40% 95 th 2.4 1.2 3.2 1.8 39% 90 th 2.0 1.0 2.7 1.5 37% 75 th 1.5 0.8 2.0 1.1 39% Median 1.1 0.6 1.5 0.8 38% 25 th 0.8 0.4 1.1 0.6 38% 1 st 0.4 0.2 0.5 0.3 37%

a Pathways impacted by policy-relevant sources incl ude inhalation, soil and indoor dust ingestion (and 3 exclude policy-relevant background sources , such as diet and drinking water). 4 5 5.4.3 Near Roadway Urban Case Study 6 7 The near roadway urban case study blood Pb estimat es are shown in Exhibit 5-17. As discussed 8 in Section 5.2.1, the median blood Pb estimates generated by the IEUBK m odel fall in the same 9 range as those for the primary Pb smelter case study. However, the upper percentile estimates 10 are substantially lower than for the primary Pb smelter because of the lower maximum exposures 11 in the near roadway case. The estimated proport ion of Pb uptake from policy-relevant sources 12 stays relatively constant acro ss the blood Pb percentiles. 13 14 Exhibit 5-17. Projected Blood Pb Levels ( μ g/dL) for Near Roadway Urban Case Study 15 Concurrent Blood Pb Lifetime Average Blood Pb

Statistic IEUBK Leggett IEUBK Leggett Average Proportionfrom PolicyRelevant Sources a
99.9 th 6.5 4.2 9.1 6.9 62% 99.5 th 5.0 3.1 7.0 4.9 56% 99 th 4.4 2.7 6.2 4.1 58% 95 th 3.1 1.9 4.4 2.9 56% 90 th 2.6 1.6 3.6 2.4 58% 75 th 1.9 1.2 2.7 1.7 56% Median 1.4 0.8 1.9 1.2 63% 25 th 1.0 0.6 1.4 0.9 60% 1 st 0.4 0.3 0.6 0.4 56%

a Pathways impacted by policy-relevant sources incl ude inhalation, soil and indoor dust ingestion (and 16 exclude policy-relevant background sources , such as diet and drinking water). 17 18Estimation of Human Health Risk

ICF International 6-1 DRAFT – December 2006 

### 6. Estimation of Human Health Risk 1 2 This chapter presents the methods and result s for the characterizati on of health impacts 3 associated with Pb exposures for the three human health case studies. As discussed in Chapter 2, 4 IQ changes in exposed children have been selected as the endpoint for risk quantification in the 5 pilot phase assessment, on the basis of a large bod y of evidence establishing the occurrence of 6 the IQ effects at low exposure and blood Pb leve ls. In addition, the estimation of these impacts 7 is facilitated by the availability of recent high-quality studies of the relationship between 8 children’s blood Pb and IQ loss. It is noted, how ever, that low-level Pb exposures have been 9 found to be associated with a wide range of a dverse effects in children and adults beyond those 10 quantified here (USEPA 2006b). 11 12 6.1 Methods 13 14 6.1.1 Overview of Approach 15 16 The estimation of IQ loss is performed using th e distribution of blood Pb estimates for exposed 17 populations derived via the probabilistic mode l described in Section 5.1.3.4. This model 18 estimates a population-weighted di stribution of estimated individua l blood Pb concentrations by 19 first taking 10,000 population-weighed random sample s from the GM blood Pb concentrations 20 estimated for each block or block group in the ca se studies. Each of these 10,000 GM blood Pb 21 levels is then combined with an adjustment factor (sampled from a lognormal distribution 22 reflecting inter-individual variab ility in behavior a nd biokinetics related to Pb exposure) to 23 generate a blood Pb level for a simulated child. This procedure ultimately generates blood Pb 24 levels for 10,000 simulated children for each case study location. 25 26 To calculate the IQ loss impacts of the case st udy Pb exposures, exposure-response functions for 27 IQ loss differentiated for the two blood metrics considered in the pilot phase (i.e., concurrent and 28 lifetime average) are used to convert each of the blood Pb levels for the 10,000 simulated 29 children into resulting IQ loss. As described below, the blood Pb-IQ relationships are derived 30 from the results of a recent analysis of pooled epidemiological data by Lanphear et al. (2005). 31 Individual IQ loss estimates are calculated for each of the lifetime average and concurrent blood 32 Pb estimates using two forms of the blood Pb-IQ model from Lanphear et al. (2005) matched to 33 these blood Pb metrics. The output s of the risk assessment are the distributions of estimated IQ 34 loss across the exposed populations, char acterized as percentile values. 35 36 6.1.2 Blood Pb-IQ Relationships 37 38 As noted above, the blood Pb-IQ models were deri ved from the results of a large epidemiological 39 analysis by Lanphear et al. (2005) , in which the relationships be tween blood Pb concentrations 40 and IQ test results in seven populations were analyzed. The 1,333 subjects in the study 41 population included subjects of four studies in the United States, and one each from Mexico, 42 Australia, and Kosovo. A number of statistica l techniques were used to characterize the 43 relationships between several blood Pb metrics and test results for individual cohorts and for the 44 pooled study population. For the pilot phase assessm ent, model forms were selected that related 45 IQ change to two blood Pb metrics, lifetime averag e and concurrent. These metric were meant to 46Estimation of Human Health Risk

ICF International 6-2 DRAFT – December 2006 be analogous to the metrics defined in the Lanph ear et al. (2005) study, where lifetime average 1 was defined as the average of all blood Pb m easurements taken from a given individual, and 2 concurrent as the blood Pb measurement taken clos est to the time of IQ te sting. For purposes of 3 the pilot phase of the assessment, lifetime av erage blood Pb was the estimated time-weighted 4 average blood Pb concentration from age 6 mont hs to 84 months, calculated as described in 5 Section 5.1.3, using either the IEUB K or Leggett model. Concurrent blood Pb was estimated, for 6 purposes of the pilot phase assessment, as th e average modeled blood Pb concentration from 7 ages 73 to 84 months, inclusive (s ee Section 5.1.3), the rational be ing a desire to have a blood Pb 8 metric matching as closely as possible to the av erage age at IQ testing (6.9 years) in the 9 Lanphear et al. study. 10 11 The log-linear model for IQ loss derived from the Lanphear et al. pooled analysis was adapted 12 for use in the pilot phase assessment. The coe fficients in the model ar e different for lifetime 13 average and concurrent bl ood Pb, as shown below: 14 15 Concurrent: IQ = 99.3 – 2.70 × ln (Concurrent Blood Pb) 16 17 Lifetime Average: IQ = 100.9 – 3.04 × ln (Lifetime Average Blood Pb) 18 19 The form of these models is consistent with the observed greater slope of the blood Pb-IQ 20 relationship seen at low blood Pb levels. Lanphear et al. (2005) also investigated piecewise 21 linear models to capture this relationship and f ound that the slope of the blood Pb-IQ relationship 22 at blood Pb concentrations less than 7.5 μg/dL wa s significantly greater than the linear slope 23 estimated above that level. 24 25 Applying the models shown above to the estima tion of IQ changes i nvolves a degree of 26 uncertainty. First, because of th e log linear form of the models, they predict very large changes 27 in IQ for small changes in blood Pb at low concen trations. Thus, it is important to stay within 28 the range of the exposure data used in fitti ng the functions when predicting IQ loss for 29 hypothetical exposed populations. 30 31 In addition, the constant term s in the models represent central tendency estimates of 32 “background” (low exposure) IQ distributions for the study populat ions. These values may not 33 be applicable to the population for which IQ is being estimated. Thus, applying the models “as 34 is” to the case study population would involve making possibly unj ustifiable assumptions about 35 baseline IQ levels. 36 37 For these reasons, the Lanphear et al. (2005) mode ls were adjusted in the following manner for 38 application in the pilot phase assessment. For each model, a blood Pb “cutpoint” was derived 39 that corresponded to the approximate 5 th percentile blood Pb levels seen in the Lanphear et al. 40 pooled study population. Adverse effects were no t estimated for predicted individual blood Pb 41 levels below the cutpoints. The cutpoints vary for the blood Pb metrics; for concurrent blood Pb, 42 the cutpoint is 2.4 μg/dL, while for lifetim e average blood Pb, the cutpoint is 6.1 μg/dL 43 (Lanphear et al. 2005, Table 4). 44 45Estimation of Human Health Risk

ICF International 6-3 DRAFT – December 2006 The use of the blood Pb cutpoints in IQ loss es timation is not intended to imply that these 1 concentrations are thresholds below which adverse effects do not occur. Rather, they are 2 intended to reflect practical limits on IQ predic tion based on the low numbers of Lanphear et al. 3 (2005) study subjects in these c oncentration ranges, and the corre sponding large un certainties in 4 the estimates of the blood Pb-IQ re lationships below the cutpoints. 5 6 Changes in IQ associated with predicted indi vidual blood Pb levels a bove the cutpoints were 7 estimated relative to the corresponding cutpoint as: 8 9 Concurrent: IQ loss (relative to cutpoint) = - 2.70 × ln (concurrent blood Pb / 2.4) 10 11 Lifetime Average: IQ loss (r elative to cutpoint) = - 3.04 × ln (lifetime average blood Pb / 6.1) 12 13 The outputs of the IQ modeling were thus estimates of changes in IQ relative to an individual 14 exposed at the cutpoint level, rather than absolute IQ loss estimates. 15 16 6.2 Risk Assessment Results 17 18 As noted in Section 6.1, the primary outputs of the pilot phase risk as sessment are population 19 distributions of estimated blood Pb concentratio ns and IQ loss, along with evaluations of the 20 proportional contribution of indivi dual exposure pathways to tota l lifetime Pb uptake. This 21 section presents the results for each of the thr ee case studies. Detailed results of the risk 22 assessment can be found in Appendix J. 23 24 Risk results presented in this section and in Appendix J reflect both IEUBK- and Leggett-based 25 blood Pb modeling. As noted earlier in Ch apter 5 (see Sections 5.1.2.2 and 5.1.3.3) and as 26 evident in the risk results exhibits presented in this section, modeling completed for the pilot 27 phase has consistently shown a trend of Leggett-derived bl ood Pb levels being lower than 28 IEUBK-derived levels for the same exposure scen ario. This trend is the opposite of findings 29 presented in Pounds and Leggett (1998). This ap parent discrepancy elev ates the importance of 30 model evaluation focused on IEUBK and Leggett (as applied in the pilot phase) which is 31 currently ongoing. It also emphasizes the fact that results of the pilot phase should be considered 32 provisional and subject to update in the full-scale assessment. No te that the results of model 33 evaluation, once completed, will be conveyed by separate cover. 34 35 6.2.1 Primary Pb Smelter Case Study 36 37 Exhibit 6-1 summarizes the blood Pb and IQ loss di stributions for the primary Pb smelter current 38 conditions scenario, calculated using the IEUB K and Leggett blood Pb models, with IQ loss 39 values based on lifetime average a nd concurrent values. All othe r input assumptions and values 40 used in the assessment are described in Chapter 5. The first column of this exhibit labels the 41 percentile values of the outputs from the probab ilistic model, and the second column shows the 42 number of children who would have blood Pb and IQ loss values above th e percentile value, 43 given the estimated population at risk in the study area. 44 45 46Estimation of Human Health Risk

ICF International 6-4 DRAFT – December 2006 Exhibit 6-1. Projected IQ Losses from the 1 Primary Pb Smelter Current Conditions Scenario 2 IEUBK Model Leggett Model Concurrent

Blood Pb Lifetime Average Blood Pb Concurrent Blood Pb Lifetime Average Blood Pb

Percentile Population Above Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model)

99.9 th 4 21.9 6.0 28.6 4.7 13.9 4.7 22.9 4.0

99.5 th 19 12.4 4.4 16.9 3.1 6.7 2.8 11.1 1.8

99 th 39 7.4 3.0 10.6 1.7 4.2 1.5 6.8 <1

95 th 194 3.7 1.2 5.3 -- 2.0 -- 3.1 --

90 th 388 2.9 <1 4.1 -- 1.5 -- 2.3 --

75 th 970 2.0 -- 2.7 -- 1.0 -- 1.6 -- Median 1,940 1.3 -- 1.8 -- 0.7 -- 1.1 --

25 th 2,910 0.9 -- 1.2 -- 0.5 -- 0.7 --

1 st 3,841 0.4 -- 0.5 -- 0.2 -- 0.3 --
3 It can be seen that the large majority of expos ed children are predicted to have blood Pb levels 4 below both the concurrent and lifetime average cu tpoint values (i.e., no IQ loss associated with 5 Pb exposure is estimated). No IQ loss is predicted for the median or 75 th percentile child by 6 either the IEUBK or Leggett models, based on eith er the lifetime average or concurrent blood Pb 7 values. IQ losses are predicted for the larg est proportion of child ren by the IEUBK model 8 (because it predicted higher blood Pb concentrati on than Leggett) when the concurrent blood Pb 9 metric is used as the input to the blood Pb-IQ model. Estimated IQ loss values range from less 10 than1 for the 90 th percentile child to 6.0 for the 99.9 th percentile. Under th e three other modeling 11 options, IQ losses are predicted starting between the 95 th and 99 th percentiles and range from less 12 than 1 to 4.7. 13 14 Predicted blood Pb and IQ loss distributions for th e primary Pb smelter attainment scenario are 15 essentially identical (Exhibit 6-2) to those for th e current conditions scenario, except for the very 16 highest percentiles. This is as expected becau se the exposure concentrations change between the 17 two air quality scenarios only fo r the seven highest exposure bl ocks (see Section 5.1.4), where 18 only about 0.5 percent of the expos ed children reside. Thus, the es timated IQ losses are slightly 19 smaller for the 99.5 th and 99.9 th percentile children under the attainment scenario, while the 20 remaining estimates are the same, within simulati on error, to estimates derived for the current 21 conditions scenario. 22 23Estimation of Human Health Risk

ICF International 6-5 DRAFT – December 2006 Exhibit 6-2. Projected IQ Losses from the Primary Pb Smelter Attainment Scenario 1 IEUBK Model Leggett Model

Concurrent

Blood Pb Lifetime Average Blood Pb Concurrent Blood Pb Lifetime Average Blood Pb Percentile Population Above Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model)
99.9 th 4 18.4 5.5 24.9 4.3 12.7 4.5 20.7 3.7

99.5 th 19 11.2 4.2 15.5 2.8 6.4 2.6 10.6 1.7

99 th 39 7.9 3.2 11.3 1.9 4.3 1.6 7.1 0.5

95 th 194 3.7 1.2 5.4 -- 2.0 -- 3.1 --

90 th 388 2.9 0.5 4.1 -- 1.5 -- 2.3 --

75 th 970 2.0 -- 2.8 -- 1.0 -- 1.6 -- Median 1,940 1.4 -- 1.9 -- 0.7 -- 1.1 --

25 th 2,910 0.9 -- 1.2 -- 0.5 -- 0.7 --

1 st 3,841 0.4 -- 0.5 -- 0.2 -- 0.3 --
2 In addition to calculating blood Pb and IQ loss di stributions, the probabilistic model also records 3 the contribution of each exposure pathway to tota l lifetime average Pb upt ake at each percentile 4 of the blood distributions. Pb uptak e is defined as the total amount of Pb that is absorbed into the 5 blood compartments of the biokinetic models. The probabilistic model calculates pathway6 specific Pb uptakes for each census block and block group independently of the biokinetic 7 models, using the same uptake ca lculations equations as used in the IEUBK and Leggett models 8 (Section 5.1.3.3). When a census block or block group is chosen in the simulation model, the 9 uptake values are saved. When percentiles of the blood Pb/IQ loss distri butions are calculated, 10 the associated pathway contributions for the bl ock group are retrieved. These values give an 11 approximate picture of the importance of specifi c exposure pathways at specific percentile 12 values; they do not, however, reflect individual variability in Pb uptake within the blocks or 13 block groups. 14 15 The average proportion of total Pb uptake cont ributed by policy-releva nt sources and policy16 relevant background pathways diffe rs widely across the blood Pb/I Q loss percentiles (Exhibit 6- 17 3). At the lowest percentiles, policy-relevant background (in this case study, they are diet and 18 drinking water) contributes the majority (about 54 percent) of total Pb uptake. The proportional 19 contribution from policy-relevant background then decreases rapidly unt il it accounts for only 20 about two percent of the to tal Pb uptake at the 99.9 th percentile of the simulation blood Pb. 21 22 Among the exposure pathways associated with policy-relevant source s, inhalation exposure 23 accounts for a very small proportion (less than fi ve percent) of Pb uptake across all blood Pb 24 percentiles. Ingestion of soil pr ovides about 30 percent of total uptake for the low percentiles, 25 increasing to just more than half of the total (59 percent) at the 90 th percentile, then decreasing 26 for the highest blood Pb percentiles . As might be expected, the bl ocks and block groups with the 27Estimation of Human Health Risk

ICF International 6-6 DRAFT – December 2006 highest percentile (99 th and above) individual blo od Pb and IQ estimates appear to have very 1 high house dust Pb levels, which accounts for the bulk of total Pb uptake. 2 3 The proportions graphed in Exhibit 6-3 represent the average of th e contributions from all eight 4 exposure scenario/blood Pb mode l/blood Pb metric combinations for the primary Pb smelter case 5 study. 1 The estimated contributions from the indi vidual pathways for specific percentile blood 6 Pb values differ only slightly (generally, a few percent) across the vari ous modeling approaches. 7 8 Exhibit 6-3. Average Exposure Pathw ay Contributions to Total Pb Uptake, 9 Primary Pb Smelter Case Study a 10 11

0% 25% 50% 75% 100%

1st 25th Median 75th 90th 95th 99th 99.5th 99.9th
Blood Pb/IQ Loss PercentilePathway Contribution

Inhalation

Soi Ingestion

House Dust Ingestion

Policy-Relevant Background Policy-Relevant Sources

12

a ” Policy-Relevant Sources” exposures include air, soil, and house dust. Diet and water exposures constitute 13 “Policy-Relevant Background” in this case study. 14 15 6.2.2 Secondary Pb Smelter Case Study 16 17 Exhibit 6-4 summarizes the estimated blood Pb a nd IQ loss distributions for the secondary Pb 18 smelter case study when the modeled (MPE) estimates of soil concentrations are used as inputs. 19 In this scenario, as well as when the hybrid soil concentration model is used, the AGG (Air + 20 Soil) model (as described further in Secti on 4.1) was used to estimate house dust Pb 21 concentrations. 22 23 As expected, due to the lower exposure concentr ations, the estimated bl ood Pb levels and IQ 24 losses are smaller than those estimated for the primary Pb smelter. Based on the concurrent 25 blood Pb distributions obtained from the IEUBK m odel, IQ losses are predic ted only for children 26

1 The eight estimates come from two exposure scenarios (c urrent conditions and attainment), two blood Pb models (IEUBK and Leggett), and two blood Pb metr ics (concurrent and lifetime average).Estimation of Human Health Risk

ICF International 6-7 DRAFT – December 2006 above the 95 th percentile of the individual blood Pb di stribution. Based on the estimated number 1 of resident children near the secondary Pb sme lter, approximately 84 children would be at the 2 95 th percentile blood Pb or above. 3 4 Exhibit 6-4. Projected IQ Losses from the Secondary Pb Smelter – 5 Modeled (MPE) Soil Concentration Estimates 6 IEUBK Model Leggett Model

Concurrent

Blood Pb Lifetime Average Blood Pb Concurrent Blood Pb Lifetime Average Blood Pb Percentile Population Above Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model)

99.9 th 2 3.7 1.8 4.7 -- 2.1 -- 3.0 -- 99.5 th 8 3.0 1.1 3.9 -- 1.6 -- 2.3 -- 99 th 17 2.7 <1 3.5 -- 1.4 -- 1.9 -- 95 th 84 1.9 <1 2.5 -- 1.0 -- 1.4 -- 90 th 167 1.7 -- 2.1 -- 0.9 -- 1.2 -- 75 th 418 1.2 -- 1.6 -- 0.6 -- 0.9 -- Median 836 0.9 -- 1.1 -- 0.5 -- 0.7 -- 25 th 1,254 0.7 -- 0.8 -- 0.3 -- 0.5 -- 1 st 1,655 0.3 -- 0.4 -- 0.2 -- 0.2 -- 7 For this case study, predicted IQ change s range from less than 1 point for the 99 th percentile to 8 1.8 points for the 99.9 th percentile child. No IQ losses are predicted using the lifetime blood Pb 9 distribution from the IEUBK model or using eith er the lifetime average or concurrent metric 10 generated by the Leggett model. All of the 99.9 th percentile blood Pb estimates are less than the 11 lifetime average (6.1 μg/dL) or conc urrent (2.4 μg/dL) cutpoints. 12 13 Exhibit 6-5 shows the blood Pb and IQ distribu tions for the secondary Pb smelter case study 14 when the hybrid soil Pb estimation model is used. As discussed in Section 5.1.4, hybrid soil 15 concentration estimates are three times the MPE modeled values. Because of the higher soil 16 concentrations, it can be seen that the estimated Pb and IQ loss percentiles are slightly higher 17 than when the modeled (MPE) soil concentrations are used. IQ losses are predicted for children 18 above about the 90 th percentile (correspondi ng to 167 children); based on the IEUBK concurrent 19 blood Pb estimates, IQ losses ra nge from less than 1 at the 95 th percentile to 2.3 at the 99.9 th 20 percentile. A very small IQ loss (0.1 point) is also predicted by th e IEUBK lifetime average 21 blood Pb metric. As was the case for the modeled soil estimates, no IQ losses are predicted 22 when the Leggett model is used to predict e ither lifetime average or concurrent blood Pb 23 distributions. 24 25 26Estimation of Human Health Risk

ICF International 6-8 DRAFT – December 2006 Exhibit 6-5. Projected IQ Losses from the Secondary Pb Smelter Case Study – 1 Hybrid Soil Concentration Estimates 2 IEUBK Model Leggett Model

Concurrent

Blood Pb Lifetime Average Blood Pb Concurrent Blood Pb Lifetime Average Blood Pb Percentile Population Above Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model)

99.9 th 2 4.7 2.3 6.3 <1 2.3 -- 3.3 -- 99.5 th 8 3.7 1.4 4.9 -- 1.9 -- 2.7 -- 99 th 17 3.3 1.0 4.4 -- 1.7 -- 2.5 -- 95 th 84 2.4 <1 3.2 -- 1.2 -- 1.8 -- 90 th 167 2.0 -- 2.7 -- 1.0 -- 1.5 -- 75 th 418 1.5 -- 2.0 -- 0.8 -- 1.1 -- Median 836 1.1 -- 1.5 -- 0.6 -- 0.8 -- 25 th 1,254 0.8 -- 1.1 -- 0.4 -- 0.6 -- 1 st 1,655 0.4 -- 0.5 -- 0.2 -- 0.3 -- 3 The proportional contribution of th e individual exposure pathways to total Pb uptake in the 4 secondary Pb smelter case study is very different from that seen for the primary Pb smelter case 5 study. Unlike the primary Pb smelter case study, the average pathway contributions remain 6 approximately constant across the blood Pb/IQ loss percentiles, all vary ing less than about two 7 percent around their mean values. This is due primarily to the relatively flat exposure 8 distributions; most census blocks have very lo w ambient air and house dust Pb concentrations, 9 and policy-relevant background exposure pathways (diet, water, and natu rally occurring soil Pb) 10 contribute a large percentage of total Pb uptake across all the blood Pb percen tiles (Exhibit 6-6). 11 12 As expected, the relative path way contributions differ when the two different sets of soil 13 concentration estimates are used as inputs to the blood Pb/IQ loss models. The soil pathway 14 contribution, averaged across the four combina tions of exposure scenar io and blood Pb model, 15 increases from 7.8 percent to about 19 per cent when hybrid, rather than modeled, soil 16 concentration estimates are used. The contri butions from the other pathways decrease in 17 compensation for the higher soil Pb levels. 18 19Estimation of Human Health Risk

ICF International 6-9 DRAFT – December 2006 Exhibit 6-6. Pathway Contributions to Total Pb Uptake, 1 Secondary Pb Smelter Case Study a 2

0% 10% 20% 30% 40% 50% 60% 70%

1st 25th Median 75th 90th 95th 99th 99.5th 99.9th

Blood Pb/IQ Loss PercentilePathway Contribution

Diet

Inhalation

Soil Ingestion (M PE Estimate)

House Dust Ingestion

Policy-Relevant Background

Policy-Relevant Sources

Soil Ingestion (Hybrid Estmate)

3

a “Policy-Relevant Background” includes diet and water intake, plus a contribution from naturally occurring Pb in 4 soil of 15 μ g/g). 5 6 6.2.3 Near Roadway Urban Case Study 7 8 Exhibit 6-7 summarizes the results of the pilot ph ase risk assessment for the near roadway urban 9 case study. IQ losses are predicted to occur for children above the 75 th percentile IEUBK 10 concurrent blood Pb estimate, ranging from less than 1 point at the 90 th percentile to 2.7 points at 11 the 99.9 th percentile. Based on the estimated 319 childre n living in the census blocks included in 12 this case study, 32 children are predicte d to be exposed at or above the 90 th percentile blood Pb 13 level, and approximately three children above the 99 th percentile blood/IQ level. IQ losses are 14 predicted for smaller proportions of children when the IEUBK model is used to estimate lifetime 15 average blood Pb values and for both blood Pb metr ics when estimated with the Leggett model. 16 17Estimation of Human Health Risk

ICF International 6-10 DRAFT – December 2006 Exhibit 6-7. Projected IQ Losses from the Near Roadway Urban Case Study 1 IEUBK Model Leggett Model

Concurrent

Blood Pb Lifetime Average Blood Pb Concurrent Blood Pb Lifetime Average Blood Pb Percentile Population Above Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model) Total Blood

Pb (μg/dL) IQ Loss(LogLinear Model)
99.9 th 0 6.5 2.7 9.1 1.2 4.2 1.5 6.9 <1

99.5 th 2 5.0 2.0 7.0 <1 3.2 <1 4.9 --

99 th 3 4.4 1.6 6.2 -- 2.7 <1 4.1 --

95 th 16 3.1 0.7 4.4 -- 1.9 -- 2.9 --

90 th 32 2.6 <1 3.6 -- 1.6 -- 2.4 --

75 th 80 1.9 -- 2.7 -- 1.2 -- 1.7 --
Median 159 1.4 -- 1.9 -- 0.8 -- 1.2 --

25 th 239 1.0 -- 1.4 -- 0.6 -- 0.9 --

1 st 316 0.4 -- 0.6 -- 0.3 -- 0.4 --
2 As was the case for the secondary Pb smelte r case study, the contribution of the individual 3 exposure pathways is relatively constant across blood Pb/IQ loss percentiles (Exhibit 6-8). In 4 contrast to the secondary Pb smelter, however, po licy-relevant sour ces contribute th e majority of 5 total Pb uptake (56 to 63 percent) across the bloo d Pb percentile distribution. Policy-relevant 6 background exposures (diet and dr inking water, for this case st udy) contribute between 37 and 7 44 percent to total Pb uptake. 8 9 10Estimation of Human Health Risk

ICF International 6-11 DRAFT – December 2006 Exhibit 6-8. Average Exposure Pathw ay Contributions to Total Pb Uptake, 1 Near Roadway Urban Case Study a 2

0% 10% 20% 30% 40% 50% 60% 70%

1st 25th Median 75th 90th 95th 99th 99.5th 99.9th Blood Pb/IQ Loss P ercentilePathway Contribution

Inhalation

Soil Ingestion

House Dust Ingestion

Policy-Relevant Background Policy-Relevant Sources

3

a “Policy-Relevant Sources” exposures in clude air, soil, and house dust. Diet and water are the “Policy-Relevant 4 Background” exposures in this case study. 5 6 6.3 Uncertainty and Variability in the IQ Loss Estimates 7 8 6.3.1 Sources of Uncertainty 9 10 There are two sets of factors that contribute the overall uncertainty in the IQ loss estimates. The 11 first, discussed in Section 5.3, are those factors which affect the blood Pb estimates from which 12 the IQ loss distributions are calc ulated. These include the choice of the model used to estimate 13 blood Pb levels, the GSD describing the individual variability in response to Pb exposures, 14 uncertainties in exposure concentration estimates, and uncertainties in the exposure, intake, and 15 uptake factors used to estimates of Pb uptake (absorbed dose). 16 17 The second set of factors that needs to be consider ed includes those th at are associated 18 specifically with the estimation of IQ loss from the individual blood Pb estimates. In the 19 sensitivity analysis described in Section 6.4, the impact of using two different models (log-linear 20 and piecewise linear) derived from the Lanphear et al. (2005) epidemiological study are 21 examined. Blood Pb-IQ models have been de veloped from a number of other high-quality 22 studies (Canfield et al. 2003, fo r example), and such models could also be applied to the 23 individual blood Pb levels calculated in the probabilistic model. 24 25 The nature of the blood Pb metric used to estimate IQ loss also st rongly affects the results of the 26 analysis. Both the slope parameters and the cutp oints are very different in the concurrent and 27 lifetime average blood Pb-IQ model equations summa rized above. In the sensitivity analyses, 28Estimation of Human Health Risk

ICF International 6-12 DRAFT – December 2006 described in the following section, the effect of us ing these two metrics, as well as models for the 1 “peak” blood Pb, on the modeled IQ loss distributions was examined. 2 3 The decision to use the blood Pb cutpoints also introduces uncertainty into the assessment. 4 Using different cutpoints (i.e., making different judgments about the predictive power of the 5 Lanphear et al. (2005) blood Pb-IQ models at lo w blood Pb concentrations) changes the both the 6 numbers and proportions of children for whom adve rse effects (IQ loss) ar e predicted to occur, 7 and the magnitude of the predicted IQ changes. The impact of different assumptions about the 8 blood Pb cutpoints is also investigated in the sensitivity analysis described below. 9 10 6.3.2 Sensitivity Analysis Methodology 11 12 For the pilot phase risk assessment, a series of “one-at-a-time” analyses has been conducted to 13 evaluate the impacts of indi vidual modeling methods and parameter inputs on estimated blood 14 Pb and IQ loss distributions. The general approa ch employed was to evaluate changes in risk 15 metrics associated with varying si ngle models or input parameter va lues away from those used in 16 a “baseline” analysis. Th e set of models and input parameter va lues associated with the baseline 17 analysis are summarized in Exhibi t 6-9. This exhibit also indicat es the analytical steps on which 18 a sensitivity analysis was performed and the al ternate model or parameter values that were 19 examined in the analysis. 20 21 The baseline risk analysis focused on the curren t conditions scenario for the primary Pb smelter 22 case study. In this analysis, i nhalation and soil exposure concentrations were estimated as 23 described in Section 4.1. House dust Pb concen trations were derived based on the H6 site24 specific regression model for the census block and block groups n ear the facility, and with the 25 AGG (air + soil) model for the remainder of th e exposed population. The baseline individual 26 blood Pb concentrations were derived using the IEUBK model estimates for each census block or 27 block group, assuming individual blood Pb concen trations were lognormally distributed with a 28 GSD of 1.6. Individual IQ loss was estimated ba sed on concurrent blood Pb concentration using 29 the Lanphear et al. (2005) log-lin ear model with IQ loss not pred icted for individuals with blood 30 Pb concentrations below the cutpoint value of 2.4 μg/dL. Note that while we believe the 31 baseline models and parameter values are believed to provide a credible estimate of blood Pb and 32 IQ changes, it is not suggested th at the baseline model is uniquely the “best,” or that it provides 33 demonstrably “central tendency” risk estimates. 34 35 The bottom row of Exhibit 6-9 summarizes the models and parameters that were varied 36 (individually) during the sensitivity analysis. Changes in soil a nd air exposure concentrations 37 were not changed in the course of the sensitivity analysis. In c ontrast, the impact of different 38 models for estimating house dust concentrations (AGG [air only] and AG G [air + soil]) was 39 evaluated. GM blood Pb concentrations for the blocks and block groups were calculated based 40 on the Leggett biokinetic model, as well as an “empirical” blood Pb model developed by 41 Lanphear et al. (1998) (see Appe ndix K), and the impacts of using the different models on 42 predicted changes in IQ was also evaluated. Variations in para meters related to Pb exposure, 43 intake, and uptake estimation were also evaluate d. In one set of sens itivity runs, the GI 44 absorption fractions for dietary and drinking water ingestion were varied above and below their 45 estimated central tendency values used in th e baseline assessment. For the second set of 46Estimation of Human Health Risk

ICF International 6-13 DRAFT – December 2006 sensitivity runs, alternative estimates of daily soil and dust ingestion an d soil and dust absorption 1 fractions for soil and dust were evaluated for their impacts on estimated blood Pb and IQ loss 2 distributions. These values were derived by Von Lindern et al. (2003) from a systematic 3 statistical analysis of soil, dust, and blood Pb distributions at a large mining/smelting 4 “Superfund” site. In addition, the probabilistic model was run with two alternative values of the 5 blood Pb GSD. 6 7Estimation of Human Health RiskICF International 6-14 DRAFT – December 2006Exhibit 6-9. Baseline and Sensitivity Analysis Blood Pband Risk Estimation Models and AssumptionsModelingElementCaseStudy/ScenarioAir, SoilExposureConc.HouseDust PbConc.ModelBloodEstimationModelExposure/Intake/UptakeFactorsIndividualBlood PbVariability(GSD)Blood PbIQ ModelBlood PbMetricBlood PbCutpointBaselineEstimatePrimary PbSmelter/CurrentConditionsModeled(ISCPRIME),regression(soil)Regression(H6 andAGG [Air +Soil])IEUBK(Batchmode ageprofile)Baseline(Exhibit 5-11)1.6 Lanphearet al.(2005), loglinearConcurrent(age 73-84months)2.4(concurrent),6.1 (lifetimeaverage)SensitivityAnalysisUnchanged Unchanged ExclusivelyAGG (Air +Soil), AGG(Air)Leggett,Lanphearet al.(1998)Low, highdietaryabsorption;Von Lindern etal. (2003) soil,dust exposurefactorestimates1.3, 1.9;lower andupperboundvalues onGSD forchildrenaroundexposuresourcesLanphearet al.(2005),piece-wiselinear,upper andlowerconfidencelimit slopeparameterestimatesLifetime, (6- 84months),peak(highestannualaverage)1.2(concurrent),3.1 (lifetimeaverage)Estimation of Human Health Risk

ICF International 6-15 DRAFT – December 2006 Alternative approaches to IQ loss estimation were also investigated. The results obtained from 1 the piecewise linear form of the Lanphear et al . (2005) model were compared to the baseline 2 (log-linear) modeling approach. Al ternative estimates of IQ loss were also derived using the 3 upper and lower confidence limit slope parameters from the Lanphear et al. (2005) model. 4 5 As noted above, the distribution of IQ loss wa s also estimated based on lifetime average and 6 peak individual blood Pb values (instead of the base line concurrent metric). Finally, the effect of 7 reducing the Pb cutpoint value fo r estimating IQ loss was also test ed. Detailed descriptions of 8 the changes in models and parameter values are provided in Section 6.4. 9 10 6.4 Sensitivity Analysis Results 11 12 As discussed in 6.3, sensitivity analyses were conducted by varying one model or set of 13 parameter values at a time from the “baseline” assumptions and examining the impact of the 14 changes on blood Pb and IQ loss distributions. This section summarizes the results of these 15 sensitivity analyses. In all of the following e xhibits, the sensitivity analysis models differ from 16 the baseline only with regard to the single model or input paramete r value that is being varied. 17 18 6.4.1 Alternative House Dust Pb Concentration Models 19 20 As shown in Section 6.2.1, house dust Pb exposures can contribute a large proportion of total Pb 21 uptake, particularly for the highly exposed bloc ks and block groups in the primary Pb smelter 22 case study. Thus, the impacts of employing alte rnative models for estimating house dust Pb 23 concentrations were examined as part of the sensitivity analysis. 24 25 In the baseline assessment, house dust Pb concen trations were estimated using two different 26 models. The “H6” site-specific regression model was used for blocks and block groups within 27 1.5 kilometers of the facility, while the AGG (Air + Soil) model was used to estimate house dust 28 Pb levels for areas more distant from the smelte r (see Section 4.1.4). In th e sensitivity analysis, 29 two variants of the AGG mode l were employed (Exhibit 6-10), one in which the Air + Soil 30 variant was applied to al l of the blocks and block groups, and the other where the Air Only form 31 of the model was used. In all cases, th e IEUBK model was used to estimate blood Pb 32 distributions. The resultant blood Pb distributions are only mildly affected by the choice of the 33 house dust Pb model – the median values are near ly the same (between 1.4 and 1.5 μg/dL). At 34 the higher percentile values, the model estimates diverge more significantly because the H6 35 model predicts much higher house dust Pb concentr ations for the blocks and block groups near 36 the facility, relative to the other models. 37 38Estimation of Human Health Risk

ICF International 6-16 DRAFT – December 2006 Exhibit 6-10. Effects of Alternative House Dust Pb Concentration Models 1 on Estimated Blood Pb Distributions a,b 2

House Dust Pb Model Change versus Baseline

Percentile Baseline (Composite Model),
μ g/dL AGG Model (Air Only),

μ g/dL c AGG Model (Air + Soil),
μ g/dL d AGG Model (Air Only) AGG Model (Air + Soil)

99.9th 17.0 13.6 11.2 -20% -34% 99.5th 10.5 7.8 7.2 -25% -32% 99th 7.3 6.1 6.2 -15% -15% 95th 3.6 3.8 3.9 5% 7% 90th 2.8 3.1 3.1 8% 7% 75th 1.9 2.1 2.1 9% 9% Median 1.3 1.5 1.4 12% 10% 25th 0.9 1.0 1.0 13% 9% 1st 0.4 0.4 0.4 15% 9%

a See Chapter 4, Section 4.1, for further discussion of the AGG regression model. 3

b Concurrent blood Pb estimates from the IEUBK model and the log-linear form of the Lanphear et al. (2005) blood 4 Pb-IQ model are used for all estimates. 5

c AGG Model (Air Only) House Dust (μg/g) = 60 + 844 * Ambient Air Pb (μg/m 3 ) 6

d AGG Model (Air +Soil)House Dust (μg/g) = 31.1 + 638 * Ambient Air Pb (μg/m 3 ) + 0.364 * Soil Pb (μg/g) 7 8 This pattern is reflected in the estimated IQ lo ss distributions shown in Exhibit 6-11. Again, the 9 decreasing slope of the log-linear model reduces the differences in estimated IQ loss at the 10 higher percentiles. 11 12 Exhibit 6-11. Effects of Alternative House Dust Pb Models 13 on Estimated IQ Distributions a 14
House Dust Pb Model Change versus Baseline

Percentile IQ Change Based on Composite Model House Dust Estimates IQ Change Based on AGG Model

(Air Only) IQ Change Based on AGG Model (Air + Soil) AGG Model (Air Only) AGG Model (Air + Soil)

99.9th -5.3 -4.7 -4.1 -11% -22% 99.5th -4.0 -3.2 -3.0 -20% -26% 99th -3.0 -2.5 -2.6 -15% -14% 95th -1.1 -1.2 -1.3 12% 17% 90th -0.5 -0.7 -0.6 46% 43% 75th -- -- -- -- -- Median -- -- -- -- -- 25th -- -- -- -- -- 1st -- -- -- -- --

a Concurrent blood Pb estimates from the IEUBK model and the log-linear form of the Lanphear et al. (2005) 15 blood Pb-IQ model are used for all estimates. 16 17Estimation of Human Health Risk

ICF International 6-17 DRAFT – December 2006 6.4.2 Alternative Blood Pb Models 1 2 To evaluate the sensitivity of IQ loss estimates to the selectio n of a blood Pb model, the IEUBK 3 model was selected as the baseline and used to generate the concu rrent baseline blood Pb 4 distribution and IQ loss distributi ons, using as inputs the media c oncentration estimates from the 5 primary Pb smelter current conditions scenario. The IEUBK input parameter values for this 6 model run are presented in Exhibit 5-11. The results from this baseline IEUBK simulation were 7 compared to results generated using two alternative blood Pb models. 8 9 First, the Leggett model was applied, with the results summarized in Section 6.2.1. 2 In addition, 10 an “empirical” blood Pb model developed by Lanph ear et al. (1998) was a pplied, which predicts 11 blood Pb levels for 16 month-old ch ildren. This model was derived by statistical analyses of the 12 relationships between multimedia Pb exposures (a ir, soil, house dust Pb loading, drinking water, 13 and presence/absence of Pb paint), socioeconom ic variables, and blood Pb measurement in 14 young children obtained as part of a national survey conducted in 1997. The best fitting models 15 from the analysis predict ch ildren’s long-term blood Pb leve ls as a function of soil Pb 16 concentrations and house dust Pb loading (the am ount of Pb present per unit area of household 17 surfaces; in this case, on the floor.) To apply th e Lanphear et al. (1998) m odel, it was necessary 18 to convert the estimated house dust exposure concen trations in the primary Pb smelter case study 19 into estimates of dust Pb loading. As discu ssed in Appendix K, this was done by conducting a 20 regression analysis of house dust Pb loading a nd concentration measurements from 305 housing 21 units from the 1997 survey. The resulting re lationship between house dust loading and 22 concentration was then used to generate equi valent dust loading estimates for each block and 23 block group, which were then used as inputs to the Lanphear et al. (1998) model (Appendix K). 24 25 The blood Pb distribution estimates genera ted by the IEUBK and Leggett models are 26 summarized in Exhibit 6-12. As illustrated in this exhibit, the median blood Pb estimates from 27 the models differ substantially, with the Leggett median estimate (0.7 μg/dL) being about one28 half that from the baseline IEUBK model (1.3 μg/d L). The higher percenti les also differ greatly, 29 although on a proportional basis, the estimates converge slightly acro ss the three blood Pb 30 models. The Lanphear et al. ( 1998) model predicts blood Pb le vels for 16 month-old children, 31 when exposures and blood Pb levels tend to be considerably higher than for older children, and 32 thus the blood Pb levels that it predicts (w hich are considerably higher than the IEUBK 33 concurrent estimates) at all per centiles are not directly comparable to the values presented in 34 Exhibit 6-12. The IQ loss estimates predic ted based on the Lanphear et al. (1998) model, 35 however, should be roughly comparable, since they predict the same persistent, possibly 36 permanent, decrements in function due to exposures at different ages. 37 38

2 As noted in Section 6.2, modeling completed for the pilo t phase has consistently shown a trend of Leggett-derived blood Pb levels being lower than IEUBK-derived levels for the same exposure scenario. This trend is the opposite of findings presented in Pounds and Leggett (1998). This apparent discrepancy elevates the importance of model evaluation focused on IEUBK and Leggett (as applied in the pilot phase) which is currently ongoing. It also emphasizes the fact that results of the pilot phase (inclu ding sensitivity analysis results) should be considered provisional and subject to update in the full-scale assessment. Note that the results of model evaluation, once completed, will be conveyed by separate cover.Estimation of Human Health Risk

ICF International 6-18 DRAFT – December 2006 Exhibit 6-12. Blood Pb Distributions Generated by the 1 Leggett and IEUBK Models 2

Blood Pb Model Change

versus Baseline

Percentile Leggett (1993) Model a,b Baseline (IEUBK) a Leggett Model

99.9th 13.9 17.0 -18% 99.5th 6.7 10.5 -36% 99th 4.2 7.3 -42% 95th 2.0 3.6 -46% 90th 1.5 2.8 -47% 75th 1.0 1.9 -47% Median 0.7 1.3 -46% 25th 0.5 0.9 -46% 1st 0.2 0.4 -46%

a Exposure factor values for the Leggett model were selected so as to produce equivalent Pb uptake for 3 each exposure pathway as those used in the IE UBK model, concurrent blood Pb (6-7 years). 4

b. Blood Pb estimate for 16-month old child, based on soil and dust pathways only. 5 6 Exhibit 6-13 summarizes the differe nces in estimated IQ losses when the three different blood Pb 7 models are used. IQ losses are predicted above the 95 th percentile by the Leggett model, above 8 the 75 th percentile by the IEUBK model, a nd by the Lanphear et al. (1998) model. 3 The 9 maximum (99.9 th percentile) IQ losses predicted are not that different across the three models 10 because the slope of the log-linear IQ mode l decreases rapidly with increased blood Pb. 11 12

3 The IQ loss blood Pb cutoff (4.0 μ g/dL) and slope factor (-2.85) corresponding to the “peak” Pb blood model of Lanphear et al. (2005) were used to calculate IQ loss based on the Lanphear et al. (1998) blood Pb model for 16- month old children. The model for concurrent blood Pb (cutoff = 2.4 μg/dL, slope factor = -2.7) was used to estimate IQ losses associated with blood Pb predictions from the IEUBK and Leggett models.Estimation of Human Health Risk

ICF International 6-19 DRAFT – December 2006 Exhibit 6-13. IQ Loss Estimates for Prim ary Pb Smelter Current Conditions Derived 1 Using the Leggett, IEUBK, and Lanphear et al. (1998) Blood Pb Models a 2

Blood Pb Model Change versus Baseline

Percentile IQ Change Based on

Leggett (1993) Model IQ Change Based on

IEUBK Blood Pb Estimates IQ Change Based on Lanphear et al. (1998) Empirical

Model Effect of Using Leggett Model Blood Pb Effect of Using Lanphear et al. (1998) Model Blood Pb 99.9th -4.7 -5.3 -5.1 -11% -4% 99.5th -2.8 -4.0 -3.7 -31% -8% 99th -1.5 -3.0 -2.9 -50% -2% 95th -- -1.1 -1.4 -100% 27% 90th -- -0.5 -0.8 -100% 55% 75th -- -- -- -- -- Median -- -- -- -- -- 25th -- -- -- -- -- 1st -- -- -- -- --

a Log-linear model IQ loss esti mates are reported throughout. 3 4 6.4.3 Alternative Diet and Wat er Absorption Fractions 5 6 As noted in Section 6.2, policy-relevant backgr ound pathways (specifica lly, diet and drinking 7 water) contribute a substantial propor tion of total Pb uptake in all of the case studies. Under the 8 primary Pb smelter current conditions scenario, diet and drinking water account for more than 9 half of total Pb uptake at the low end of the blood Pb distribution, a nd lesser proportions at 10 higher percentiles as the amount of upta ke from other pathways increases. 11 12 Dietary and drinking water Pb upt ake is determined by several sets of model inputs, including 13 age-specific dietary intakes, dr inking water Pb concentrations, and drinking water consumption. 14 In addition, the GI uptake fractions (i.e., abso rption fraction for dietary Pb [AFDiet] and 15 absorption fraction for Pb in drinking water [AFW ater]) determine the proportion of ingested Pb 16 that is absorbed. For the pilot phase, the simp lest approach to varyi ng these polic y-relevant 17 background pathway Pb uptakes was determined to be adjusting AFDiet and AFWater values in 18 parallel for both pathways. In the baseline analysis, AFDiet and AFWater are both 0.5 (50 19 percent). For the sensitivity analysis, the va lues were varied downward to 40 percent and 20 upward to 60 percent. These values were thought to be reasonable lower- and upper-end 21 estimates based on a review of recent literat ure (USEPA 2006b; Madda loni et al. 2005). 22 23 Increasing or decreasing the dri nking water and dietary absorption fractions to these values has 24 moderate impacts on the blood Pb distributions (Exhibit 6-14). Median concurrent blood Pb 25 estimates for the low, baseline, and high estima tes are 1.0, 1.3, and 1.5 μg/dL, respectively. For 26 the higher blood Pb percentiles, the estimates derived with the low AFDiet and AFWater values 27 are about 70 percent of those es timated using the baseline values , and the estimates derived with 28 the high-end values are approximately 15 percent higher th an the baseline. 29 30Estimation of Human Health Risk

ICF International 6-20 DRAFT – December 2006 Exhibit 6-14. Impacts of Ch anging Diet and Drinking Wat er GI Absorption Fractions 1 on Estimated Blood Pb Distributions a 2

GI Absorption Fractions for Drinking Water, Diet Change Relative to Baseline

Percentile AFDiet, AFWater = 40% Baseline (AFDiet, AFWater = 50%) 1 AFDiet, AFWater = 60% AFDiet, AFWater = 40% AFDiet, AFWater = 60%

99.9th 12.4 17.0 21.7 -27% 28%
99.5th 6.3 10.5 11.9 -40% 13%
99th 4.5 7.3 8.5 -38% 17%
95th 2.5 3.6 4.0 -30% 11%
90th 2.1 2.8 3.1 -28% 9%
75th 1.5 1.9 2.2 -25% 12%
Median 1.0 1.3 1.5 -23% 12%
25th 0.7 0.9 1.0 -20% 13%
1st 0.3 0.4 0.4 -20% 14%

a All estimates are for the primary Pb smelter current conditions scenario using IEUBK concurrent blood 3 Pb metrics. 4 5 Exhibit 6-15 shows the corresponding impacts of variations to AFDiet and AFWater on IQ loss 6 distributions for the primary Pb smelter curren t conditions scenarios, based on concurrent blood 7 Pb estimates from the IEUBK model. When low AF values are used, IQ loss is predicted for 8 children above the 90 th percentile. When the baseline and higher values are used, IQ losses are 9 predicted for children above the 75 th percentile. The 99.9 th percentile IQ losses range from 4.4 10 (low AF values) to 5.9 points (high AF values). 11 12 Exhibit 6-15. Impacts of Changi ng Diet and Drinking Water GI 13 Absorption Fractions on Estimated IQ Loss Distributions a 14

GI Absorption Fractions for Water, Diet Change Relative to Baseline

Percentile AFDiet, AFWater

= 40% Baseline (AFDiet, AFWater = 50%) 1 AFDiet, AFWater

= 60% AFDiet, AFWater

= 40% AFDiet, AFWater

= 60%

Maximum -6.6 -7.0 -8.8 -5% 27%
99.9th -4.4 -5.3 -5.9 -16% 13%
99.5th -2.6 -4.0 -4.3 -35% 9%
99th -1.7 -3.0 -3.4 -44% 14% 95th -0.1 -1.1 -1.4 -88% 25%
90th
-- -0.5 -0.7 -100% 52%
75th
-- -- -- -- -- Median
-- -- -- -- -- 25th
-- -- -- -- -- 1st
-- -- -- -- --

a All estimates are derived using IEUBK concurrent blood Pb distributions, log-linear blood Pb-IQ model. 15 16Estimation of Human Health Risk

ICF International 6-21 DRAFT – December 2006 6.4.4 Alternative Values for Soil and House Du st Ingestion and for Soil and Dust 1 Absorption Fractions 2 3 Soil and house dust exposures play an important role in determining Pb uptake and blood Pb 4 distributions in the primary Pb smelter case study, particularly in the highly-exposed blocks and 5 block groups near the facility. Thus, alternative assumptions re lating to soil and dust ingestion 6 rates and GI absorption fractions were examined to determine their impacts on blood Pb and IQ 7 loss distributions. 8 9 Von Lindern et al. (2003) developed a combina tion of parameter estimates related to soil and 10 dust Pb contributions to blood Pb based on a compre hensive statistical analyses of environmental 11 and blood Pb measurements taken over a period of 12 years near a large mining and smelting 12 “Superfund” site in Idaho. Based on their anal yses, Von Lindern et al. estimated that dust 13 ingestion accounted for about 42 percent and soil ingestion for approximately 58 percent of the 14 total soil plus dust influence on blood Pb levels. Therefore, the “soil dust weighting factor” input 15 to the IEUBK model was adjusted to 58 percent fr om the IEUBK default value. Von Lindern et 16 al. distinguished between “community,” “neighborhood,” and “yar d soil” contributions to total 17 Pb intake. No such distinction was made in this assessment – the estimated soil and dust 18 concentrations for each block and block group were assumed to be representative of all three 19 types of contributions. 20 21 Von Lindern et al. (2003) also es timated that the average (absolut e) GI absorption fraction for Pb 22 uptake from soil and dust was 18 percent more than the period covered by th eir analysis. In the 23 sensitivity analysis, this value (18 percent) was substituted for both the site-specific soil and 24 house dust GI absorption fraction values used in the baseline modeling, wh ich were 26 percent 25 and 48 percent, respectively. 26 27 Exhibit 6-16 illustrates that using the alternativ e soil and dust intake and uptake factors from 28 Von Lindern et al. (2003) results in lower blood Pb estimates than the when the baseline values 29 are used. While the higher pr oportion of house dust ingestion might be expected to result in 30 higher Pb uptake, especially in the most highly exposed block gr oups, this potential increase is 31 offset by the lower estimated soil and dust ingest ion fractions (18 percent for both media versus 32 48 and 26 percent, respectively, in the baseline anal ysis). The median blood Pb estimate is thus 33 reduced to 1.0 μg/dL, compared to 1.4 for th e baseline parameter values, and the blood Pb 34 percentile estimates above the median are 25 to 40 percent lower than the baseline estimates. 35 36 Use of the Von Lindern et al. (2003) soil-dust wei ghting factor and GI absorption fractions has a 37 corresponding effect on the estim ated IQ loss distributions (E xhibit 6-17). IQ losses are 38 predicted above the 90 th percentile when the Von Lindern et al. parameters are used. When the 39 baseline parameter estimates are used, IQ losses are predicted for children above the 75 th 40 percentile. The 99.9 th percentile IQ change under the baseli ne is 5.3 points, whereas, when the 41 alternative values for the soil-dust weighting factor and absorption fraction are used, the 42 corresponding IQ loss estimate is 4.4 points. 43 44Estimation of Human Health Risk

ICF International 6-22 DRAFT – December 2006 Exhibit 6-16. Effects of Von Lindern et al. (2003) Soil and Dust Intake Parameters on 1 Estimated Blood Pb Distributions, Primary Pb Smelter Case Study 2

Soil, House Dust Exposure Factor Values Change from Baseline Percentile Baseline Exposure Factors a Von Lindern et al. Exposure Factors b Alternative Exposure

Factors

99.9th 17.0 12.4 -27% 99.5th 10.5 6.3 -40% 99th 7.3 4.5 -38% 95th 3.6 2.5 -30% 90th 2.8 2.1 -28% 75th 1.9 1.5 -25% Median 1.3 1.0 -23% 25th 0.9 0.7 -20% 1st 0.4 0.3 -20%

a Soil-Dust Weighting factor = 45%, AFSoil = 26%, AFDust = 48 % 3

b Soil-Dust Weighting factor = 58%, AFSoil = AFDust = 18 % 4 5 Exhibit 6-17. Effects of Von Lindern et al. (2003) Soil and Dust Intake Parameters on 6 Estimated IQ Loss Distributions, Primary Pb Smelter Case Study a 7 Soil, House Dust Exposure Factor Values Change from Baseline

Percentile IQ Change Derived from Baseline Exposure Factors a,b IQ Change Derived from Von Lindern et al. Exposure Factors a,b Alternative Exposure

Factors

99.9th -5.3 -4.4 -16% 99.5th -4.0 -2.6 -35% 99th -3.0 -1.7 -44% 95th -1.1 -0.1 -88% 90th -0.5 -- -100% 75th -- -- -- Median -- -- -- 25th -- -- -- 1st -- -- --

a Log-linear blood Pb-IQ loss model 8

b See notes to Exhibit 6-16 9 10 6.4.5 Alternative Blood Pb Geom etric Standard Deviations 11 12 To this point, the sensitivity analysis has addres sed models and parameters that affect the GM 13 blood Pb estimates for the individual blocks and block groups. The parameter to be evaluated 14 next, the GSD for individual variability, affects the distribution of individual blood Pb values 15 derived from the block and block group GM values. 16 17Estimation of Human Health Risk

ICF International 6-23 DRAFT – December 2006 The baseline (central tendency) estimate for th e blood Pb GSD used in a ll three case studies is 1 1.6 (see Section 5.1.4.4). Based on a review of the literature conducted for this assessment, GSD 2 values for children in limited geographical areas were found to range from approximately 1.3 to 3 1.9. Larger variability has been seen in national and regional data (CDC 2004; Maddaloni 2005), 4 but it is likely that the children in these populatio ns faced more varied exposures than those in 5 smaller populations. The values 1.3 and 1.9 were t hus considered to be plausible “lower” and 6 “upper” estimates of the GSD for individual vari ability, with no implicat ion intended that they 7 represent absolute limits or defined percentiles of actua l population variability. 8 9 As expected, changing the GSD value leaves th e median blood Pb estimates unchanged (Exhibit 10 6-18) and reducing the GSD value to 1.3 results in the upper percentile values that are lower than 11 the corresponding baseline estimates, with the pr oportional decrease increas ing toward the “tail” 12 of the analysis. The pattern is clear up to the 99 th percentile, but the 99.5 and 99.9 th percentile 13 blood Pb estimates calculated with the lower GSD value are similar to, or greater than, those 14 calculated with the baseline values. This is ce rtainly an artifact due to simulation uncertainty 15 (see Exhibit 5-13). 16 17 Exhibit 6-18. Effect of Varyin g Individual Variability Estimates 18 on Predicted Blood Pb Distributions a 19

Blood Pb Individual Variability Change Relative to Baseline Percentile

GSD = 1.3 Baseline (GSD = 1.6) GSD = 1.9 GSD = 1.3 GSD = 1.9 99.9th 16.4 17.0 22.8 -3% 34% 99.5th 10.9 10.5 13.5 4% 29% 99th 6.8 7.3 9.8 -6% 35% 95th 2.9 3.6 4.7 -19% 31% 90th 2.3 2.8 3.5 -18% 23% 75th 1.7 1.9 2.2 -11% 13% Median 1.3 1.3 1.3 0% 3% 25th 1.0 0.9 0.8 13% -8% 1st 0.6 0.4 0.3 62% -32%

a Concurrent blood Pb estimates are calculated for the primary Pb smelter current conditions scenario 20 using the IEUBK model. 21 22 Blood Pb estimates above the median are consistently elevated compared to the baseline when 23 the higher estimate for population vari ability (1.9) is used to calcul ate individual blood Pb levels. 24 The estimates for the upper percentiles derived us ing the upper GSD estimate are on the order of 25 30 to 35 percent higher than th e corresponding baseline values. 26 27 Exhibit 6-19 shows the differences in estimates of IQ loss when the alternative GSD values are 28 used. For the low GSD estimate, IQ losses are predicted for children above the 90 th percentile, 29 while for the baseline and high-end GSDs, IQ losses are predicted for children above the 75 th 30 percentile. The highest predic ted IQ losses derived using the three GSD estimates are quite 31 similar (5.2, 5.3, and 6.1, respectively), the closen ess of the estimates again reflecting the 32 shallow slope of the log- linear blood Pb-IQ loss model at high blood Pb levels. 33 34Estimation of Human Health Risk

ICF International 6-24 DRAFT – December 2006 Exhibit 6-19. Effect of Changing the GSD for Individual Variability 1 on Predicted IQ Loss Distributions a 2

Blood Pb Individual Variability Change Relative to Baseline Percentile GSD = 1.3 Baseline (GSD = 1.6) GSD = 1.9 GSD = 1.3 GSD = 1.9

Maximum -6.7 -7.0 -10.6 -3% 53% 99.9th -5.2 -5.3 -6.1 -2% 15% 99.5th -4.1 -4.0 -4.7 3% 17% 99th -2.8 -3.0 -3.8 -6% 27% 95th -0.5 -1.1 -1.8 -51% 66% 90th -- -0.5 -1.0 -100% 124% 75th -- -- -- -- -- Median -- -- -- -- -- 25th -- -- -- -- -- 1st -- -- -- -- --

a IQ loss estimates are derived based on concurrent IEUBK blood Pb estimates for the primary Pb 3 smelter current conditions scenario. 4 5 6.4.5 Alternative Blood Pb-IQ Models and Uncert ainty Related to Statistical Fit of 6 Models 7 8 To this point, all of the IQ loss estimates have been calculated based on th e log-linear version of 9 the Lanphear et al. (2005) model. For the sensitiv ity analysis, three alternative parameterizations 10 of the blood Pb-IQ models were us ed to calculate IQ changes. The first two sets of estimates 11 were derived using the estimated upper and lowe r confidence limits on the log linear slope 12 parameters from the concurrent blood Pb-IQ model derived by Lanphear et al. (2005). The 13 equations for these model were: 14 15 IQ loss (relative to cutpoint) = - 3.74 × ln (concurrent blood Pb / 2.4) 16 17 IQ loss (relative to cutpoint) = - 1.66 × ln (concurrent blood Pb / 2.4) 18 19 with the first being the “upper” confidence limit (t he value that predicts the largest change in 20 IQ), and the second being th e lower confidence limit. 21 22 The third alternative was the piecewise linear fo rm of the model presen ted in Lanphear et al. 23 (2005): 24 25 Concurrent Blood Pb < 2.4 μg/dL: IQ change not calculated 26 27 Concurrent Blood Pb < 10 μg/dL: IQ = 97.2 – 0.80 × Blood Pb 28 29 Concurrent Blood Pb > 10 μg/dL: IQ = 89.2 – 0.13 × Blood Pb 30 31 The intercept in the third equati on is simply the IQ loss estimate d from the second equation at a 32 blood Pb concentration of 10 μg/dL. Thus, the fo rm of this model is a “hockey-stick” with a 33Estimation of Human Health Risk

ICF International 6-25 DRAFT – December 2006 slope of -0.80 below 10 μg/dL, and a slope of -0.13 above 10 μg/dL. As for the log-linear 1 model, IQ changes were not estimated if indivi dual blood Pb levels were below the concurrent 2 blood Pb cutpoint discussed above. 3 4 Exhibit 6-20 summarizes the IQ loss distributions derived us ing the alternative blood Pb-IQ 5 models with the concurrent bl ood Pb metric. In all cases, block and block group blood Pb 6 concentrations were calculated with IEUBK, and a GSD value of 1.6 was used to describe 7 individual variability in response to Pb exposures. Of the altern atives evaluated, the predictions 8 generated by the piecewise linear model differ the most from the baseline estimates. The 90 th 9 and 95 th percentile IQ losses pred icted by the two models are quite similar, but the 99 th and 10 higher percentile IQ loss predic tions from the piecewise model are on the order of twice those 11 from the log-linear model. This is due to diffe rence in form and the much higher slope of the 12 piecewise linear model in the range from the blood Pb cutpoint to 10 μg/dL. 13 14 The lower and upper confidence limit model pred ictions parallel the predictions from the 15 baseline model, as expected. The lower conf idence limit (LCL) model predicts IQ losses for 16 children above the 75 th percentile, as high as 1.2 points at the 99.9 th percentile. The upper 17 confidence limit (UCL) model, like the baseline es timate, predicts IQ losses for children above 18 the 75 th percentile, but the maximum (99.9 th percentile) IQ loss is pred icted to be on the order of 19 8.1 points, compared to about 5.3 po ints for the baseline model. 20 21 Exhibit 6-20. Effects of Alternative Blood Pb-IQ Models on Predicted IQ Distributions 22 Blood Pb-IQ Model Change Relative to Baseline

Percentile 95% LCLon LogLinear Model (1.66) Baseline(LogLinear Model) a 95% UCLon LogLinear Model (3.72) Piecewise Linear Model 95% LCL 95% UCL Piecewise Linear

99.9 th -1.2 -5.3 -8.1 -8.9 -78% 53% 69% 99.5 th -0.8 -4.0 -5.9 -8.1 -80% 49% 103% 99 th -0.6 -3.0 -4.3 -3.9 -80% 44% 30% 95 th -0.2 -1.1 -1.6 -1.0 -77% 48% -12% 90 th -0.1 -0.5 -0.7 -0.4 -76% 49% -23% 75 th

-- -- -- -- -- -- -- Median -- -- -- -- -- -- -- 25 th

-- -- -- -- -- -- -- 1 st

-- -- -- -- -- -- --

a All IQ Loss estimates are for the primary Pb smelter cu rrent conditions scenario, based on IEUBK concurrent blood 23 Pb estimates. 24 25Estimation of Human Health Risk

ICF International 6-26 DRAFT – December 2006 6.4.7 Alternative Blood Pb Metrics 1 2 In Section 6.2, the concurrent and lifetime averag e blood Pb distributions, and IQ loss estimates 3 generated from these distributions, were presented for each of the case studies. In addition, the 4 impact of using the “peak” blood Pb estimates (the highest annual average, which was found to 5 occur at ages 6 through 18 months) on the magnitude of IQ loss predictions was also evaluated. 6 The Lanphear et al. (2005) log-lin ear model form was used to derive all of these results. The 7 concurrent, lifetime average, and peak blood Pb-based models, while sharing the same 8 mathematical form, differ in tw o respects. First, they have different slope parameters (2.7, 3.04, 9 and 2.85, respectively for the concurrent, lifetime average, and peak blood Pb metrics). In 10 addition, the cutpoint values below which IQ loss es are not calculated va ries across the models 11 (see Section 6.1.2). The cutpoint fo r concurrent blood Pb is 2.4 μg/dL, the cutpoint for lifetime 12 average blood Pb is 6.1 μg/dL, and the cutpoint for peak exposures is 4.0 μg/dL, corresponding 13 to the 5 th percentile blood Pb values measured in the Lanphear et al. (2005) pooled cohort. 14 15 Exhibit 6-21 shows that both the lifetime average and peak blood Pb values generated using the 16 IEUBK model are substantially great er than the baseline (concurrent) estimates. This is because 17 the lifetime average metric contains a substan tial contribution from younger ages, when blood Pb 18 is estimated to be higher than for the concurrent children (aged 6 to 7 years). The peak (highest 19 annual) blood Pb is, by definition, taken from the exposure period when blood Pb levels are 20 highest. Across the percentiles, the lifetime av erage blood Pb estimates are approximately 35 to 21 40 percent higher than the corr esponding baseline valu es, and the peak estimates are about 22 80 percent higher. These observatio ns are consistent with publishe d findings in the literature 23 regarding varying blood Pb leve ls with age (USEPA 2006a). 24 25 Exhibit 6-21. Distributions of Concurren t, Lifetime Average, and Peak Blood Pb 26 Distributions from the Prim ary Pb Smelter Case Study 27
Blood Pb Metric

Percentile Lifetime Average Blood Pb,

μ g/dL Baseline (Concurrent Blood Pb),

μ g/dL a Peak (Highest Annual) Blood Pb,

μ g/dL b

99.9th 23.2 17.0 33.4 99.5th 14.3 10.5 18.0 99th 10.4 7.3 13.4 95th 5.1 3.6 6.7 90th 4.0 2.8 5.3 75th 2.7 1.9 3.5 Median 1.8 1.3 2.4 25th 1.2 0.9 1.7 1st 0.5 0.4 0.7

a IEUBK concurrent (age 6 to 7 years) blood Pb estimates. 28

b IEUBK highest 12-month average (age 6 to 18 months) blood Pb estimates. 29 30Estimation of Human Health Risk

ICF International 6-27 DRAFT – December 2006 These differences in blood Pb predictions strong ly affect the magnitude of predicted IQ losses 1 (Exhibit 6-22), as do the differences in the model parameters. Although the lifetime average 2 blood Pb percentiles are higher than the baseli ne concurrent estimat es, the magnitude of 3 predicted IQ loss is lower. Wh en interpreting the results in Exhibit 6-22, it is important to 4 remember that IQ changes are calculated using different blood Pb cutpoint s and slope factors for 5 each blood Pb metric. 6 7 When the lifetime average blood Pb distribution is input into the lifetime average blood Pb-IQ 8 model, only children above the 95 th percentile are predicted to e xperience IQ changes, and the 9 greatest (99.9 th percentile) estimate is an IQ loss of 4.1 points. These IQ changes are 10 significantly lower than the baseli ne predictions based on the conc urrent blood Pb distributions. 11 The primary reason for this pattern is the relati vely high lifetime average blood Pb cutpoint (6.1 12 μg/g), compared to the concurrent cutpoint of 2.4 μg/dL. 13 14 Exhibit 6-22. Effects of Using Different Blood Pb Metrics to Predict IQ Loss 15
Blood Pb Metric Difference from Concurrent

Percentile IQ Change Based on Lifetime Average Blood Pb a IQ Change Based on Concurrent Blood Pb b IQ Change Based on

Highest Annual Blood Pb c Lifetime Average Peak

99.9th -4.1 -5.3 -7.5 -23% 42% 99.5th -2.6 -4.0 -5.7 -35% 44% 99th -1.6 -3.0 -4.9 -46% 64% 95th -- -1.1 -2.9 -100% 165% 90th -- -0.5 -2.3 -100% 395% 75th -- -- -1.1 -- -- Median -- -- -- -- -- 25th -- -- -- -- -- 1st -- -- -- -- --

a IQ estimates derived using Lanphear et al. (2005) log- linear model using parameter values for lifetime average 16 blood Pb levels. 17

b Derived using Lanphear et al. (2005) parameter values for concurrent blood Pb levels. 18

c Derived using Lanphear et al. (2005) parameter values for peak blood Pb exposures. 19 20 In contrast, the IQ losses predicted based on the distribution of peak blood Pb levels are 21 considerably higher than the base line estimates. IQ losses are pr edicted for children above the 22 50 th percentile blood Pb, and range from 1.1 points (75 th percentile) to 7.5 points (99.9 th 23 percentile). In the case of the peak blood Pb values, the difference between the blood Pb cutoff 24 values is not great enough to overcome the effect of much greater peak blood Pb estimates. 25 26 6.4.8 Alternative Blood Pb Cutpoi nt for IQ Loss Estimation 27 28 Because of the impact on IQ loss estimates of th e blood Pb cutoff values used as the “floor” for 29 estimating IQ loss, the impact of changing these valu es was also investigated. The data available 30 from the Lanphear et al. (2005) study does not pr ovide a foundation for a lternative cutpoints for 31 use in sensitivity analysis. For the pilot phase, the simple expedi ent of reducing the cutpoints for 32Estimation of Human Health Risk

ICF International 6-28 DRAFT – December 2006 concurrent and lifetime average blood Pb by 50 pe rcent was adopted. The slope parameters for 1 the two models remained unchanged. 2 3 Exhibit 6-23. Effect of Reducing Blood Pb Cutoff Values for IQ Estimation a 4

Blood Pb Cutpoint Change from Baseline
Percentiles Baseline Blood Pb Thresholds Thresholds = 1/2 Baseline Values Reduced Cutpoint

99.9th -5.3 -8.3 57% 99.5th -4.0 -6.3 58% 99th -3.0 -5.0 68% 95th -1.1 -3.0 171% 90th -0.5 -2.3 413% 75th -- -1.3 -- Median -- -0.3 -- 25th -- -- -- 1st -- -- --

a All IQ loss estimates are derived from IEUBK blood Pb estimates for the primary Pb smelter 5 current conditions scenario. 6 7 As shown in Exhibit 6-23, when the concurre nt blood Pb cutpoint is reduced from 2.4 to 8 1.2 μg/dL, children above the 25 th (rather than the 75 th ) percentile in the primary Pb smelter case 9 study are predicted to experience IQ loss, and the maximum (99.9 th percentile) IQ loss using 10 IEUBK and concurrent blood Pb blood Pb-IQ func tion is increased from 5.3 to 8.3 points. A 11 change in the cutpoint for the lifetime blood Pb metric would be expected to have similar 12 impacts on the IQ changes. 13 14 6.4.9 Summary of Sensitivity Analysis 15 16 The preceding discussion demonstrates a wide range of impacts on blood Pb and IQ loss 17 distributions associated with alternative mode l selection and parameter values. Exhibit 6-24 18 summarizes the impacts of the variations in m odels and input assumptions on predicted blood Pb 19 and IQ distributions. Ch anges in the median 90 th , 95 th , and 99 th percentile values relative to the 20 corresponding baseline estimates are displayed for each set of models and parameter values that 21 were varied. 22 23 As expected, the predicted changes in blood Pb a nd IQ values in Exhibi t 6-24 tend to move in 24 parallel, although with a re verse sign. However, va riations in models or parameter values affect 25 the predicted blood Pb or IQ to varying degrees owing to the non-linearity of the statistical blood 26 Pb-IQ models and variations in the blood Pb-IQ loss cutpoints when different blood Pb metrics 27 (concurrent, lifetime average, or peak) are use d. Many changes in assumptions that result in 28 significant decreases in predicted blood Pb leve ls accordingly result in a lesser predicted IQ 29 change, or no predicted IQ change at some pe rcentiles. Some relatively small changes in blood 30 Pb associated with the sensitivity analysis abolis h the estimated IQ loss entirely (identified as “- 31 100%” changes in Exhibit 6-24) beca use the predicted blood Pb falls below the cutpoint. In such 32 cases, both the numbers of children for which IQ losses are predicted and the magnitude of IQ 33Estimation of Human Health Risk

ICF International 6-29 DRAFT – December 2006 losses among the affected children are reduced. It is also important to remember that the 1 proportional changes displayed in the exhibit apply to much sma ller absolute blood Pb and IQ 2 loss values at the median and 90 th percentile than at the 95 th and 99 th , owing to the “skewed” 3 nature of the blood Pb and IQ loss distributions. 4 5 The factors most strongly affec ting the magnitude of IQ loss estim ates are the nature of the blood 6 Pb metric from which they are estimated and assu mptions related to the blood Pb cutpoint for IQ 7 loss estimation. The estimated GSD values for individual variability in response to Pb exposures 8 also strongly affect the predicted IQ loss distri butions, particularly at the upper percentiles. 9 Among the factors related to blood Pb modeling, th e selection of blood Pb model has the most 10 impact on estimated IQ changes. Relatively mode st changes in GI absorption changes for diet 11 and water Pb surprisingly produce relatively larg e proportional changes in the predicted blood Pb 12 and IQ distributions, as do changes in soil/dust exposure factor values. As noted, above, 13 however, the highest proportional changes associated with these modeling alternatives are seen 14 at the lower percentile values, where the absolute magnitudes of the change from the baseline 15 can be rather small. 16 17Estimation of Human Health RiskICF International 6-30 DRAFT – December 2006Exhibit 6-24. Impacts of Alternative Models and Parameters on Blood Pb and IQLoss Estimates in the Sensitivity Analysis.Change Versus BaselinePercentileHouse Dust PbConcentrationModelBlood PbEstimationModelWater, DietAbsorptionFractionsSoil, DustExposureIntake/UptakeFactorsIndividualBlood PbVariability(GSD)Blood Pb-IQModelBlood Pb MetricReducedBlood PbCutpointBlood Pb99th -15% -42% to +58% -38% to +17% -38% -6% to +35% -- +43% to +84% --95th -5% to -7% -46% to 84% -30% to +11% -30% -19% to +31% -- +42% to +85% --90th -8% to -7% -47% to 84% -28% to +9% -28% -18% to +23% -- +41% to +86% --Median -12% to -10% -46% to +108% -23% to +12% -23% 0% to 3% -- +37% to +83% --IQ Loss99th -14% to -15% -50% to -2% -44% to +14% -44%-6% to +27% -80% to +44% -46%to +64% +68%95th 12% to 17% -100% to +27% -88% to +25% -88% -51% to +66% -77% to +48% -100% to +165% +171%90th 43% to 46% -100% to +55% -100% to +52% -100% -100% to +124% -76% to +49% -100% to +365% +413%Median -- -- -- -- -- -- -- --Ecological Risk Assessment

ICF International 7-1 DRAFT – December 2006 

### 7. Ecological Risk Assessment 1

2 The ecological risk assessment for this NAAQS re view consisted of th ree case study screening 3 assessments and a national-scale surface water and sediment screening assessment. The case 4 study screening assessments were designed to estimate the potential for ecological risks 5 associated with exposures to Pb emitted into ambient air for three case studies: primary Pb 6 smelter, secondary Pb smelter, and near roadway non-urban locati on. The national-scale 7 screening assessment evaluated the potential for ec ological risks associated with the atmospheric 8 deposition of Pb released into ambient air at surface water and sediment monitoring locations 9 across the United States. This chapter describes the methods applied for this assessment (Section 10 7.1), presents the exposure and risk results (S ection 7.2), and discusses the uncertainties and 11 limitations of the assessment (Section 7.3). 12
13 7.1 Methods 14
15 This section describes the screen ing methods used to determine th e potential for ri sks of adverse 16 effects on ecological receptors associated with exposures of Pb emitted into ambient air and 17 subsequently deposited to other environmenta l media. Section 7.1.1 summarizes the risk 18 characterization methods used to characterize ecological risks for the case studies; Section 7.1.2 19 describes the methods used to conduct the natio nal-scale surface water and sediment screening 20 assessment; and Section 7.1.3 describes the ecologi cal effects characterization methods and the 21 ecotoxicity screening values used in this assessment. 22
23 7.1.1 Case Studies 24
25 The Hazard Quotient (HQ) approach was used to compare estimated media concentrations with 26 ecotoxicity screening values in soils, surf ace waters, and sediments around the primary Pb 27 smelter, and for soils only around the secondary Pb smelter case study location and the near 28 roadway non-urban case study locations. The HQ is calculated as the ratio of the media 29 concentration to the ecotoxicity screening va lue. The HQ is represented by the following 30 equation: 31
32 HQ = (estimated media concentration) / (ecotoxicity screening value) 33
34 For each case study, HQ values were calculated for each location where either modeled or 35 measured media concentrations were available. Separate soil HQ valu es were calculated for 36 each ecological receptor group for which an ecotoxici ty screening value has been developed (i.e., 37 birds, mammals, soil invertebrates, and plants ). HQ values less than 1.0 suggest that Pb 38 concentrations in a specific me dium are unlikely to pose signifi cant risks to ecological receptors 39 whereas HQ values greater than 1.0 indicate that the expected exposure ex ceeds the ecotoxicity 40 screening value. 41
42 7.1.2 National-Scale Surface Water and Sediment Screening Assessment 43
44 The HQ method was used to characterize risk fo r the surface water portion of the national-scale 45 surface water and sediment screening assessm ent, where measures of dissolved Pb 46Ecological Risk Assessment

ICF International 7-2 DRAFT – December 2006 concentrations in surface waters at monitoring lo cations across the United States were compared 1 with both acute and chronic water column (surf ace water) ecotoxicity screening values. To 2 identify which sites might pose a risk to aquatic communities, the frequencies with which 3 dissolved Pb concentrations exceeded the select ed chronic and acute surface water ecotoxicity 4 screening values for the protecti on of aquatic life (described in Section 7.1.3.2) were examined. 5 Because of uneven sampling frequency across samp ling locations, a screen was implemented to 6 exclude from further analysis sites expected to pose negligible risks to aquatic communities. 7 Locations were eliminated if the frequency with which a sample exceeds the chronic surface 8 water screening value for dissolved Pb was 5 pe rcent or less of available data, and the acute 9 screening value was never exceeded. The sa mpling locations that were dropped from the 10 assessment using this screen are identified in Appendix H with the code “na” in the column 11 indicating the number of Pb measurements that exceeded the screening value out of the total 12 number of samples analyzed for dissolved Pb. 13
14 As described in Section 7.1.3.2, a re view of the data on water hardness in the NAWQA data set 15 indicated that the initial chronic screening value was too high to identify all locations where the 16 chronic surface water screening value for the pr otection of aquatic lif e was exceeded. As a 17 result, a second screen was conducted. In the se cond screen, for each sampling location with one 18 or more measured dissolved Pb concentrations above the QL but less than 1.2 μg/L (the initial 19 screening value assuming a water hardness of 50 μ g/L CaCO 3 ), measurements of CaCO 3 were 20 used to calculate a lo cation-specific chronic su rface water screening value. If none of the 21 dissolved Pb measurements at a particular loca tion exceeded this location-specific value, the 22 location was not considered further. If one or more of the dissolved Pb measurements at a 23 particular location did exceed the location-specific value, the location was retained for further 24 analysis. 25
26 Where dissolved Pb concentrations were found to exceed the chronic surface water screening 27 values at a frequency of greater than 5 percent of avai lable data or where the acute surface water 28 screening value was exceeded by at least one m easurement, the next step involved identifying 29 the most likely sources of Pb input to the surface waters to focus the screen on those locations for 30 which air is a major contributor to the Pb concentrations in th e water column. The following 31 considerations were used to exclude sites for which air contributions could not be apportioned. 32
33

• Where the surrounding land use was for mining, th e Pb in the surface water could not be 34 apportioned between runoff/erosion and air em issions. Therefore, these sites were 35 categorized separately. 36
37

• If there is a major permitted effluent discha rge upstream of a sampling location with Pb 38 specified in the permit, it is possible that this discharge wa s responsible for some of the 39 Pb measured in the surface water. Therefor e, air contributions could not be readily 40 apportioned. 41
42

• If there were no facilities known to emit Pb to the air within a 50-km radius of the 43 sampling location, the Pb in the surface water was assumed to come largely from non-air 44

sources. 45
46Ecological Risk Assessment

ICF International 7-3 DRAFT – December 2006 Based on this screen of monitoring locatio ns, 15 non-mining surface water locations were 1 identified with elevated Pb concentrations possibl y attributable to policy-re levant sources. Risk 2 characterization for the sediment portion of th e screening assessment was conducted for these 15 3 locations. This risk characterization was perf ormed using the HQ method, where measures of 4 total Pb concentrations in sediments were comp ared with the sediment ecotoxicity screening 5 values for the protection of benthic communities. Concentrations that exceeded the sediment 6 screening values were identifie d and possible sources of Pb in put to these sediments were 7 examined. The first step involved identifyi ng sediment sampling locations in the NAWQA 8 database that were co-located with the surface water samples identified for the surface water 9 portion of the screening assessment. Three of the 15 sites did not have co-located sediment and 10 surface water results. In these cases, sediment sampling results from nearby sampling locations 11 were identified on the basis of latitude, longitude, and name of the site location. For the 12 sediment sampling sites that were categorized as near matches, it is important to note that the 13 surface water samples at the same location had not exceeded the chronic surface water screening 14 value in the surface water portion of the screening assessment. Ther efore, the value of the “near15 match” locations is limited. 16
17 7.1.3 Ecotoxicity Screening Values 18
19 The sections that follow describe the ecotoxicity screening values used in this ecological risk 20 assessment to evaluate the soil, surface water, and sediment concentrations of Pb identified in the 21 case studies and in the national- scale screening assessment. S ection 7.1.3.1 describes the soil 22 ecotoxicity screening values developed for this assessment. Section 7.1.3.2 describes the surface 23 water quality screening values, and Section 7.1.3.3 describes the sediment screening values. 24
25 7.1.3.1 Soil Screening Values 26
27 In identifying soil screening values (SSVs) for this assessment, the Ecological Soil Screening 28 Levels (Eco-SSLs) developed by EPA’s S uperfund program (USEPA 2003; USEPA 2005c) 29 were considered. These values are defined as “c oncentrations of contaminants in soil that are 30 protective of ecological receptors that commonly come in contact with soil or ingest biota that 31 live in or on soil.” They were derived separately for four cate gories of ecological receptors: 32 plants, soil invertebrates, bird s, and mammals. For the purpose s of Superfund screening level 33 ecological risk assessments at i ndividual hazardous waste sites, Eco-SSLs are used to exclude 34 from further investigation thos e receptor/exposure pathway combin ations that pose negligible 35 risks. Eco-SSLs are therefore derived using c onservative assumptions and parameter values. 36

The development of Eco-SSLs for each receptor category involved the assessment of several 37 species within each category. Available toxicity test data for these species were evaluated. In 38 the case of plants and soil invertebrates, these data are expressed as contaminant concentrations 39 in soil (mg contaminant/kg soil), are evaluated with considerati on of characteristics affecting 40 bioavailability (e.g., pH, organic c ontent, etc), and a single Eco-SSL is derived for each category. 41 Unlike Eco-SSLs for plants and soil invertebra tes, those for the avian and mammalian wildlife 42 categories involve a two step proc ess: (1) derivation of a toxic ity reference value (TRV) in mg 43 contaminant per kg body weight per day, and (2) application of the TRV and exposure parameter 44 values to derive an Eco-SSL in mg contaminan t per kg soil for three different species in each 45 category. In the first step, a single toxicity reference value (TRV) was developed (e.g., the 46Ecological Risk Assessment

ICF International 7-4 DRAFT – December 2006 reference dose for the most sens itive of the adverse effects on birds that might cause population1 level effects) that applied to al l species in each wildlife categor y. In the second step, however, 2 species-specific exposure assumptions regarding in cidental soil ingestion, diet composition, and 3 contaminant uptake from soil by prey generally re sulted in different Eco-SSL values, expressed 4 as soil concentrations, for the different specie s in each receptor category. The receptor category 5 Eco-SSL was then set equal to the lowest species-specific Eco-SSL. 6
7 In developing SSVs for use in the Pb NAAQS review, the exposure assumptions and parameter 8 values used in the second step of the de rivation of the Superfund Eco-SSLs for birds and 9 mammals were examined, and as appropriate, augmented or replaced with alternatives 10 considered more appropriate for the Pb NAAQS review (see Appendix L). SSVs for birds and 11 mammals for this assessment were derived using the Eco-SSL methodology and TRVs and 12 alternative values for some of the exposure para meters. For purposes of this assessment, the 13 SSV for avian wildlife of 38 mg /kg dry weight was developed us ing data for the woodcock, the 14 species on which the avian Eco-SSL was base d (USEPA 2003; USEPA 2005c). A more 15 nationally representative value was used fo r woodcock diet composition (i.e., 75 percent 16 earthworms, 25 percent soil arthropods), and al ternative values, supported by available data, 17 were used for food ingestion rate (i.e., 0.20 kg food dry weight/kg-day), inci dental soil ingestion 18 rate (i.e., 11.4 percent of the dry weight diet), and absorption of ingested Pb (i.e., 50 percent). 19 The same 50 percent absorption efficiency was used to calculate an SSV for mammals of 56 20 mg/kg dry weight. This absorpti on efficiency assumption was base d on studies that suggest that 21 the oral bioavailability of Pb in soils or food fo r mammals is between 3 and 50 percent of its oral 22 bioavailability when added as soluble Pb acet ate to food or water (ATSDR 2005; Ruby et al. 23 1999). The mammalian SSV developed for the Pb NAAQS review remains somewhat more 24 conservative than the avian SSV because the proporti on of earthworms in the diet of shrews (the 25 mammalian species with the highest exposure) is likely less th an the assumed 100 percent. 26 Shrews also ingest arthropods, and an arthropod model for Pb upt ake from soil predicts lower 27 concentrations in the arthropods than in ear thworms at the same soil concentrations. 28
29 The SSVs developed for this assessment are shown in Exhibit 7-1. For plants and soil 30 invertebrates, the Eco-SSLs were adopted as the SSVs for the Pb NAAQS review. 31
32 Exhibit 7-1. Soil Screening Values (SSVs) for Pb 33

Receptor SSV (mg Pb/kg Soil Dry Weight) Source

Plants 120

Soil invertebrates 1700 USEPA 2005c

Avian wildlife 38

Mammalian wildlife 112 Appendix L

34 35Ecological Risk Assessment

ICF International 7-5 DRAFT – December 2006 7.1.3.2 Water Quality Criteria 1 2 The AWQC were developed by U.S. EPA to provide guidance to states and authorized tribes to 3 use in adopting water quality standards and are ba sed on toxicity testing in aquatic organisms, 4 including fish, invertebrates and algae. AWQC values for chronic e xposures are called the 5 criterion continuous concentra tion (CCC) and for acute exposu res are called the criterion 6 maximum concentration (CMC), and they are availa ble for freshwater and marine environments. 7 For a CCC to be exceeded, a 4-day average water concentration must exceed the CCC more than 8 once every three years (USEPA 1984). 9
10 The freshwater AWQC, both the CCC and CMC, for Pb are derived to vary with water hardness, 11 and when initially developed pertained to tota l recoverable Pb. Duri ng a re-evaluation of 12 AWQC for metals in 1993, EPA concluded that it would be more accurate to base the AWQC on 13 that portion of the metal in the water column that is readily bioavailable, that is, the dissolved 14 fraction (USEPA 1993). As a result, EPA examined the re lationship of total to dissolved 15 concentrations for a series of me tals to develop a conversion factor for each metal. For Pb, the 16 conversion varies with water hardness and is the same for both the CCC and CMC (USEPA 17 1993). The current equations/values may be rei ssued based on pH at the conclusion of the 18 current revisions to the Pb AWQC scheduled to be completed in 2007. The CCC and CMC are 19 calculated using the following formulas (USEPA 1984): 20
21 CCC = exp{1.273 [ ln(hardness) ] – 4.705} x CF 22 CMC = exp{1.273 [ ln(hardness) ] – 1.460} x CF 23
24 where: 25
26 CF = conversion factor = 1.46203 -[ ln(hardness) x 0.145712] 27 Hardness = mg/L of calcium carbonate (CaCO 3 ). 28
29 For the primary Pb smelter case study, freshwater AWQC were calculated based on water body30 specific measured hardness values for the water bodies located in the vicinity of the facility that 31 were sampled and analyzed for Pb (ELM 2005). Exhibit 7-2 lists thes e site-specific AWQC. 32Ecological Risk Assessment

ICF International 7-6 DRAFT – December 2006 1 Exhibit 7-2. Calculated Pb AW QC for Water Bodies Analyzed 2 for the Primary Pb Smelter Case Study 3

Sample Cluster or Area Hardness a (mg CaCO 3 /L) Chronic AWQC for Pb - CCC ( μ g/L) Acute AWQC for Pb - CMC ( μ g/L)

Joachim Creek Cluster 1 283.3 7.7 197 Cluster 2 276.7 7.5 192 Cluster 3 281.7 7.6 195 Cluster 4 272.2 7.3 188
Cluster 5 273.3 7.4 189 Mississippi River Upstream 210 5.6 143 Near facility 227.5 6.1 156
Downstream 235 6.3 161 Pond and Drainage Areas U-shaped pond cluster 166.67 4.4 112 CHRDDP 465 12.8 328
RRDP-02 240 6.4 165
DAMUP 280 7.6 195

a Hardness measures were taken from ELM (2005). 4 CCC = criterion continuous concentration (chronic criterion), U.S. EPA’s National AWQC for Pb 5 CMC = criterion maximum concentration (acute criterion), U.S. EPA’s National AWQC for Pb 6
7 To simplify the national-scale surface water screening assessment, a more conservative 8 freshwater CCC of 1.2 μg/L dissolved Pb associ ated with a low water hardness of 50 mg/L 9 CaCO 3 was used as an initial screening step. At sampling locations where that value was 10 exceeded, the next step was to find co-located measurements of water hardness (expressed as 11 mg/L of CaCO 3 ) and to use those values to calculate a location-specific CCC and CMC for the 12 identified sampling location. 13
14 Location-specific CCC and CMC values for the national-scale screen ing assessment were 15 derived based on water hardness measurements av ailable for those locations. The number of 16 water hardness measurements at a given sampling station in the NAWQA data set ranged from 17 zero to more than 60. Where there were two or more measurements of water hardness, the 18 arithmetic mean of the available measurements across all years was used to represent the water 19 hardness at the sampling location. Where there we re no analyses of wate r hardness at a given 20 sampling location, the next closest sampling stat ion (from station ID number and latitude and 21 longitude) was identified and water hardness associated with this station was used for the 22 sampling location in question. Where there was a single value reported for water hardness, that 23 value was used without comparing it to values for any next nearest sampling locations. 24
25 Rather than discard any sampling stations from the screening assessment, the use of single or 26 nearby water hardness measurements to calcu late a CCC and CMC for a location was deemed 27 acceptable. For sampling stations with more th an two measurements of water hardness, the 28Ecological Risk Assessment

ICF International 7-7 DRAFT – December 2006 arithmetic mean of the measurements is the measure most consistent with the limited data at 1 other stations. 2
3 A review of the data on water hardness in the NAWQA data set for 1994 to 2004 indicated that 4 the initial screening valu e of 1.2 μg/L was too high to identif y all locations for which dissolved 5 Pb concentrations exceeded the CCC for the prot ection of aquatic life. Many waters in the 6 United States are softer than orig inally thought (i.e., measured CaCO 3 concentrations were as 7 low as 1 mg/L). A second screening was therefore conducted in which dissolved Pb 8 measurements greater than the quantitation limit (QL) but less than 1.2 μg/L were reviewed. 9
10 In the second screen, for each sampling locati on with one or more measured dissolved Pb 11 concentrations above the QL but less than 1.2 μg/L, measurements of CaCO 3 were used to 12 calculate a location-specif ic CCC as described above. 1 If none of the dissolved Pb 13 measurements at a particular location exceeded its location-specific CCC, the location was not 14 considered further. If one or more of the disso lved Pb measurements at a particular location did 15 exceed the location-specific CCC, the location wa s retained for further analysis. The Pb 16 measurements for the additional locations identi fied in the second screening are provided in 17 Appendix H. 18
19 7.1.3.3 Sediment Screening Values 20
21 A number of sediment ecotoxicity values were considered in identifying chronic sediment 22 screening values for this assessment. These incl uded several types of criteria for freshwater 23 sediments derived using different approaches; Exhi bit 7-3 lists the candida te sediment screening 24 values; Appendix M reviews the details of thei r derivation. The EPA ap proach for sediment 25 quality criteria (i.e., equilibrium parti tioning sediment benchmarks) (USEPA 2005d) utilizes 26 EPA’s AWQC with site-specific information on acid volatile sulfides and organic carbon. As 27 such site-specific information was not availabl e for the case study location at which sediment 28 was assessed, or for the nationa l-scale screening assessment, sediment quality assessment 29 guidelines developed by MacDonald et al. (2000; reported in MacDonald et al. 2003) were 30 identified for use in this ecological risk assessm ent at all case study locations and in the national31 scale screening assessment. 32
33 The MacDonald et al. (2000; 2003) gu idelines include two values fo r Pb: (1) a threshold effect 34 concentration (TEC) of 35.8 mg/kg and (2) a probable effect con centration (PEC) of 128 mg/kg. 35 The TEC was derived as the geometric mean of lite rature values described as being at the lower 36 end of the effects continuum. The PEC was derived as the geometric mean of literature values 37 described as more prevalently associated with adverse effect s. Appendix M provides more 38 details on their basis. These values are consider ed to be applicable to the United States (and 39 Canada). 40
41

1 For each sampling site, the arithmetic mean of all CaCO 3 samples, the single hardness measurement, or the water hardness from a nearby sampling site was used.Ecological Risk Assessment

ICF International 7-8 DRAFT – December 2006 Exhibit 7-3. Pb Sediment Screening Values Evaluated for Use in 1 This Ecological Risk Assessment 2

Description Value (mg Pb/kg Dry Sediment)

EPA Region 4 Sediment Screening Value (derived by MacDonald 1994) 30.2

EPA Region 5 Ecological Screening Level for Sediment (adopted consensus TEC from MacDonald et al. 2000; see below) 35.8

EPA Region 6 Ecological Benchmark for Sediment – Freshwater (TNRCC 2001) 35

Consensus TEC for freshwater sediments (MacDonald 2000) 35.8

Consensus PEC for freshwater sediments (MacDonald 2000) 128

Equilibrium partitioning (USEPA 2005d) - Low risk of adverse biological effects Equilibrium partitioning (USEPA 2005d) - May have adverse biological effects Equilibrium partitioning (USEPA 2005d) - Adverse biological effects expected Requires site-specific measures of acid volatile sulfide and organic carbon

fraction in the sediment

Abbreviations: 3 TNRCC = Texas National Resource Conservation Commis sion; TEC = Threshold Effect Concentration; 4 Consensus PEC = Probable effect concentration (sed iment concentration above which harmful effects are 5 likely to be observed, MacDonald et al. [2000]); Consensus TEC = Threshold effect concentration 6 (sediment concentration below which harmful effects ar e unlikely to be observed, MacDonald et al. [2000]) 7 8 The consensus-based TEC for Pb was calculated as the geometric mean of five literature values, 9 ranging from a threshold effect le vel of 35 mg/kg from Smith et al . (1996) to a minimal effect 10 threshold of 42 mg/kg from Environment Canada (EC) and Ministere de l’Environnement du 11 Quebec (MENVIQ) (EC and MENVIQ 1992). The consensus-based PEC for Pb was calculated 12 as the geometric mean of five values, ranging fr om a probable effect level of 91.3 mg/kg (Smith 13 et al. 1996) to a severe effect level of 250 mg/kg (Persaud et al. 1993). Moreover, they were 14 derived via collaboration of many partners in the Freshwater Sediment Quality Assessment 15 Initiative (MacDonald et al. 2003) which included revi ew of a wide range of options before 16 selecting an approach. 17
18 Note that there are some limitations to using th e TEC and PEC values for assessing risk to the 19 benthic community. A major limitation is that site -specific differences in the bioavailability of 20 Pb are not taken into account. 21
22 7.2 Exposure and Risk Estimates 23
24 Chapter 4 presents the methods and data used to develop the media concentrations used as the 25 measures of exposure in the ecological risk asse ssment. These media concentrations of Pb are 26 summarized and the associated risk HQs are pres ented in the sections that follow for the case 27 studies: primary Pb smelter (Section 7.2.1), secondary Pb sm elter (Section 7.2.2), and near 28Ecological Risk Assessment

ICF International 7-9 DRAFT – December 2006 roadway non-urban (Section 7.2.3). The national- scale surface water and sediment screening 1 assessment Pb concentrations and HQs are presented in Section 7.2.4. 2
3 7.2.1 Primary Pb Smelter Case Study 4
5 Sampling clusters based on likely exposure patter ns were created for this case study location by 6 pooling empirical data collected from various sa mpling locations within the case study location. 7 Section 4.1.3 summarizes the total Pb soil concentrations, and Sec tion 4.1.5 discusses the surface 8 water concentrations and sediment Pb concentr ations for the primary Pb smelter case study. 9 Appendix F presents a detailed description of the a ggregates that form each cluster. Soil, surface 10 water, and sediment average concentrations a nd HQ results are presented in Exhibits 7-4, 7-5, 11 and 7-6, respectively. 12
13 Exhibit 7-4. HQs Calculated for So ils for Primary Pb Smelter Case Study a 14

Location of Sample Cluster Average Pb Concentration (mg/kg) HQ for Plants HQ for Soil Invertebrates HQ for Avian Wildlife HQ for Mammalian

Wildlife

1 - West Bank of Joachim Creek 425 3.55 0.25 11.19 3.80 2 - Crystal City 62.6 0.54 0.04 1.70 0.58 3 - Near Fester's Airport 48.5 0.40 0.03 1.28 0.43

a HQ values greater than 1.0 are highlighted in bold type. 15 16 As shown in Exhibit 7-4, all of the soil sampli ng clusters had HQs that exceeded 1.0 for birds, 17 including those “control” locations outside the assumed impact area of the primary Pb smelter. 18 The west bank of the Joachim Creek samples al so had HQs greater than 1.0 for plants and 19 mammals. The west bank of Joachim Creek, which is inside the zone of influence of the primary 20 smelter, had HQ values that were substantiall y greater than those fo r the control areas. 21
22 As discussed in Section 3.1.6, there is wildlife habitat in the vicin ity of Joachim Creek, and birds 23 and mammals often use riparian (s tream-side) areas for feeding a nd for moving between habitats. 24 25Ecological Risk Assessment

ICF International 7-10 DRAFT – December 2006 Exhibit 7-5. HQs Calculated for Surface Waters 1 for Primary Pb Smelter Case Study 2

Sample Location and Cluster ID Dissolved Pb Concentration HQ using CCC (Chronic) HQ using CMC (Acute)

Joachim Creek Cluster 1 ND a 0.39 0.02 Cluster 2 ND 0.40 0.02 Cluster 3 ND 0.39 0.02 Cluster 4 ND 0.41 0.02
Cluster 5 ND 0.41 0.02 Mississippi River Upstream ND 0.54 0.02 Near facility ND 0.49 0.02
Downstream ND 0.48 0.02 Emission Deposition Cluster 1 ND 0.69 0.03 CHRDDP ND 0.24 0.01
RRDP-02 ND 0.47 0.02
DAMUP ND 0.40 0.02

a ND = not detected; method detection limit was 3 μ g/L; assumed for this assessment 3 that surface water concentration level was at the detection limit. 4 5 Exhibit 7-6. HQs Calculated for Sediments in Surface Waters 6 for Primary Pb Smelter Case Study 7

Location and Cluster Sample ID Average Measured Pb Concentration in Sediment (mg/kg) HQ

Joachim Creek Cluster 1 36.5 1.0 Cluster 2 56.0 1.6 Cluster 3 78.7 2.2 Cluster 4 30.0 0.84
Cluster 5 34.5 0.96 Mississippi River Upstream 14.7 0.41 Near facility 30.1 0.84
Downstream 12.1 0.34 Pond and Drainage Areas U-shaped ephemeral pond cluster 170 4.8
ED1 110 3.1
ED2 14.8 0.41

a HQ values greater than 1.0 are highlighted in bold type. 8 9 10Ecological Risk Assessment

ICF International 7-11 DRAFT – December 2006 The surface water sampling clusters, which assume d a Pb concentration e qual to the detection 1 level, had HQs less than 1.0. However, three sedi ment sample clusters in Joachim Creek (1, 2, 2 and 3) had HQs ranging from 1.0 to 2.2, and the U-shaped ephemeral pond and one drainage area 3 had sediment HQs greater than 3 but less than 5. 4
5 7.2.2 Secondary Pb Smelter Case Study 6
7 For the secondary Pb smelter case study, as described in Sect ion 4.2.3, two sets of modeled 8 average Pb soil concentrations were devel oped for the human exposure and health risk 9 assessments and also used as exposure estimate s for the ecological risk assessment. The 10 averages for 1-, 5-, or 10-km interval distan ces from the secondary Pb smelter facility are 11 summarized in Exhibit 7-7. The associated soil HQs calculated for each interval are also 12 presented in Exhibit 7-7. 13 14 Exhibit 7-7. HQs Calculated for Soils for Secondary Pb Smelter Case Study a 15 Distance Range (m) Modeled Soil Concentration Data Sets (mg/kg) HQ for Plants HQ for Soil Invertebrates HQ for Birds HQ for Mammals

Total Pb Soil Conc. with Background

0 1000 86.6 0.72 0.05 2.28 0.77
1000 2000 20.7 0.17 0.012 0.54 0.18
2000 3000 17.3 0.14 0.010 0.46 0.15
3000 4000 16.3 0.14 0.010 0.43 0.15
4000 5000 15.8 0.13 0.009 0.42 0.14
5000 10000 15.4 0.13 0.009 0.41 0.14
10000 20000 15.1 0.13 0.009 0.40 0.13

Scaled 3x Total Pb Soil Conc. with Background

0 1000 260 2.17 0.15 6.84 2.32
1000 2000 62.3 0.52 0.037 1.64 0.56
2000 3000 51.8 0.43 0.030 1.36 0.46
3000 4000 48.9 0.41 0.029 1.29 0.44
4000 5000 47.3 0.39 0.028 1.24 0.42
5000 10000 46.2 0.39 0.027 1.22 0.41
10000 20000 45.4 0.38 0.027 1.19 0.41

a HQ values greater than 1.0 are highlighted in bold type. 16 17 The modeled soil concentrations within 1 km of the facility showed HQs of greater than 1.0 for 18 avian wildlife. All soil concentrations for locat ions greater that 1 km from the facility were 19 associated with HQs less than 1.0 for this data set. The scaled soil concentr ation data set resulted 20 in avian HQs greater than 1.0 for all distance intervals evaluated, including th e farthest interval 21 modeled, 10 to 20 km from the facility. The sc aled soil concentrations within 1 km of the 22 facility also showed HQs greater than 1.0 for plants, birds, and mammals. 23Ecological Risk Assessment

ICF International 7-12 DRAFT – December 2006 As for the previous case study, there is wildlife ha bitat near the secondary Pb smelter facility. 1 However, as discussed in Section 3.2.6, none of the species residing in the county where this 2 facility is located are currently on state or fe deral threatened and endangered species lists. 3
4 7.2.3 Near Roadway Non-Urban Case Study 5
6 Average Pb soil concentrations and risk charact erization results for th e two non-urban case study 7 locations—Corpus Christi, Texas and Atlee, Virgin ia—are presented in this section. Exhibit 7-8 8 presents the average Pb soil concentrations (which are detailed in Section 4.4.1) and HQ 9 calculated for the Corpus Christi, Texas n ear roadside soil concentration data. 10
11 Exhibit 7-8. Soil HQs Calculated for the No n-Urban Near Roadway Case Study Location 12 in Corpus Christi, Texas a 13
Soil HQs Sample Location –

Distance

from Roadway (m) Sample Depth (cm) Total Pb Concentration (mg/kg) Plants Invertebrates Avian Wildlife Mammalian Wildlife

2 2.5 340 2.83 0.20 8.95 3.04
2 10 650 5.42 0.38 17.1 5.80
2 20 15 0.13 0.019 na b 0.13
4 2.5 140 1.17 0.082 3.68 1.25

a HQ values greater than 1.0 are highlighted in bold type. 14

b na = not applicable. Woodcock are specialized for f eeding on earthworms by probing into soils, and may 15 be able to reach a depth of 6 to 7 cm, which is the approximate length of the bill (Keppie and Whiting 1994). 16 17 HQs for birds and mammals were greater than 1.0 at both 2 and 4 meters (m) from the road. 18 Plants also had HQs ranging from 2.83 to 5.42 at the 2-m distance, depending on depth. 19
20 Exhibit 7-9 presents the location- specific Pb soil concentration data and HQs calculated for the 21 ecological exposure area in Atlee, Virginia, fo r the four ecological receptor groups. The soil 22 concentration data for this loca tion are described in Section 4.4.2. The HQs associated with the 23 overall average Pb concentration for the potentia l ecological exposure area are also presented. 24 25Ecological Risk Assessment

ICF International 7-13 DRAFT – December 2006 Exhibit 7-9. Soil HQs Associated with Non-Urban Near Roadway 1 Location in Atlee, Virginia a 2

Soil HQs Location ID (Approximate Distance from Nearest Road) Average Total Pb Concentration (mg/kg) b Plants Soil Invertebrates Avian Wildlife Mammalian Wildlife

6 (2 m) 525 4.4 0.31 14 4.7 5 (2 m) 59 0.49 0.035 1.6 0.53 16 (10 m) 140 1.2 0.082 3.7 1.3 22 (10 m) 100 0.83 0.059 2.6 0.89 10 (15 m) 99 0.83 0.058 2.6 0.88 4 (15 m) 50.5 0.42 0.030 1.3 0.45 2 (15 m) 48 0.40 0.028 1.3 0.43 1 (30 m) c 40.5 0.34 0.024 1.1 0.36 3 (30 m) c 31.5 0.26 0.019 0.83 0.28 Average 123 1.0 0.072 3.2 1.1

a HQ values greater than 1.0 are highlighted in bold type. 3

b Sampling depths of 0 to 15 inches. 4

c Sampled soils in a surface water runoff detention basin. 5 6 HQs for birds were greater than 1.0 at all locations except one (Location ID 3) at 30 meters from 7 the interstate. Plant and mammal HQs were gr eater than 1.0 at two of the nine locations 8 (Location IDs 6 and 16) within 2 and 10 meters of the road, respec tively, although the HQs 9 associated with Location ID 16, 10 m from the in terstate, were only slightly above 1.0. HQs for 10 the soil invertebrate receptor group were below 1.0 at all locations. 11
12 7.2.4 National-Scale Surface Water and Sediment Screening Assessment 13
14 This section presents the Pb exposure measures and risk characterization results of the national15 scale surface water and sediment screening asse ssment. The water column and sediment Pb 16 concentration data used to estimate ecological exposures are described in Section 4.5.2 and 4.5.3, 17 respectively. Section 7.2.4.1 presents the su rface water dissolved Pb concentrations and 18 associated risk HQs, and Section 7.2.4.2 presents the total Pb sediment concentrations and 19 associated risk HQs. 20
21 7.2.4.1 Surface Water Column Portio n of the Screening Assessment 22
23 The initial screen of dissolved Pb concentrations for measuremen ts equal to or greater than 24 1.2 μg/L identified 42 sampling locations for which one or more measurements exceeded that 25 screening value. Data for each measurement of di ssolved Pb at these locations are provided in 26 Appendix H. The number of samples analyzed for dissolved Pb at each station ranged from 1 to 27 60, and the number of measuremen ts that exceeded 1.2 μg/L ranged from 1 to 50. For purposes 28 of this assessment, given the limited analyses for dissolved Pb, all 42 sampling locations were 29 retained for analysis. Eight additional loca tions were added during a second screen, which 30 identified sampling locations with one or more measured dissolved Pb concentrations above the 31 QL, but less than 1.2 μg/L, and that exceed site specific CCC values (see Appendix H). 32
33Ecological Risk Assessment

ICF International 7-14 DRAFT – December 2006 Chronic Risks: Exhibit 7-10 presents the chronic HQs for the locations identified in the initial 1 screen (i.e., those locations not specified as “na” in Appendix H). This exhibit is organized in 2 order of increasing water hardness, and therefore, increasing values of the site-specific CCCs. 3 The land use is as reported in the NAWQA database. 4
5 For the measures of CaCO 3 at each sampling location, the mean (± one standard deviation) 6 concentration and sample size are presented. For stations for which there were no water 7 hardness data (ND), an endnote to the exhi bit describes how the CCC was estimated. 8
9 The dissolved Pb measurements for each loca tion were characterized in four ways: 10
11 1. The number of measured dissolved Pb concentr ations that exceeded the CCC out of the 12 total number of samples analyzed for dissolved Pb; 13 2. The number of samples that were less than the QL where the QL is greater than the CCC 14 for dissolved Pb (i.e., the number of sample s that might or might not exceed the CCC); 15 3. The mean of the measured Pb concen trations that exceeded the CCC; and 16
4. The maximum measured dissolved Pb concentration. 17
18 Two types of HQs are presented in Exhibit 7-10. The first type of HQ represents an average HQ 19 for the measurements that exceeded the CCC. This HQ answers the question “when the CCC is 20 exceeded, on average, what is the magnitude of the HQ?” The second HQ represents a 21 maximum HQ based on the maximum dissolved Pb concentration. This HQ assumes that the 22 maximum value represents at least a four-day average concentration. For many sampling sites, 23 both HQs are the same because only a single me asurement of dissolved Pb exceeded the CCC. 24 Exhibit 7-10 illustrates that many of the larger chronic HQ values are associated with mining 25 sites. 26
27 28Ecological Risk AssessmentICF International7-15 DRAFT –December 2006Exhibit 7-10. Results of Chronic AquaticRisk Screening Assessment –HQs for MeasuredDissolved Pb Concentrations Comparedto Chronic AWQC (i.e., Criterion Continuous Concentration or CCC) for the Protection of Aquatic Life for PbaDissolved Pb MeasurementsCalcium Carbonate Hazard QuotientsBasinIDStateStationIDLandUseMean ±SD(mg/L)NPbCCC(μg/L)No.> CCC/ TotalNNo. <QL,whichis > CCCMean of[Pb]s> CCC(μg/L)Max [Pb](μg/L)Mean[Pb] /CCCMax[Pb] /CCC44 UCOL CO 393557105512400 Mining1(b)1 0.013 1/1 0 1.26 1.2696.92 96.9259 COOK AK 600715152572800 Reference 6 1 0.11 1/1 0 1.64 1.6414.91 14.9146 NROK ID 12392155 Forest 8.8± 2.8 18 0.17 4/17 10 0.224 0.2611.32 1.5458 OAHU HA 16212700 Mixed9 2 0.17 1/2 1 0.337 0.3371.98 1.982 CONN CT 1119375 Mixed 9.5± 2.9 19 0.18 5/20 13 0.302 0.3771.68 2.092 CONN CT 1124000 Mixed 14.7± 4.9 30 0.30 11/23 9 0.759 12.53 3.3344 UCOL CO 375732107394000 MiningND(c)0 0.35 2/2 0 21.6 2361.71 65.7144 UCOL CO 385437107015600 Mining 17 1 0.35 1/1 0 6.02 6.0217.20 17.202 CONN CT 1127000 Mixed 17.4± 8.7 29 0.36 3/22 14 0.408 0.4721.13 1.3146 NROK ID 12419000 Mixed 17.9± 1.5 29 0.37 2/26 16 0.995 1.5792.69 4.271 NECB RI 1112900 Mixed 21± 10 13 0.44 3/3 0 1.103 1.5512.51 3.5346 NROK ID 12413470 Mining 25± 7.2 34 0.54 33/33 0 4.62 11.628.56 21.5244 UCOL CO 9046530 Mining 31.6 ± 3.3 62 0.70 53/60 7 3.39 84.84 11.4344 UCOL CO 392907106013900 Mining35.3 ± 4.0 3 0.80 2/3 1 1.50 21.88 2.5046 NROK ID 12413123 Mining36.3 1 0.82 1/1 0 29.78 29.7836.32 36.3244 UCOL CO 385240106583600 Other/Mixed 39 2 0.89 1/4 2 0.971 0.9711.09 1.0944 UCOL CO 392947106024500 Mining41.3 ± 4.0 3 0.95 2/3 1 1.50 21.58 2.1146 NROK WA 12422500 Urban 43± 27 20 0.99 4/28 24 1.131 1.2331.14 1.2546 NROK WA 12422000 Urban 43.1± 25.7 18 0.99 2/20 18 1.158 1.1581.17 1.1746 NROK ID 12413150 Mining 50.2 1 1.2 1/1 0 10.518 10.5188.77 8.7744 UCOL CO 9146200 Mining 84.7± 28 52 2.1 0/3 0 2 2 0.95 0.9531 OZRK AR 7050500 Mixed 102 ± 39 13 2.6 1/8 0 9 93.46 3.4645 RIOG NM 8331000 Mixed 113± 18 37 2.9 1/12 0 3 31.03 1.0350-51CCYK WA 461850120005800 Other/MixedND(d)0 3.3 0/1 0 1.2 1.2 0.36 0.3631 OZRK MO 7061155 Mining136 1 3.5 0/1 0 3 3 0.86 0.8631 OZRK MO 7018100 Forest 142 1 3.7 1/2 0 7 71.89 1.8931 OZRK MO 7061135 Mining 160 1 4.2 1/1 0 11 112.62 2.6255 NVBR NV 94196783 Urban 175 ± 57106 4.6 0/5 02.2 3 0.48 0.65Ecological Risk AssessmentICF International7-16 DRAFT –December 2006Exhibit 7-10. Results of Chronic AquaticRisk Screening Assessment –HQs for MeasuredDissolved Pb Concentrations Comparedto Chronic AWQC (i.e., Criterion Continuous Concentration or CCC) for the Protection of Aquatic Life for PbaDissolved Pb MeasurementsCalcium Carbonate Hazard QuotientsBasinIDStateStationIDLandUseMean ±SD(mg/L)NPbCCC(μg/L)No.> CCC/ TotalNNo. <QL,whichis > CCCMean of[Pb]s> CCC(μg/L)Max [Pb](μg/L)Mean[Pb] /CCCMax[Pb] /CCC40 SPLT CO 6752260 MixedND(e)0 4.9 0/4 0 4 4 0.82 0.8240 SPLT CO 6752270 MixedND(e)0 4.9 0/4 0 2 2 0.41 0.4140 SPLT CO 6752280 MixedND(e)0 4.9 0/4 0 3 3 0.61 0.6149 CAZB AZ 9517000 Agriculture 190± 36 48 5.0 0/1 0 2 2 0.40 0.4047 GRSL UT 404026111273001 Rangeland 195 1 5.2 0/5 02.34 2.828 0.45 0.5447 GRSL UT 403938111300201 UrbanND(f)0 5.2 0/2 0 1.432 1.432 0.28 0.2845 RIOG NM 8317200 Urban 196 ± 68 145.2 0/3 0 2.33 3 0.45 0.5847 GRSL UT 404431111282901 UrbanND(g)0 5.3 0/2 0 3.754 4.271 0.71 0.8147 GRSL UT 404847111240501 RangelandND(h)0 5.8 0/2 05.495 8.403 0.951.45Abbreviations: SD = standard deviation; N = Total number of samples analyzed for specified analyte; [Pb] = dissolved Pb concentration; QL = quantitation limit1(where QL = 0.08 μg/L dissolved Pb, somemeasured concentrations that were greaterthan the detection limit, but less than theQL were estimated and are2considered≥QL for purposes of this exhibit); No. = Number of samples analyzed for dissolved Pb; Basin ID four-letter codes are describedin Appendix H; State3codes = state postal abbreviations; ND = no data4aRecords are presented in order of increasing mean water hardness (mg/L as CaCO3) and hardness-specific CCC. HQ valuesgreater than 1.0 are highlighted in5bold type.6bThis measurement of water hardness seems unreasonably low.7cNo water hardness data for this sampling station or nearby stations; used CCC determined for nearest station (i.e., 385437107015600).8dNo water hardness data for this sampling station; used CCC based on a value of 128 mg/L as CaCO3from nearest station (i.e., 461517119402500).9eNo water hardness data for this sampling station; used CCC based on a value of 187 mg/L as CaCO3from nearest station (i.e., 6754000).10fNo data for this station; used CCC based on water hardness value for the other single value for Silver Creek (station No. 404026111273001).11gNo data for this station; one nearby station had values of 234 and 223 mg/L as CaCO3; another nearby station had values of 195 and 176 mg/L as CaCO3. This12analysis therefore assumed a water hardness of 200 mg/L to estimate the CCC for this location.13hNo water hardness data for this station; used CCC based on a value of 219 mg/L as CaCO3from nearest station (i.e., 404751111423501).14Ecological Risk Assessment

ICF International 7-17 DRAFT –December 2006 Acute Risks: Exhibit 7-11 presents the acute aquatic risk HQs for the locations identified in the 1 initial screen (i.e., those locations not specif ied as “na” in Appendix H). This exhibit is 2 organized in order of increasing water hardness, and therefore, increasi ng values of the site3 specific CMCs. In this exhibit, dissolv ed Pb is characterized in two ways: 4
5 1. The number of measured dissolved Pb concen trations that exceed the CMC out of the 6 total number of samples analyzed for dissolved Pb; and 7 2. The maximum measured dissolved Pb concentration. 8
9 Given that the CMCs are between 20 a nd 25 times higher than the CCCs, the only HQ 10 considered here is the maximum dissolved Pb co ncentration compared with the CMC. Exhibit 11 7-11 shows that only three sampling sites had acute HQ values that exceeded 1.0 (in bold), and 12 all three of these are located near mining sites. 13
14 Exhibit 7-11. Results of Acute Aquatic Risk Screening Assessment –HQs for Measured Dissolved Pb Concentrations Co mpared to Acute AWQC (i.e., Criterion Maximum Concentration or CMC) for th e Protection of Aquatic Life for Pb a

Basin

ID Dissolved Pb Measurements State Station ID Land Use Pb CMC (μg/L) No. > CMC / Total N Max [Pb] (μg/L) HQ: Max [Pb] / CMC

44 UCOL CO 393557105512400 Mining 0.34 1/1 1.26 3.71
59 COOK AK 600715152572800 Reference 2.7 0/1 1.64 0.61
46 NROK ID 12392155 Fore st 4.2 0/17 0.261 0.06
58 OAHU HA 16212700 Mix ed 4.4 0/2 0.337 0.08
2 CONN CT 1119375 Mix ed 4.6 0/20 0.377 0.08
2 CONN CT 1124000 Mixed 7.6 0/23 1 0.13
44 UCOL CO 375732107394000 Mi ning 9.0 0/1 6.02 0.67 44 UCOL CO 385437107015600 Mining 9.0 2/2 23 2.56
2 CONN CT 1127000 Mix ed 9.2 0/22 0.472 0.05
46 NROK ID 12419000 Mix ed 9.5 0/26 1.579 0.17
1 NECB RI 1112900 Mi xed 11 0/3 1.551 0.14 46 NROK ID 12413470 Mining 14 0/33 11.62 0.83 44 UCOL CO 9046530 Mining 18 0/60 8 0.44 44 UCOL CO 392907106013900 Mining 20 2/3 2 0.10 46 NROK ID 12413123 Mining 21 1/1 29.78 1.42
44 UCOL CO 385240106583600 Ot her/Mixed 23 0/4 0.971 0.04 44 UCOL CO 392947106024500 Mining 24 0/3 2 0.08
46 NROK WA 12422500 Urban 25 0/28 1.233 0.05
46 NROK WA 12422000 Urban 25 0/20 1.158 0.05 46 NROK ID 12413150 Mining 30 0/1 10.518 0.35 44 UCOL CO 9146200 Mining 54 0/3 2 0.04
31 OZRK AR 7050500 Mixed 66 0/8 9 0.14
45 RIOG NM 8331000 Mi xed 74 0/12 3 0.04 50-51 CCYK WA 461850120005800 Other/Mixed 84 0/1 1.2 0.01

31 OZRK MO 7061155 Mining 90 0/1 3 0.03
31 OZRK MO 7018100 Forest 94 0/2 7 0.07 31 OZRK MO 7061135 Mining 110 0/1 11 0.10Ecological Risk Assessment

ICF International 7-18 DRAFT –December 2006 Exhibit 7-11. Results of Acute Aquatic Risk Screening Assessment –HQs for Measured Dissolved Pb Concentrations Co mpared to Acute AWQC (i.e., Criterion Maximum Concentration or CMC) for th e Protection of Aquatic Life for Pb a

Basin

ID Dissolved Pb Measurements State Station ID Land Use Pb CMC (μg/L) No. > CMC / Total N Max [Pb] (μg/L) HQ: Max [Pb] / CMC

55 NVBR NV 94196783 Urban 120 0/5 3 0.03 47 GRSL UT 404026111273001 Rangeland 130 0/5 2.828 0.02 49 CAZB AZ 9517000 Agriculture 130 0/1 2 0.02
45 RIOG NM 8317200 Ur ban 130 0/3 3 0.02
47 GRSL UT 403938111300201 Urban 130 0/2 1.432 0.01
40 SPLT CO 6752270 Mixed 130 0/4 2 0.02
40 SPLT CO 6752280 Mixed 130 0/4 3 0.02
40 SPLT CO 6752260 Mixed 130 0/4 4 0.03
47 GRSL UT 404431111282901 Urban 140 0/2 4.271 0.03 47 GRSL UT 404847111240501 Rangeland 150 0/2 8.403 0.06 Abbreviations: Same as Exhibit 7-10. 1

a Records are presented in order of increasing hardne ss-specific CMC. HQ valu es greater than 1.0 are 2 highlighted in bold type. 3
4 Aquatic Risks at Mining Sites Compared with other Sites: The locations of interest for the 5 Pb NAAQS review are those loca tions for which deposition from air might be a significant input 6 of Pb into the surface water. It may be assume d that air inputs are not easily distinguishable 7 from other non-air inputs near mining sites. Ther efore, the results for the mining sites (Exhibit 8 7-12) were separated from the other types of locations (Exhibit 7-13). Locations for which the 9 HQ was less than 1.0 were eliminated to summar ize the results for the aquatic chronic risk 10

screen. 11
12 Exhibit 7-12 summarizes the two chronic and one acute risk HQs for the 10 mining sites for 13 which the chronic HQ exceeded 1.0. The exhibit is presented in order of increasing HQ. 14
15Ecological Risk Assessment

ICF International 7-19 DRAFT –December 2006 Exhibit 7-12. Results of Aquatic Risk Sc reening Assessment – Mining Sites at which 1 Dissolved Pb Measurements Exceed AWQC a 2
Pb Measurements Hazard Quotient

Basin

ID State Station ID Land Use Pb CCC (μg/L) No. > CCC / Total N No. < QL, which is > CCC Mean [Pb] / CCC Max [Pb] / CCC Max [Pb] / CMC

44 UCOL CO 392947106024500 Mining 0.95 2/3 1 1.58 2.11 0.08 44 UCOL CO 392907106013900 Mining 0.80 2/3 1 1.88 2.50 0.10 31 OZRK MO 7061135 Mining 4.2 1/1 0 2.62 2.62 0.10 44 UCOL CO 9046530 Mining 0.70 53/60 7 4.84 11.43 0.44 46 NROK ID 12413470 Mining 0.54 33/33 0 8.56 21.52 0.83 46 NROK ID 12413150 Mining 1.2 1/1 0 8.77 8.77 0.35 44 UCOL CO 385437107015600 Mining 0.35 1/1 0 17.20 17.20 0.67 46 NROK ID 12413123 Mining 0.82 1/1 0 36.32 36.32 1.42 44 UCOL CO 375732107394000 Mining 0.35 2/2 0 61.71 65.71 2.56 44 UCOL CO 393557105512400 Mining 0.013 1/1 0 96.92 96.92 3.71

Abbreviations: Same as for Exhibits 7-10; CCC = criteri on continuous concentration (or chronic AWQC); CMC = 3 criterion maximum concentration (or acute AWQC). 4

a In order of increasing Hazar d Quotient for the CCC aquatic toxicity benc hmark. HQ values greater than 1.0 are 5 highlighted in bold type. 6
7 Many of the sampling sites near mining land uses are characterized by soft water (i.e., CaCO 3 8 less than 50 mg/L and CCC less than 1.2 μg/L). Note that some of the largest HQs are 9 associated with sites that had one or two samp les for dissolved Pb and no or few samples for 10 CaCO 3 . Thus, the uncertainty associated with th e chronic HQ values for these sites is higher 11 than for the two locations that have been well characterized (i.e., with 33 and 60 samples 12 analyzed for dissolved Pb). For the mining site s, it was assumed that drainage from the mines 13 provides the majority of the Pb input to the surface waters, and these site s were not examined 14 further. 15
16 Exhibit 7-13 summarizes the HQs for the 15 non-mi ning sites for which the chronic HQs exceed 17 1.0 in order of increasing HQ. Th ese locations are in areas classi fied in the NAWQA database as 18 urban and mixed, but also include forest, rangel and, and a “reference” site in Alaska. The 19 highest HQ is for the Alaska reference site and is based on one measurement of dissolved Pb and 20 one measurement of CaCO 3 , which was extremely low (i.e., 6 mg/L). Thus, the uncertainty 21 associated with this HQ is high. 22
23Ecological Risk Assessment

ICF International 7-20 DRAFT –December 2006 Exhibit 7-13. Results of Aquatic Risk Screen ing Assessment - Locations at which Dissolved 1 Pb Measurements Exceed AWQC, Excluding Mining Sites a 2

Pb Measurements Hazard Quotient

Basin

ID State Station ID Land Use Pb CCC (μg/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 385240106583600 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 404847111240501 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 600715152572800 Reference 0.11 1/1 0 14.91 14.91 0.61 Abbreviations: Same as Exhibit 7-11; CCC = criterion continuous concentration (or chronic AWQC); CMC = 3 criterion maximum concentration (or acute AWQC). 4

a In order of increasing Hazar d Quotient for the CCC aquatic toxicity benc hmark. HQ values greater than 1.0 are 5 highlighted in bold type. Additional information characterizing these locations is provided in Appendix H. 6
7 Contribution of Air to Pb Concentratio n at Non-Mining Si te Locations: For the sites in 8 Exhibit 7-13, three additional sources of informa tion were examined in an attempt to determine 9 whether air deposition is likely to be a significant source of Pb for the surface water. These 10 sources are as follows: 11
12 1. Land Use. More extensive data on the land us es surrounding the sampling sites were 13 obtained from the 1992 National Land Cover Da tabase for land uses within 20 km of 14 each sampling site, if available. 15 2. Air Pb Emissions. The number of facili ties that emit Pb to ai r, and the total annual 16 quantity of Pb emitted to air, within 20 km and 50 km of the sampling site were 17 determined from the 2002 National Emissions Inventory (USEPA 2006i). Facility 18 emissions to the air within 20 km are likel y to be much more important than the 19 emissions between 20 and 50 km away. Data on non-point and mobile emissions of Pb at 20 the county level were obtained from the same source. 21 3. NPDES. For each sampling site, the watershed was examined to identify facilities in the 22 National Pollutant Discharge Elimination Sy stem (NPDES) database upstream of the 23 sampling site for which Pb was a constituent id entified for the facility. For each facility 24 identified, available data were examined on permitted limits for Pb releases and on 25 measured Pb releases. 26
27 The detailed results of these analyses are presen ted in Appendix H, and th e result highlights are 28 presented in Exhibit 7-14. 29Ecological Risk Assessment

ICF International 7-21 DRAFT –December 2006 1 Exhibit 7-14. Results of National-Scale Surface Water Risk Screening Assessment - 2 Locations at which Dissolved Pb Measuremen ts Exceed AWQC, Excluding Mining Sites a 3 Pb Emissions (tons/year)

Basin ID (and State) Station ID Pb CCC (μg/L) Facilities < 20 km Facilities < 50 km Mobile& Nonpoint in County % Strip Mines,

etc. No. NPDES upstream (metals) No. [Pb]

> CCC / N HQ Max [Pb] / CCC
45 RIOG NM 8331000 2.9 0.068 0.095 0.029 0.19 3 (0) 1/12 1.03

44 UCOL CO 38524010658360 0 0.89 0.0 0.00029 0.036 0.0 ND 1/4 1.09

46 NROK WA 12422000 0.99 0.41 0.43 0.24 0.0 3 (0) 2/20 1.17

46 NROK WA 12422500 0.99 0.39 0.43 0.24 0.0 3 (0) 4/28 1.25

2 CONN CT 1127000 0.36 6.1 7.0 0.14 0.11 15 (0) 4/22 1.31

47 GRSL UT 40484711124050 1 5.8 0.0 0.36 0.001 1 0.06 1 (1) 1/2 1.45

46 NROK ID 12392155 0.17 0.0 0.0014 0.22 0.0 1 (1) 4/17 1.54

31 OZRK MO 7018100 3.7 0.0 0. 34 0.018 0.05 ND 1/2 1.89

58 OAHU HA 16212700 0.17 4.9 4.9 0.49 0.0 ND 1/2 1.98

2 CONN CT 1119375 0.18 0.68 13.7 0.014 0.07 5 (0) 5/20 2.09

2 CONN CT 1124000 0.3 0.081 11. 3 0.92 0.09 1 (0) 11/2 3 3.33

31 OZRK AR 7050500 2.6 0.0 0.0051 0.0064 0.02 1 (0) 1/8 3.46

1 NECB RI 1112900 0.44 4.1 11.7 0.15 0.44 14 (6) 3/3 3.53

46 NROK ID 12419000 0.37 0.34 0.43 0.6 0.0 11 (4) 2/26 4.27

31 COOK AK 60071515257280 0 0.11 0.0 0.0 0. 1 0.0 ND 1/1 14.91 Abbreviations: Same as Exhibit 7-10; CCC = Criterion Continuous Concentration (or chronic AWQC); CMC = 4 Criterion Maximum Concentration (or acute AWQC). 5

a In order of increasing Hazard Quotient (HQ) for the CCC aquatic toxicity benchmark. HQ values greater than 1.0 6 are highlighted in bold type. Additional information characterizing these locations is provided in Appendix H. 7 8 Of the 15 sampling locations in Exhibit 7-14, only three appear to be with in 20 km of facilities 9 emitting relatively large quantities of Pb to the atmosphere (i.e., more than 1 ton per year): one is 10 in Oahu, Hawaii, one in Jewett City, Connecticut , and one in Manville, Rhode Island (see bold 11 entries in column “Facilities < 20 km”). Two a dditional sampling locations appear to be within 12 50 km of facilities emitting relatively large qu antities of Pb, both in Connecticut; however, 13 whether these facilities are clos e enough to influence the Pb con centrations in the water column 14 at these sampling sites is unknown. 15
16 Of the three sampling locations within 20 km of facilities emitting more than 1 ton of Pb per 17 year, the location in Rhode Island might also be receiving a large fract ion of its Pb from 18 upstream discharges from metal ore processing facilities (i.e., there are six such discharges out of 19 fourteen NPDES permitted facilities ups tream of this sampling location). 20
21 7.2.4.2 Sediment Portion of the Screening Assessment 22
23 The sediment sampling locations used to represent each of the 15 non-mining sites are identified 24 in Exhibit 7-15. Co-located sediment and surface wa ter data were available for 9 of the 15 sites. 25 In addition, there were three “nea r matches,” with a sediment sampling station on the same creek 26 or river, but somewhat upstream or downstr eam of the surface wa ter sampling location. 27Ecological Risk Assessment

ICF International 7-22 DRAFT –December 2006 1 Exhibit 7-15. Results of Sedi ment Risk Screening Assessment – Stations With Exact or Near Match to Surface Water Stations at which Dissolved Pb Measurements Exceed AWQC, Excluding Mining Sites a

Basin ID State Station ID Land Use Match Name of Location

45 RIOG NM SW + Sed 8331000 Mixed Yes Rio Grande at Isleta

SW 3852401-

06583600 Other/Mixed Slate River above Coal Creek near Crested Butte 44 UCOL CO Sed 3854371- 07015600 Mining No – nearest location is in

different water body Oh-be-joyful Creek at mouth near rested Butte
SW 1127000 Mixed Quinebaug River at Jewett City

2 CONN CT Sed 1126850 Mixed Near match with location

on same

river Quinebaug River (Aspinock Pond) at Clayville

SW 12422500 Urban Spokane River at Spokane 46 NROK WA Sed 12424000 Cropland No – nearest location is in

different water body Hangman Creek at Spokane, Washington

SW 12422000 Urban Spokane River below Green Street at Spokane 46 NROK WA Sed 12424500 Urban Near match with location

on same

river Spokane River at 7 Mile Bridge Near Spokane, Washington

46 NROK ID SW + Sed 12392155 Forest Yes Lightning Creek at Clark Fork, Idaho

47 GRSL UT SW + Sed 4048471- 11240501 Rangeland Yes Silver Creek at Wanship, Utah

SW 1119375 Mixed Willimantic River at Merrow, Connecticut 2 CONN CT No Sed Match na No other station in

county na

31 OZRK MO SW + Sed 7018100 Forest Yes Big River Near Richwoods, Missouri

58 OAHU HA SW + Sed 16212700 Mixed Yes Waikakalaua Street Near Wahiawa, Oahu, Hawaii

1 NECB RI SW + Sed 1112900 Mixed Yes Blackstone River at Manville, Rhode Island

SW 1124000 Mixed Quinebaug River at Quinebaug, Connecticut 2 CONN CT Sed 1126850 Mixed Near match – see also SW 1127000 Quinebaug River (Aspinock Pond) at Clayville

46 NROK ID SW + Sed 12419000 Mixed Yes Spokane River Near Post Falls, Idaho

31 OZRK AR SW 7050500 Mixed Yes Kings River near Berryville

31 COOK AK 6007151- 52572800 Reference Yes EF ORE C Near MTH near Johnson Glacier Near Tuxedni Bay, Alaska Abbreviations: SW = surface water sampling station; Sed = sediment sampling station. 2

a Locations listed in order of increasing Hazard Quotient for the CCC aquatic toxicity benchmark. 3
4 Measured Total Pb Sediment Concentrations: Exhibit 7-16 summarizes the total Pb sediment 5 concentrations measured at 12 of the locations in Exhibit 7-15: nine locations with co-located 6 sediment and surface water data and the three “near-match” locations in Exhibit 7-15. The 7Ecological Risk Assessment

ICF International 7-23 DRAFT –December 2006 NAWQA database appears to have only one meas urement of Pb sediment concentrations for 1 each of these 12 locations. Severa l of those samples were taken in the early 1990s, earlier than 2 most of the surface water Pb concentrations that were summarized in the water column risk 3 screen. Nonetheless, assuming that sediment concentrations change slowly over time, which 4 may be one reason for the single samples in the NA WQA database, these data are of some value. 5 For comparison purposes, Exhibit 7-16 also provides the surface water HQ for Pb estimated by 6 dividing the maximum dissolved Pb concentratio n by the CCC. For the 12 sites in Exhibit 7-16, 7 the total Pb concentrations in sediments seemed to be trimodal. Seven total Pb concentrations 8 were between 23 and 68 mg/kg; two were at 239 and 240 mg/kg, and three were between 1620 9 and 2900 mg/kg. 10
11 Chronic Risks to Benthic Communities and Co ntribution of Air Pb Emissions to those 12 Risks: This section presents the HQs associated w ith measured total Pb concentrations in 13 sediments at the 12 sites presented in Exhib it 7-16. The HQs were calculated by dividing 14 potential Pb exposure concentrations by the TEC and PEC benchmarks for benthic communities 15 (MacDonald et al. 2000; 2003). The possible contributions of air Pb emissions to risks at these 16 locations are also discussed. 17
18 As shown in Exhibit 7-16, 9 of 12 sites had TEC -based HQs that exceeded 1.0; TEC-based HQs 19 at the remaining three sites we re less than 1.0. The three si tes with HQs less than 1.0 are 20 unlikely to pose risks to benthic aquatic communities based on the available data. None of these 21 three sites were those likely to be affected by air emissions of Pb from point sources (i.e., Pb 22 emissions were less than 0.07 tons pe r year at all three sites). 23
24 PEC-based HQs at five sites exceeded 1.0, indi cating probable adverse effects to ecological 25 receptors at these sites. Thr ee of these five exceeded a PEC-based HQ of 10, indicating a very 26 high probability of adverse effects to ecological receptors at these sites, and possibly higher 27 severity of effects than at the locations with lo wer HQ values. It is believed that none of these 28 three sites were likely to be affected, however, by air emissions. One in Idaho was downstream 29 from several NPDES-permitted discharges of me tals to surface waters (station ID 12419000). 30 The other two sites are located in Utah and Montana, suggesti ng possible historic sediment 31 contamination from mining operations. 32Ecological Risk AssessmentICF International 7-24 DRAFT – December 2006Exhibit 7-16. Concentrations of Total Pb in Sediments at Locations Near or Co-Located with the 15 Sites at which Dissolved1Pb Concentrations Exceeded the AWQC, Excluding Mining Sitesa2Pb Emissions(tons/year)Sediment HazardQuotientsBasin ID StateStationIDLandUseMatch DateTotal [Pb](mg/kgdrysediment)SWHQb:max[Pb]/CCCFac <20 kmFac <50 kmNo.UpstreamNPDESpermitsformetalsb[Pb]/TEC [Pb]/PEC45 RIOG NMSW + Sed8331000Mixed Yes 02/25/93 231.030.068 0.095 0 0.64 0.182 CONN CTSed1126850Mixed Near 07/20/93 681.136.1 7.0 01.90.5346 NROK WASed12424500Urban Near 08/05/98 47.31.140.39 0.43 01.30.3746 NROK IDSW + Sed12392155Forest Yes 07/29/98 24.91.320.0 0.0 1 0.70 0.1947 GRSL UTSW + Sed4048471-11240501Rangeland Yes 07/22/99 29001.450.0 0.36 181 2331 OZRK MOSW + Sed7018100Forest Yes 08/25/92 23001.890.0 0.34 ND64 1858 OAHU HASW + Sed16212700Mixed Yes 07/12/00 591.984.9 4.9 ND1.60.461 NECB RISW + Sed1112900Mixed Yes 06/30/99 2402.514.1 11.7 66.7 1.92 CONN CTSed1126850Mixed Near 07/20/93 682.530.081 11.3 01.90.5346 NROK IDSW + Sed12419000Mixed Yes 08/03/98 16202.690.34 0.43 445 1331 OZRK ARSW + Sed7050500Mixed Yes 11/02/93 283.460.0 0.01 0 0.78 0.2231 COOK AKSW + Sed6007151-52572800Reference Yes 07/25/00 23914.910.0 0.0 06.7 1.9aExhibited in increasing order of the surface water (SW) column risk hazard quotient (HQ). HQs exceeding 1.0 are highlighted inbold type.3bData from Exhibit 7-14 for corresponding locations.4Abbreviations:[Pb] = total Pb concentration in sediments (mg/kgdry sediment). CCC = Criterion Continuous Concentration (or chronic AWQC). For Study5Basin IDs, see Appendix H. TEC = threshold-effect concentration, and PEC = probable-effect concentration, both from the consensus-based sediment quality6criteria approach published byMacDonald et al. (2000; 2003).7Ecological Risk Assessment

ICF International 7-25 DRAFT – December 2006 Of the three locations in Exhibit 7-16 for which air emissions of Pb from point sources might be 1 contributing to ongoing Pb contamination of surface water and sediments (i.e., the 2 nd , 7 th , and 2 8 th rows for locations in Connecticut, Hawaii, and Rhode Island, respectively), only one, the 3 Blackstone River in Manville, Rhode Island, is likely also to rece ive significant Pb inputs from 4 upstream NPDES-permitted sites. That site also might have received historic inputs of Pb from 5 the use of leaded gasoline and subsequent eros ion of soils to the rive r. The other two, the 6 Quinebaug River in Connecticut (a near match between the Jewett City and Clayville locations) 7 and the water body at Waikakalaua Street near Wahiawa, Oahu, Hawaii, had no other obvious 8 inputs of Pb in our assessment other than the poi nt sources of air emissi ons within 20 km. Both 9 of those sites, however, are in “mixed” urbanized areas, and ther efore could quite possibly also 10 have historic Pb deposition from leaded gasoli ne and ongoing inputs of Pb to sediments from 11 erosion of soils contaminated by leaded gasoline. 12
13 7.3 Limitations and Uncertainties 14
15 This section summarizes primary limitations and uncertainties for each of the case studies and 16 the national-scale screening assessment. Sect ion 7.3.1 addresses uncerta inties and limitations 17 associated with the primary Pb smelter cas e study; Section 7.3.2 describes them for the 18 secondary Pb smelter facility case study; Sect ion 7.3.3 discusses them for the near roadway non19 urban case studies; and Section 7.3. 4 addresses limitations and uncer tainties associated with the 20 national-scale screening assessment for risks to aquatic organisms from Pb deposition from air to 21 surface waters. 22
23 Uncertainties that apply across the case studies and national-scale screening assessment include 24 but are not limited to the following: 25
26

• The case study analyses and national-scal e surface water and sediment screening 27 assessment are limited to specific case study lo cations and other locations nationwide for 28 which dissolved Pb data in surface water were available. Efforts were made to ensure 29 that the exposure estimates were attributab le to background Pb concentrations and air 30 emissions of Pb; however, it is uncertain whether other sources might have actually 31 contributed to the Pb exposure estimates. 32
33

• The selected ecotoxicity screening values fo r surface waters and sediments might not be 34 sufficient to identify risks to some thr eatened or endangered species or unusually 35 sensitive aquatic ecosystems. 36
37

• The database supporting the current AWQC for Pb is over 20 years old. There are data to 38 indicate that Pb bioconcentrat es to some extent in inve rtebrates (e.g., bioconcentration 39 factors, or BCFs, of 500 to 1,700), and, to a lesser extent, in fish (e.g., BCFs of 42 to 45 40 in two species) in freshwater ecosystems. However, in 1984, data were insufficient to 41 estimate Final Tissue Residue Levels associated with adverse effects in fish, and thus the 42 BCFs did not influence the CCC value (USEPA 1984). Also, EPA is evaluating whether 43 pH may also be an indicator of bioava ilability in addition to water hardness. 44
45Ecological Risk Assessment

ICF International 7-26 DRAFT – December 2006 • No adjustments were made for sediment-speci fic characteristics that might affect the 1 bioavailability of Pb in sediments in the deri vation of the sediment quality criteria used 2 for this ecological risk assessment. Similarl y, characteristics of soils for the case study 3 locations were not evaluated fo r measures of bioavailability. 4
5

• Although the SSV calculated for birds is based on realistic parameter values for average 6 diet composition, 90 th percentile soil and diet inge stion rates, and a high-end Pb 7 assimilation efficiency, a recent site-speci fic determination of a soil concentration 8 protective of soil-invertebrate-consuming bird s suggested that the value of 38 mg/kg dry 9 soil is still overly c onservative. Use of 90 th percentile values for two parameters and the 10 maximum Pb assimilation efficiency sugge sted by available data may be overly 11 conservative. 12
13 7.3.1 Primary Pb Smelter Case Study 14
15 The ELM Sampling and Analysis Plan was designed to investigate possible ecological risks from 16 all sources of Pb (and other contaminants) attribut able to the primary Pb smelter without a need 17 to attribute the source of Pb in ecological ly sensitive areas (ELM 2003; ELM 2005). For 18 purposes of the Pb NAAQS review, it is important to distinguish areas im pacted primarily by air 19 deposition of Pb from areas impacted primarily by Pb from other sources (e.g., soil runoff, 20 groundwater discharge to surface water). 21
22 The soil sampling locations within a 2.1-km radius were all in areas that might have been subject 23 to Pb inputs from Joachim Creek during flooding events. As such, the stations might not 24 represent the concentrations of Pb in soils that result from direct air emissions from the smelter. 25 This limitation may overstate the risks from de position of Pb emitted from the facility. 26
27 7.3.2 Secondary Pb Smelter Case Study 28
29 The ecological risk assessment used modeled rath er than soil concentration measurements to 30 estimate exposure because measured data were not available. Fate and transport modeling 31 limitations and uncertainties are de scribed in Section 4.2.3.1. As di scussed in this section, the 32 modeled soil concentrations were compared to measured soil concentrations from areas 33 surrounding another secondary Pb sm elter and were found to be a pproximately three times lower 34 than the measured concentrations. Therefore, th e modeled results were scaled up three times to 35 address the potential under-predict ion of the soil model when the results were compared to the 36 surrogate measurements. 37
38 7.3.3 Near Roadway Non-Urban Case Study 39
40 Few measurements were available to evaluate ecol ogical risk associated with contaminated soils 41 near roadways in less developed areas wh ere presence of ecological receptors may be 42 anticipated. It is also uncerta in how representative of othe r roadways these data are. 43
44Ecological Risk Assessment

ICF International 7-27 DRAFT – December 2006 It is unlikely that there is significant avian a nd mammalian use of habitats within 2 meters of 1 heavily traveled roads; therefore, HQs above 1.0 at locations at 2 meters might not be associated 2 with ecological impacts beyond those a ssociated with traffic on the road. 3
4 For the Atlee, Virginia location, soil concentr ations were measured at sampling locations 5 between 2 to 30 meters of intensel y traveled roads. This assess ment did not evaluate avian and 6 mammalian wildlife habitat use within 30 meters of roadways. Without this evaluation, the 7 Atlee, Virginia assessment is likely to overestim ate the ecological risks of Pb in roadway soils. 8
9 The assessment did not address surface water ecosyst em impacts of Pb from near roadway runoff 10 of Pb contaminated soils. This may underestim ate risks to aquatic receptors via this exposure 11 pathway. 12
13 7.3.4 National-Scale Surface Water and Sediment Screening Assessment 14
15 7.3.4.1 Surface Water Column Screen 16
17 The analysis revealed only tw o or three NAWQA sampling locations nationwide where there 18 appear to be potential risks to the aquatic commu nity from Pb that might have originated from 19 atmospheric deposition. However, this is likely to be a large unde restimate of the true number of 20 such sites for several reasons: 21
22

• The NAWQA Study Units cover less than 50 percent of the land area of the United 23 States. 24

• Dissolved Pb was an analyte at only 16 percent of all NAW QA sampling locations. 25

• Dissolved Pb was measured only once or twice at many locations. 26

• For waters with a hardness of less than 47 mg/L as CaCO 3 , the CCC for dissolved Pb is 27 less than the quantitation limit for dissolved Pb that was used until the fall of 2000 (i.e., 28 1 μg/L). 29

• Fewer than 15 percent of samples analyzed for dissolved Pb between 1994 and 2004 were 30 assessed with the lower quantitation limit of 0.08 μg/L, which is a value that is 31 sufficiently low to match the CCC for waters with a hardness as low as 4.7 mg/L CaCO 3

. 32
33 The first two bullet points alone suggest that the number of such sites nationwide might easily be 34 at least ten times higher than what was represen ted in the NAWQA database. In addition, where 35 the land use around a sampling location was clas sified as “mining,” no investigation was 36 conducted to determine whether air emissions from a nearby smelter might also be contributing 37 to the Pb in the water. 38
39 There are many sources of uncertainty in the re sults presented for the sampling locations for 40 which there were some data, including the following: 41
42

• Many sampling locations are represented by onl y one or two measurements of dissolved 43 Pb. 44

• The water hardness for some sampling locations was not measured or is represented by 45 only one or two measurements. 46Ecological Risk Assessment

ICF International 7-28 DRAFT – December 2006 • Where there are multiple measures of both dissolved Pb and water hardness at a given 1 location, no attempt was made to match sampling dates and times to develop time2 specific CCC values. 2 3

• The water hardness measured at some locations was less than the lowest value of 4 20 mg/L of CaCO 3 used to develop the equation to calculate a CCC. The CCC equation 5 is not necessarily valid at values less than 20 mg/L CaCO 3

. 6

• It is not known how quickly di ssolved Pb concentrations cha nged at any of the locations. 7

• The database supporting the current AWQC fo r Pb is over 20 years old; new AWQC for 8 Pb should be available in 2007. 9
10 7.3.4.2 Sediment Screen 11
12 Results of this analysis cannot conclusively link any of the locations with probable adverse 13 effects of Pb in sediments on benthic communitie s to ongoing air emissions of Pb. There is no 14 clear correlation between HQs fo r the surface water column and HQs for the sediments. Some 15 locations with the highest Pb concentrations in sediments have no obvious air or NPDES16 permitted discharge sources of Pb. Some locatio ns (e.g., Utah and Montan a) suggest historical 17 drainage of Pb from mining activities. Othe r locations with high HQs for benthic communities 18 (e.g., near or in cities) suggest historical deposition of Pb from the use of leaded gasoline. 19 Erosion of Pb-contaminated soils from near roadways to these surface waters may be a 20 significant ongoing source of Pb input in such areas. 21
22 This analysis was limited to those 15 locations from the NAWQA database at which dissolved 23 concentrations of Pb in surface waters exceeded the chronic AWQC for Pb. Those 15 locations 24 are believed to represent a sma ll fraction of surface waters in the United States for reasons 25 described in Section 7.3.4.1. 26
27 An additional limitation is that where the land use around a sampling location was classified as 28 “mining,” no investigation was conducted to de termine whether air emissions from a nearby 29 smelter might also be contributing to the Pb in se diments. It was assume d that direct runoff and 30 erosion from the mining sites to the surface waters would have contributed the bulk of the Pb in 31 sediments. 32
33 Further limitations accrue from the sediment samp ling data. There were only nine exact matches 34 and three near matches between the 15 surface wate r sampling locations of interest and locations 35 at which sediment samples also were analyzed. Furthermore, there was a single sediment sample 36 at each of the locations of interest, some of which were taken in the early 1990s. 37
38 Finally, no adjustments were made for sediment-s pecific characteristics that might affect the 39 bioavailability of Pb in sediments in the derivati on of the sediment quality criteria used for this 40 risk screen. 41

2 The coefficient of variation (i.e., the standard deviation divided by the mean – see Appendix H for standard deviation and mean values for CaCO 3 measurements) for water hardness measurements was less than 10 or 20 percent for many stations; however, at some locations, the coefficient of variation exceeded 50 percent, indicating higher fluctuations in water hardness measurements.References

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